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Aligned with Whom? Direct and social goals for AI systems
1
Aligned with Whom?
Direct and social goals for AI systems1
Anton Korinek (Brookings, University of Virginia, and GovAI) and Avital Balwit (Oxford)
May 2022
As artificial intelligence (AI) becomes more powerful and widespread, the AI alignment problem –
how to ensure that AI systems pursue the goals that we want them to pursue – has garnered
growing attention. This article distinguishes two types of alignment problems depending on whose
goals we consider, and analyzes the different solutions necessitated by each. The direct alignment problem considers whether an AI system accomplishes the goals of the entity operating it. In contrast, the social alignment problem considers the effects of an AI system on
larger groups or on society more broadly. In particular, it also considers whether the system
imposes externalities on others. Whereas solutions to the direct alignment problem center around more robust implementation, social alignment problems typically arise because of conflicts
between individual and group- level goals, elevating the importance of AI governance to mediate
such conflicts. Addressing the social alignment problem requires both enforcing existing norms on
their developers and operators and designing new norms that apply directly to AI systems.
Keywords: agency theory, delegation, direct alignment, social alignment, AI governance
I. Introduction
We are building artificially intelligent systems which are increasingly competent and general.
Already, A I systems can have impacts that occur almost instantly and anywhere in the world.2
While speed and reach are factors that AI shares with other technologies like malware or
nuclear weapons, AI systems are also different, becoming increasingly autonomous and behaving like agents of their own.
When we delegate tasks to AI systems, an important challenge is to endow the systems with
“desirable goals” — this is frequently labeled the AI alignment problem. For a good survey of
recent research on AI alignment see, e.g., Ngo (2020). However, when speaking of alignment, it
1 This paper was prepared for the Oxford Handbook of AI Governance. We would like to thank Ondřej
Bajgar, Damon Binder, Justin Bullock, Alexis Carlier, Carla Zoe Cremer, Allan Dafoe, Ben Garfinkel,
Lewis Hammond, Fin Moorhouse, Luca Righetti, Toby Shevlane, Joseph Stiglitz, and participants at the
Spring 2021 Handbook of A I Governance conference for helpful comments and discussions. Any
remaining misalignment is our own. Korinek acknowledges financial support from the David M.
Rubenstein Fellowship program at the Brookings Institution. The views expressed herein are those of the
authors and do not necessarily reflect the views of the Brookings Institution.
2 An example are AI -powered financial trading systems (see e.g. Boukherouaa and Shabsigh, 2021).
2
is important to be explicit about whose goals an AI system is aligned with. This paper
distinguishes between two types of AI alignment that are both important in AI development but
that require distinct governance approaches:
Direct alignment: when an AI system is pursuing goals consistent with the goals of its operator,
irrespective of whether it imposes externalities on other parties.3
Social alignment: when an AI system is purs uing goals that are consistent with the broader
goals of society, taking into account the welfare of everybody who is impacted by the system.
There are multiple challenges to achieving direct alignment, including identifying the right goal to
give to an AI system, conveying that goal, and getting the AI system to correctly implement the
goal.4 Social alignment adds the challenge of including all of those impacted — not merely the
operator of a system — to the extent that society views this as desirable w hen determining what
goal to give the system.
What would it entail to consider all those affected? We describe a conceptual benchmark for social alignment based on welfare economics that we call ideal social alignment and analyze
how social alignment differs from direct alignment in that it considers externalities. We provide
examples of ideal social welfare functions that could, in principle, be employed to formally study social alignment. Since ideal social alignment is unattainable in practice, we explain how it is
possible to determine a partial ordering of social preferences and that in practice, social
alignment means aligning AI systems with this more limited set of instructions. Social alignment
will generally r equire external interventions and a broader governance framework. We discuss
how regulatory solutions provided by social norms, laws, markets, and architecture could be used to achieve social alignment, and we highlight the importance of AI governance to achieve
social alignment.
We believe ensuring that AI systems satisfy both direct and social alignment will become ever more important in the future. Currently, we delegate fairly narrow tasks to AI systems — we ask
them to classify images, rank our search results, recommend movies and music, and
autocomplete bits of our emails. But w e are starting to use AI in increasingly higher stakes
environments, like evaluating loan and job applicants, financial trading, utility management, and national defense. Impor tantly, the range and complexity of tasks that we turn over to our AI
systems are continuing to grow.
AI Alignment — the challenge of how to endow AI systems with goals that are consistent with
our goals — becomes ever more important as delegation from hu mans to AI systems (i)
3 Throughout the paper, we use the convention of referring to the entity that is creating, operating, and
controlling an AI system as the “operator.” In principle, each of these tasks could be performed by
different entities, adding additional complexity to the challenge of AI alignment.
4 There are many alternative ways of defining alignment but with similar flavor. For example, an AI system
could be aligned to the human’s instructions, intentions, revealed preferences, informed preferences, interests, or values, among other options. See G abriel (2020) for a fuller discussion.
3
happens more often, creating more opportunities for misalignment to cause harm ; (ii) involves
higher stakes situations, where misalignment would be more costly or even catastrophic ; and
(iii) occurs in situations where we have less ability to provide oversight, which makes it more
difficult to assess whether the system is aligned and less likely that we catch alignment failures
early.
II. Concepts
We start by clarifying several theoretical concepts that are relevant for our discussion of
alignment. Readers who are most interested in the comparison of direct and social alignment
may wish to skip to the following section.
Agents and goals
Agents : We call entities that interact with their environment “agents” whenever it is useful to
describe them as pursuing goals.5 A goal is a summary description of what an entity is
attempting to achieve. Humans clearly fit our description of agents —we think of ourselves and
each other as pursuing goals, and this is useful because inferring people’s goals makes for a
more efficient description of what to expect from them next. For example, if a driver sees a
person walking straight up to a crosswalk, it is useful to infer that the person’s goal is to cross the street, and this is more efficient than to ponder how the person’s leg movements will
translate into the person’s physical location over the ensuing seconds.
But our concept of agency is broader than humans. It also includes non -human entities such as
organizations or governments, which can be descr ibed as pursuing their own sets of goals. For
example, a business organization may be described as following the goal of producing a
product to earn profits; a university as advancing research and education; a government as
pursuing the safety and well -being of its citizens.
And, importantly for the purposes of this article, our concept also includes artificial intelligence.
Many modern AI systems are directly programmed to maximize a specific objective function,
making them act in a goal -oriented way. Mo re broadly, AI systems are agents in the sense that
we define because they are designed to pursue specific goals, for example classifying images, driving cars, controlling robots etc.
In our characterization of agency, we explicitly ask whether it is “us eful” to describe an entity as
pursuing a goal. This implies that the delineation is fluid and depends on the context. For
example, if we start with a very simple mechanical structure and transform it into a progressively
5 This is a shallow definition of agency that is, however, useful for our purposes here. It is inspired by but
distinct from Dennett’s work on stances (see e.g. Dennett, 1987). In different contexts, other definitions
may be more useful - for example, in ethics, a moral agent is an entity that is morally accountable for its
actions. For an elaboration on alternative concepts of agency, see e.g. Franklin and Graesser (1996) or
Orseau et al. (2018).
4
more intelligent robot, there is no specific threshold at which it becomes an agent —but it will
become more and more efficient and useful to describe it according to the goals it pursues
rather than by the physical laws describing its mechanical structure. To provide further
examples, it w ill rarely be useful to describe a rock as an agent, but there will be many
situations in which it is useful to describe a Boston Dynamics Spot robot6 as an agent.
Moreover, the same entity may be usefully described as an agent in some situations and
contexts but not in others. For example, we may want to describe a robot as an agent while it is
operating but as a piece of metal when we recycle it for scrap.
Goals : In the social sciences, an entity’s goals (or objectives) are frequently described using a
set of preferences, i.e. , an ordering that describes how the entity values different outcomes
relative to each other. For example, a preference relation such as A > B reflects that the entity
prefers outcome A over outcome B.
7
There is a dualism between an agent’s goals and her actions —we can either describe the
actions which emanate from her goals or describe the goals which drive her actions. When an
agent’s goals are fully specified in a given environment, the agent’s actions are also f ully
specified (except where there are ties) because we can work out what actions the agent will find
optimal to take, and vice versa because we can infer what their goals are from their actions. We can simply translate back and forth from goal space into action space.
Simple depictions of agents in the social sciences frequently take an agent’s goals as a primitive
that is exogenously given, for example by postulating a certain utility function for the agent. Then they proceed to analyze what actions the agent would take to achieve the most desired
outcome. However, such descriptions risk over -simplifying the behavior of human agents by
leaving out subgoals, competing goals, or constraints on the described goal. For example,
humans track the distance between current and desired states across multiple value dimensions
that are in tension with each other in ways that change dynamically over time (see e.g. ,
Juechems and Summerfield, 2019). This may pose significant challenges in determining human
goals and may , for example, produce behavior that is inconsistent with any utility function.
8 As
we will discuss in more detail below, the difficulty of correctly determining goals is a major
challenge for AI alignment.
6 See https://www.bo stondynamics.com/products/spot
7 Another way to express goals is in the form of a “utility function” u(X) that assigns a numerical value to
each possible X which ranks the different possibilities. Utility functions are more restrictive than
preference orderings. In other words, every utility function defines a set of preferences, but not every set
of preferences can be captured by a utility function. For example, for lexicographic preferences, doing so is impossible.
8 Specifically, for a set of preferences to map into a utility function requires several technical assumptions
that may be violated, including completeness, transitivity, and continuity.
5
Delegation and a lignment
Delegation is when an entity charges another with the fulfillment of her goals. Following the
language of economics, we use the term “principal” for the entity that delegates a task and
“agent” for the entity who is charged with the task.9
Examples of delegation are as ol d as humanity itself and could already be found in hunter
gatherer societies.10 As societies became more complex and hierarchical, the delegation of
tasks and the need for goal alignment also started to involve governmental or religious organizations and, later, corporations. Humans and organizations both started to delegate tasks
to each other to better accomplish their goals. For example, entrepreneurs founded corporations to pursue their goals, and corporations hired workers.
Successful delegation is advantageous for the principal by enabling her to better accomplish her goals. The reasons for this advantage include that the agent may have a comparative
advantage in the task at hand, such as different or greater capabilities than the principal, better
knowledge, or simply a lower opportunity cost of time. To make delegation successful, there
needs to be a sufficient degree of alignment between the principal and the agent, but the
principal also needs to provide the agent with a sufficient degree of discr etion —a point that is
not typically emphasized in traditional principal -agent theory in economics but that was already
made by Weber (1922) and that is also emphasized in the recent literature on AI governance
(see e.g. , Young et al, 2021). In particular, the agent needs sufficient freedom of action to
employ her greater capabilities, her better knowledge and judgment or her additional time to be useful to the principal. However, this freedom of action is what creates problems when there is misalignment.
Economists and other social scientists have long studied principal -agent relationships, i.e. , how
to align the actions of agents with the outcomes desired by principals (see e.g. Jensen and
Meckling, 1976, for one of the most influential contributions). In that body of work, the principal
and agent have exogenously given goals that differ from each other, and imperfect information makes it difficult for the principal to observe whether the agent has acted in her best interest or
has abused the discretion she was afforded. The main research question centers around how
the principal can provide the agent with incentives to act in her interest —for example, how to
include carrots and sticks in work contracts to incentivize workers to exert the optimal level of
effort.
9 Unfortunately this nomenclature involves some overloading of the term “agent” - in the previous section,
we called any entity that can be described as pursuing a goal as an agent; in this section, we follow the
conventions of principal -agent theory. Throughout the remainder of this article, the meaning of the term
will be clear from the context.
10 In fact, some of the interactions among other social species such as bees or ants can also be
described as simple forms of delegation.
6
In AI alignment, by contrast, the question is different and in some sense more fundamental: how
to endow an agent with goals that lead to outcomes that are desired by the principal.11 AI
alignment is usually described as goal alignment, but what ult imately matters for the principal
are the agent’s actions. When the relationship between goals and actions is clear and is known
perfectly and when the goals of principal and agent coincide, then there is no direct alignment problem. In Figure 1, this woul d correspond to each of the mappings that are indicated with
arrows holding perfectly, i.e. , (1) the principal has identified the goals that will lead to her
desired actions well, (2) they are correctly conveyed to the agent, who (3) in turn translates the conveyed goals into the desired actions. However, this is an idealized benchmark.
Delegation Principal Agent
Action space Desired actions Pursued actions
↕ (1) ↕ (3)
Goal space Desired goals ↔
(2) Pursued goals
Figure 1: Principal -agent alignment in action space and in goal space
In practice, misalignment between the principal’s desired and realized outcomes can arise in any of the three steps outlined in the figure. This allows us to distinguish the source of alignment
problems into the following categories:
(1) Identifying the principal’s desired goals: The principal needs to figure out what her goals
are.
(2) Conveying the goals to the agent: Next, the principal needs to correctly transmit her
desired goals to the agent so the agent can pursue them.
(3) Translating the goals into actions: The agent needs to correctly implement the
transmitted goals by pursuing the corresponding actions.
11 On the surface, the two described situations - incentivizing a human agent with distinct goals to pursue
the principal’s goals versus creating an AI agent from scratch who pursues the principal’s goals - seem
very different. However, given the dualism between actions and goals, there is in fact a deeper
equivalence between the two. Addressing the classic principal -agent problem in economics can be
viewed as a situation in which the principal has only limited ability to affect the agent’s architecture (e.g. to
reprogram the agent’s primal drive to avoid hard work) and needs to find workarounds (“incentives”) to
make the agent pursue the desired goal. Programmers frequently experience similar situations. For
example, the architecture of ML libraries, say TensorFlow, constrains how they can write their code and
makes some results far easier to obtain than others. In other situations, they need to write workarounds
building on clunky legacy applications to efficiently obtain the desired behavior.
Conversely, human principals sometimes have the ability to “reprogram” agents. For example, parents
greatly appreciate the importance of instilling proper goals into their offspring; managers and military
leaders know the importance of “inspiring” their agents to pursue desired goals; and a significant part of
our human culture (religion, morals, etc) revolves around reprogramming humans’ goals in a way to make
our societies operate more harmoniously.
7
Below, we will elaborate on each of these steps in detail.
As we observed before, the term “AI alignment problem” is frequently used to describe step (2),
or a combination of steps (1) and (2), i.e. , how to provide an AI system with a set of goals that
correctly reflect our goals, whereas the term “AI control problem” is used to capture the broader
challenge of how to ensure that the actions of an AI system are desirable.12 However, the three
steps illustrated in our figure are closely related to each other. If the mappings described in
steps (1), ( 2) and ( 3) held perfectly, it would be possible to focus exclusively on how to align the
goals of human principals and AI agents. In practice, however, it will be necessary to consider
all four steps simultaneously as they cannot be cleanly separated from each other.
Direct and s ocial alignment of AI
So far we have discussed how to ensure that the actions performed by an AI system reflect the desires of a principal. However, we have been silent on who exactly the principal is. We
distinguish two separate concepts, direct and social alignment, that relate to whether we view the principal as the operator of an AI system or as society at large. The disti nction between the
two has not been sufficiently recognized in the existing literature on AI alignment.
As defined in the introduction, we use the term direct alignment to refer to whether an AI
system is pursuing goals that are consistent with the goals of its operator, and social alignment
to refer to whether an AI system is pursuing goals that are consistent with the broader goals of society, taking into account everybody who is affected by the system and internalizing any
externalities. The two forms o f alignment problems are also related to the broader challenge of
developing cooperative AI (see e.g. , Dafoe et al, 2020).
In the following two sections, we will discuss the challenges of direct and social alignment in
detail and will elaborate further on when the two differ from each other. But before doing so, let
us provide two examples to highlight the difference between the two.
Example 1: Ted develops a new resume screening algorithm. Since he wants the system to be
free of racial bias, he leaves out the variable “race” from the training dataset. However, the
system quickly learns correlates of race such as name, address, and educational institution from
the existing bias in the training dataset and uses these to arrive at biased hiring decisions.
This represents a failure of direct alignment. Ted was eager to avoid racial bias but did not
realize that his implementation led to precisely the bias that he was concerned about.
Example 2: Mark develops a recommendation model to maximize user engagement on a social
network platform. When he finds out that the system leads to stark increases in political polarization, he does not change course.
12 For example, Bostrom (2014) describes value alignment as one element of AI control alongside other
mechanisms such as capability controls.
8
This represents a failure of social alignment. The AI system pursued—and successfully
achieved—its assigned g oal, but it imposed large externalities on society by increasing
polarization.
III. Direct alignment
From the perspective of a principal operating an AI system, there are three interrelated
challenges to ensuring direct alignment of an AI system: the first is the challenge of determining
what goals to pursue, the second is conveying the goal to an AI system, and the third is getting
the AI system to correctly translate the goal into actions.
Determining the goal
The first challenge is to work out the princ ipal’s goals, i.e. , to translate the principal’s desired
outcomes into abstract goals. Determining what we want can be difficult. Human goals are not
easily interpretable; they are often amorphous or intuitively understood but difficult to express.
Our brains are opaque and pursue multiple value dimensions depending on circumstances (see
e.g., Juechems and Summerfield, 2019). When we are not sure what we want, it is difficult to
align an AI system with our goals.
A key aspect of determining the principal’ s goal is how to scope the goal appropriately so it does
not conflict with other goals that are valuable to the principal. Humans have a broad set of goals
that involve many different subgoals that we automatically and often subconsciously weigh
against each other when they are in conflict. When we determine what goals to assign to AI
systems, we have to ensure that the systems do not optimize one subgoal to the detriment of
others. This becomes more and more important as our AI systems become more powerful and
their capacity to optimize over a single goal increases.
Example 3: Mark develops a recommendation model to maximize user engagement on a social
network platform. He is dismayed to find out that the system also increases political polarization.
This is a classic example of specifying an excessively narrow goal and obtaining unexpected side effects. In the described example, Mark did not anticipate that his recommendation model
would also affect the political views of its users.
Conveying the g oal
After a principal determines the content of her goals, she faces the technical challenge of
transmitting the goal to an AI system. When humans convey goals to each other in natural
language, they understand the context, which makes it easier to resolve ambiguities.
9
It is more challenging to convey goals to AI systems. AI systems do not share the same
understanding of the world that we humans share and will likely not be able to resolve
ambiguities by making common -sense deductions. Instead, we need to translate the goal into
something machine readable and provide the instructions, training, or feedback necessary for
the system to “understand” and execute. Part of the challenge is to clarify what our concepts
mean. For example, if we told an AI system to “make us happy,” what do we mean by that term?
Do we mean pure hedonic experience, do we mean general life satisfaction, or any other range
of viable meanings?
Example 4: Tim tells his AI -powered smartphone assistant to “call Jon” as he gets ready to go
out and party. He is embarrassed that his 11 p.m. phone call wakes up his boss rather than
reaching his brother, who is listed as “Jonathan” in his contact list.
This is a n example of an AI system misinterpreting the goal of its principal because it did not
correctly understand the context. A human assistant would have known not to call a work
contact late at night and would have understood that “Jon” may refer to “Jonathan.”
Sometimes, the challenges of determining and conveying can be addressed jointly. For example, inverse reinforcement learning allows an AI system to learn the objective function of
its principal through observing their behavior (Ng and Russell, 2000). Through this method, the
content and form of the goal blend together.
Implementing the goal
Once the principal’s goals are transmitted to the agent, they need to be implemented through appropriately chosen actions by the AI system. Implementation has many t echnical aspects that
we will not discuss here.
13 These include ensuring a system is free of bugs and robust, including
that it operates reliably in circumstances other than what it was initially trained and tested on. While implementation is key for getti ng a result that the principal is happy with, some do not
view it as a pure component of alignment.
14
Example 1 above described a resume screening system that resulted in biased hiring decisions
because of biased training data that was not corrected for. This is an example of an
implementation failure.
Distinguishing direct alignment from s ocial alignment
The described challenges of direct alignment also apply to social alignment. The key difference
is that the first step, determining the goal, no longer i nvolves a single principal who is operating
13 For a thorough and cutting -edge technical introduction see e.g. Russell and Norvig (2020).
14 For example, some definitions of alignment only capture the intentions of the AI system, not the
outcome, whether ex ante the AI system was trying to achieve the human’s goal. If an AI system tries to
accomplish the goal, but some implementation failure causes the system to crash before doing so,
perhaps it should not be viewed as a failure of alignment.
10
the system, but instead the broader goals of others in society who would be impacted by the AI
system. This is what makes the social alignment of AI a central theme of AI governance.
Many contributions in the ex isting literature on AI alignment refer to either direct alignment or
social alignment without explicitly addressing the distinction between the two and the necessity
of paying attention to both. For example, Paul Christiano (2018b) appears to focus largel y on
direct alignment in his definition of intent alignment: “AI A is aligned with an operator H, if A is
trying to do what H wants it to do.” It is possible that the operator in this definition wants the AI
to do something that will impose large externali ties on others. Yudkowsky (2004) seems to
describe a form of social alignment in defining an alignment benchmarked that he terms
coherent extrapolated volition (CEV) as “our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together...” He explicitly refers to “ our
wish...had [ we] grown up farther together .”
Other definitions are unclear on whether they imply direct or social alignment. Christiano
(2018a) defines the project of alignment as “aligning AI sys tems with human interests,” leaving
unspecified which humans these interests belong to. For example, Stuart Russell (2019) defines
the problem of value alignment as ensuring that we do not “perhaps inadvertently, imbue machines with objectives that are imperfectly aligned with our own” (p. 149). Brian Christian
(2020), author of “The Alignment Problem,” defines the titular problem as “how to ensure that
these models capture our norms and values, understand what we mean or intend, and, above
all, do what we want” (p. 19). Both of these definitions do not make it clear who “we” or “our”
refers to —all of humanity, some representative group, a synonym for one? Evan Hubinger
(2020) writes that an AI agent is “impact aligned (with humans) if it doesn't take actions that we
would judge to be bad/problematic/dangerous/catastrophic.” It is not clear who these “humans”
are—just the principals/operators, or all humans —or how large a consensus this “we” has to
attain.
IV. Social alignment
In contrast to the direct alignm ent problem, which looks at whether an AI system is consistent
with the goals of its operator, the social alignment problem looks at whether the system pursues goals that are consistent with the goals of society at large.
In the following, we start by des cribing a benchmark for social alignment based on welfare
economics that we call ideal social alignment . We analyze how social alignment differs from
direct alignment by emphasizing that it considers externalities on others. We provide examples of ideal social welfare functions that could, in principle, be employed to formally study social
alignment. However, we also lay out the practical difficulties of establishing what society’s
preferences are, and we analyze how to deal with situations in which there i s no clear way of
mediating conflicting objectives within society. See also Baum (2017) for a description of the
difficulties in establishing society -wide ethics for AI.
11
Next, we analyze to what extent existing norms (broadly defined) for the operators of AI systems
are sufficient to ensure that the systems are socially aligned. We distinguish two sets of
problems that give rise to misalignment: The first arises when the operators of AI systems
violate existing social norms and are themselves not socially aligned; the second arises when
our current social norms are insufficient to address some novel externality caused by new AI
capabilities. Achieving social AI alignment requires progress on both fronts. We describe a set
of policy tools that can be used to further the social alignment of AI.
Ideal s ocial alignment
To describe a simple idealized benchmark for social AI alignment, we impose two assumptions.
First, we assume that society has a complete and well -defined set of preferences over all
choices that are relevant for the AI system. Second, we assume that society itself creates,
operates , and fully controls the AI system. Under these idealized conditions, the challenges of
social alignment of AI would be reduced to the challenges of direct alignment that we described
in the previous section, since society would essentially act as the operator.
Technically speaking, the first assumption of complete preferences implies that society’s
preferences define a full ordering of all the available choices, i.e. , that whenever society faces a
set of choices, say A, B or C, there are social preferences that tell us how they rank compared
to each other, which ones are preferred, or which ones are equally desirable for society.15 One
possible ranking would be for example A > B > C. As we will explore further below, this is a
strong assumption; in practice, there are many areas in which society does not have such clear
preferences.
Theoretically, ideal social alignment could also be achieved if an AI syste m is operated by a true
altruist who implements society’s preferences. Most human beings are not purely egoistic but
also care about the well -being of others —they have altruistic preferences, meaning they
explicitly consider and accommodate the preferences of a broader group than just themselves.
Their consideration of the welfare of others may range from minor, i.e. , they care about others
much less than about themselves, to perfect altruism, i.e. , their decision making considers the
welfare of another person just as much as their own. The direct alignment problem only considers the effects of the AI system on others to the extent that the person or entity creating
the system is altruistic. If those who set AI goals are perfectly altruistic, like this theoretical
altruist, then the social alignment problem is solved—their individual goals will be aligned to
social goals. However, whenever the preferences that the altruist perceives about society diff er
from society’s actual preferences —or whenever society’s preferences change—the supposed
altruist would become a dictator. This is a significant risk whenever there is no democratic process that represents a corrective force to express society’s preferen ces.
15 In mathematics, a full ordering (or total order) is a binary relation on a set that satisfies, among other
conditions, that it is transitive and that any two elements are comparable. See e.g.
https://en.wikipedia.org/wiki/Total_order The assumption that society’s preferences represent a full
ordering is also a n ecessary condition for describing them via social welfare functions.
12
As reflected in the term, ideal social alignment is more a conceptual benchmark than a guideline
for actual implementation. Even in a well -functioning representative democracy, it is unlikely that
society will agree on all outcomes. However, the benchmark is useful to sharply delineate the
differences between direct and social alignment.
Externalities: At the center of the distinction between direct and social alignment are
externalities. In our context, externalities exist whenever an AI system aff ects others without
their agreement and without the beneficiary compensating others for it. For example, an AI system that engages in surveillance of individuals or otherwise intrudes into their privacy
imposes externalities onto them. Likewise, a system t hat manipulates consumers into buying
goods on false pretexts inflicts externalities on its victims. Similarly, a system that evaluates applicants for jobs, loans, or other transactions but engages in unfair discrimination imposes
externalities on its subj ect. At a society- wide level, a system that promises social engagement to
its users but creates echo chambers and deteriorates democratic processes while doing so
inflicts significant externalities on society at large. Similarly, AI systems that displace human
jobs without generating sufficient new jobs depress economy -wide labor demand, reduce
wages , and thereby contribute to rising inequality. These effects represent what economists call
pecuniary externalities because they arise from changes in market pr ices (in the given case,
declines in workers’ wages). AI systems may also confer positive externalities on society —for
example, a tool that aids in scientific discovery may have benefits for society that are far greater
than the benefits that the developer of the tool obtains.
Framed this way, the goal of social alignment is to internalize the externalities of AI systems —
making sure that AI systems consider the benefits and costs not only for their operators but also
for all other members of society. In so me cases, society may view the potential externalities of
an AI system as so harmful that it is best for the system not to be implemented. For example,
many forms of mass surveillance may simply be too harmful to be worth any benefits they create. In gener al, however, it is not necessarily desirable to reduce all externalities to zero, just
to properly take them into account. For example, an AI system that creates large amounts of
value at the cost of displacing some jobs may well be worth implementing. Soc ial alignment
simply requires that the balance of the useful and deleterious effects reflects what society would
choose as the optimal balance.
Social welfare functions are a widely used concept to weigh benefits and costs that can be
applied for the ideal social alignment of AI systems. Social welfare functions are a
generalization of utility functions to social choices and work in a similar fashion: Given a set of
possible choices for society, a social welfare function assigns to each choice a welfare sc ore
that captures how desirable it is for society, and the choice that obtains the highest score is by
definition the preferred one that society would adopt. A widely used example are utilitarian (also
called Benthamite) welfare functions, which sum up the utilities of all members of society with
equal weight. A specific case among utilitarian social welfare functions that evaluate
consumption choices is when individuals are attributed utility functions that are linear in
consumption—in that case, social we lfare is simply the sum of all consumption, and maximizing
social welfare is equivalent to maximizing economic efficiency, i.e. , maximizing total
13
consumption without any regard for distributive concerns. Conversely a Rawlsian (or maximin)
social welfare function captures the desire to maximize the utility of the worst -off member of
society, reflecting a strong desire for equality.
The analogy to utility functions makes social welfare functions an appealing concept for AI
developers —utility maximization is a cornerstone of training AI systems. This makes it important
to be aware of the limitations of social welfare functions. One limitation of social welfare
functions as commonly used is that they focus on consequentialist specifications of individual
utility, which may miss other considerations that society views as important for ethical decision
making, e.g. , deontological considerations.16 Imposing a bad welfare function, even with the
best of intentions, will create social harm. Another important limitati on is that social welfare
functions pre -suppose a complete set of social preferences that are well -defined over all
choices, but there are many situations in which this may not be achievable. This is what we will
turn to next.
Disagreement and partial s ocial preferences
Social alignment can only occur where there are well -defined social preferences. However,
members of society frequently disagree on what are the most desirable choices. Unfortunately,
there is no general way to compile the preferences of multiple individuals into well -defined and
rational social preferences over all available choices. Condorcet (1785) observed that
democratic voting will not in general produce a full set of rational social preferences.17 Arrow
(1950) showed in his doctoral t hesis that this is a general property of all mechanisms to
aggregate individual preferences into social preferences, except for a dictatorial rule whereby a single member of society dictates all choices.
18 These negative results imply that it is generally
not possible to come up with the complete set of preferences that would be necessary for AI systems to implement the ideal social alignment that we described above. However, that does
not imply that we need to give up on social alignment entirely.
Instead , social alignment can still focus on aligning AI systems with those social preferences
that can be clearly established. Even though members of society may not agree on how to rank
all available choices, they will agree on how to rank many of the most important choices. They
may agree that A > B and A > C but may not be able to rank B and C relative to each other. The
social choices over which there is general agreement within society represent a partial ordering
of all the available choices. We can increas e the set of choices for which a social preference
can be established if we weaken the standard from universal agreement to somewhat lower
16 Although it is possible to add such considerations with a negative weight in consequentialist
specifications of welfare functions, it is difficult to determine desirable weights.
17 Specifically, when three or more people are asked to express their preferences over three or more
alternative choices in pairwise votes, they frequently arrive at outcomes like A > B, B > C and C > A,
making it impossible to establish a full order of the available social choices.
18 Even seemingly straightforward mechanisms such as a welfare function that is the sum of utility
functions of all members of society will not satisfactorily address the problem, since it may lead to Pareto-dominated outcomes. See Eckersley (2019) for a fuller description of the problem in the context of AI
alignment.
14
standards, e.g. , near-universal agreement. For example, society will generally agree that it is
desirable to save liv es or to refrain from actively discriminating against minorities, even if a small
fraction of the population disagrees. The resulting partial ordering provides a limited set of
instructions for social choices —even though it cannot identify a full set of social preferences that
apply to all circumstances.
Social alignment requires that AI systems observe the partial ordering provided by social
preferences. Formally, we call an AI system socially aligned if its choices correspond to the
partial ordering implied by social preferences. Conversely, an AI system violates social
alignment if it makes choices that contradict the partial ordering implied by social preferences, i.e., if society generally agrees that it would make different choices.
Given the partial nature of the ordering implied by social preferences, there will be situations in
which society genuinely disagrees , so social preferences do not provide instructions for what a
socially aligned AI system should do. Existing social norms cannot provide guidance for the AI
system or its operator what choices to make. In some contexts, there may not be a preferred
social choice, but there may be agreement among members of society that AI systems and their
operators should have the liberty and freedom to mak e their own choices, for example the
freedom of how to design a new product or how to compete in the market (while respecting the
rules) . In open societies, such freedoms are in themselves an important value.
However, in other contexts, unresolved conflicts within society imply that the choices of AI
systems and their operators are likely to be contentious, no matter what choice they make. One of the fundamental goals of governance is to resolve such conflicts, to determine how to
establi sh social preferences in such situations, and how to establish norms that encapsulate
these preferences. The question of what social preferences ought to be in the realm of AI —and
over which social choices they are defined—is a central theme of AI governance .
Rights -based approaches are a common mechanism by which society represents partial social
preferences because they reflect certain defined entitlements and freedoms for members of
society while leaving ample space for other choices over which there may be disagreement. For
example, one of the most fundamental rights -based approaches, human rights, encapsulates a
set of basic rights and freedoms for which there is general agreement among the countries
adopting the declaration that all human beings are entitl ed to them (Universal Declaration of
Human Rights, 1948). In the digital realm, an example is the EU’s General Data Protection
Regulation (GDPR, 2016), which adds many new rights that have become relevant only
recently, for example the right of access to i nformation or the right to be forgotten. Bajgar and
Horenovsky (2021) describe how rights -based approaches may be useful for long- term AI safety
and regulation.
Spheres of s ocial alignment
The social alignment problem can manifest at several different scales, ranging from small subgroups of society to larger spheres such as humanity as a whole. The commonality between
15
them is that they all include cases where someone other than the AI system’s operator is
affected by externalities.
In general, the extent of agreement within a group decreases with group size. Smaller
communities may find it easier to come to an agreement on what outcomes are desirable for an
AI system to pursue than larger groups of people such as the citizens of a nation or humanity as
a whole. This implies that group preferences will lead to increasingly partial orderings as the
group size increases, i.e. , larger groups will agree less on what outcomes to pursue than
smaller, more homogenous groups.
A good example of how attitudes towar ds social alignment differ depending on group size is
when there are competitive dynamics between subgroups of society. Consider two large
corporations developing AI systems that are in fierce competition with each other —when one
corporation improves its s ystem, it gains market share at the expense of the other. The
stakeholders of each corporation, including its workers, shareholders, and suppliers, form a
subgroup of society, and this subgroup has a clear interest in the corporation doing well. If a
corporation’s AI system pursues outcomes that are in the collective interest of that subgroup,
then the system is socially aligned at the subgroup level, i.e. , at the level of the corporation.
Looking outside the corporation, there are clearly negative externalities between the two
corporations. However, our social norms at the national and international level allow for such
competition and do not find competitive dynamics objectionable as long as they benefit
consumers and satisfy other applicable laws. The partial ordering reflecting our society -wide
norms includes the requirement to act lawfully, to avoid biases, etc., but gives corporations
freedom to engage in lots of actions, including the freedom to compete with each other. Under
the described circumstan ces, the corporation’s AI system is aligned at the society -wide level
even though it imposes large negative externalities on competitors.
For purposes of illustration, and without being exhaustive, we discuss a few different exemplary spheres of social al ignment:
Group- level : A social alignment problem could manifest at the group level —for example, within
a community, club, university, corporation, or city. If an AI system run by members of the group
imposes externalities on the group that violate the social norms within the group, it is socially
misaligned at the group level.
Country- Level : For many questions, individual countries are the most important sphere at
which to consider social alignment. In the modern world (perhaps with the exception of the EU), most laws and regulations originate at the country level since countries are the politically most
powerful actors. This is also true for AI regulation to forestall alignment problems.
However, social alignment at the country level is not necessarily sufficient —countries are
frequently subject to competitive dynamics, especially in the military context, where advances in
16
AI may give rise to significant shifts in power dynamics (see e.g. , Armstrong et al, 2016). This
directly leads to the next and widest sphere at which social alignment is desirable.
World -level : Social alignment at the world level is the broadest, least restrictive, but perhaps
also most fundamental sphere of alignment for AI systems. It requires that an AI system
pursues desired outc omes on which humanity at large broadly agrees. Although there are many
areas of significant disagreements among the world’s citizens, there are also areas of almost -
universal agreement. Examples include the desirability of basic forms of AI safety to avoi d
human extinction, or that the most momentous decisions undertaken by autonomous weapons
systems should have humans in the loop ( e.g. Human Rights Watch, 2012). Another area of
near-universal agreement may be that it is undesirable to develop a super -human AI system
that displaces all human labor without ensuring that humans have sufficient material resources
to survive such a radical shift . Articulating and formalizing global social norms on these topics is
a pressing area of concern.
Implementing s ocial alignment
No matter in which sphere, attaining social alignment of an AI system may be more challenging
than attaining direct alignment —the main goal for the creator of an AI system is to solve the
direct alignment problem so that the system pursues the outcomes he desires. Social alignment
may be an after thought.
The social alignment of an AI system would be ensured if its operator is perfectly altruistic and
internalizes all externalities that the system imposes on others in an ethical fashion. However,
more generally, the operator may not care about imposing harm on others as long as the
system achieves the outcomes they desire for themselves. In the following, we discuss the available avenues to achieve social alignment.
Assessing AI impacts : A precondition for evaluating social alignment is knowing about the
impacts and potential externalities generated by an AI system. Sometimes harms arise without the operator of an AI system even being aware of it, and the operator may not have sufficient
incentives to find out. Moreover, lack of transparency makes it easier to cover up harms. AI
impact assessments could help. Lessons can be learned from environmental impact
assessments (EIAs), which are routinely required for actions of government agencies, or for
government -funded, -permitted or - licensed activities —for example, for building a highway,
airport, or oil pipeline. AI impact assessments could quantify the potential risks and benefits of
AI systems. Such assessments could be mandated for AI projects tha t are implemented by
government entities, that receive government funding, or that have sufficiently broad societal
effects. They could also be used on a voluntary basis, just like EIAs have become relatively
common in the private sector.
Existing norms : Once the potential externalities of an AI system are known, the next question
is how social norms, regulations , and laws can ensure the social alignment of AI systems.
Society already has a rich set of norms for social alignment that have e volved over centuries,
17
consisting of informal social customs and habits as well as formal laws and regulations. These
norms constrain the behaviors of both individual humans and non- human agents such as
governments, corporations , or nonprofits. Whenever these entities operate AI systems, the
existing norms that they are subject to by extension represent norms for the behaviors of their
AI systems. To provide a stark example, a civilian must not program an AI -based robot to kill
someone.
The social alignment of the operators of AI systems thus leads to a certain “default” level of
social alignment for AI. Conversely, when these actors violate the social norms that they are
subject to, they give rise to alignment problems that we may call social misalignment from
violating existing norms. We start by discussing how informal and formal social norms address
social alignment. Then we discuss why we believe that it is also increasingly desirable to impose new constraints directly on AI systems in addition to exist ing norms on their operators in
order to guarantee that AI systems are socially aligned.
Informal social norms are constraints on agents’ behavior that are enforced in an informal,
decentralized way by a community. Social norms have evolved together with humanity to
facilitate human cooperation. They can be seen in action, for example , when employees,
consumers , or shareholders pressure companies to abstain from behaviors that they view as
unethical.
The importance of informal social norms is frequently underemphasized— for example in
economic analyses, when individuals are counterfactually depicted as perfectly selfish actors —
and there is significant room for improving social AI alignment by establishing the right norms within the AI ecosyst em (see e.g. , Klinova, 2022). Social norms can be powerful in driving the
behavior of individual humans. For example, social norms among AI developers as to what
types of systems are considered ethical and desirable and what is considered unethical provide
effective constraints on what systems AI companies develop. We can already see the effect of
social norms on AI development in that many AI companies have begun creating codes of
ethics or employing teams that directly focus on AI ethics and society (Bess en et al., 2021).
Similarly, social norms among the broader public can translate into consumer pressure—not purchasing from AI companies that don’t live up to their expectation of social alignment.
However, social norms alone are insufficient to govern our complex modern societies. They are
most effective at the human community level. Non- human entities such as corporations and
governments are not directly susceptible to social norms —only indirectly via their human
agents —opening the door to what some hav e called “ administrative evil ” (see e.g. Young et al.,
2021). More formal governance modes such as laws and regulations are therefore indispensable.
Laws and Regulations impose constraints on agents that are enforced with formal, state-
backed or - administ ered penalties. There are several ways which such legal constraints can
contribute to social alignment.
18
● Prohibitions and mandates: There are some uses of AI that society will deem too harmful
to allow, and for these it may make sense to pass legislation w hich forbids them. For
example, the Campaign to Stop Killer Robots has launched an effort to ban lethal
autonomous weapons that could kill without human oversight.19 Similarly, mandates can
be enacted to ensure that AI systems meet certain socially desirable minimum
standards, for example in the realm of safety. A closely related measure is to assign harmed individuals rights that can be enforced via litigation (see e.g. , Kessler, 2010).
● Taxes and subsidies: Taxes and subsidies are a classic fix for exter nalities, and they
apply just as well to the case of social AI alignment. They are preferable to outright bans and mandates when an activity creates externalities so the unregulated amount of that activity would be undesirable, but when a total ban would be excessive. Moreover, the
revenue from taxes can be distributed to those who experience the harms.
20 For
example, tracking of individuals represents a privacy intrusion but may also offer some
useful benefits. Instead of banning it outright, imposing taxes or user fees may reduce it
to a more desirable level. Similarly, if society’s goals include an equitable income
distribution, excessive automation that destroys jobs and undermines worker incomes
could be taxed, with the resulting revenue distributed to workers losing their jobs, but it
would be undesirable to ban automation.
New norms for s ocial AI alignment
The growing powers and capabilities of AI systems create new and ever more powerful ways in
which the public interest may be infringed upon, i.e. , new externalities, which call for new social
norms and create new potential forms of social misalignment. When AI systems gain new
capacities, society can be unprepared to govern. For example, society had one set of norms for surveillance and privacy when surveillance was very labor -intensive and correspondingly costly
to undertake , so governments focused surveillance only on very high- value targets, such as
suspected high- value criminals. Legal constraints on mass surveillance would have been
redundant, gi ven the high cost of surveillance. Now that AI systems can perform many forms of
surveillance cheaply at large scale, new legal constraints on surveillance activities have become
necessary.
More generally, every time a new AI capability is developed, they may bring up new social
alignment problems that call for new social norms. Aside from privacy norms, additional examples in areas that we already touched upon include the need for new norms for AI systems
that become increasingly adept at manipulating consumers, new fairness norms for AI systems
that make high- impact decisions that have hitherto been reserved to humans. As the labor
market effect of AI and other forms of automation become more severe—and more pernicious
for workers —there is also a new need for norms for when and how AI developers should
compensate the exposed workers. Even more starkly, if AI makes human workers economically
19 See https://www.stopkillerrobots.org/
20 Allowing the harmed individuals themselves to impose a user fee is equivalent to taxing the harm and
distributing the revenue to the harmed individuals.
19
redundant, society will need to establish new norms for how to provide humans with income
when labor income is no longer an option (see Korinek and Juelfs, 2022).
Social Alignment Norms Imposed on Whom? Up until the recent past, governance to ensure
the social alignment of AI systems has relied entirely on society imposing norms on the
operators of AI systems, who were in turn charged with ensuring the alignment of their systems.
This case, which we may call social alignment by extension, is illustrated in panel (a) of Figure 2, which employs arrows to indicate that an entity imposes norms on another entity. Such an
arrangement would be all that is needed if (i) the operator was perfectly aligned with the social
norms, and (ii) if the direct alignment between the operator and the AI system held perfectly.
However, when one of these two conditions is violated, it makes it desirable for society to
directly impose social norms on AI systems, as illustrated in panel (b) of Figure 2. Let us
consider each of the two conditions in turn.
Society
↓ Society
Operator
↓ Operator → AI System
AI System
(a) Socia l alignment by extension (b) Social alignment imposed on AI
Figure 2: Two modes of imposing social alignment norms on AI systems
When the operator of an AI system is not in compliance with social norms, then imposing social norms directly on AI systems may substitute for the operator’s lack of compliance. Such an
arrangement may also make it easier to monitor the social alignment of the operator. Consider
for example an unethical corporation that pursues blind profit maximization to the detriment of
other values of society. If the AI systems deployed by such a corporation need to satisfy certain
enforceable norms —such as being unbiased—then the space for unethical behavior of the
corporation is curtailed. In fact, norms imposed on AI systems may even make i t possible to
regulate behaviors that violate social norms but used to be difficult to regulate before. For example, when lending decisions were made individually by loan officers, it was harder to
establish whether they were unbiased than it is with algor ithms.
When an operator is generally aligned with social norms but has not fully solved the direct
alignment problem between her and an AI system, then norms that are imposed directly on the
AI system may also help. Such norms can be thought of as best practices, and they may contribute to all three steps of the direct alignment problem that we laid out above —determining
the right goal, conveying the goal, and implementing the goal. For example, they may help a
20
well-intended but inexperienced entrepreneur t o ensure that the AI system she develops does
not unintentionally impose harm on society.
As AI systems become more agentic and have ever more discretion over decisions that used to
be reserved for humans, we believe that imposing norms directly on AI syst ems is becoming
increasingly important.21
V. Conclusion
As AI systems become more powerful and are deployed in a growing number of areas, aligning
them with our goals becomes ever more vital. However, the expression “our goals” is often used
too loosely. It is crucial to emphasize that AI alignment has two distinct dimensions —direct and
social alignment. The two dimensions require somewhat different approaches, but we need to
solve both to ensure a future that is desirable for humanity. Direct ali gnment ensures that AI
systems pursue goals consistent with the objectives of their operators, irrespective of whether
they impose externalities on other parties. By contrast, social alignment ensures that AI systems
pursue goals that are consistent with t he broader objectives of society, internalizing externalities
and taking into account the welfare of everybody who is impacted by them.
Modern AI systems have the capacity to powerfully optimize for the goals that we endow them
with. They are becoming better and better at doing what we are asking them to do and reaching
their programmed goals —no matter if these goals correspond to our true goals or if we
mistakenly assign them the wrong goals , for example excessively narrow subgoals that lead to
disastrous unintended side effects because they fail to fully capture what we want.
Regarding direct alignment, we need to work on determining, conveying, and implementing the
goals that we want AI systems to pursue in a robust manner. Regarding social alignment,
society needs to determine what social goals and norms we want AI systems to pursue. Social
preferences can only determine a partial ordering over all available choices. It is important to
expand that ordering as much as possible by resolving social disagreements and conflicts, and
to appeal to our better angels as we do this so that our preferences reflect our ethical values. To
the extent that society finds agreement, it is also important to develop the right institutions to implement our preferences. We argue that this requires imposing norms on the developers and
operators of AI systems as well as new norms that are directly imposed on AI systems.
As AI systems have become more powerful and their use in our world has become more
widespread in recent y ears, we have also witnessed a growing number of cases of social
alignment failures, from automated decision systems with biases against disadvantaged groups
to social networks that increase polarization and undermine our political systems. Yet progress
is continuing, and the powers of our AI systems are continuing to evolve. This makes it urgent to
accelerate our efforts to better address the social alignment of AI. If we already have difficulty
21 In related work, Korinek (2021) proposes the establishment of an AI Control Council to further these
objectives.
21
addressing the AI alignment problems we face now, how can we hope to do so in the future
when the powers of our AI systems have advanced by another order of magnitude? Creating
the right governance institutions to address the social AI alignment problem is therefore one of
the most urgent challenges of our time.
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Mediating Artificial Intelligence Developments through Negative and Positive Incentives
Abstract
--------
The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to “win”. Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices. Moreover, in the latter regimes, rewards do not suffer from the issue of over-regulation as is the case for punishment. Overall, our findings provide valuable insights into the nature and kinds of regulatory actions most suitable to improve safety compliance in the contexts of both smooth and sudden technological shifts.
Introduction
------------
With the current business and governmental anxiety about AI and the promises made about the impact of AI technology, there is a risk for stake-holders to cut corners, preferring rapid deployment of their AI technology over an adherence to safety and ethical procedures, or a willingness to examine their societal impact [[1](#bib.bib1), [2](#bib.bib2), [3](#bib.bib3)].
Agreements and regulations for safety and ethics can be enacted by involved parties so as to ensure their compliance concerning mutually adopted standards and norms [[4](#bib.bib4)]. However, experience with a spate of international treaties, like those of climate change, timber, and fisheries agreements [[5](#bib.bib5), [6](#bib.bib6), [7](#bib.bib7)] has shown, the autonomy and sovereignty of the parties involved will make monitoring and compliance enforcement difficult (if not impossible). Therefore, for all to enjoy the benefits provided by safe, ethical and trustworthy AI, it is crucial to design and impose appropriate incentivising strategies in order to ensure mutual benefits and safety-compliance from all sides involved.
Given these concerns, many calls for developing efficient forms of regulation have been made [[8](#bib.bib8), [2](#bib.bib2), [9](#bib.bib9)].
In this paper, we aim to understand how different forms of incentives can be efficiently used to influence safety decision making within a development race for domain supremacy through AI (DSAI), resorting to population dynamics and Evolutionary Game Theory (EGT) [[10](#bib.bib10), [11](#bib.bib11), [12](#bib.bib12)].
Although AI development is used here to frame the model and to discuss the results, both model and conclusions may easily be adopted for other technology races, especially where a winner-takes-all situation occurs [[13](#bib.bib13), [14](#bib.bib14), [15](#bib.bib15)].
We posit that it requires time to reach DSAI, modelling this by a number of development steps or technological advancement rounds [[16](#bib.bib16)]. In each round the development teams (or players) need to choose between one of two strategic options: to follow safety precautions (the SAFE action) or ignore safety precautions (the UNSAFE action). Because it takes more time and more effort to comply with precautionary requirements, playing SAFE is not just costlier, but implies slower development speed too, compared to playing UNSAFE. We consequently assume that to play SAFE involves paying a cost c>0𝑐0c>0italic\_c > 0, while playing UNSAFE costs nothing (c=0𝑐0c=0italic\_c = 0). Moreover, the development speed of playing UNSAFE is s>1𝑠1s>1italic\_s > 1 whilst the speed of playing SAFE is normalised to s=1𝑠1s=1italic\_s = 1.
The interaction is iterated until one or more teams establish DSAI, which occurs probabilistically, i.e. the model assumes, upon completion of each round, that there is a probability ω𝜔\omegaitalic\_ω that another development round is required to reach DSAI—which results in an average number W=(1−ω)−1𝑊superscript1𝜔1W=(1-\omega)^{-1}italic\_W = ( 1 - italic\_ω ) start\_POSTSUPERSCRIPT - 1 end\_POSTSUPERSCRIPT of rounds per competition/race [[12](#bib.bib12)]. We thus do not make any assumption about the time required to reach DSAI in a given domain. Yet once the race ends, a large benefit or prize B𝐵Bitalic\_B is acquired that is shared amongst those reaching the target simultaneously.
The DSAI model further assumes that a development setback or disaster might occur, with a probability assumed to increase with the number of occasions the safety requirements have been omitted by the winning team(s) at each round. Although many potential AI disaster scenarios have been sketched [[1](#bib.bib1), [17](#bib.bib17)], the uncertainties in accurately predicting these outcomes have been shown to be high.
When such a disaster occurs, the risk-taking participant loses all its accumulated benefits, which is denoted by prsubscript𝑝𝑟p\_{r}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT, the risk probability of such a disaster occurring when no safety precaution is followed (see Materials and Methods section for further details).
As shown in [[16](#bib.bib16)], when the time-scale of reaching the target is short, such that the average benefit over all the development rounds, i.e. B/W𝐵𝑊B/Witalic\_B / italic\_W, is significantly larger compared to the intermediate benefit b𝑏bitalic\_b obtained in every round, there is a large parameter space where societal interest is in conflict with the personal one: unsafe behaviour is dominant despite the fact that safe development would lead to a greater social welfare (see region II in Figure [2](#Sx3.F2 "Fig 2 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") and Supporting Information (SI) for details). The reason is that, those who completely ignore safety precautions can always achieve the big prize B𝐵Bitalic\_B when playing against safe participants.
The two other zones, i.e. region I and region III in Figure [2](#Sx3.F2 "Fig 2 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), do not suffer from a dilemma between individual and group benefits as is the case for region II. Whereas in region I safe development is preferred due to excessively high risks, region III prefers unsafe, risk taking behaviour, both from an individual and societal perspective.
From a regulatory perspective, only region II requires additional measures that ensure or enhance safe and globally beneficial outcomes, avoiding any potential disaster. Large-scale surveys and expert analysis of the beliefs and predictions about the progress in AI, indicate that the perceived time-scale for supremacy across domains through AI as well as regions is highly diverse [[18](#bib.bib18), [19](#bib.bib19)].
Also note that despite focusing on DSAI in this paper, the proposed model is generally applicable to any kind of long-term competing situations such as technological innovation development and patent racing where there is a significant advantage (i.e. large B𝐵Bitalic\_B) to be achieved by reaching an important target first [[13](#bib.bib13), [14](#bib.bib14), [15](#bib.bib15)]. Other domains include pharmaceutical development where firms could try to cut corners by not following safe clinical trial protocols in an effort to be the first to develop a pharmaceutical produce (i.e. a cure for cancer), in order to take the highest possible share of the market benefit [[20](#bib.bib20)]; Besides tremendous economic advantage, a winner of a vaccine race such as for Covid-19 treatment, can also gain significant political and reputation influence [[21](#bib.bib21)].
In this paper, we explore whether and how incentives such as reward and punishment can help in avoiding disasters and generate a wide benefit of AI-based solutions. Namely, players can attempt to prevent others from moving as fast as they want (i.e., an elementary form of punishment of wrong-doers) or help others to speed up their development (rewarding right-doers), at a given cost. Slowing down unsafe participants can be obtained by reporting misconduct to authorities and media, or by refusal to share and collaborate with companies not following the same deontological principles. Similarly, rewards can correspond to support, exchange of knowledge, staff, etc. of safety conscious participants. Note that reasons for intervening with the development speed of competitors may also be nefarious, e.g. cyber-attacks, in order to get a speed advantage. The current work only considers interventions by safe players as a result of the unsafe behaviour of co-players. We show that both negative and positive incentives can be efficient and naturally self-organize (even when costly). However, we also show that such incentives should be carefully introduced, as they can have negative effects otherwise. To this end, we identify the conditions under which positive and negative incentives are conducive to desired collective outcomes.
### Related Work
Although there have been a number of proposals and debates on how to avert, regulate, or mediate a race for technological supremacy [[8](#bib.bib8), [2](#bib.bib2), [22](#bib.bib22), [4](#bib.bib4), [23](#bib.bib23), [9](#bib.bib9), [24](#bib.bib24)], few formal modelling studies were proposed [[1](#bib.bib1), [16](#bib.bib16)]. The current paper takes the next step, further filling this gap. Namely, it will resort to Evolutionary Game Theory (EGT) methods to investigate how positive and negative incentives can improve the outcomes of DSAI and, more generally, a broad class of innovation race dynamics.
Incentives such as punishment and rewards have been shown to provide important mechanisms to promote the emergence of positive behaviour (such as cooperation and fairness) in the context of social dilemmas [[25](#bib.bib25), [26](#bib.bib26), [27](#bib.bib27), [28](#bib.bib28), [29](#bib.bib29), [30](#bib.bib30), [31](#bib.bib31), [32](#bib.bib32), [33](#bib.bib33), [34](#bib.bib34), [35](#bib.bib35)].
Notwithstanding, all existing modelling approaches to AI governance [[1](#bib.bib1), [16](#bib.bib16)] do not
study how incentives can be used to enhance safety compliance. Moreover, there have been incentive-modelling studies addressing other kinds of risk, such as climate change and nuclear war, see e.g. [[36](#bib.bib36), [32](#bib.bib32), [37](#bib.bib37)]. Following from an analysis of several large global catastrophic risks [[17](#bib.bib17)], it has been shown that the race for domain supremacy through AI and its related risks are rather unique. Analyses of climate change disasters primarily focus on participants’ unwillingness to take upon themselves some personal cost for a desired collective target, and implies a collective risk for all parties involved [[32](#bib.bib32)]. In contrast, in a race to become leader in a particular AI application domain, the winner(s) will extract significant advantage relative to that of others. More importantly, this AI risk is also more directed towards individual developers or users than collective ones.
Materials and methods
---------------------
### DSAIR model definition
Let us depart from the innovation race or domain supremacy through AI race (DSAIR) model developed in [[16](#bib.bib16)]. We adopt a two-player repeated game, consisting of, on average, W𝑊Witalic\_W rounds.
At each development round, players can collect benefits from their intermediate AI products, subject to whether they choose playing SAFE or UNSAFE. By assuming some fixed benefit, b𝑏bitalic\_b, resulting from the AI market, the teams share this benefit in proportion to their development speed.
Hence, for every round of the race, we can write, with respect to the row player i𝑖iitalic\_i, a payoff matrix denoted by ΠΠ\Piroman\_Π, where each entry is represented by ΠijsubscriptΠ𝑖𝑗\Pi\_{ij}roman\_Π start\_POSTSUBSCRIPT italic\_i italic\_j end\_POSTSUBSCRIPT (with j𝑗jitalic\_j corresponding to a column), as follows
| | | | |
| --- | --- | --- | --- |
| | Π=𝑆𝐴𝐹𝐸𝑈𝑁𝑆𝐴𝐹𝐸𝑆𝐴𝐹𝐸( −c+b2−c+bs+1) 𝑈𝑁𝑆𝐴𝐹𝐸sbs+1b2.\Pi=\bordermatrix{~{}&\textit{SAFE}&\textit{UNSAFE}\cr\textit{SAFE}&-c+\frac{b}{2}&-c+\frac{b}{s+1}\cr\textit{UNSAFE}&\frac{sb}{s+1}&\frac{b}{2}\cr}.roman\_Π = start\_ROW start\_CELL end\_CELL start\_CELL end\_CELL start\_CELL SAFE end\_CELL start\_CELL UNSAFE end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL SAFE end\_CELL start\_CELL italic\_( end\_CELL start\_CELL - italic\_c + divide start\_ARG italic\_b end\_ARG start\_ARG 2 end\_ARG end\_CELL start\_CELL - italic\_c + divide start\_ARG italic\_b end\_ARG start\_ARG italic\_s + 1 end\_ARG end\_CELL start\_CELL italic\_) end\_CELL end\_ROW start\_ROW start\_CELL UNSAFE end\_CELL start\_CELL end\_CELL start\_CELL divide start\_ARG italic\_s italic\_b end\_ARG start\_ARG italic\_s + 1 end\_ARG end\_CELL start\_CELL divide start\_ARG italic\_b end\_ARG start\_ARG 2 end\_ARG end\_CELL start\_CELL end\_CELL end\_ROW . | | (1) |
The payoff matrix can be explained as follows. First of all, whenever two SAFE players interact, each will pay the cost c𝑐citalic\_c and share the resulting benefit b𝑏bitalic\_b. Differently, when two UNSAFE players interact, each will share the benefit b𝑏bitalic\_b without having to pay c𝑐citalic\_c.
When a SAFE player interacts with an UNSAFE player, the SAFE one pays a cost c𝑐citalic\_c and receives a (smaller) part b/(s+1)𝑏𝑠1b/(s+1)italic\_b / ( italic\_s + 1 ) of the benefit b𝑏bitalic\_b, while the UNSAFE one obtains the larger part sb/(s+1)𝑠𝑏𝑠1sb/(s+1)italic\_s italic\_b / ( italic\_s + 1 ) without having to pay c𝑐citalic\_c. Note that ΠΠ\Piroman\_Π is a simplification of the matrix defined in [[16](#bib.bib16)] since it was shown that the parameters defined here are sufficient to explain the results in the current time-scale.
We will analyse evolutionary outcomes of safety behaviour within a well-mixed, finite population consisting of Z𝑍Zitalic\_Z players, who repeatedly interact with each other in the AI development process. They will adopt one of the following two strategies [[16](#bib.bib16)]:
* •
AS: always complies with safety precaution, playing SAFE in all the rounds.
* •
AU: never complies with safety precaution, playing UNSAFE in all the rounds.
The payoff matrix defining averaged payoffs for AU vs AS is given by
| | | | |
| --- | --- | --- | --- |
| | 𝐴𝑆𝐴𝑈𝐴𝑆( B2W+Π11Π12) 𝐴𝑈p(sBW+Π21)p(sB2W+Π22),\bordermatrix{~{}&\textit{AS}&\textit{AU}\cr\textit{AS}&\frac{B}{2W}+\Pi\_{11}&\Pi\_{12}\cr\textit{AU}&p\left(\frac{sB}{W}+\Pi\_{21}\right)&p\left(\frac{sB}{2W}+\Pi\_{22}\right)\cr},start\_ROW start\_CELL end\_CELL start\_CELL end\_CELL start\_CELL AS end\_CELL start\_CELL AU end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL AS end\_CELL start\_CELL italic\_( end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL roman\_Π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT end\_CELL start\_CELL italic\_) end\_CELL end\_ROW start\_ROW start\_CELL AU end\_CELL start\_CELL end\_CELL start\_CELL italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL end\_CELL end\_ROW , | | (2) |
where, solely with the purpose of presentation, we denote p=1−pr𝑝1subscript𝑝𝑟p=1-p\_{r}italic\_p = 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT.
As was shown in [[16](#bib.bib16)] by considering when AU is risk-dominant against AS, three different regions can be identified in the parameter space s𝑠sitalic\_s-prsubscript𝑝𝑟p\_{r}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT (see Figure [2](#Sx3.F2 "Fig 2 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods")), details are provided in SI): (I) when pr>1−13ssubscript𝑝𝑟113𝑠p\_{r}>1-\frac{1}{3s}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG 3 italic\_s end\_ARG, AU is risk-dominated by AS: safety compliance is both the preferred collective outcome and selected by evolution; (II) when 1−13s>pr>1−1s113𝑠subscript𝑝𝑟11𝑠1-\frac{1}{3s}>p\_{r}>1-\frac{1}{s}1 - divide start\_ARG 1 end\_ARG start\_ARG 3 italic\_s end\_ARG > italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s end\_ARG: even though it is more desirable to ensure safety compliance as the collective outcome, social learning dynamics would lead the population to the state wherein the safety precaution is mostly ignored; (III) when pr<1−1ssubscript𝑝𝑟11𝑠p\_{r}<1-\frac{1}{s}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT < 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s end\_ARG (AU is risk-dominant against AS), then unsafe development is both preferred collectively and selected by social learning dynamics.
It is worthy of note that adding a conditional strategy (that, for instance, plays SAFE in the first round and thereafter adopts the same move its co-player used on the previous round) does not influence the dynamics or improve safe outcomes (see details in SI). This is contrary to the prevalent models of direct reciprocity in the repeated social dilemmas context [[12](#bib.bib12), [38](#bib.bib38), [39](#bib.bib39)].
Therefore, additional measures need to be put in place for driving the race dynamics towards a more beneficial outcome. To this end, we came to explore in this work the effects of negative (sanctions) and positive (rewards) incentives.
### Punishment and reward in innovation races
Given the DSAIR model one can now introduce incentives that affect the development speed of the players. These incentives reduce or increase the speed of development of a player as this is the key factor in gaining b𝑏bitalic\_b as well as B𝐵Bitalic\_B once the game ends [[16](#bib.bib16)]. While there are many ways to incorporate them, we assume here a minimal model where the effect on speed is constant and fixed over time, hence not cumulative with the number of unsafe or safe actions of the co-player. Given this constant assumption, a negative incentive reduces the speed of a co-player taking an UNSAFE action to a lower but constant speed-level. Similarly, a positive incentive increases the speed of a co-player that took a safe action to a fixed higher speed-level. In both cases these incentives are attributed in the next round, after observing the UNSAFE or SAFE action respectively. Moreover, both positive and negative incentives are considered to be costly, meaning that the strategy that awards them will reduce its own speed by providing the incentive. Given these assumptions the following two strategies are studied in relation to the AS and AU strategies defined earlier:
* •
A strategy PS that always plays SAFE but will sanction the co-player after she has played UNSAFE in the previous round. The punishment by PS imposes a reduction sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT on the opponent’s speed as well as a reduction sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT on her own speed (see Figure [1](#Sx3.F1 "Fig 1 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), orange line/area).
* •
A strategy RS that always chooses the SAFE action and will reward a SAFE action of a co-player by increasing her speed with sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT while paying a cost sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT on her own speed (see Figure [1](#Sx3.F1 "Fig 1 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), blue line/area).
The analysis performed in the Results section aims to show whether having PS or/and RS in the population leads to more societal welfare in the region (II), where there is a conflict between individual and societal interests. The methods used in this analysis are discussed in the next section.
### Evolutionary Dynamics for Finite Populations
We employ EGT methods for finite populations [[12](#bib.bib12), [40](#bib.bib40), [41](#bib.bib41)], whether in the analytical or numerical results obtained here. Within such a setting, the players’ payoffs stand for their *fitness* or social *success*, and social learning shapes the evolutionary dynamics, according to which the most successful players will more often tend to be imitated by other players. Social learning is herein modeled utilising the so-called pairwise comparison rule [[40](#bib.bib40)], assuming that a player A𝐴Aitalic\_A with fitness fAsubscript𝑓𝐴f\_{A}italic\_f start\_POSTSUBSCRIPT italic\_A end\_POSTSUBSCRIPT adopts the strategy of another player B𝐵Bitalic\_B with fitness fBsubscript𝑓𝐵f\_{B}italic\_f start\_POSTSUBSCRIPT italic\_B end\_POSTSUBSCRIPT with probability assigned by the Fermi function,
PA,B=(1+e−β(fB−fA))−1subscript𝑃𝐴𝐵superscript1superscript𝑒𝛽subscript𝑓𝐵subscript𝑓𝐴1P\_{A,B}=\left(1+e^{-\beta(f\_{B}-f\_{A})}\right)^{-1}italic\_P start\_POSTSUBSCRIPT italic\_A , italic\_B end\_POSTSUBSCRIPT = ( 1 + italic\_e start\_POSTSUPERSCRIPT - italic\_β ( italic\_f start\_POSTSUBSCRIPT italic\_B end\_POSTSUBSCRIPT - italic\_f start\_POSTSUBSCRIPT italic\_A end\_POSTSUBSCRIPT ) end\_POSTSUPERSCRIPT ) start\_POSTSUPERSCRIPT - 1 end\_POSTSUPERSCRIPT,
where β𝛽\betaitalic\_β conveniently describes the intensity of selection. The long-term frequency of each and every strategy in a population where several of them are in co-presence, can be computed simply by calculating the stationary distribution of a Markov chain whose states represent those strategies. In the absence of behavioural exploration or mutations, end states of evolution inevitably are monomorphic. That is, whenever
such a state is reached, it cannot be escaped via imitation. Thus, we further assume that, with some mutation probability, an agent can freely explore its behavioural space (in our case, consisting of two actions, SAFE and UNSAFE), randomly adopts an action therein. At the limit of a small mutation probability, the population consists of at most two strategies at any time. Consequently, the social dynamics can be described using a Markov Chain, where its state represents a monomorphic population and its transition probabilities are given by the fixation probability of a single mutant [[42](#bib.bib42), [43](#bib.bib43)]. The Markov Chain’s stationary distribution describes the time average the population spends in each of the monomorphic end states (see already the examples in Figure [3](#Sx3.F3 "Fig 3 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") for illustration).
Denote by πX,Ysubscript𝜋𝑋𝑌\pi\_{X,Y}italic\_π start\_POSTSUBSCRIPT italic\_X , italic\_Y end\_POSTSUBSCRIPT the payoff a strategist X obtains in a pairwise interaction with strategist Y𝑌Yitalic\_Y (defined in the payoff matrices). Suppose there exist at most two strategies in the population, say, k𝑘kitalic\_k agents using strategy A (0≤k≤Z0𝑘𝑍0\leq k\leq Z0 ≤ italic\_k ≤ italic\_Z) and (Z−k)𝑍𝑘(Z-k)( italic\_Z - italic\_k ) agents using strategies B. Thus, the (average) payoff of the agent that uses A and B can be written as follows, respectively,
| | | | |
| --- | --- | --- | --- |
| | ΠA(k)=(k−1)πA,A+(Z−k)πA,BZ−1,ΠB(k)=kπB,A+(Z−k−1)πB,BZ−1.formulae-sequencesubscriptΠ𝐴𝑘𝑘1subscript𝜋𝐴𝐴𝑍𝑘subscript𝜋𝐴𝐵𝑍1subscriptΠ𝐵𝑘𝑘subscript𝜋𝐵𝐴𝑍𝑘1subscript𝜋𝐵𝐵𝑍1\begin{split}\Pi\_{A}(k)&=\frac{(k-1)\pi\_{A,A}+(Z-k)\pi\_{A,B}}{Z-1},\\
\Pi\_{B}(k)&=\frac{k\pi\_{B,A}+(Z-k-1)\pi\_{B,B}}{Z-1}.\end{split}start\_ROW start\_CELL roman\_Π start\_POSTSUBSCRIPT italic\_A end\_POSTSUBSCRIPT ( italic\_k ) end\_CELL start\_CELL = divide start\_ARG ( italic\_k - 1 ) italic\_π start\_POSTSUBSCRIPT italic\_A , italic\_A end\_POSTSUBSCRIPT + ( italic\_Z - italic\_k ) italic\_π start\_POSTSUBSCRIPT italic\_A , italic\_B end\_POSTSUBSCRIPT end\_ARG start\_ARG italic\_Z - 1 end\_ARG , end\_CELL end\_ROW start\_ROW start\_CELL roman\_Π start\_POSTSUBSCRIPT italic\_B end\_POSTSUBSCRIPT ( italic\_k ) end\_CELL start\_CELL = divide start\_ARG italic\_k italic\_π start\_POSTSUBSCRIPT italic\_B , italic\_A end\_POSTSUBSCRIPT + ( italic\_Z - italic\_k - 1 ) italic\_π start\_POSTSUBSCRIPT italic\_B , italic\_B end\_POSTSUBSCRIPT end\_ARG start\_ARG italic\_Z - 1 end\_ARG . end\_CELL end\_ROW | | (3) |
Now, in each time step, the probability of change by ±plus-or-minus\pm±1 of a number of k𝑘kitalic\_k agents using strategy A can be specified as[[40](#bib.bib40)]
| | | | |
| --- | --- | --- | --- |
| | T±(k)=Z−kZkZ[1+e∓β[ΠA(k)−ΠB(k)]]−1.superscript𝑇plus-or-minus𝑘𝑍𝑘𝑍𝑘𝑍superscriptdelimited-[]1superscript𝑒minus-or-plus𝛽delimited-[]subscriptΠ𝐴𝑘subscriptΠ𝐵𝑘1T^{\pm}(k)=\frac{Z-k}{Z}\frac{k}{Z}\left[1+e^{\mp\beta[\Pi\_{A}(k)-\Pi\_{B}(k)]}\right]^{-1}.italic\_T start\_POSTSUPERSCRIPT ± end\_POSTSUPERSCRIPT ( italic\_k ) = divide start\_ARG italic\_Z - italic\_k end\_ARG start\_ARG italic\_Z end\_ARG divide start\_ARG italic\_k end\_ARG start\_ARG italic\_Z end\_ARG [ 1 + italic\_e start\_POSTSUPERSCRIPT ∓ italic\_β [ roman\_Π start\_POSTSUBSCRIPT italic\_A end\_POSTSUBSCRIPT ( italic\_k ) - roman\_Π start\_POSTSUBSCRIPT italic\_B end\_POSTSUBSCRIPT ( italic\_k ) ] end\_POSTSUPERSCRIPT ] start\_POSTSUPERSCRIPT - 1 end\_POSTSUPERSCRIPT . | | (4) |
The fixation probability of a single mutant adopting A, in a population of (Z−1)𝑍1(Z-1)( italic\_Z - 1 ) agents adopting B, is specified by [[40](#bib.bib40), [43](#bib.bib43)]
| | | | |
| --- | --- | --- | --- |
| | ρB,A=(1+∑i=1Z−1∏j=1iT−(j)T+(j))−1.subscript𝜌𝐵𝐴superscript1superscriptsubscript𝑖1𝑍1superscriptsubscriptproduct𝑗1𝑖superscript𝑇𝑗superscript𝑇𝑗1\rho\_{B,A}=\left(1+\sum\_{i=1}^{Z-1}\prod\_{j=1}^{i}\frac{T^{-}(j)}{T^{+}(j)}\right)^{-1}.italic\_ρ start\_POSTSUBSCRIPT italic\_B , italic\_A end\_POSTSUBSCRIPT = ( 1 + ∑ start\_POSTSUBSCRIPT italic\_i = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_Z - 1 end\_POSTSUPERSCRIPT ∏ start\_POSTSUBSCRIPT italic\_j = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_i end\_POSTSUPERSCRIPT divide start\_ARG italic\_T start\_POSTSUPERSCRIPT - end\_POSTSUPERSCRIPT ( italic\_j ) end\_ARG start\_ARG italic\_T start\_POSTSUPERSCRIPT + end\_POSTSUPERSCRIPT ( italic\_j ) end\_ARG ) start\_POSTSUPERSCRIPT - 1 end\_POSTSUPERSCRIPT . | | (5) |
When considering a set {1,…,s}1…𝑠\{1,...,s\}{ 1 , … , italic\_s } of distinct strategies, these fixation probabilities determine the Markov Chain transition matrix M={Tij}i,j=1s𝑀superscriptsubscriptsubscript𝑇𝑖𝑗𝑖𝑗
1𝑠M=\{T\_{ij}\}\_{i,j=1}^{s}italic\_M = { italic\_T start\_POSTSUBSCRIPT italic\_i italic\_j end\_POSTSUBSCRIPT } start\_POSTSUBSCRIPT italic\_i , italic\_j = 1 end\_POSTSUBSCRIPT start\_POSTSUPERSCRIPT italic\_s end\_POSTSUPERSCRIPT, with Tij,j≠i=ρji/(s−1)subscript𝑇𝑖𝑗𝑗
𝑖subscript𝜌𝑗𝑖𝑠1T\_{ij,j\neq i}=\rho\_{ji}/(s-1)italic\_T start\_POSTSUBSCRIPT italic\_i italic\_j , italic\_j ≠ italic\_i end\_POSTSUBSCRIPT = italic\_ρ start\_POSTSUBSCRIPT italic\_j italic\_i end\_POSTSUBSCRIPT / ( italic\_s - 1 ) and Tii=1−∑j=1,j≠isTijsubscript𝑇𝑖𝑖1subscriptsuperscript𝑠formulae-sequence𝑗1𝑗𝑖subscript𝑇𝑖𝑗T\_{ii}=1-\sum^{s}\_{j=1,j\neq i}T\_{ij}italic\_T start\_POSTSUBSCRIPT italic\_i italic\_i end\_POSTSUBSCRIPT = 1 - ∑ start\_POSTSUPERSCRIPT italic\_s end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_j = 1 , italic\_j ≠ italic\_i end\_POSTSUBSCRIPT italic\_T start\_POSTSUBSCRIPT italic\_i italic\_j end\_POSTSUBSCRIPT. The normalized eigenvector of the transposed of M𝑀Mitalic\_M associated with the eigenvalue 1 provides the above described stationary distribution [[42](#bib.bib42)], which defines the relative time the population spends while adopting each of the strategies.
#### Risk-dominance
An important approach for comparing two strategies A and B is that of in which direction the transition is stronger or more probable, that of an A mutant fixating in a population of agents employing B, ρB,Asubscript𝜌𝐵𝐴\rho\_{B,A}italic\_ρ start\_POSTSUBSCRIPT italic\_B , italic\_A end\_POSTSUBSCRIPT, or that of a B mutant fixating in the population of agents employing A, ρA,Bsubscript𝜌𝐴𝐵\rho\_{A,B}italic\_ρ start\_POSTSUBSCRIPT italic\_A , italic\_B end\_POSTSUBSCRIPT. In the limit, for large population size (large Z𝑍Zitalic\_Z), this condition can be simplified to [[12](#bib.bib12)]
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| | πA,A+πA,B>πB,A+πB,B.subscript𝜋𝐴𝐴subscript𝜋𝐴𝐵subscript𝜋𝐵𝐴subscript𝜋𝐵𝐵\pi\_{A,A}+\pi\_{A,B}>\pi\_{B,A}+\pi\_{B,B}.italic\_π start\_POSTSUBSCRIPT italic\_A , italic\_A end\_POSTSUBSCRIPT + italic\_π start\_POSTSUBSCRIPT italic\_A , italic\_B end\_POSTSUBSCRIPT > italic\_π start\_POSTSUBSCRIPT italic\_B , italic\_A end\_POSTSUBSCRIPT + italic\_π start\_POSTSUBSCRIPT italic\_B , italic\_B end\_POSTSUBSCRIPT . | | (6) |
Fig 1: Effect of positive and negative incentives on players’ speed. On the on hand, when player 1 is of type PS (blue circle on x-axis), i.e. sanctioning unsafe actions, it reduces the future speed of player 2 when she is of type AU (orange circle on the y-axis), while paying a speed cost, possibly equivalent to the reduction in speed that the AU player is experiencing (orange line). In general the reduction of speeds of player 1 and 2 fall into the area marked by the orange rectangle. On the other hand, when player 1 is of type RS (blue circle on x-axis), i.e. rewarding safe actions, it increases the speed of player 2 (green circle on y-axis), while paying a speed cost that reduces the RS player’s speed. Differently from before, the speed effect is in opposing directions for the two players. The blue rectangle marks the area of the speed of player 1 and player 2. In the analysis in the paper, first the case of equal speed effects is considered (lines) before analysing different speed effects (rectangles) between both players.
Fig 2: Frequency of AU in a population of AU and AS. Region (II): The two solid lines inside the plots delineate the boundaries pr∈[1−1/s,1−1/(3s)]subscript𝑝𝑟11𝑠113𝑠p\_{r}\in[1-1/s,1-1/(3s)]italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ∈ [ 1 - 1 / italic\_s , 1 - 1 / ( 3 italic\_s ) ] where safety compliance is the preferred collective outcome yet evolution selects unsafe development. Regions (I) and (III) display where safe (respectively, unsafe) development is not only the preferred collective outcome but also the one selected by evolution. Parameters: b=4𝑏4b=4italic\_b = 4, c=1𝑐1c=1italic\_c = 1, W=100𝑊100W=100italic\_W = 100, B=104𝐵superscript104B=10^{4}italic\_B = 10 start\_POSTSUPERSCRIPT 4 end\_POSTSUPERSCRIPT, β=0.01𝛽0.01\beta=0.01italic\_β = 0.01, Z=100𝑍100Z=100italic\_Z = 100.
Fig 3: Transitions and stationary distributions in a population of three strategies AU, AS, with either PS (top row) or RS (bottom row), for three regions. Only stronger transitions are shown for clarity. Dashed lines denote neutral transitions. Parameters: sα=sβ=1.0subscript𝑠𝛼subscript𝑠𝛽1.0s\_{\alpha}=s\_{\beta}=1.0italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT = italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = 1.0, c=1𝑐1c=1italic\_c = 1, b=4𝑏4b=4italic\_b = 4, W=100𝑊100W=100italic\_W = 100, B=10000𝐵10000B=10000italic\_B = 10000, β=0.01𝛽0.01\beta=0.01italic\_β = 0.01, Z=100𝑍100Z=100italic\_Z = 100.
Fig 4: (a) Risk-dominant condition of PS against AU, as defined in Equation [7](#Sx4.E7 "7 ‣ Negative incentives are a double-edged sword ‣ Results"), for different ratio sα/ssubscript𝑠𝛼𝑠s\_{\alpha}/sitalic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT / italic\_s. The two solid lines correspond to when the ratio is 0 and 1, corresponding to the boundaries pr∈[1−1/s,1−1/(3s)]subscript𝑝𝑟11𝑠113𝑠p\_{r}\in[1-1/s,1-1/(3s)]italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ∈ [ 1 - 1 / italic\_s , 1 - 1 / ( 3 italic\_s ) ]. The larger the ratio the smaller the Region (II) (between this line and the black line) is decreased, which disappears when sα=ssubscript𝑠𝛼𝑠s\_{\alpha}=sitalic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT = italic\_s. Panel (b): frequency of AU in a population of AS, AU, and PS (for sα=3s/4subscript𝑠𝛼3𝑠4s\_{\alpha}=3s/4italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT = 3 italic\_s / 4). Region (II) is split into two (IIa) and (IIb) where PS is now also be preferred to AU in the first one. Parameters: b=4𝑏4b=4italic\_b = 4, c=1𝑐1c=1italic\_c = 1, W=100𝑊100W=100italic\_W = 100, B=10000𝐵10000B=10000italic\_B = 10000, β=0.01𝛽0.01\beta=0.01italic\_β = 0.01, Z=100𝑍100Z=100italic\_Z = 100.
Fig 5: AU Frequency: Reward (top row) vs punishment (bottom row) for varying sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, for three regions. In (I), both lead to no AU, as desired. In (II), punishment is more efficient except for when reward is rather costly but highly cost-efficient (the areas inside the white triangles). It is noteworthy that RS has very low frequency in all cases, as it catalyses the success of AS. In (III), RS always leads to the desired outcome of high AU frequency, while PS might lead to an undesired result of a reduced AU frequency (over-regulation) when highly efficient (non-red area). Parameters: b=4𝑏4b=4italic\_b = 4, c=1𝑐1c=1italic\_c = 1, W=100𝑊100W=100italic\_W = 100,
B=10000𝐵10000B=10000italic\_B = 10000, s=1.5𝑠1.5s=1.5italic\_s = 1.5, β=0.01𝛽0.01\beta=0.01italic\_β = 0.01, population size, Z=100𝑍100Z=100italic\_Z = 100.
Results
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### Negative incentives are a double-edged sword
As explained in Methods PS reduces the speed of an AU player from s𝑠sitalic\_s to s−sβ𝑠subscript𝑠𝛽s-s\_{\beta}italic\_s - italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, while reducing its own speed from 1111 (since it plays always SAFE) to 1−sα1subscript𝑠𝛼1-s\_{\alpha}1 - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT. Hence one can define s′=1−sαsuperscript𝑠′1subscript𝑠𝛼s^{\prime}=1-s\_{\alpha}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT = 1 - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT as the new speed for PS and s′′=s−sβsuperscript𝑠′′𝑠subscript𝑠𝛽s^{\prime\prime}=s-s\_{\beta}italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT = italic\_s - italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT as the new speed for AU. Depending on the values of sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, these speeds may also be zero or even negative, which represent situations where no progress is being made or where punishment even destroys existing development, respectively. In the following we consider these situations in two different ways. First, a theoretical analysis is performed for the situation where sβ=sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}=s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT. Second, this assumption is relaxed and a numerical study of the generalised case is provided.
There are two scenarios to consider when sβ=sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}=s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT: (i) when sα≥ssubscript𝑠𝛼𝑠s\_{\alpha}\geq sitalic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ≥ italic\_s and (ii) when it is not. In scenario (i), s′superscript𝑠′s^{\prime}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT and s′′superscript𝑠′′s^{\prime\prime}italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT are non-positive, resulting in an infinite number of rounds since the target can never be reached. The average payoffs of PS and AU when playing against each other are thus −c𝑐-c- italic\_c and 00, respectively (assuming that when a team’s development speed is non-positive, its intermediate benefit, b𝑏bitalic\_b, is zero).
The condition for PS to be risk-dominant against AU (see Equation [6](#Sx3.E6 "6 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") in Methods, and noting that the payoff of PS against another PS is the same as that of AS against another AS) reads
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| | (1−pr)(sB2W+Π22)<B2W+Π11−c.1subscript𝑝𝑟𝑠𝐵2𝑊subscriptΠ22𝐵2𝑊subscriptΠ11𝑐(1-p\_{r})\left(\frac{sB}{2W}+\Pi\_{22}\right)<\frac{B}{2W}+\Pi\_{11}-c.( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) < divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT - italic\_c . | |
For sufficiently large B𝐵Bitalic\_B (fixing W𝑊Witalic\_W), this condition is reduced to, pr>1−1/ssubscript𝑝𝑟11𝑠p\_{r}>1-1/sitalic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - 1 / italic\_s. That is, PS is risk-dominant against AU for the whole region (II), thereby ensuring that safe behaviour becomes promoted in that dilemma region.
Considering the second case in scenario (ii), where sα<ssubscript𝑠𝛼𝑠s\_{\alpha}<sitalic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT < italic\_s, the game is repeated for W−ss−sα+1=W−sαs−sα𝑊𝑠𝑠subscript𝑠𝛼1𝑊subscript𝑠𝛼𝑠subscript𝑠𝛼\frac{W-s}{s-s\_{\alpha}}+1=\frac{W-s\_{\alpha}}{s-s\_{\alpha}}divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG + 1 = divide start\_ARG italic\_W - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG start\_ARG italic\_s - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG rounds, which we denote here by r𝑟ritalic\_r. Hence, the payoffs of PS and AU when playing with each other are given by, respectively
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| | 1r(π12+(r−1)π12′),1𝑟subscript𝜋12𝑟1subscriptsuperscript𝜋′12\frac{1}{r}\left(\pi\_{12}+(r-1)\pi^{\prime}\_{12}\right),divide start\_ARG 1 end\_ARG start\_ARG italic\_r end\_ARG ( italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT + ( italic\_r - 1 ) italic\_π start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT ) , | |
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| | pr(B+π21+(r−1)π21′),𝑝𝑟𝐵subscript𝜋21𝑟1subscriptsuperscript𝜋′21\frac{p}{r}\left(B+\pi\_{21}+(r-1)\pi^{\prime}\_{21}\right),divide start\_ARG italic\_p end\_ARG start\_ARG italic\_r end\_ARG ( italic\_B + italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT + ( italic\_r - 1 ) italic\_π start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) , | |
where
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| | π12′={−cif s>sα≥1−c+(1−sα)bs+1−2sαif sα<1,subscriptsuperscript𝜋′12cases𝑐if 𝑠subscript𝑠𝛼1𝑐1subscript𝑠𝛼𝑏𝑠12subscript𝑠𝛼if subscript𝑠𝛼1\pi^{\prime}\_{12}=\begin{cases}-c&\mbox{if }s>s\_{\alpha}\geq 1\\
-c+\frac{(1-s\_{\alpha})b}{s+1-2s\_{\alpha}}&\mbox{if }s\_{\alpha}<1\end{cases},italic\_π start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT = { start\_ROW start\_CELL - italic\_c end\_CELL start\_CELL if italic\_s > italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ≥ 1 end\_CELL end\_ROW start\_ROW start\_CELL - italic\_c + divide start\_ARG ( 1 - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ) italic\_b end\_ARG start\_ARG italic\_s + 1 - 2 italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG end\_CELL start\_CELL if italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT < 1 end\_CELL end\_ROW , | |
and
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| | π21′={bif s>sα≥1(s−sα)bs+1−2sαif sα<1.subscriptsuperscript𝜋′21cases𝑏if 𝑠subscript𝑠𝛼1𝑠subscript𝑠𝛼𝑏𝑠12subscript𝑠𝛼if subscript𝑠𝛼1\pi^{\prime}\_{21}=\begin{cases}b&\mbox{if }s>s\_{\alpha}\geq 1\\
\frac{(s-s\_{\alpha})b}{s+1-2s\_{\alpha}}&\mbox{if }s\_{\alpha}<1\end{cases}.italic\_π start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT = { start\_ROW start\_CELL italic\_b end\_CELL start\_CELL if italic\_s > italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ≥ 1 end\_CELL end\_ROW start\_ROW start\_CELL divide start\_ARG ( italic\_s - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ) italic\_b end\_ARG start\_ARG italic\_s + 1 - 2 italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG end\_CELL start\_CELL if italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT < 1 end\_CELL end\_ROW . | |
Thus, for sufficiently large B𝐵Bitalic\_B, PS is risk dominant against AU when
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| | psB2W+prB<B2W,𝑝𝑠𝐵2𝑊𝑝𝑟𝐵𝐵2𝑊p\frac{sB}{2W}+\frac{p}{r}B<\frac{B}{2W},italic\_p divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + divide start\_ARG italic\_p end\_ARG start\_ARG italic\_r end\_ARG italic\_B < divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG , | |
which is simplified to:
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| | pr>1−1s+2Wr.subscript𝑝𝑟11𝑠2𝑊𝑟p\_{r}>1-\frac{1}{s+2Wr}.italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s + 2 italic\_W italic\_r end\_ARG . | | (7) |
This condition is easier to achieve for smaller r𝑟ritalic\_r.
Since r𝑟ritalic\_r is an increasing function of sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT, to optimise the safety outcome, the highest possible sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT should be adopted, i.e. the strongest possible effort in slowing down the opponent should be made. Figure [4](#Sx3.F4 "Fig 4 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods")a shows the condition for different values of sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT in relation to s𝑠sitalic\_s (fixing the ratio sα/ssubscript𝑠𝛼𝑠s\_{\alpha}/sitalic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT / italic\_s). Numerical results in Figure [4](#Sx3.F4 "Fig 4 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods")b for a population of PS, AS and AU corroborate this analytical condition. Equation [7](#Sx4.E7 "7 ‣ Negative incentives are a double-edged sword ‣ Results") splits the region (II) into two parts, (IIa) and (IIb), where PS is now also be preferred to AU in the first one. In part (IIa), the transition is stronger from AU to PS than vice versa (see Figure [3](#Sx3.F3 "Fig 3 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods")b). Recall that in the whole region (II) the transition is stronger from AS to AU, thus leading to a cyclic pattern between these three strategies.
When relaxing the assumption that sβ=sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}=s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT (see SI for the detailed calculation of payoffs), the effect of punishment for all variations of the parameters can be studied. The results are shown in Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") (bottom row), for all the three regions shown in Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") in inverse order. First, when looking at the right panel (bottom row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), one can observe that punishment does not alter the desired outcome (safety behaviour is the preferred outcome) in region (I), i.e. safe behaviour remains dominant. Significant less unsafe behaviour is observed in region (II) , i.e. the middle panel (bottom row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), where it is not desirable, especially when sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT is small and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT is sufficiently large (purple area). However, punishment has an undesirable effect in region (III), i.e. the left panel (bottom row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), as it leads to reduction of AU when punishment is highly efficient (see the non-red area) while AU remains the preferred collective outcome in that region. The reason is that, for sufficiently small sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and large sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT (such that s′>0superscript𝑠′0s^{\prime}>0italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 and s′>s′′superscript𝑠′superscript𝑠′′s^{\prime}>s^{\prime\prime}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT), PS gains significant advantage against AU, thereby dominating it even for low prsubscript𝑝𝑟p\_{r}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT.
In summary, reducing the development speed of unsafe players leads to a positive effect, especially when the personal cost is much less than the effect it induces on the unsafe player. Yet at the same time, it may lead to unwanted sanctioning effects in the region where risk-taking should be promoted.
### Reward vs punishment for promoting safety compliance
Here we investigate how positive incentives, as explained in Methods, influence the outcome in all three regions. The payoff matrix showing average payoffs among three strategies AS, AU and RS reads
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| --- | --- | --- | --- |
| | 𝐴𝑆𝐴𝑈𝑅𝑆𝐴𝑆( B2W+Π11Π12B(1+sβ)W+Π11) 𝐴𝑈p(sBW+Π21)p(sB2W+Π22)p(sBW+Π21)𝑅𝑆Π11Π12B(1+sβ−sα)2W+Π11.\bordermatrix{~{}&\textit{AS}&\textit{AU}&\textit{RS}\cr\textit{AS}&\frac{B}{2W}+\Pi\_{11}&\Pi\_{12}&\frac{B(1+s\_{\beta})}{W}+\Pi\_{11}\cr\textit{AU}&p\left(\frac{sB}{W}+\Pi\_{21}\right)&p\left(\frac{sB}{2W}+\Pi\_{22}\right)&p\left(\frac{sB}{W}+\Pi\_{21}\right)\cr\textit{RS}&\Pi\_{11}&\Pi\_{12}&\frac{B(1+s\_{\beta}-s\_{\alpha})}{2W}+\Pi\_{11}\cr}.start\_ROW start\_CELL end\_CELL start\_CELL end\_CELL start\_CELL AS end\_CELL start\_CELL AU end\_CELL start\_CELL RS end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL AS end\_CELL start\_CELL italic\_( end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL roman\_Π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT end\_CELL start\_CELL divide start\_ARG italic\_B ( 1 + italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT ) end\_ARG start\_ARG italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL italic\_) end\_CELL end\_ROW start\_ROW start\_CELL AU end\_CELL start\_CELL end\_CELL start\_CELL italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL RS end\_CELL start\_CELL end\_CELL start\_CELL roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL roman\_Π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT end\_CELL start\_CELL divide start\_ARG italic\_B ( 1 + italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ) end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL end\_CELL end\_ROW . | | (8) |
The payoff of RS against another RS is given under the assumption that reward is sufficiently cost-efficient, such that 1+sβ>sα1subscript𝑠𝛽subscript𝑠𝛼1+s\_{\beta}>s\_{\alpha}1 + italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT > italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT; otherwise, this payoff would be Π11subscriptΠ11\Pi\_{11}roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT. On the one hand, one can observe that RS is always dominated by AS. On the other hand, the condition for RS to be risk-dominant against AU is given by:
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| | p(sB2W+Π22+sBW+Π21)<Π12+B(1+sβ−sα)2W+Π11,𝑝𝑠𝐵2𝑊subscriptΠ22𝑠𝐵𝑊subscriptΠ21subscriptΠ12𝐵1subscript𝑠𝛽subscript𝑠𝛼2𝑊subscriptΠ11p\left(\frac{sB}{2W}+\Pi\_{22}+\frac{sB}{W}+\Pi\_{21}\right)<\Pi\_{12}+\frac{B(1+s\_{\beta}-s\_{\alpha})}{2W}+\Pi\_{11},italic\_p ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT + divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) < roman\_Π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT + divide start\_ARG italic\_B ( 1 + italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT ) end\_ARG start\_ARG 2 italic\_W end\_ARG + roman\_Π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT , | |
which, for sufficiently large B𝐵Bitalic\_B (fixing W𝑊Witalic\_W), is equivalent to
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| --- | --- | --- | --- |
| | pr>1−1+sβ−sα3s.subscript𝑝𝑟11subscript𝑠𝛽subscript𝑠𝛼3𝑠p\_{r}>1-\frac{1+s\_{\beta}-s\_{\alpha}}{3s}.italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 + italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT - italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT end\_ARG start\_ARG 3 italic\_s end\_ARG . | | (9) |
Hence, RS can improve upon AS when playing against AU whenever sβ>sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}>s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT > italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT (recall that the condition for AS to be risk-dominant against AU is pr>1−1/(3s)subscript𝑝𝑟113𝑠p\_{r}>1-1/(3s)italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - 1 / ( 3 italic\_s )). It is different from the peer punishment strategy PS that can lead to improvement even when sβ≤sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}\leq s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT ≤ italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT.
Thus, under the above condition, a cyclic pattern emerges (see Figure [3](#Sx3.F3 "Fig 3 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods")b): from AS to AU, to RS, then back to AS. In contrast to punishment, the rewarding strategy RS has a very low frequency in general (as it is always dominated by the non-rewarding safe player AS). Nonetheless, RS catalyses the emergence of safe behaviour.
Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") (top row) shows the frequencies of AU in a population with AS and RS, for varying sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, in comparison with those from the punishment model, for the three regions. One can observe that, in region (II), i.e. the middle panel (top row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), punishment is more (or at least as) efficient than reward in suppressing AU except
for when incentivising is rather costly (i.e. sufficiently large sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT) but highly cost-efficient (sβ>sαsubscript𝑠𝛽subscript𝑠𝛼s\_{\beta}>s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT > italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT) (the areas inside the white triangles; see also Figure [7](#Sx7.F7 "Fig 7 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information") in SI for clearer difference with larger β𝛽\betaitalic\_β). It is because only when incentive is highly cost-efficient, RS can take over AU effectively (see again Equation [9](#Sx4.E9 "9 ‣ Reward vs punishment for promoting safety compliance ‣ Results")); and furthermore, the larger both sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT are, the stronger the transition from RS to AS, to a degree that can overcome the transition from AS to AU. For an example satisfying these conditions, where sα=1.5subscript𝑠𝛼1.5s\_{\alpha}=1.5italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT = 1.5 and sβ=3.0subscript𝑠𝛽3.0s\_{\beta}=3.0italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = 3.0, see Figure [10](#Sx7.F10 "Fig 10 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information") in SI.
In regions (I) and (III), i.e. the right and left panels (top row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), similarly to punishment, the rewarding strategy does not change the outcomes, as is desired. Note however that differently from punishment, in region (I), i.e. the right panel (top row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), only AS dominates the population, while in the case of punishment, AS and PS are neutral and together dominate the population (see Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), comparing panels c and f). Most interestingly, rewards do not harm region (III), i.e. the left panel (top row) of Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods"), which suffers from over-regulation in the case of punishment because of the stronger transitions from RS to AS and AS to AU. Additional numerical analysis shows that all these observations are robust for larger β𝛽\betaitalic\_β (see SI, Figure [7](#Sx7.F7 "Fig 7 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information")).
In SI, we also consider the scenario where both peer reward and punishment are present, in a population of four strategies, AS, AU PS and RS (see Figures [8](#Sx7.F8 "Fig 8 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information") and [9](#Sx7.F9 "Fig 9 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information")). Since PS behaves in the same way as AS when interacting with RS, there is always a stronger transition from RS to PS. It results in an outcome in terms of AU frequency similar to the case when only PS is present, suggesting that, in a self-organized scenario, peer-punishment is more likely to prevail than peer-rewarding when individuals face a technological race.
Finally, it is noteworthy that all results obtained in this paper are robust if one considers that with some probability in each round UNSAFE players can be detected resulting in those UNSAFE players losing all payoff in that round [[16](#bib.bib16)]. This observation confirms the observation in that in a short-term AI regime only participants’ speeds matter (in relation to the disaster risk, prsubscript𝑝𝑟p\_{r}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT), and controlling the speeds is important to ensure a beneficial outcome (see also [[16](#bib.bib16)]).
Conclusion
----------
In this paper we study the dynamics associated with technological races, those having the objective of being the first to bring some AI technology to market as a case study. The model proposed, however, is general enough for applicability to other innovation dynamics which face the conflict between safety and rapid development [[13](#bib.bib13), [20](#bib.bib20)]. We address this problem resorting to a multiagent and complex systems approach, while adopting well established methods from evolutionary game theory and populations dynamics .
We propose a plausible adaptation of a baseline model [[16](#bib.bib16)] which can be useful when thinking about policies and regulations, namely incipient forms of community enforcing mechanisms, such as peer rewards and sanctions. We identify the conditions under which these incentives provide the desired effects while highlighting the importance of clarifying the risk disaster regimes and the time-scales associated with the problem. In particular, our results suggest that punishment — by forcibly reducing the development speed of unsafe participants — can generally reduce unsafe behaviour even when sanctions are not particularly efficient. In contrast, when punishment is highly efficient, it can lead to over-regulation and an undesired reduction of innovation, noting that a speedy and unsafe development is acceptable and more beneficial for the whole population whenever the risk for setbacks or disaster is low compared to the extra speed gained by ignoring safety precautions. Similarly, rewarding a safe co-player to speed up its development may, in some regimes, stimulate safe behaviours, whilst avoiding the detrimental impact of over-regulation.
These results show that, similarly to peer incentives in the context of one-shot social dilemmas (such as the Prisoner’s Dilemma and the Public Goods Game) [[25](#bib.bib25), [26](#bib.bib26), [27](#bib.bib27), [28](#bib.bib28), [30](#bib.bib30), [31](#bib.bib31), [32](#bib.bib32), [33](#bib.bib33), [34](#bib.bib34), [35](#bib.bib35)], strategies that target development speed in DSAIR can influence the evolutionary dynamics, but interestingly, they produce some very different effects from those of incentives in social dilemmas [[44](#bib.bib44)]. For example, we have shown that strong punishment, even when highly inefficient, can lead to improvement of safety outcome; while punishment in social dilemmas can promote cooperation only when highly cost-efficient. On the other hand, when punishment is too strong, it might lead to an undesired effect of over-regulation (reducing innovation where desirable), which is not generally the case in social dilemmas.
Our model and analysis of elementary forms of incentives thus provides an instrument for policy makers to
ponder on the supporting mechanisms (e.g. positive and negative incentives), in the context of technological races [[45](#bib.bib45), [46](#bib.bib46), [47](#bib.bib47)].
Concretely, both sanctioning of wrong-doers (e.g. rogue or unsafe developers/teams) and rewarding of right-doers (e.g. safe-compliant developers/teams) can lead to enhancement of the desirable outcome (it being that of innovation or risk-taking in low risk cases, and safety-compliance in higher risk cases). Notably, while the former can be detrimental for innovation in low risk cases, it leads to a stronger enhancement for a wider range of effect-to-cost ratio of incentives.
Thus, when it is not clear from the beginning what is the risk level associated (with the technology to be developed), then positive incentives appear to be the safer choice than negative ones (in line with historical data on rewards usage in innovation policy in the UK [[46](#bib.bib46)] as well as suggestions for Covid-19 vaccine innovation policy [[21](#bib.bib21)]). This is the case for many kinds of technological races especially when data about the effect of a new technology is usually lacking and only becomes available when it has been created and used enough (see the Collingridge Dilemma [[48](#bib.bib48)]), as are the cases of the domain supremacy race through AI [[18](#bib.bib18), [19](#bib.bib19)] and the race for creating the first Covid-19 vaccines [[49](#bib.bib49), [21](#bib.bib21)]. On the other hand, when one can determine early on that the associated level of risk is sufficiently high (i.e. above a certain threshold as determined in our analysis), negative incentives might provide a stronger mechanism. For instance, high risk technologies such as new airplane models and medical products [[50](#bib.bib50)] might benefit from putting strong sanctioning mechanisms in place.
In short, our analysis has shown, within an idealised model of an AI race and using a game theoretical framework, that some simple forms of peer incentives, if used suitably (to avoid over-regulation, for example) can provide a way to escape the dilemma of acting safely even when speedy unsafe development is preferred. Future studies may look at more complex incentivising mechanisms [[47](#bib.bib47)] such as reputation and public image manipulation [[51](#bib.bib51), [52](#bib.bib52)], emotional motives of guilt and apology-forgiveness [[53](#bib.bib53), [54](#bib.bib54)], institutional and coordinated incentives [[28](#bib.bib28), [36](#bib.bib36)], and the subtle combination of different forms of incentive (e.g., stick-and-carrot approach and incentives for agreement compliance) [[55](#bib.bib55), [56](#bib.bib56), [34](#bib.bib34), [32](#bib.bib32), [57](#bib.bib57)].
Acknowledgements
----------------
T.A.H., L.M.P. and T.L. have been supported by Future of Life Institute grant RFP2-154. L.M.P. also acknowledges support from FCT/MEC NOVA LINCS PEst UID/CEC/04516/2019. F.C.S. acknowledges support from FCT Portugal (grants PTDC/EEI-SII/5081/2014, PTDC/MAT/STA/3358/2014, and UID/CEC/50021/2020). T.L. acknowledges support by the FuturICT2.0 (www.futurict2.eu) project funded by the FLAG-ERA JCT 2016.
Supporting information
----------------------
### Details of analysis for three strategies AS, AU, CS
Let CS be a conditionally safe strategy, playing SAFE in the first round and choosing the same move as the co-player’s choice in the previous round. We recall below the detailed calculations for this case, as described in [[16](#bib.bib16)], just for completeness.
The average payoff matrix for the three strategies AS, AU, CS reads (for row player)
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| | Π=𝐴𝑆𝐴𝑈𝐶𝑆𝐴𝑆( B2W+π11π12B2W+π11) 𝐴𝑈(1−pr)(sBW+π21)(1−pr)(sB2W+π22)(1−pr)[sBW+sW(π21+(Ws−1)π22)]𝐶𝑆B2W+π11sW(π12+(Ws−1)π22)B2W+π11.\centering\Pi=\bordermatrix{~{}&\textit{AS}&\textit{AU}&\textit{CS}\cr\textit{AS}&\frac{B}{2W}+\pi\_{11}&\pi\_{12}&\frac{B}{2W}+\pi\_{11}\cr\textit{AU}&(1-p\_{r})\left(\frac{sB}{W}+\pi\_{21}\right)&(1-p\_{r})\left(\frac{sB}{2W}+\pi\_{22}\right)&(1-p\_{r})\left[\frac{sB}{W}+\frac{s}{W}\left(\pi\_{21}+(\frac{W}{s}-1)\pi\_{22}\right)\right]\cr\textit{CS}&\frac{B}{2W}+\pi\_{11}&\frac{s}{W}\left(\pi\_{12}+(\frac{W}{s}-1)\pi\_{22}\right)&\frac{B}{2W}+\pi\_{11}\cr}.\@add@centeringroman\_Π = start\_ROW start\_CELL end\_CELL start\_CELL end\_CELL start\_CELL AS end\_CELL start\_CELL AU end\_CELL start\_CELL CS end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL AS end\_CELL start\_CELL italic\_( end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL italic\_) end\_CELL end\_ROW start\_ROW start\_CELL AU end\_CELL start\_CELL end\_CELL start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) [ divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + divide start\_ARG italic\_s end\_ARG start\_ARG italic\_W end\_ARG ( italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT + ( divide start\_ARG italic\_W end\_ARG start\_ARG italic\_s end\_ARG - 1 ) italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) ] end\_CELL start\_CELL end\_CELL end\_ROW start\_ROW start\_CELL CS end\_CELL start\_CELL end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL divide start\_ARG italic\_s end\_ARG start\_ARG italic\_W end\_ARG ( italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT + ( divide start\_ARG italic\_W end\_ARG start\_ARG italic\_s end\_ARG - 1 ) italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) end\_CELL start\_CELL divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL start\_CELL end\_CELL end\_ROW . | | (10) |
The conditions (i) SAFE population has a larger average payoff than that of UNSAFE one, i.e. ΠAS,AS>ΠAU,AUsubscriptΠ𝐴𝑆𝐴𝑆subscriptΠ𝐴𝑈𝐴𝑈\Pi\_{AS,AS}>\Pi\_{AU,AU}roman\_Π start\_POSTSUBSCRIPT italic\_A italic\_S , italic\_A italic\_S end\_POSTSUBSCRIPT > roman\_Π start\_POSTSUBSCRIPT italic\_A italic\_U , italic\_A italic\_U end\_POSTSUBSCRIPT, meaning by definition that a collective outcome is preferred and (ii) when is it the case that AS and CS are more likely to be imitated against AU (i.e., risk-dominant) will be derived below. First, for condition (i), it must hold that
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| | B2W+π11>(1−pr)(sB2W+π22).𝐵2𝑊subscript𝜋111subscript𝑝𝑟𝑠𝐵2𝑊subscript𝜋22\frac{B}{2W}+\pi\_{11}>(1-p\_{r})\left(\frac{sB}{2W}+\pi\_{22}\right).divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT > ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) ( divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) . | | (11) |
Thus,
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| | pr>1−B+2Wπ11sB+2Wπ22,subscript𝑝𝑟1𝐵2𝑊subscript𝜋11𝑠𝐵2𝑊subscript𝜋22p\_{r}>1-\frac{B+2W\pi\_{11}}{sB+2W\pi\_{22}},italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG italic\_B + 2 italic\_W italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_ARG start\_ARG italic\_s italic\_B + 2 italic\_W italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT end\_ARG , | | (12) |
which is equivalent to (since B/W≫bmuch-greater-than𝐵𝑊𝑏B/W\gg bitalic\_B / italic\_W ≫ italic\_b)
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| | pr>1−1s.subscript𝑝𝑟11𝑠p\_{r}>1-\frac{1}{s}.\ \quaditalic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s end\_ARG . | | (13) |
This inequality means that, whenever the risk of a disaster or personal setback, prsubscript𝑝𝑟p\_{r}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT, is larger than the gain that can be gotten from a greater development speed, then the preferred collective action in the population is safety compliance.
Now, for condition (ii),
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| | B2W+π11+π12>(1−pr)(3sB2W+π21+π22).𝐵2𝑊subscript𝜋11subscript𝜋121subscript𝑝𝑟3𝑠𝐵2𝑊subscript𝜋21subscript𝜋22\frac{B}{2W}+\pi\_{11}+\pi\_{12}>(1-p\_{r})\left(\frac{3sB}{2W}+\pi\_{21}+\pi\_{22}\right).divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT + italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT > ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) ( divide start\_ARG 3 italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT + italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) . | | (14) |
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| --- | --- | --- | --- |
| | sW(π12+(Ws−1)π22)+B2W+π11>(1−pr)[sB2W+sBW+sW(π21+(Ws−1)π22)+π22],𝑠𝑊subscript𝜋12𝑊𝑠1subscript𝜋22𝐵2𝑊subscript𝜋111subscript𝑝𝑟delimited-[]𝑠𝐵2𝑊𝑠𝐵𝑊𝑠𝑊subscript𝜋21𝑊𝑠1subscript𝜋22subscript𝜋22\begin{split}&\frac{s}{W}\left(\pi\_{12}+(\frac{W}{s}-1)\pi\_{22}\right)+\frac{B}{2W}+\pi\_{11}\\
&>(1-p\_{r})\left[\frac{sB}{2W}+\frac{sB}{W}+\frac{s}{W}\left(\pi\_{21}+(\frac{W}{s}-1)\pi\_{22}\right)+\pi\_{22}\right],\end{split}start\_ROW start\_CELL end\_CELL start\_CELL divide start\_ARG italic\_s end\_ARG start\_ARG italic\_W end\_ARG ( italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT + ( divide start\_ARG italic\_W end\_ARG start\_ARG italic\_s end\_ARG - 1 ) italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) + divide start\_ARG italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + italic\_π start\_POSTSUBSCRIPT 11 end\_POSTSUBSCRIPT end\_CELL end\_ROW start\_ROW start\_CELL end\_CELL start\_CELL > ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) [ divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG 2 italic\_W end\_ARG + divide start\_ARG italic\_s italic\_B end\_ARG start\_ARG italic\_W end\_ARG + divide start\_ARG italic\_s end\_ARG start\_ARG italic\_W end\_ARG ( italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT + ( divide start\_ARG italic\_W end\_ARG start\_ARG italic\_s end\_ARG - 1 ) italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ) + italic\_π start\_POSTSUBSCRIPT 22 end\_POSTSUBSCRIPT ] , end\_CELL end\_ROW | | (15) |
which are both equivalent to (since B/W≫bmuch-greater-than𝐵𝑊𝑏B/W\gg bitalic\_B / italic\_W ≫ italic\_b)
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| --- | --- | --- | --- |
| | pr>1−13s.subscript𝑝𝑟113𝑠p\_{r}>1-\frac{1}{3s}.italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG 3 italic\_s end\_ARG . | | (16) |
The two boundary conditions for (i) and (ii), as given in Equations [13](#Sx7.E13 "13 ‣ Details of analysis for three strategies AS, AU, CS ‣ Supporting information") and [16](#Sx7.E16 "16 ‣ Details of analysis for three strategies AS, AU, CS ‣ Supporting information"), splits s−pr𝑠subscript𝑝𝑟s-p\_{r}italic\_s - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT parameter space into three regions, as exhibited in Figure [6](#Sx7.F6 "Fig 6 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information")a:
* (I)
when pr>1−13ssubscript𝑝𝑟113𝑠p\_{r}>1-\frac{1}{3s}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG 3 italic\_s end\_ARG: This corresponds to the *AIS compliance zone*, in which safe AI compliance is both preferred collectively and that unconditionally (AS) and conditionally (CS) safe development is the social norm (an example for s=1.5𝑠1.5s=1.5italic\_s = 1.5 is given in Figure [6](#Sx7.F6 "Fig 6 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information")b: pr>0.78subscript𝑝𝑟0.78p\_{r}>0.78italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 0.78);
* (II)
when 1−13s>pr>1−1s113𝑠subscript𝑝𝑟11𝑠1-\frac{1}{3s}>p\_{r}>1-\frac{1}{s}1 - divide start\_ARG 1 end\_ARG start\_ARG 3 italic\_s end\_ARG > italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s end\_ARG: This intermediate zone is the one that captures a dilemma because, collectively, safe AI developments are preferred, though the social dynamics pushes the whole population to the state where all develop AI in an unsafe manner. We shall refer to this zone as the *AIS dilemma zone* (for s=1.5𝑠1.5s=1.5italic\_s = 1.5, 0.78>pr>0.330.78subscript𝑝𝑟0.330.78>p\_{r}>0.330.78 > italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT > 0.33, see Figure [6](#Sx7.F6 "Fig 6 ‣ Calculation for 𝜋_{𝑃𝑆,𝐴𝑈} and 𝜋_{𝐴𝑈,𝑃𝑆} in general case ‣ Supporting information")c);
* (III)
when pr<1−1ssubscript𝑝𝑟11𝑠p\_{r}<1-\frac{1}{s}italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT < 1 - divide start\_ARG 1 end\_ARG start\_ARG italic\_s end\_ARG: This defines the *AIS innovation zone*, in which unsafe development is not only the preferred collective outcome but also the one the social dynamics selects.
### Calculation for πPS,AUsubscript𝜋𝑃𝑆𝐴𝑈\pi\_{PS,AU}italic\_π start\_POSTSUBSCRIPT italic\_P italic\_S , italic\_A italic\_U end\_POSTSUBSCRIPT and πAU,PSsubscript𝜋𝐴𝑈𝑃𝑆\pi\_{AU,PS}italic\_π start\_POSTSUBSCRIPT italic\_A italic\_U , italic\_P italic\_S end\_POSTSUBSCRIPT in general case
Below R𝑅Ritalic\_R denotes the average number of rounds; B1subscript𝐵1B\_{1}italic\_B start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT and B2subscript𝐵2B\_{2}italic\_B start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT the benefits PS and AU might obtain from the winning benefit B𝐵Bitalic\_B when either of them wins the race by being the first to have made W𝑊Witalic\_W development steps; b1subscript𝑏1b\_{1}italic\_b start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT and b2subscript𝑏2b\_{2}italic\_b start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT the intermediate benefits PS and AU might obtain in each round of the game; plosssubscript𝑝𝑙𝑜𝑠𝑠p\_{loss}italic\_p start\_POSTSUBSCRIPT italic\_l italic\_o italic\_s italic\_s end\_POSTSUBSCRIPT is the probability that all the benefit is not lost when AU wins and draws the race; Clearly, all these values depend on the development speeds (s′superscript𝑠′s^{\prime}italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT for PS and s′′superscript𝑠′′s^{\prime\prime}italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT for AU).
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| --- | --- | --- |
| | πPSvsAU=1R(s′,s′′)[π12+B1(s′,s′′)+(R(s′,s′′)−1)(−c+b1(s′,s′′))]subscript𝜋𝑃𝑆𝑣𝑠𝐴𝑈1𝑅superscript𝑠′superscript𝑠′′delimited-[]subscript𝜋12subscript𝐵1superscript𝑠′superscript𝑠′′𝑅superscript𝑠′superscript𝑠′′1𝑐subscript𝑏1superscript𝑠′superscript𝑠′′\pi\_{PS\ vs\ AU}=\frac{1}{R(s^{\prime},s^{\prime\prime})}\left[\pi\_{12}+B\_{1}(s^{\prime},s^{\prime\prime})+(R(s^{\prime},s^{\prime\prime})-1)(-c+b\_{1}(s^{\prime},s^{\prime\prime}))\right]italic\_π start\_POSTSUBSCRIPT italic\_P italic\_S italic\_v italic\_s italic\_A italic\_U end\_POSTSUBSCRIPT = divide start\_ARG 1 end\_ARG start\_ARG italic\_R ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) end\_ARG [ italic\_π start\_POSTSUBSCRIPT 12 end\_POSTSUBSCRIPT + italic\_B start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) + ( italic\_R ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) - 1 ) ( - italic\_c + italic\_b start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) ) ] | |
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| --- | --- | --- |
| | πPSvsAU=ploss(s′,s′′)×1R(s′,s′′)[π21+B2(s′,s′′)+(R(s′,s′′)−1)b2(s′,s′′)]subscript𝜋𝑃𝑆𝑣𝑠𝐴𝑈subscript𝑝𝑙𝑜𝑠𝑠superscript𝑠′superscript𝑠′′1𝑅superscript𝑠′superscript𝑠′′delimited-[]subscript𝜋21subscript𝐵2superscript𝑠′superscript𝑠′′𝑅superscript𝑠′superscript𝑠′′1subscript𝑏2superscript𝑠′superscript𝑠′′\pi\_{PS\ vs\ AU}=p\_{loss}(s^{\prime},s^{\prime\prime})\times\frac{1}{R(s^{\prime},s^{\prime\prime})}\left[\pi\_{21}+B\_{2}(s^{\prime},s^{\prime\prime})+(R(s^{\prime},s^{\prime\prime})-1)b\_{2}(s^{\prime},s^{\prime\prime})\right]italic\_π start\_POSTSUBSCRIPT italic\_P italic\_S italic\_v italic\_s italic\_A italic\_U end\_POSTSUBSCRIPT = italic\_p start\_POSTSUBSCRIPT italic\_l italic\_o italic\_s italic\_s end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) × divide start\_ARG 1 end\_ARG start\_ARG italic\_R ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) end\_ARG [ italic\_π start\_POSTSUBSCRIPT 21 end\_POSTSUBSCRIPT + italic\_B start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) + ( italic\_R ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) - 1 ) italic\_b start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) ] | |
where B1(s′,s′′)={Bif s′>0&s′′≤0Bif s′>0&W−ss′′>W−1s′B/2if s′>0&W−ss′′=W−1s′0otherwise subscript𝐵1superscript𝑠′superscript𝑠′′cases𝐵if superscript𝑠′0superscript𝑠′′0𝐵if superscript𝑠′0𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′𝐵2if superscript𝑠′0𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′0otherwise B\_{1}(s^{\prime},s^{\prime\prime})=\begin{cases}B&\mbox{if }s^{\prime}>0\ \&\ s^{\prime\prime}\leq 0\\
B&\mbox{if }s^{\prime}>0\ \&\ \frac{W-s}{s^{\prime\prime}}>\frac{W-1}{s^{\prime}}\\
B/2&\mbox{if }s^{\prime}>0\ \&\ \frac{W-s}{s^{\prime\prime}}=\frac{W-1}{s^{\prime}}\\
0&\mbox{otherwise }\end{cases}italic\_B start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL italic\_B end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ≤ 0 end\_CELL end\_ROW start\_ROW start\_CELL italic\_B end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG > divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL end\_ROW start\_ROW start\_CELL italic\_B / 2 end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG = divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL end\_ROW start\_ROW start\_CELL 0 end\_CELL start\_CELL otherwise end\_CELL end\_ROW
B2(s′,s′′)={Bif s′≤0&s′′>0Bif s′′>0&W−ss′′<W−1s′B/2if s′′>0&W−ss′′=W−1s′0otherwise subscript𝐵2superscript𝑠′superscript𝑠′′cases𝐵if superscript𝑠′0superscript𝑠′′0𝐵if superscript𝑠′′0𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′𝐵2if superscript𝑠′′0𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′0otherwise B\_{2}(s^{\prime},s^{\prime\prime})=\begin{cases}B&\mbox{if }s^{\prime}\leq 0\ \&\ s^{\prime\prime}>0\\
B&\mbox{if }s^{\prime\prime}>0\ \&\ \frac{W-s}{s^{\prime\prime}}<\frac{W-1}{s^{\prime}}\\
B/2&\mbox{if }s^{\prime\prime}>0\ \&\ \frac{W-s}{s^{\prime\prime}}=\frac{W-1}{s^{\prime}}\\
0&\mbox{otherwise }\end{cases}italic\_B start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL italic\_B end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ≤ 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 end\_CELL end\_ROW start\_ROW start\_CELL italic\_B end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 & divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG < divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL end\_ROW start\_ROW start\_CELL italic\_B / 2 end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 & divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG = divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL end\_ROW start\_ROW start\_CELL 0 end\_CELL start\_CELL otherwise end\_CELL end\_ROW
b1(s′,s′′)={(1−pfo)s′bs′+s′′+p𝑓𝑜bif s′>0&s′′>0bif s′>0&s′′≤00otherwise subscript𝑏1superscript𝑠′superscript𝑠′′cases1subscript𝑝𝑓𝑜superscript𝑠′𝑏superscript𝑠′superscript𝑠′′subscript𝑝𝑓𝑜𝑏if superscript𝑠′0superscript𝑠′′0𝑏if superscript𝑠′0superscript𝑠′′00otherwise b\_{1}(s^{\prime},s^{\prime\prime})=\begin{cases}(1-p\_{fo})\frac{s^{\prime}b}{s^{\prime}+s^{\prime\prime}}+p\_{\mathit{fo}}b&\mbox{if }s^{\prime}>0\ \&\ s^{\prime\prime}>0\\
b&\mbox{if }s^{\prime}>0\ \&\ s^{\prime\prime}\leq 0\\
0&\mbox{otherwise }\end{cases}italic\_b start\_POSTSUBSCRIPT 1 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_f italic\_o end\_POSTSUBSCRIPT ) divide start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT italic\_b end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT + italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG + italic\_p start\_POSTSUBSCRIPT italic\_fo end\_POSTSUBSCRIPT italic\_b end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 end\_CELL end\_ROW start\_ROW start\_CELL italic\_b end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ≤ 0 end\_CELL end\_ROW start\_ROW start\_CELL 0 end\_CELL start\_CELL otherwise end\_CELL end\_ROW
b2(s′,s′′)={(1−pfo)s′′bs′+s′′if s′>0&s′′>0(1−pfo)bif s′≤0&s′′>00otherwise subscript𝑏2superscript𝑠′superscript𝑠′′cases1subscript𝑝𝑓𝑜superscript𝑠′′𝑏superscript𝑠′superscript𝑠′′if superscript𝑠′0superscript𝑠′′01subscript𝑝𝑓𝑜𝑏if superscript𝑠′0superscript𝑠′′00otherwise b\_{2}(s^{\prime},s^{\prime\prime})=\begin{cases}(1-p\_{fo})\frac{s^{\prime\prime}b}{s^{\prime}+s^{\prime\prime}}&\mbox{if }s^{\prime}>0\ \&\ s^{\prime\prime}>0\\
(1-p\_{fo})b&\mbox{if }s^{\prime}\leq 0\ \&\ s^{\prime\prime}>0\\
0&\mbox{otherwise }\end{cases}italic\_b start\_POSTSUBSCRIPT 2 end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_f italic\_o end\_POSTSUBSCRIPT ) divide start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT italic\_b end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT + italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 end\_CELL end\_ROW start\_ROW start\_CELL ( 1 - italic\_p start\_POSTSUBSCRIPT italic\_f italic\_o end\_POSTSUBSCRIPT ) italic\_b end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ≤ 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 end\_CELL end\_ROW start\_ROW start\_CELL 0 end\_CELL start\_CELL otherwise end\_CELL end\_ROW
R(s′,s′′)={+∞if s′≤0&s′′≤0W−1s′+1if s′>0&s′′≤0W−ss′′+1if s′≤0&s′′>01+min{W−ss′′,W−1s′}otherwise 𝑅superscript𝑠′superscript𝑠′′casesif superscript𝑠′0superscript𝑠′′0𝑊1superscript𝑠′1if superscript𝑠′0superscript𝑠′′0𝑊𝑠superscript𝑠′′1if superscript𝑠′0superscript𝑠′′01𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′otherwise R(s^{\prime},s^{\prime\prime})=\begin{cases}+\infty&\mbox{if }s^{\prime}\leq 0\ \&\ s^{\prime\prime}\leq 0\\
\frac{W-1}{s^{\prime}}+1&\mbox{if }s^{\prime}>0\ \&\ s^{\prime\prime}\leq 0\\
\frac{W-s}{s^{\prime\prime}}+1&\mbox{if }s^{\prime}\leq 0\ \&\ s^{\prime\prime}>0\\
1+\min\{\frac{W-s}{s^{\prime\prime}},\frac{W-1}{s^{\prime}}\}&\mbox{otherwise }\end{cases}italic\_R ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL + ∞ end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ≤ 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ≤ 0 end\_CELL end\_ROW start\_ROW start\_CELL divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG + 1 end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT > 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ≤ 0 end\_CELL end\_ROW start\_ROW start\_CELL divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG + 1 end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT ≤ 0 & italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 end\_CELL end\_ROW start\_ROW start\_CELL 1 + roman\_min { divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG , divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG } end\_CELL start\_CELL otherwise end\_CELL end\_ROW
ploss(s′,s′′)={p(=1−pr)if s′′>0&W−ss′′≤W−1s′1otherwise subscript𝑝𝑙𝑜𝑠𝑠superscript𝑠′superscript𝑠′′casesannotated𝑝absent1subscript𝑝𝑟if superscript𝑠′′0𝑊𝑠superscript𝑠′′𝑊1superscript𝑠′1otherwise p\_{loss}(s^{\prime},s^{\prime\prime})=\begin{cases}p(=1-p\_{r})&\mbox{if }s^{\prime\prime}>0\ \&\ \frac{W-s}{s^{\prime\prime}}\leq\frac{W-1}{s^{\prime}}\\
1&\mbox{otherwise }\end{cases}italic\_p start\_POSTSUBSCRIPT italic\_l italic\_o italic\_s italic\_s end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT , italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT ) = { start\_ROW start\_CELL italic\_p ( = 1 - italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT ) end\_CELL start\_CELL if italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT > 0 & divide start\_ARG italic\_W - italic\_s end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ ′ end\_POSTSUPERSCRIPT end\_ARG ≤ divide start\_ARG italic\_W - 1 end\_ARG start\_ARG italic\_s start\_POSTSUPERSCRIPT ′ end\_POSTSUPERSCRIPT end\_ARG end\_CELL end\_ROW start\_ROW start\_CELL 1 end\_CELL start\_CELL otherwise end\_CELL end\_ROW
Fig 6: Panel (a) as in Figure 1 in the main text, added here for ease of following. Panels (b) and (c) show the transition probabilities and stationary distribution (see Methods). In panel (c) AU dominates, corresponding to region (II), whilst in panel (b) AS and CS dominate, corresponding to region (I). For a clear presentation, we indicate just the stronger directions. Parameters: b=4𝑏4b=4italic\_b = 4, c=1𝑐1c=1italic\_c = 1, W=100𝑊100W=100italic\_W = 100, B=104𝐵superscript104B=10^{4}italic\_B = 10 start\_POSTSUPERSCRIPT 4 end\_POSTSUPERSCRIPT, Z=100𝑍100Z=100italic\_Z = 100, β=0.1𝛽0.1\beta=0.1italic\_β = 0.1 ; In panel (b) pr=0.9subscript𝑝𝑟0.9p\_{r}=0.9italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT = 0.9; in panel (c) pr=0.6subscript𝑝𝑟0.6p\_{r}=0.6italic\_p start\_POSTSUBSCRIPT italic\_r end\_POSTSUBSCRIPT = 0.6; in both (b) and (c) s=1.5𝑠1.5s=1.5italic\_s = 1.5.
Fig 7: AU Frequency: Reward (top row) vs punishment (bottom row) for varying sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, for three regions, for stronger intensity of selection (β=0.1𝛽0.1\beta=0.1italic\_β = 0.1). Other parameters are the same as in Figure [5](#Sx3.F5 "Fig 5 ‣ Risk-dominance ‣ Evolutionary Dynamics for Finite Populations ‣ Materials and methods") in the main text. The observations in that figure is also robust for larger intensities of selection.
Fig 8: Transitions and stationary distributions in a population of four strategies AU, AS, PS and RS, for three regions. Only stronger transitions are shown for clarity. Dashed lines denote neutral transitions. In addition, note that PS is equivalent to AS when interacting with PS, i.e. there is always a stronger transition from RS to PS than vice versa. Parameters as in Figure 2.
Fig 9: AU Frequency for varying sαsubscript𝑠𝛼s\_{\alpha}italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT and sβsubscript𝑠𝛽s\_{\beta}italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT, in a population of four strategies AS, AU, PS and RS, for three regions. The outcomes in all regions are similar to the case of punishment (without reward) in Figure 5. The reason is that there is always a stronger transition from RS to PS than vice versa. Parameters as in Figure 5.
Fig 10: Transitions and stationary distributions in a population of three strategies AU, AS, with either PS (top row) or RS (bottom row), in region (II) (pr=0.75𝑝𝑟0.75pr=0.75italic\_p italic\_r = 0.75): left column (β=0.01𝛽0.01\beta=0.01italic\_β = 0.01), right column (β=0.1𝛽0.1\beta=0.1italic\_β = 0.1). The parameters of incentives fall in the white triangles in Figures 5 and 7: sα=1.5,sβ=3formulae-sequencesubscript𝑠𝛼1.5subscript𝑠𝛽3s\_{\alpha}=1.5,\ s\_{\beta}=3italic\_s start\_POSTSUBSCRIPT italic\_α end\_POSTSUBSCRIPT = 1.5 , italic\_s start\_POSTSUBSCRIPT italic\_β end\_POSTSUBSCRIPT = 3. We observe that the frequency of AU is lower in case of reward than that of punishment. Other parameters as in Figure 2.
|
6bea2002-ef5f-45bf-9827-2949d5a84cc0
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Eliciting Latent Knowledge (ELK) - Distillation/Summary
This post was inspired by the [AI safety distillation contest](https://forum.effectivealtruism.org/posts/ei4pYFJKcbGAdGnNb/calling-for-student-submissions-ai-safety-distillation). It turned out to be more of a summary than a distillation for two reasons. Firstly, I think that the main idea behind ELK is simple and can be explained in less than 2 minutes (see next section). Therefore, the main value comes from understanding the specific approaches and how they interact with each other. Secondly, I think some people shy away from reading a 50-page report but I expect they could get most of the understanding from reading/skimming this summary (I'm aware that the summary is longer than anticipated but it's still a >5x reduction of the original content).
I summarized the [ELK report](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge) and the results of the [ELK competition](https://www.alignmentforum.org/posts/QEYWkRoCn4fZxXQAY/prizes-for-elk-proposals). I personally think ELK is very promising and therefore want to make extra sure that the summary is readable and accurate. In case anything is unclear or wrong, please let me know.
Context and Introduction
========================
[Eliciting Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge) was written by [Paul Christiano](https://paulfchristiano.com/), [Ajeya Cotra](https://www.openphilanthropy.org/about/team/ajeya-cotra) and [Mark Xu](https://markxu.com/about). Before getting to the main summary, I want to provide some background and context. In addition to the summary of the main report, I provide a summary of the results from the ELK prize contest in the end. My aim is to give a brief summary of “everything related to ELK” with links to the long versions.
Paul Christiano worked on various projects in AI safety before founding the [Alignment Research Center](https://alignment.org/) (ARC). He lead the LLM alignment team at OpenAI and is known for, among other contributions, [AI safety via debate](https://arxiv.org/abs/1805.00899) and [Iterated Distillation and Amplification](https://ai-alignment.com/iterated-distillation-and-amplification-157debfd1616) (IDA).
From my very limited outside perspective and some of his podcast appearances (e.g. at [80K](https://80000hours.org/podcast/episodes/paul-christiano-a-message-for-the-future/) or the [AI x-risks podcast](https://axrp.net/episode/2021/12/02/episode-12-ai-xrisk-paul-christiano.html)), Paul Christiano seems to run into similar problems with many current alignment techniques. Whether you choose inverse RL, AI safety via debate or IDA, there is always the risk that sufficiently powerful models create problems that can only be solved by understanding what the model “truly thinks”--by eliciting latent knowledge (ELK). In the case of AI safety via debate, for example, you have to trust the individual debate AIs. With a good solution to ELK, we could make sure the debate AIs are trustworthy.
Of course, other people want to understand what’s going on in Neural Networks (NNs) as well. [Chris Olah](https://colah.github.io/), for example, has successfully worked on technical NN interpretability for many years now (see e.g. [circuits](https://distill.pub/2020/circuits/zoom-in/)) and there are many other approaches to NN transparency in academia. From what I understand, there are two main differences between ELK and these other approaches.
First, ELK operates on a different scale. NN interpretability often looks at low-level neurons or circuits (which gets messy when your model has Billions of parameters) while ELK aims to generate natural language answers or descriptions that reflect the model's true beliefs as if it “had drunk a truth serum”. In the optimal case, we don’t need to understand the model in detail, the model just pro-actively tells us what it believes on its own.
Secondly, ARC approaches ELK more theoretically than is common in technical alignment research. Conventionally, technical alignment researchers come up with a theory, then test it in practice, find the flaws and improve it. ARC, on the other hand, skips the experimentation step and tries to find theoretical counterexamples for their current proposals. Once they found a counterexample, they try to come up with a new proposal that fixes the problem and then iterate. In the best case, they arrive at a proposal for which they can’t find any major theoretical flaws and then implement that.
Summary of the ELK report
=========================
I will try to stay close to the structure and naming patterns that the ELK report uses. In my summary, I will necessarily remove some nuance and detail. However, I still want it to be accurate. Therefore, if you think something is wrong or misleading, please notify me. All figures have been copied from the original report. None of them are my own.
Toy Scenario: The SmartVault
----------------------------
Assume you want to design a security system to protect a diamond from theft. You choose the SmartVault as your mechanism of protection. The SmartVault has many actuators (robot arms, trap doors, etc) to keep the diamond in its place.
Some of the possible SmartVault actions are obvious to humans, e.g. opening the front door or using the trap door. Other actions and especially action sequences are hard or impossible to understand, e.g. a complicated sequence of all of its safety mechanisms. While you don’t understand what actions the SmartVault is taking, you are able to evaluate the state before and after the action, e.g. by looking through the camera (see figure above).
Since you are unable to operate the actuators on your own, you want to train an AI system to operate them for you. To train the AI system, you let the model predict an observation, then evaluate/judge the predicted observation and repeat. The SmartVault AI will learn to produce predicted observations that look good (see columns 1-3 below).
**Problem - observations leave out key information:**As shown in column 4 above, the second predicted observation *appeared*good but the actual reality was bad. The robber stole the gem by putting a screen matching the desired observation in front of the camera.
One possible solution to this problem would be to install more sensors, e.g. of different kinds or from multiple angles. However, this doesn’t solve the original problem, it just makes it more complicated. A thief might still find a way to trick all sensors at once, e.g. by replacing the gem with a fake gem during a moment of inattention. *The main problem with this approach is that the SmartVault AI is still trying to fool us, e.g. the AI wants to create the appearance of the gem being still there while, in reality, the gem has already been stolen.*
Our preferred alternative is to elicit the latent knowledge about the reliability of the model’s sensors, i.e. the model should just tell us that the thief has tampered with the sensors.
**Solution approach - asking questions:**The model seems to “understand” that the sensors were tampered with and that the current gem is fake. These concepts are not complicated--they are object-level observations about what happened in the room and don’t require an understanding of more complicated concepts such as “lying” or “intentions”. Therefore, as long as we can ask the right questions, the model should be able to give us answers that accurately reflect the true state of the gem--the question is just how we get it to always as accurate as it can with the knowledge it has.
The SmartVault setup is, of course, just a simple toy example. However, I think the situation is very translatable to more realistic scenarios. Imagine, for example, a GPT-N-based assistant with a superhuman world model that is supposed to help human decision-makers with policy design. We would want this AI to give us truthful information and inform us about potential misunderstandings rather than just confirming our priors.
Assume, for example, we ask the AI assistant to design the best education policy for our state. We would want to avoid a scenario where the AI just suggests a policy because it predicts that we would think it is the best policy (or that we could be convinced of it) even though it’s not true. Rather, we would want the AI to give us the best policy, pro-actively explain important details and caveats and communicate back and forth because the AI is “convinced” that it has found the best education policy. A good solution to ELK would ensure that we get the honest and helpful AI assistant rather than the one that attempts to say the things it expects us to want to hear.
Baseline: what you’d try first and how it could fail
----------------------------------------------------
*Meta: I omit large parts (e.g. the BayesNets) of this section because I think they aren’t strictly necessary to understand it. Feel free to check them out in the main report, they are definitely helpful.*
On a basic level, you can view the SmartVault AI as figuring out what it should predict and then creating a video for the human to judge. We can add a reporter part to the model that can answer our questions about how the SmartVault AI figures out what’s going on. Since it has access to all the inside information of the SmartVault AI, it should, in theory, be able to elicit latent knowledge.
### Training strategy - generalize from easy questions to hard questions:
We could train the SmartVaultAI with the reporter on a set of easy questions where we are confident to know the right answer. In the optimal case, the reporter AI learns which kind of questions we are interested in and then extrapolates how to answer them in more complicated scenarios where we might not be able to understand the exact mechanisms of the SmartVault.
For example, in a simple scenario, we might train the reporter to mention that the camera has been tampered with whenever we are confident that this has happened. We would then want the reporter to generalize this concept and say something like “Oh btw, sensor 413 has been twisted and sensor 113 is frozen. Therefore, it looks like the gem is still there even though it was stolen.” in a scenario where we don’t understand what happened inside the SmartVault.
### Failure mode - the reporter learns the wrong model
The above strategy fails when the reporter learns to report the wrong model during training. The reporter could either tell us what we *should* predict (direct translator), i.e. that the sensors are frozen, or what we *would*predict (simulator), i.e. what our naive human world model would say. In other words, in the failure mode, the reporter learned to simulate whatever we were thinking rather than reporting the true latent knowledge.
Just from the training performance, we are unable to distinguish the direct translator from the simulator. Both strategies have identical training losses since they give the same answers to all training inputs. We can also not distinguish them during deployment since that would require verifying the latent information which is exactly what we need the reporter for.
I think an imperfect yet helpful analogy comes from advisors of political leaders. Optimally, a leader would want their advisors to give him/her true and helpful information to make good decisions. However, due to bad training mechanisms, e.g. the selection of the advisors or their incentives, leaders sometimes gather Yes-men around themselves. These Yes-men have learned to tell the leader what he/she wants to hear (rather than what’s truly going on) without raising suspicion. In some way, they have been trained to predict the leader’s model rather than the true world model. They have become the human simulator and not the direct translator.
Since we are looking for a worst-case solution to ELK and were able to find failure modes for this strategy, we need to search for better strategies.
Research methodology
--------------------
The ELK report’s research methodology can be seen as a game between a builder and a breaker (copied from the report).
1. The builder proposes a training strategy for eliciting latent knowledge.
2. The breaker proposes a test case in which the strategy might fail.
3. The builder describes the desired reporter they hope will be learned in that test case.
4. The breaker describes a bad reporter that could be learned instead.
5. The builder can then try to argue that the breaker’s scenario is implausible. This may involve asking the breaker to specify more details of the scenario; then the builder identifies inconsistencies in the scenario or argues that actually, the strategy would learn the desired reporter after all.
Whenever the builder succeeds, we go back to step 2 and the breaker proposes a new counterexample. Whenever the breaker wins, we go back to step 1 and the builder proposes a new algorithm.
Ultimately, ARC intends to play this game until they either a) find an algorithm that defeats all counterexamples they can think of or b) find a counterexample that defeats all algorithms they can think of. If they succeed with a), they would likely implement this algorithm in practice (or collaborate on the implementation with other alignment organizations).
Importantly, **ARC focuses on worst-case examples**, i.e. if a counter-example works in the average case but fails in the worst case, the strategy counts as defeated. They are concerned that strategies that don’t fulfill this criterion will fail once AIs have superhuman capabilities. Furthermore, we might not be able to anticipate the problems with superhuman AIs and therefore need a robust strategy that scales to arbitrary capabilities.
A **counterexample is defined** as the reporter giving an answer that is unambiguously bad, i.e. a) the reporter understands the question, b) the reporter's answer is unambiguously wrong about a concrete fact that a human can understand (e.g. the diamond is in the room), and c) the model knows that its answer is wrong, e.g. it predicts something that is at odds with its internal latent knowledge.
In general, the report starts with very high-level considerations and gets as detailed as necessary to play the builder-breaker game, e.g. when the counterexample already breaks the strategy on a high level it is not necessary to develop a more detailed version of it unless the new strategy addresses the counterexample.
Constructing a dataset that separates “correct” from “looks correct to a human”
-------------------------------------------------------------------------------
The reason that the previous strategy failed was that the training could produce both an undesired human simulator or the desired direct translator. Maybe we can solve this problem by smartly designing the training data such that the model has to learn the direct translator rather than the bad reporter. We’ll explore three strategies in the following.
### Strategy 1 - have a human operate the SmartVault and ask them what happened
Rather than using a SmartVault AI with labels from a human observer, we could let another human operate the SmartVault who actively tries to fool the human observer. The human operator would then create the training labels (instead of the observer) and thus create a much larger set of scenarios since it is easier to understand the SmartVault from the inside than the outside. Furthermore, this strategy would yield data where the human observer was fooled and we can thus double-check what the model output was for these scenarios.
The previous strategy failed because the SmartVault AI learned to simulate the *observer’s* model rather than predict the actual SmartVault model. The new strategy **defeats the previous counterexample**because the human *operator’s* model of the SmartVault is much closer to the true model. Therefore, whenever the reporter learns to simulate the *observer’s* model, it won’t get a perfect loss on the training distribution since the labels are based on the *operator's* knowledge. As a consequence, the model is disincentivized to learn to simulate the observer.
**New counterexample:** While this strategy solves the previous counterexample, **it merely shifts the underlying problem**. The previous bad reporter learned to simulate the human observer. The new bad reporter learns to simulate the combination of the human observer and human operator. In that case, it has a perfect loss on the training data (since the labels were created by the human operator) but could still report what the human reporter and observer predict to see rather than what they should be seeing. Once again, the reporter has learned to simulate the humans rather than directly translating what’s truly going on in the model. In cases where the human model is incorrect, e.g. a flaw beyond our current understanding, the reporter would not point out the error if it learned to be the simulator.
### Strategy 2 - have AI help humans improve our understanding
The previous strategies failed because there are parts of the world/SmartVault humans don’t understand. Therefore, a new strategy could be to use AI to improve our understanding of the world/SmartVault.
For this strategy, we **assume that there is an AI that can do science (=learning about the world) honestly and describe the results as well as possible** even though this AI does not exist yet.
In the previous strategy, the reporter learned to simulate the human model. If the science AI can improve our understanding of the world, the human operator can then create new test cases that they were unable to construct previously. The bad reporter learned to simulate the unimproved human model rather than directly reporting the true problem. Therefore, the simulator would fail to report the new test cases and could be distinguished from the direct reporter. Thus, **this strategy defeats the previous counterexample**.
**New counterexample:**It is plausible that the science AI that helps humans understand the world better would use much more compute to do science than the SmartVault AI uses for training. As an intuition, imagine the amount of compute necessary to train [AlphaFold](https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology) and compare it to the compute necessary to train an AI that explains how and why a certain protein folds a specific way. It is plausible that the second task is more complex than the first and thus requires a more powerful AI. **This is a counterexample because** *ARC attempts to find a model that is a competitive solution to ELK,*i.e. one that doesn’t require more compute than the SmartVault AI training procedure. This counterexample relies on the notion that doing science is less efficient than SGD (the optimization algorithm the SmartVault AI is trained with).
### Strategy 3 - have humans adopt the optimal model
In the previous strategy, we realized that doing science could be more compute-intensive than training the SmartVault AI with SGD. Therefore, we need a strategy that is as efficient as SGD.
One *speculative* approach for this is [imitative generalization](https://alignmentforum.org/posts/JKj5Krff5oKMb8TjT/imitative-generalisation-aka-learning-the-prior-1). Roughly speaking, imitative generalization (copied from the report):
* Considers the space of changes the humans could make to their model;
* Learns a function which maps (proposed change to model) to (how a human — with AI assistants — would make predictions after making that change);
* Searches over this space to find the change that allows the humans to make the best predictions.
Intuitively, imitative generalization narrows the gap between "how the model would answer a question" and "how a human would answer a question". In its application to ELK, imitative generalization is used to generate a function that learns the human prior and is then able to propose changes for the AI model and make better predictions.
*Meta: I'm not confident I have understood this section correctly. I'll update it when it turns out to be wrong.*
We could use imitative generalization to improve the human model with the same amount of compute that is used to train the SmartVault AI. The human could then used the improved model to test the reporter in the same way they used the science AI in the previous section. The report notes that this strategy is very speculative but we assume, for the sake of argument, that it works.
**This defeats the previous counterexample**since it reduces the compute requirements from the improvement strategy to the same level as the SmartVault AI training. The science AI likely has a higher training requirement than SGD but imitative generalization is competitive with SGD.
This strategy comes with a **new counterexample - ontology mismatch**. In the best case, imitative generalization would update small parts of the human model while keeping existing concepts such as robber or diamond. However, in the worst case, the best model resulting from imitative generalization may throw out most or all existing human concepts, e.g. it thinks about the world only on the atomic level or uses concepts alien to humans. Such a model would be completely useless in practice. In that case, we have found an abstract model that explains the world best but humans can’t use it.
Ontology identification
-----------------------
Ontology broadly describes “how we map the real world to concepts in our heads” or “how we think about the world and relationships in it”. For ELK, ontology identification is an important question because it is not clear that AIs learn the same ontology as humans.
It is easy to come up with some of these **ontology mismatches.**For example, the AI could view the world through a string theory lens while humans choose the standard model of physics. Or the AI could use very abstract concepts and trends that are inaccessible to us humans, e.g. because they would require a proper understanding of complex relationships between multiple centuries.
However, a mismatch between string theory and the standard model of physics is not very likely. It’s much more plausible that the AI thinks about the world with concepts that are completely alien to us. For a more concrete example, we might be able to shed light on some of the concepts that transformers store in their attention heads with interpretability methods (see e.g. [transformer circuits](https://transformer-circuits.pub/2021/framework/index.html)) but many concepts used by LLMs might be completely different from our ways of thinking about natural language.
Ontology identification is a core problem for ELK because the reporter is only useful if they are able to communicate with the human observer using concepts the human can understand.
*Meta: there is an additional discussion on learned optimization that I will skip here because it doesn’t seem necessary to understand the core concepts of ELK. If you’re interested check out the main report + appendix.*
Specifying a regularizer to penalize bad reporters
--------------------------------------------------
A possible solution strategy to distinguish between the bad reporter (human simulator) and the direct translator is to regularize the SmartVault AI reporter during training. Note, that this regularization is not intended as a solution to ontology identification, i.e. the regularizer doesn’t ensure that the ontology is similar, but is a solution approach for the baseline strategy presented earlier.
### Strategy 1 - penalize complexity
The bad reporter (human simulator) might be more complex than the direct translator, i.e. the task of simulating the entire human model might be harder than the task of translating the inner state of the SmartVault AI to the human model. Thus, if we penalized complexity, we might be able to prevent the simulator.
**Counterexample:**This strategy relies on the fact that the human model is more complex than the SmartVault AI’s prediction model. However, we can easily imagine a very complex SmartVault AI model. In that case, the regularizer flips its purpose and incentivizes the reporter to learn the easier human simulator rather than the actual true model and thus achieves the opposite effect.
### Strategy 2 - penalize computation time
Simulating the human intuitively requires doing inference in the entire human model whereas direct translation only requires to “look up” the correct answer in the SmartVault AI model. Therefore, if we penalized computation time, we might be able to consistently get the direct translator.
Intuitively, this strategy feels very similar to the previous one and would thus be vulnerable to the same counterexample but they differ in the details. Naively, the bigger the SmartVault model gets, the more expensive direct translation becomes while human simulation stays constant. However, the SmartVaultAI already uses a lot of compute to make a prediction. This computation could be “co-used” by the reporter and therefore not scale with the size of the SmartVault model. We could imagine a world in which the direct translator only uses constant additional compute on top of the growing compute demands of the SmartVault predictor.
However, there **is a new counterexample.**This strategy relies on the assumption that the additional computation the reporter requires on top of the SmartVault predictor is larger than the computation required for the human simulator. But we can imagine scenarios in which this is not true. As an extreme case, we can think of scenarios that require the reporter to solve exponentially hard problems on top of the SmartVault predictor such as deciphering an encrypted message. Once we get into this realm, simulating the human model is less compute-intensive and the strategy fails again.
### Strategy 3 - penalize depending on “downstream” variables
Assume we understand the variables the reporter’s answer depends on and lay them out in chronological order. Then it would be suspicious if the reporter’s model depends on the very last variable rather than one much earlier. Intuitively, the variable “where things happened”, e.g. where the gem was stolen should be much more influential on the final report than the last thing to happen. The bad reporter (human simulator) might, however, primarily use the last node as it might be most predictive of what the human expects to see next.
**Counterexample:**We can create scenarios in which the behavior is flipped, i.e. the bad reporter corresponds to the early variable and the good reporter to the last variable. Assume, for example, we have a very competent robber. They plan to steal the diamond but first tamper with the camera in advance. Later, the robber returns to actually steal the diamond. In that case, the bad reporter uses the knowledge presented early because they can predict the rest based on the tampering. The direct translator, on the other hand, relies on later information, i.e when the robber returns to get steal the diamond. Therefore, the regularizer flips its purpose and we found a counterexample.
### Strategy 4 - penalize reporters that work with many different predictors
Ultimately, we want to find a property that distinguishes the human simulator from the direct translator. One such property could be how robust to changes the different reporters are. The direct translator depends on a lot of exact details of the SmartVault model. Therefore, small changes in the model can lead to gibberish answers for the direct translator. The bad translator, on the other hand, depends more on the human model than on the exact details of the SmartVault model. Therefore, it might not break down as easily when we perturb the SmartVault model. This could be punished and used for a regularizer. *Meta: I will not dive into the details of how to construct this regularizer but you can find it in the original report.*
**Counterexample:**We could imagine a bad reporter that has minimal robustness and would thus be favored by the regularizer. This bad reporter would memorize what the predictor is supposed to look like and then report gibberish when it looks different from its expectation. In this case, the bad reporter only works in one case and is thus maximally “unrobust” when we perturb the SmartVault model. Therefore, this strategy can’t be used to distinguish between the honest and the bad reporter in the worst case.
### Strategy 5 - penalize inconsistencies across different inputs
During training, we could look at many different input-output patterns of the reporter. For example, there might be two nearly identical scenarios in one of which tampering is obvious but not in the other. If the reporter describes the tampering only in one scenario this increases the probability that it is a human simulator since it only reports tampering when it is obvious to humans. The direct translator on the other hand should report tampering in both cases since they are so similar. We could design a regularizer that punishes this behavior.
**Counterexample:**This strategy merely changes the game from “look not robust” in the previous strategy to “look consistent” in the current strategy. However, ARC was not sure that the “look consistent” strategy is actually feasible with the same amount of effort. They note, that there could be a strategy in this direction that creates a strong advantage for the direct translator even if they haven’t found it yet. So we should think of this as a *tentative counterexample*.
Why ARC is excited about tackling worst-case ELK
------------------------------------------------
ARC thinks that a worst-case solution to ELK tackles one of the or the most exciting problem in current alignment. More concretely, they present four reasons why they think ELK is promising.
First, **a worst-case solution to ELK would constitute major theoretical progress for outer and inner alignment**. ELK’s main role in outer alignment is to make sure the AI training leads to good models. On a basic level, ELK enables us to rate the consequences of the AI's possible actions. We could then score these predictions and choose the ones with high ratings. Without a solution to ELK, the AI’s predicted consequences might not accurately reflect its internal state and thus lead us to choose the wrong actions.
On a higher level, we can investigate how the internal model of the AI relates to concepts we care about such as “humans” or “alive” during and after training. ELK might also pose a solution to inner alignment since both require finding regularizers that prefer “honest” policies over dishonest ones. There are, of course, more problems with alignment but solving worst-case ELK would pose a major step in the right direction.
Second, **ELK is crucial but much narrower than alignment**. To solve ELK, you get around concepts such as “agency”, “human values”, “counterfactuals”, “truly understanding a model”, “Goodhearting” and more. Since all of these pose very hard problems it’s nice to bypass them whenever possible. Furthermore, if the previous reason is correct, this would imply that it’s not necessary to solve all these hard problems to get a pretty good solution to alignment. Maybe ELK is the minimal sufficient condition for alignment.
Third, **we have to avoid untruthful powerful AIs anyway.**It’s intuitive that powerful AI systems that know critical facts about the world but hide them from their human operators would be bad on many levels. Therefore, it is important to work on ELK even if it doesn’t lead to alignment.
Broadly speaking there are two scenarios: a) ELK could turn out to be quite easy and just scale to larger models without ever running into ontology mismatches and the likes. b) ELK is hard for superintelligent AIs but works on weaker AIs. In this case, we might be able to use recursive strategies to combine multiple weaker AIs into more powerful AIs.
Fourth, **this research seems valuable even if ARC can’t solve the worst case.**Thinking about ELK has multiple side benefits like providing a host of possible solutions to test in practice even if there exists a theoretical counterexample. For example, even if the regularizer has theoretical worst-case counterexamples, it might still work pretty well in practice. Furthermore, thinking about ELK clarifies the trustworthiness of other approaches such as AI safety via debate or Iterated Distillation and Amplification, e.g. it provides a better understanding of risks coming from end-to-end optimization on subtasks.
In general, ARC seems excited about ELK. They state that they are making real progress and are cautiously optimistic about finding a good solution.
ELK Prize results
=================
To get more strategies for ELK, ARC ran the [ELK challenge](https://www.alignmentforum.org/posts/QEYWkRoCn4fZxXQAY/prizes-for-elk-proposals). They published the [results & prizes](https://www.alignmentforum.org/posts/zjMKpSB2Xccn9qi5t/elk-prize-results) for the challenge which I will briefly summarize in the following. I will only summarize the main prizes but you can check out the honorable mentions in the linked post.
### Strategy - train a reporter that is useful for another AI
Optimally, a direct translator is especially “useful” or “simple”. One possible way to translate this into practice is to train an auxiliary AI model on an auxiliary task that uses the model of the reporter and then reward the reporter if it was useful to the auxiliary model. This way, the reporter is incentivized to say true things that are decoupled from the human model and therefore disincentivized to simulate the human.
One possible way of encoding “usefulness” is to guess the predictor's activations but there are other possible implementations.
The **counterexamples to this strategy use steganography**, i.e. the reporter could hide information in its activations such that the auxiliary model sees them as useful while the reporter uses the hidden information for simulating the human model.
There were additional proposals to fix the first counterexample that essentially boil down to “make it harder to encrypt information that is invisible to the humans”. However, ARC was able to find counterexamples to all proposed fixes that boil down to “here is a new way to encrypt secret information that your proposal can’t fix”.
### Strategy - require the reporter to be continuous
The human observers only notice the tampering with the sensors if it happens slowly enough to be seen on camera, i.e. if it is captured on at least one camera frame. If something happens between two frames, the humans wouldn’t notice the tampering. This reveals an option to distinguish the bad reporter from the direct translator. The direct translator would always report tampering while the bad reporter would only report tampering when it is visible on camera but not otherwise. Therefore, we can create multiple sequences that include tampering which only differ in the frequency of the frames. The direct translator would be consistent and the bad reporter would be inconsistent. If we required the reporter to be continuous it should be equal to the direct translator since there is no room to be inconsistent.
**Counterexample: the reporter’s latent space might not be continuous.**The reporter’s inner model could, for example, be stored in natural language where ideas like “X” and “not X” don’t have a continuous interpolation. One might try to train the reporter to be more continuous but it’s not clear whether this is possible--practically and philosophically. Therefore, this might not work in practice.
### Strategy - penalize reporters for depending on too many activations from the predictor
Intuitively, the direct reporter relies more on the predictor's intermediate results while the human simulator relies more on the observations since later observations probably determine more what the human *would* think while the intermediate results would predict better what the humans *should* think. Therefore, we could try to regularize the reporter to rely less on the observations and more on “downstream” variables.
There were multiple proposals for this kind of problem including penalizing the reporter for the number of variables it depends on or giving the reporter a random subset of the predictor’s activations, and hoping that we produce a diversity of reporters some of whom answer honestly.
Unfortunately, they all fail due to similar **counterexamples**as were already presented in the main report (for [strategy 3](https://docs.google.com/document/d/1qg0fJMQPryJWT54b74_3VhfTFBrQ4j12uo0FjZD9PMU/edit#heading=h.njwzygvfphh4)). If the SmartVault model is very complicated, it might be easier to simulate the human model than explain the SmartVault model and the regularizer would flip its purpose. Then, less complexity means it’s rational to simulate the humans rather than reporting the truth.
### Strategy - compress the predictor’s state so that it can be used to answer questions but not tell what a human will believe
We could compress the predictor’s state to only contain all “relevant information”. This way, the reporter might be able to reconstruct what truly happened but there is too much information missing to reconstruct a perfect simulation of the human model.
**Counterexample: compressing state involves human simulation**. One possible way to compress the predictor’s state is to simulate the human and then compress that information. This might actually be an efficient compression strategy since it allows to throw away all information that is not meaningful to the human. In that case, the compression will result in the human simulator.
### Strategy - use the reporter to define causal interventions on the predictor
We have previously seen problems with penalizing “depending on nodes” in various examples. One possible way to fix this is to define “interventions” where we let the reporter modify the predictor’s state and rerun (parts of) the predictor’s computation. In case the reporter answers differently, it has to be the direct translator because only the direct translator cares about the true underlying model while the simulator doesn’t.
This strategy relies on the fact that these interventions can be defined in a *local*manner, i.e. each variable depends on only a few other clearly defined variables rather than a very complex web of things that might or might not be variables. If the variables are non-local (which is plausible in NNs), there is a **counterexample**. The reporter could do *fake interventions* in the non-local network whenever it knows that it is supposed to do interventions. Since the network is complex and non-local, we don’t really know whether it’s a real or a fake intervention and it could successfully fool us into believing that it intervenes. Therefore, we just add one more step of complexity to the simulator but the fundamental problem still exists.
### Strategy - train a sequence of reporters for successively more powerful predictors
Instead of training one reporter for one predictor we could train a sequence of reporters for corresponding predictors. Optimally, we would design this sequence of reporters to get more powerful over time and keep the difference between reporters small such that we can always understand the changes. We can think of this as an induction-like strategy. For a simple predictor, simulating the humans might be too complex, so we start the induction with a direct translator. Since all steps are understandable we should never move into simulation territory.
The **counterexample** comes from the assumption that there are small differences between the different reporters. It’s not clear that small changes in the capability of the reporter translate to small changes in its model. For example, a smaller reporter could think about the world in objects, a bigger reporter in atoms and an even bigger reporter in strings. We could imagine a similar switch between direct translation and human simulation. At some point, it might be easier to simulate the human model than to report the true model and therefore the reporter could flip.
### Strategy - train the predictor to use the human model as a subroutine
We split up the reporter into a content model and a human-inference-engine. The human-inference-engine is another AI model pre-trained to do inference in the human observers model. In this setup, the content model produces a state S which is then fed into the human-inference-engine to produce S’. S’ is then fed back into the content model and combined with S for a final prediction. If the reporter can leverage some computation from the human-inference-engine, it requires less computation for the final prediction. Therefore, the reporter is incentivized to become a direct translator because if it were a human simulator it wouldn’t get any leverage out of the human-inference-engine.
**Counterexample: the subroutines can be used for something else.**For example, the predictor might trick the human-inference-engine into doing some computation it really cares about and therefore outsourcing tasks that are unrelated to direct translation. Therefore, this strategy might not necessarily lead to the desired consequence. Furthermore, it could lead to worse consequences if the human-inference-engine is used to improve the human simulation.
Final comments
==============
I tried to stay relatively close to the original report because I think it is readable (even if not always optimal) and the structure makes mostly sense as it is. If you think something in my summary is wrong or unclear, please let me know.
I’m personally really excited about ELK because it seems like it could be a huge step towards more beneficial AI systems in a way that is not immediately broken by whatever comes next. It’s currently the attempt at alignment that feels most promising to me.
|
aed75cd3-c520-43ea-85d8-11747c7eb0e1
|
trentmkelly/LessWrong-43k
|
LessWrong
|
An anti-inductive sequence
I was thinking about what would it mean for a sequence of bits to be "anti-inductive". It probably is a concept that is already known (as a rule of thumb, if I can think about it, someone probably already wrote a paper on it 50 years ago), but I haven't heard about it.
*
Some sequences are predictable and can be compressed. These two concepts are deeply related, because if you can successfully predict the next part of the sequence, you don't need to actually write it down; hence compression. A completely random sequence of bits cannot be compressed or predicted.
There is a simple mathematical proof that some sequences cannot be compressed, although it doesn't say which ones. For any natural number N, there are more sequences of size exactly N, than sequences of size smaller than N. Therefore no program can generate a unique sequence shorter than N for any input sequence of size N.
*
Things get more complicated if we consider the caveat that although random sequences in general cannot be compressed, true randomness means that sometimes we accidentally get a sequence that can be compressed -- for example, with probability 1/2ᴺ we get a sequence of N zeroes, and it would sound silly to argue that we can't compress that!
The solution to this paradox is that if we decide to compress only some selected sequences, then we need to add an extra bit of information specifying whether this sequence was compressed or not. Otherwise, if we see a sequence of bits saying (in binary) "a sequence of thousand zeroes", we wouldn't know whether the intended value is this very sequence of bits taken literally, or the sequence of thousand zeroes. One bit doesn't seem like much, but actually most sequences cannot be compressed, so the cost of adding one extra bit to each of them outweighs the occasional space we would save by compressing the ones we can.
But still, if I needed a random sequence of bits to use e.g. as a password for something important... and by a miracle I generated
|
33f41b1b-e7e3-4e14-8ac6-d28a6004c121
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Why the technological singularity by AGI may never happen
Artificial general intelligence is often assumed to improve exponentially through recursive self-improvement, resulting in a technological singularity. There are hidden assumptions in this model which should be made explicit so that their probability can be assessed.
Let us assume that:
1. The Landauer limit holds, meaning that:
* Reversible computations are impractical
* Minimum switching energy is of order 10−21 J per operation
* Thus, energy cost at kT of order 1 EUR per 1022 FLOPs (details)
2. General intelligence scales sublinear with compute:
* Making a machine calculate the same result in half the time costs more than twice the energy:
* Parallelization is never perfect (Amdahl's law)
* Increasing frequency results in a quadratic power increase (P∝fV2)
* Similarly, cloning entire agents does not speed up most tasks linearly with the number of agents ("You can't produce a baby in one month by getting nine women pregnant.")
* Improving algorithms will have a limit at some point
My prior on (1) is 90% and on (2) about 80%.
Taken together, training ever larger models may become prohibitively expensive (or financially unattractive) for marginal gains. As an example, take an AGI with an intelligence level of 200 points, consuming 1 kW of power. Increasing its intelligence by a few points may come at 10x the power requirement. Mock visualization:
If these assumptions hold, then the exponential increase in capabilities would likely break down before a singularity is reached.
|
e8df2667-275b-4b57-9683-8533314701a8
|
trentmkelly/LessWrong-43k
|
LessWrong
|
How to Live Well: My Philosophy of Life
|
b54bf6e6-d02c-45a9-810d-9ed989a810ae
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Remembering people's name with Anki
Anki changed my life in a lot of way. But the most obvious one is that, thanks to Anki, I do finally recall people's name ! You can find my example, with my name and picture, on my website.
How I know that anki really works
I'm really bad at remembering faces and names[1]. With anki, it did change a lot. I started a new job in September 2017 as a post-doc. A month before starting, I went on the website of the laboratory I was joining, and I red the list of my future colleagues. I then created a card with their picture, first-name, last name. If the picture were not on their professional webpage, I did try to find them on the web and in social media. In September, when I started my job, I already knew their name[2].
I was able to easily see the effect of Anki, because I realized that, while I knew the name of the colleague who have an Anki card, I still had trouble recalling the name of the few colleague who had no Anki cards. I.e. if a problem had no picture of themselves on the web, I wasn't able to recall their name. In particular, it was hard for me to learn the name of the administrative staff, because their name and pictures do not appear on our website. .
Using Anki to learn name has a strange side-effect. When I see the face of a colleague, I need to recall to which picture of Anki he looks like, and then I can recall his[3] name. So, it's still a costly process in my head, since using Anki created an unusual indirection.
Of course, I could ask anyone without picture on the web whether I could take a picture to add in my database. Bit I believe it would have sound strange.
Remaining problems
One problem with the Anki method is that it only works when I made cards. However, most of the time, I don't think about taking time to make the cards. I should certainly remember the name of people I meet in conferences, they could be future colleagues, they could lead to job opportunities or to scientific collaborations. Recalling their name and where we meet is
|
73429ef5-e19f-400c-892f-19f8545fdccd
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Versions of AIXI can be arbitrarily stupid
Many people (including me) had the impression that [AIXI](https://en.wikipedia.org/wiki/AIXI) was ideally smart. Sure, it was uncomputable, and there might be "up to finite constant" issues (as with anything involving Kolmogorov complexity), but it was, informally at least, "the best intelligent agent out there". This was reinforced by [Pareto-optimality](http://arxiv.org/pdf/cs/0204040v1.pdf) results, namely that there was no computable policy that performed at least as well as AIXI in all environments, and strictly better in at least one.
However, Jan Leike and Marcus Hutter have proved that AIXI can be, in some sense, [arbitrarily bad](http://jmlr.org/proceedings/papers/v40/Leike15.pdf). The problem is that AIXI is not fully specified, because the universal prior is not fully specified. It depends on a choice of a initial computing language (or, equivalently, of an initial Turing machine).
For the universal prior, this will only affect it up to a constant (though this constant could be arbitrarily large). However, for the agent AIXI, it could force it into continually bad behaviour that never ends.
For illustration, imagine that there are two possible environments:
1. The first one is Hell, which will give ε reward if the AIXI outputs "0", but, the first time it outputs "1", the environment will give no reward for ever and ever after that.
2. The second is Heaven, which gives ε reward for outputting "0" and 1 reward for outputting "1", and is otherwise memoryless.
Now simply choose a language/Turing machine such that the ratio P(Hell)/P(Heaven) is higher than the ratio 1/ε. In that case, for any discount rate, the AIXI will always output "0", and thus will never learn whether its in Hell or not (because its too risky to do so). It will observe the environment giving reward ε after receiving "0", behaviour which is compatible with both Heaven and Hell. Thus keeping P(Hell)/P(Heaven) constant, and ensuring the AIXI never does anything else.
In fact, it's worse than this. If you use the prior to measure intelligence, then an AIXI that follows one prior can be arbitrarily stupid with respect to another.
|
102cd176-b803-49a5-9648-187221b87d94
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Visions of Summer Solstice
Previously:
* Winter Solstice
* The Summer Solstice Paradox
* The Steampunk Aesthetic
i. Off the beaten path
> See the light as it shines on the sea? It's blinding
> But no one knows how deep it goes
> – How Far I'll Go, Moana
The experience begins with a journey.
This partly by design, but largely by necessity.
Winter Solstice is about the long arc of history. Summer Solstice is about the here and now, and why being human is something worth protecting. It's about freedom, fun, physicality. It's about figuring things out in Near Mode. It's about building things together.
Getting a particular kind of Special out of that physicality requires a particular kind of space.
It's not enough to find a small nearby park. Ideally, you want an outdoor space vast enough to feel in your bones that the sky is the limit. There is no one and nothing to help you build a tower to the stars, or to cross the ocean, or cartwheel forever in any direction. But neither is there anyone to stop you. There is only nature, and you, and your tribe, and whatever you choose to do.
If you live in a major city, this probably means you may need to undertake a nontrivial journey before finding such a place. The best places will be off the beaten path, and a bit hard to navigate to. If it were easy, humans would have already crowded around it. You might be able to have fun, but you wouldn't be able to carve out a spot for your people to invoke a Sacred Fun.
As you contemplate this from your comfortable couch and think about the journey, you may find it daunting. If you attempted it alone, you might find it frustrating and lonely.
So, don't attempt it alone.
Journey together. If you get lost along the way, getting un-lost is part of the fun. You may find something valuable in overcoming the obstacles. I do, anyway.
The best journey is one that borders on the mythologic – you pass through narrow passages, winding your way through wild undergrowth, slightly confused about where you are g
|
9ba6a8fb-683d-4d0c-9eb7-eba075d1f70f
|
StampyAI/alignment-research-dataset/special_docs
|
Other
|
Lessons for Artificial Intelligence from Other Global Risks
Lessons for Artificial Intelligence from Other Global Risks
Seth D. Baum,1 Robert de Neufville,1 Anthony M. Barrett,1 and Gary Ackerman1,2
1. Global Catastrophic Risk Institute, http://gcrinstitute.org
2. University at Albany, State University of New York , College of Emergency Preparedness,
Homeland Security and Cybersecurity, https://www.albany.edu/cehc
Forthcoming in Maurizio Tinnirello (Editor), The Global Politics of Artificial
Intelligence, Boca Raton: CRC Press. This version 19 November 2019.
Abstract
The prominence of artificial intelligence (AI) as a global risk is a relatively recent
phenomenon. Other global risks have longer histories and larger bodies of scholarship.
The study of these other risks can offer considerable insight to the study of AI risk. This
paper examines four risks: biotechnology, nuclear weapons, global warming, and asteroid
collision. Several overarching lessons are found. First, the extreme severity of global
risks is often insufficient to motivate action to reduce the risks. Second, perceptions of
global risks can be influenced by people’s incentives and by their cultural and intellectual
orientations. Third, the success of efforts to address global risks can depend on the extent
of buy-in from parties who may be negatively affected by the efforts. Fourth, global risks
and risk reduction initiatives can be shaped by broader socio-political conditions, such as
the degree of policy influence of private industry within a political jurisdiction. The paper
shows how these and other lessons can inform efforts to reduce risks from AI.
1. Introduction
Those who do not learn from history are doomed to repeat it—or so the saying goes. The
progression of artificial intelligence (AI) technology is pushing human society in new
directions, but not all of the dynamics are entirely new. Many features of the AI issue
have arisen in other contexts. That holds both for AI as it exists today and as it may exist
sometime in the future. Likewise, efforts to manage the progression of AI and improve
outcomes for society have much to learn from past experience with other issues that have
similar features. A little history can go a long way.
The process of learning from history is broadly similar to the concept of transfer
learning in the computer science of AI. Transfer learning refers to the process of saving
knowledge gained from solving one problem and then applying it to solving another,
different problem.1 Transfer learning is a significant challenge in AI and a major focus of
ongoing computer science research. Humans are in many respects substantially more
capable at transfer learning than current AI systems—AI transfer learning is currently
grappling with tasks such as recognizing different types of features within an image.2
Nonetheless, even among humans, transfer learning can take dedicated effort, especially
for complex tasks such as addressing major global issues.3
The focus of this paper is to present some insights from the study of global risks,
especially (but not exclusively) global catastrophic risks. These insights are often of
particular relevance for AI catastrophe scenarios, especially (but again not exclusively)
long-term runaway AI scenarios in which humanity is unable to control the AI and
catastrophe ensues. These AI catastrophe scenarios have several similarities to other
1
global risks, including but not limited to their potential extreme catastrophic severity. In
many cases, these other risks have been addressed and studied much more extensively
than AI has. The paper aims to accelerate the study of AI risk by leveraging the
substantial body of experience and scholarship on other global risks.
The field of AI is of course not new. It has a rich history over many decades, as is
documented in several excellent histories.4 The idea of runaway AI catastrophe is also not
new—it can be traced to early work in the 1960s5 and even the 1860s.6 What is relatively
(but again not completely) new is the treatment of AI as a social, risk, and policy issue.
Much of this is driven by the considerable recent successes of AI technology and its
many applications across society. Some of it is also driven by a specific interest in the
more dramatic long-term AI scenarios.7 Now is a good time for the AI issue to learn from
other issues.
There is a vast universe of insight available from other global risks, and one paper can
only survey a small portion of it. The portion presented here is a mix of what the present
authors are most familiar with and what we believe is most important for improving AI
outcomes. That includes some emphasis on cases from US history, though much of it is
of international relevance. Many prior studies have also applied insights from other
global risks to specific aspects of the challenge of managing AI.8 In one similar study,
Allen and Chan9 survey four emerging technologies to derive insights for AI as a US
national security issue. The present paper also surveys multiple sources of lessons,
covering four global risks of relevance for improving overall AI outcomes, especially
with respect to catastrophic risks.
It should be noted that transferring lessons from one global risk to another is not the
only reason to study multiple risks. Another reason is to address important questions that
span multiple risks, such as how to prioritize scarce resources across multiple risks and
how to address tradeoffs in potential actions that could increase one risk but decrease
another. Cross-risk tradeoffs may be of particular relevance to AI due to its potential to
affect many sectors of society, including sectors implicated in other global risks. While
AI poses certain risks, if it is developed safely and responsibly, it could bring a range of
benefits, including reductions in other global risks.10 Cross-risk allocation and tradeoff
decisions provide a compelling reason to study multiple global risks; the potential to
transfer lessons across risks provides another. We therefore believe that cross-risk
research should be emphasized in programs to understand and address AI and other
global risks.
After an overview of definitions of key terms, the paper proceeds with discussions of
four global risks, each embodying a specific theme of relevance to AI. First,
biotechnology is a field of emerging technology with numerous important social benefits
and also major risks. Second, nuclear weapons are technologies of paramount strategic
importance across the international community. Third, global warming is a major risk
that derives from profit-seeking activity by some of the largest corporations in the world
and widespread consumer use. Fourth, asteroid collision is an extreme global risk that has
garnered substantial international scientific and policy attention despite its very low
probability. The paper transfers insights from the histories of these four cases to the study
of how to effectively manage AI.
2
2. Definitions
Before proceeding, it is worth briefly pausing to define some key concepts used in this
paper, especially for the benefit of an interdisciplinary audience. The concepts of AI, risk,
and global catastrophic risk all have multifaceted and contested definitions. To a large
extent, the substance of this paper is not sensitive to the particular definition used, but it
is nonetheless worth elaborating on the definitions.
A common definition of AI is any artificial agent that can “achieve goals in a wide
range of environments”.11 However, this emphasis on goal achievement is contested. For
example, Goertzel12 explores definitions of intelligence rooted in self-organization and
involving more than just the achievement of goals. Also contentious is exactly how wide
the range of environments must be for an artificial system to qualify as AI. Indeed, a
common observation is that once computers can achieve some task (e.g., defeating
humans at chess), then this task is no longer seen as requiring AI, which is a matter of
moving goalposts.13 An inclusive definition of AI could potentially include, for example,
those “intelligent” doors at the supermarket that open when people walk up to them. An
exclusive definition could potentially exclude any system that cannot perform the same
set of cognitive tasks as the human mind in the same way the human mind would perform
them.
For the present paper, the exact scope of AI is less important than the potential
societal consequences of some AI systems (assuming, of course, that these systems
qualify as AI). The introduction refers to dramatic long-term AI scenarios including
catastrophic runaway AI. That is shorthand for a hypothesized form of future AI whose
intelligence reaches the point where it can outsmart humans and wrest control from
humanity. Such AI may undergo a process called recursive self-improvement, in which
the AI makes a smarter AI, which makes a smarter AI, ad infinitum, potentially resulting
in an “intelligence explosion” or “Singularity”.14 The forms of AI involved in this process
are often known as strong AI, artificial general intelligence (AGI), or superintelligence.
In contrast, current AI is “weak” and narrow, with capabilities only within a relatively
narrow portion of intellectual tasks, in contrast with human minds, which can succeed
across a relatively wide range of tasks.
It should be stressed that the primary arguments of this paper do not depend on the
exact form of the AI, but only on its potential to pose a significant global risk. While a
runaway AI presumably could pose a global risk, other forms of AI may as well.
Likewise, it should also be noted that a number of analysts have expressed skepticism
about runaway AI scenarios.15 Even if these scenarios can be dismissed, then the lessons
in this paper may be applicable to other AI scenarios.
This raises the question of what qualifies as a risk. To start from the basics, a risk is a
“possibility of loss or injury”.16 Risk is commonly quantified as the probability of some
loss multiplied by the severity of the loss if it occurs. Attention to global risks such as
runaway AI are commonly predicated on the view that a risk of extreme loss can be
important even if its probability is low. The exact probability of runaway AI is a
controversial matter and not essential for this paper.
This paper makes some use of the concept of global catastrophic risk. Global
catastrophe has been defined in a variety of ways, including as the death of at least 25%
of the human population17 or as significant undesirable change in the state of the global
human system.18 Some treatments emphasize the risk of human extinction, on grounds
3
that extinction would entail loss of all future generations,19 whereas others argue that sub-
extinction catastrophes involving significant permanent harm can be of comparable
importance.20
The present paper takes a wider view on global risk. Its focus is on the lessons that
can be learned from global risks, not the severity of the global events that the risks entail.
The severity is important for the lessons in some cases but not all of them. The paper
includes cases in which there are fruitful lessons even if the lesson is coming from a
global risk whose severity does not meet the standards for global catastrophe outlined
above. The point here is the insightfulness of the lesson, not the size of the risk.
3. Biotechnology
Biotechnology has been defined as “the application of science and technology to living
organisms as well as parts, products, and models thereof to alter living or nonliving
materials for the production of knowledge, goods, and services”.21 Biotechnology is in
several respects a close analog to AI. Both are classes of emerging technology with many
applications that can either decrease or increase global risks. Biotechnology can help
counter pandemics, such as by enabling the rapid synthesis of vaccines22 or using “gene
drives” to propagate disease resistance among the population of insect vectors like
mosquitoes.23 Alternatively, biotechnology can exacerbate or even cause pandemics, such
as by laboratory accidents that inadvertently release deadly pathogens24 and by making it
easier for dangerous pathogens to be weaponized.25
Infectious diseases and AI are also similar in that a small source can readily self-
replicate and spread worldwide. Biological pathogens self-replicate within their host
organisms (e.g., within human bodies) and can jump from host to host. Similarly, some
computer software can self-replicate via “copying” and “pasting” within a computer and
via transmission from computer to computer—hence the term “computer virus”.
Computer viruses may not all involve AI; whether they do may depend on one’s
definition of AI. Computer security firm Malwarebytes contends that “there are currently
no examples of AI-enabled malware in the wild” but the possibility is “realistic” with
existing AI techniques or fairly straightforward extensions of them.26 Such self-
replication and propagation would presumably be even more feasible for more advanced
forms of AI. (An important exception would be for forms of AI that require specialized
hardware to run on.)
These two similarities—both are emerging technologies with diverse applications and
potential for propagation via self-replication—make biotechnology a valuable case study
for AI. Biotechnology is of further value because it has a relatively extensive history as a
societal issue.
One early episode was the 1975 Asilomar Conference on Recombinant DNA
Molecules, aimed at assessing and managing risks from recombinant DNA research. The
applicability of the 1975 Asilomar Conference to AI governance is explored in detail by
Grace.27 Grace finds that while recombinant DNA turned out to not be as dangerous as
some scientists initially feared, the conference nonetheless had some success in getting
this portion of the scientific community to take precautions in their research. Specifically,
the US National Institutes of Health issued safety guidelines that it required for the
researchers it funded, including a moratorium on recombinant DNA. Industry labs also
voluntarily opted to comply with the NIH guidelines.
4
As Grace28 documents, a major contributor to the success of the 1975 Asilomar
Conference was substantial buy-in from the relevant scientific community. A majority of
the relevant scientists, including many leaders in the field, indicated concern about the
risk. The moratorium was widely—perhaps universally—followed, despite the lack of an
enforcement mechanism, especially in industry. It may have helped that in the 1970s,
academia already had a culture of political activism regarding issues such as
environmental degradation and the Vietnam War. The success was thus due less to
institutional requirements and more to communities of people. As Grace29 puts it,
“informal social mechanisms played an overwhelming part in producing a pause in
research and triggering further action”.
Likewise, social buy-in may be essential for successfully addressing issues in AI.
Baum30 distinguishes between extrinsic measures for addressing AI, which are imposed
on AI communities from the outside, and intrinsic measures, which originate from within.
AI governance conversations sometimes emphasize extrinsic measures, such as in calls to
ban dangerous AI technologies,31 restrict development of certain forms of AI to a single
lab with United Nations oversight,32 or install research review boards to assess which AI
research can proceed.33 However, measures such as these may fail if they do not have
substantial buy-in from the AI community, especially if the relevant forms of AI are
widely pursued. Therefore, it is important to seek buy-in from AI communities, so that
any rules or guidelines would be self-enforcing. The 1975 Asilomar Conference is an
important case in point.
The prospect of widespread AI community buy-in is of particular relevance to the
idea of relinquishing dangerous AI technology proposed by Joy.34 This proposal has been
criticized for requiring universal buy-in among computer scientists, especially in the face
of commercial or national security pressures.35 However, if sufficient buy-in could be
achieved, relinquishment might succeed even without substantial institutional pressure;
the relinquishment of recombinant DNA is a case in point. Furthermore, several studies
have expressed concern that even a global AI institution with enforcement powers could
fail to prevent dangerous secret AI projects.36 Thus, achieving informal community buy-
in may be more important than establishing formal governance institutions, though there
may nonetheless be a constructive role for both. (The challenge of monitoring AI projects
is discussed further in the section on nuclear weapons.)
A more recent and controversial case is the debate over gain of function (GOF)
research on potential pandemic pathogens (PPP). This research manipulates existing
pathogens to make them more pathogenic, in order to learn more about the pathogen and
advance the medical response to outbreaks. The research is controversial due to concerns
that the enhanced pathogen could accidentally or intentionally be released from the lab,
enter the human population, and spread. The enhancements could result in a more severe
pandemic than what would occur from the naturally occurring pathogens. An active
debate has emerged on the merits of GOF-PPP experiments. Some argue that the benefits
exceed the risks, including people involved in the experiments.37 Others argue the
opposite, including people who commonly emphasize security perspectives on
biotechnology.38
In 2012, GOF-PPP researchers agreed to a voluntary pause on these experiments.39
Then, in 2014, the US government announced a moratorium on GOF-PPP experiments in
order to assess whether the potential public health benefits of the experiments were worth
5
the risks. The US National Science Advisory Board for Biosecurity commissioned a risk-
benefit analysis to inform the debate. The ensuing report40 spans 1,006 pages but does not
reach a definitive conclusion on whether the benefits exceed the risks. One proposed
explanation for the report’s inconclusiveness is that “the areas that separate pro- and anti-
GOF advocates fall into areas of judgment and belief, and these differences cannot be
adjudicated by risk-benefit analysis”.41 Despite the lingering disagreements and
ambiguities, the US lifted the moratorium in 2017.42
The controversy surrounding the GOF-PPP case makes it arguably a more relevant
case study for AI than the case of recombinant DNA. As with GOF-PPP experiments,
experts are divided on the risks and benefits of AI, especially for the prospect and risk of
long-term runaway AI. Achieving consensus on an AI moratorium may thus be more
difficult, and there may be pressure to end the moratorium even before the risks and
potential benefits are conclusively evaluated and consensus is reached on how best to
proceed. Therefore, the GOF-PPP case may be a fertile ground for further study on
advancing AI debates and reaching clear conclusions and consensus on how to manage
potentially dangerous AI research.
An additional line of research on biotechnology worth examining is an assessment of
biotechnology stakeholder reactions to efforts to promote responsible research and
innovation, or RRI.43 RRI aims to promote an inclusive and reflective technology
research and development process, with one aim being for “societal alignment”.44
However, Kuzma and Roberts45 find significant reluctance to adopt RRI among
biotechnologists. Notably, academic biotechnologists expressed more reluctance than
their industry counterparts, due to concerns about intellectual freedom and skepticism
about non-expert outsiders imposing unwise restrictions. This finding resembles that of a
recent survey of AGI research and development projects, in which academic projects
were more likely to articulate intellectual values and industry projects were more likely to
articulate values based on benefiting humanity.46
While the RRI biotechnology study of Kuzma and Roberts47 is ongoing, they
meanwhile call for a “more practical RRI” in which RRI researchers and advocates are
actively engaged with technology projects instead of advocating RRI from the sidelines
in research journals and other venues. This would appear to be wise advice for all fields
of technology, including AI, and for all paradigms for improving technology
development processes and outcomes, including but not limited to RRI.
Grotto48 reviews the history of the governance of genetically modified organisms
(GMOs) in agriculture and derives implications for the potential regulation of AI. Grotto
contrasts the treatment of GMOs in Europe, where strict regulations drastically curtailed
the use of GMOs, to the US, where a business-friendly regulatory regime led to
widespread cultivation of GMOs. Grotto notes that these divergent regulatory
environments were not inevitabilities, but instead were linked to historical coincidences
such as European concern about food safety derived from the outbreak of mad cow
disease in the United Kingdom. Additionally, the initial regulatory decisions have had
lasting effects over several decades in both jurisdictions. The same could potentially
apply for regulations of AI.
Finally, it is worth noting the difficulties inherent in controlling even the most
pernicious forms of biotechnology. The use of biological weapons was outlawed by the
Geneva Protocol of 1925, and over 180 states have signed the 1972 Biological and Toxin
6
Weapons Convention that prohibits the acquisition and stockpiling of these weapons.
Yet, the fundamentally dual-use nature of biotechnology, where the same technique or
equipment could be used for beneficial or harmful ends, enabled several states—most
notably the Soviet Union in the 1970s and 1980s—to flout the international bioweapon
ban and embark on massive bioweapons programs. Despite multiple attempts over
several decades, the international community has been unable to craft a verification
regime for the Convention that is both practically enforceable and politically acceptable.
This might serve as a negative lesson in the difficulties of exercising international control
over technologies—like some forms of AI—that have inherently dual-use applications.
For example, image recognition techniques that can enhance public web search engines
can also enhance target recognition in weapon systems. This is not the case with respect
to nuclear weapons (discussed in the next section), where the underlying technologies are
far less dual-use in nature.
4. Nuclear Weapons
Biotechnology is similar to AI as risky, potentially self-propagating, dual-use
technologies. However, the vast majority of biotechnology applications (with the few
exceptions mentioned above) are in the civilian sector. In contrast, the destructive power
of nuclear weapons is emblematic of a military technology with unequivocal and
paramount strategic importance on the international stage. Potentially, AI could have a
similar importance, especially for more advanced forms of AI. Therefore, the extensive
history and study of nuclear weapons may be a fruitful source of insights for AI. (Nuclear
technology also has civilian applications, though these are not explored given that the
attendant dual-use issues are substantially similar to those of biotechnology, which has
already been considered.)
A potential distinction between AI and nuclear weapons is that whereas concerns
about nuclear weapons often focus on intentional harm to geopolitical adversaries,
concerns about AI (especially runaway AI) often focus on accidental harm to everyone.49
However, this distinction is at most a matter of degree. It is true that countries generally
do not aim to attack themselves with their own nuclear weapons, but AI developers also
generally do not aim to harm themselves with their own AI systems. Furthermore,
nuclear weapons must also be handled with great care to avoid accidental detonation on
home soil.50 Therefore, while nuclear weapons are not a perfect analog for AI, the
similarities may be sufficient to apply lessons from the former to the latter.
One AI topic for which nuclear weapons lessons may be especially salient is on the
prospect of a race to be the first to build advanced AI. It is sometimes proposed that a
sufficiently advanced AI, such as a strong AI or AGI, could confer extreme “winner takes
all” advantages to whomever builds it first.51 This could occur in particular if the AI
undergoes a rapid intelligence explosion but remains under the control of its builders,
who then may obtain a high degree of power over all global affairs. If control is lost,
catastrophe could ensue. A recent survey found no significant evidence of a race to build
AGI, and instead found significant cooperation between projects.52 Meanwhile, there is
some competition on other, more modest forms of AI, such as between the US and
China53 and between companies within sectors such as autonomous vehicles.54 The
prospect of an advanced AI race is plausible due to the strategic implications of the
7
technology, and it is also a concern because it could preclude sufficient caution with
respect to the safety of the technology.55
Several studies propose that the Cold War nuclear arms race may be a good analog to
an initial race to build AI,56 though a better analog would be the initial race to build
nuclear weapons. An essential feature of the initial development of nuclear weapons is
the extreme geopolitical tensions of that era. It appears to be a historical coincidence that
the relevant scientific breakthroughs in nuclear physics occurred during the run-up to and
fighting of WWII, since there is no clear link between the initial development of nuclear
physics and increasing tensions in Europe. This geopolitical context may, however,
explain the very rapid progression from the 1939 discovery of nuclear fission57 to the
establishment of nuclear weapons development projects in each of Germany, Japan, the
Soviet Union, the UK, and the US (1939 to 1942; exact project start dates are
ambiguous), and finally to the first detonations of nuclear weapons (1945). Indeed,
reading this history today, it is remarkable how quickly the events unfolded, and how
extensive were the contacts between scientists and high-level government officials.58 The
fast pace was motivated by a desire to build nuclear weapons first and therefore achieve
major—perhaps decisive—advantage in WWII and its aftermath.59
An implication of this is that an AI race could be avoided or at least managed more
carefully if major geopolitical tensions can also be avoided. This point applies not just for
advanced AI, but also for near-term AI and other technologies of military significance.
The matter is well-documented by Scharre60 in interviews with military officials and
experts and accompanying analysis. Scharre documents that militaries have thus far
largely abstained from deploying autonomous weapons, or have at most proceeded rather
cautiously, due to a variety of concerns including cost, safety, and ethics. (Autonomous
weapons can be defined as weapons capable of selecting and firing on targets without
human input.61) However, Scharre finds that militaries are much more likely to use
autonomous weapons if a major war breaks out and countries find themselves compelled
to do whatever it takes to win. One expert compares the situation to the US abstention
from unrestricted submarine warfare prior to the bombing of Pearl Harbor on 7 December
1941. In regards to whether the US would deploy autonomous weapons, the expert asks,
“Is it December 6 or December 8?” It follows that if a race for advanced AI is to be
avoided, it may be important, perhaps even crucial, to avoid major wars between the
countries that could build advanced AI.
Another important lesson from the nuclear weapons race for a potential AI race
concerns secrecy. The nuclear weapons projects were highly secretive, and espionage
was sometimes but not always successful. Notably, the US and its allies did not know
how little progress the German nuclear weapons program was making until August
1944.62 Because beating the German program was a primary goal of the US program, had
the US known earlier, it is possible that it would have ended its own program, or at least
pursued its program more carefully. Similarly, if future AI programs learn of rival
programs’ struggles and cessations, then they may also stop or proceed more carefully.
This possibility runs counter to the proposed idea that information about rival AI projects
increases risks.63
If an AI race is won, such that there is only one group in possession of advanced AI,
then the situation may resemble the period spanning from 1945 to 1949 in which only the
US possessed nuclear weapons—the so-called nuclear monopoly period.64 It is sometimes
8
proposed that advanced AI may involve a strong first-mover advantage, sometimes
referred to as “winner takes all”.65 While the extent to which the nuclear weapon
monopoly involved the same dynamic is unclear and may have been more limited, there
were nonetheless serious proposals for using the power of nuclear weapons to maintain a
monopoly.
One proposal sought to keep the nuclear monopoly under US control. The US would
have threatened nuclear attack against any country that attempted to build nuclear
weapons, or, if need be, executed such an attack.66 One view held that this would be
preferable to permitting catastrophic nuclear proliferation.67 It also would have given the
US a strong and potentially dominant position in global politics. US President Truman
ultimately declined to follow this proposal, which may suggest that the first party to build
advanced AI may likewise decline to use it to maintain monopoly and a dominant global
position.68 However, there is no guarantee that other leaders would have made the same
choice as Truman.
Another proposal—the Baruch Plan—called for an International Atomic
Development Authority that would consolidate nuclear expertise and oversee global use
of nuclear power for both peaceful and military purposes. Such an arrangement could
have maintained much of the geopolitical status quo; in particular, it would theoretically
not have required US dominance. The Soviet Union nevertheless rejected the Baruch
Plan, apparently out of concern the US and its allies would in practice dominate the new
international atomic authority.69 Potentially, had the US threatened nuclear war if the
Soviet Union refused to terminate its nuclear weapons program, it might have been more
inclined to accept the Baruch Plan, though it is unclear how the Soviets would have
reacted in this circumstance.
The Baruch Plan is perhaps the best historical precedent for several proposals for
global AI governance backed by the power of AI.70 The essence of these proposals is to
first build an AI capable of monitoring for rogue AI development projects, and then to
use this AI as the basis for enforcing global compliance with safety and ethics standards.
In some variants, the AI itself could conduct the enforcement. Such a scheme could leave
humans in charge, and could perhaps buy humans the time needed to carefully reflect on
how best to build a more powerful AI, including an AI that humans could not or would
not control. However, just as the Baruch Plan struggled to gain international consensus,
so too could a comparable plan for AI. Indeed, consensus on AI may be more elusive due
to important and potentially divisive questions about which types of AI to build.71 (A
milder variant of this scheme is for an international AI research center that consolidates
resources for AI development, modeled after CERN.72 However, the CERN model aims
for scientific breakthrough, not safe development of technology in the public interest, and
it may likewise be more applicable to the initial development of AI than the subsequent
monopoly.)
The US nuclear monopoly ended in 1949 following the Soviet development of
nuclear weapons. Since then, the world has persisted with multiple nuclear powers.
Similarly, some AI scenarios involve multiple advanced AIs. Such scenarios have been
considered especially in the context of AIs based on the digitization or emulation of
human brains,73 though they could also occur for other forms of AI.
A central feature of the ongoing era of multiple nuclear powers is the doctrine of
nuclear deterrence, in which the threat of nuclear attack dissuades rival countries from
9
waging major wars. The absence of a global war since WWII arguably affirms the
effectiveness of nuclear deterrence,74 though this is controversial: other factors may
explain international stability after WWII, including a desire to avoid any major war,
nuclear or non-nuclear, and the general satisfaction of the Soviet Union and the United
States with their positions in global affairs.75 Similarly, deterrence could potentially
facilitate the nonviolent and reasonably peaceable coexistence of rival AI powers.
Scholarship on and experience with nuclear deterrence suggests that an AI deterrence
regime may be most successful if (1) no side has the ability to destroy rivals or disable
their AI systems without suffering devastating retaliation, an ability sometimes referred
to in the nuclear weapons literature as “primacy”76 and in the AI literature as “decisive
strategic advantage”,77 (2) rival parties have incentives to avoid crises, or to deescalate
crises if they occur, a condition sometimes referred to in the nuclear weapons literature as
“crisis stability”,78 and (3) miscalculations on the intentions and activities of rivals can be
avoided. These and other aspects of nuclear deterrence could prove valuable for
managing a world of multiple rival advanced AI powers.
5. Global Warming
Out of all the global risks, global warming has probably been the subject of the most
extensive interdisciplinary scholarly inquiry—indeed, it is probably the most extensive
by a large margin. There are robust literatures on the psychology of global warming,79 the
economics,80 the epistemic and policy implications of catastrophic risk,81 military
dimensions,82 and much more. The voluminous scope of global warming research makes
it a rich source of insight for many other global risks, including AI.
Existing AI studies have just begun to scratch the surface of insight from global
warming literature. One study draws on the psychology of global warming to inform the
design of both formal regulations and informal community-based measures to improve
AI outcomes.83 Another draws on the politics and psychology of skepticism and
misinformation about global warming to explore how similar dynamics could play out
with AI.84 These are important topics, but there is a lot more lurking in the extensive
global warming literature.
The global warming literature may be of particular relevance for scenarios in which
AI is developed in the private sector. National AI development projects are plausible (and
more closely related to the pursuit of nuclear weapons), but AI is currently developed
primarily in the private sector. Indeed, AI is an important technology for some of the
largest corporations in the world. It is therefore worth studying cases in which corporate
activity poses a global risk. The case of global warming and the fossil fuel industry serves
this purpose well. What follows is a very brief history to illustrate some major dynamics.
For many years, the fossil fuel industry has sought to downplay the importance of
global warming and dispute the underlying science.85 However, this was not always the
case. Initially, some fossil fuel companies were active in the mainstream science of
global warming. This early history is of particular relevance for the current state of affairs
in AI and is worth exploring in some detail.
In 1979, Exxon installed on its Esso Atlantic supertanker custom scientific equipment
for measuring air and ocean carbon dioxide concentrations. The project assessed the
ocean’s uptake of atmospheric carbon dioxide, which at the time was an important
uncertainty in the science of global warming. The supertanker project was part of a
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broader engagement by Exxon in the mainstream scientific study of global warming
during the decade 1977-1987, as was recently documented in an investigative journalism
project by InsideClimate News.86
1988 marked the beginning of serious policy interest in addressing global warming, at
least in the US. Prompted in part by a severe drought and heat wave, the US Senate
Energy and Natural Resources Committee held a hearing in which NASA’s James
Hansen delivered a now-famous testimony expressing 99% certainty that global warming
had begun. As reported in a New York Times article, which ran at the top of the front
page, several Senators on the Committee concurred that global warming was a threat and
that action should be taken to counteract it.87
At around the same time, Exxon began supporting efforts to amplify uncertainty
about the science of global warming, apparently as a strategy to stymie policy restrictions
on its fossil fuel business. This change in practice is seen, for example, in the Global
Climate Coalition, an industry lobbyist group that Exxon co-founded in 1989. Exxon
continued its scientific research on global warming, much of which continued to support
the mainstream scientific consensus, but its public-facing communications tended to
question the science and oppose policy action.88
The divergent content of its scientific research and public communications served
different purposes. As reported by InsideClimate News, Exxon wanted its own sound
science to guide its internal planning, confer it legitimacy to help it influence policy, and
adhere to scientific standards.89 In contrast, the public communications were a business
strategy aimed at avoiding costly regulations. This strategy has a long history, dating to
1950s tobacco industry efforts to question the science linking tobacco to cancer, and it
remains in use across multiple industries, including fossil fuels.90 Exxon’s science/public
divergence permits it to claim it accepts the reality of global warming while actively
thwarting efforts to seriously address it.
The AI issue may now be where global warming was in the late 1970s to early 1980s:
public recognition has begun, but policy regulations are not yet in serious consideration.
This may explain why AI corporations are active in efforts on AI ethics: acknowledging
that AI poses serious ethical issues is not yet a threat to their core business model. Indeed,
the corporations may wish to demonstrate that they are responsible actors on AI and
therefore do not need to be regulated. (They may even want to show that they are more
responsible than their competitors, such that their competitors need to be regulated and
they do not.)
The history of global warming shows that if corporations view the issue as a
significant threat to their profits, then addressing the issue becomes quite a lot more
difficult. The corporate ethics statements may continue insofar as it improves the
corporations’ public image without committing them to any costly restrictions on their
business activities. Meanwhile, the companies may seek to publicly downplay the risks
associated with their technology, and to lobby governments to prevent regulations. This is
what the fossil fuel industry did, despite global warming posing a significant risk of
global catastrophe that has long been backed by extensive mainstream science. The risk
of global catastrophe from AI has a much more tenuous scientific basis and thus may be
considerably easier for industry to sow doubts about.91 (Conversely, improving expert
consensus on AI risk could help counteract industry obfuscation).92
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An important difference between global warming and AI is that whereas all fossil fuel
can increase global warming, not all AI technology poses a global risk. For example,
contemporary AI systems designed to play games like chess and Go may be a significant
cultural phenomenon but they are not significant threats to human welfare. In order to
avoid AI catastrophe, only certain forms of AI may need to be restricted, specifically
those that could cause catastrophe. It thus follows that a key question for AI governance
is whether the restrictions need to avoid catastrophe would cover forms of AI that are
also profitable. In this context, Baum93 coins the term “AGI profit-R&D synergy”,
defining it as “any circumstance in which long-term AGI R&D delivers short-term
profits”. If there is AGI profit-R&D synergy, then corporations may resist restrictions on
the development of AGI, even though the technology could pose a global risk. The extent
of AGI profit-R&D synergy could be an important—perhaps even crucial—factor in the
safe governance of AI.
Some arguments against regulating fossil fuels may also apply to AI. First, it is
sometimes argued that regulation stifles innovation and economic growth and restricts
consumer lifestyles. This has been a common refrain in global warming debates94 and is
starting to be heard for AI.95 Second, it is sometimes argued that regulations should be
delayed until the risks are adequately understood. This has also been a common refrain in
global warming debates, although the argument is sometimes, though not always, made
disingenuously.96 The same argument might also be made for AI, potentially, but not
necessarily, disingenuously. Whether any particular regulation would bring net benefits
(by reducing the risks from a technology more than it restricts the potential benefits) and
when regulations should be introduced are important matters for policy analysis but are
beyond the scope of this paper.
It is important to note that the pathologies of global warming governance do not apply
equally across the globe. The case of Exxon as discussed above applies in particular to
the US. Overall, the US has been relatively susceptible to corporate influence on global
warming due to a variety of political, economic, and cultural factors. For example,
Sheldon Whitehouse, a US Senator and strong advocate for environmental protection,
attributes much of the problem to the 2010 US Supreme Court decision in the case of
Citizens United v. Federal Election Commission , which permitted unlimited corporate
spending on election-related communications.97 Whitehouse reports that after this court
case, many politicians abstained from supporting action on global warming out of
concern that the fossil fuel industry would support their political opponents. In countries
with more restrictive campaign finance rules, the fossil fuel industry may tend to have
less influence on global warming policy. The same could hold for AI policy as well.
Finally, the history of global warming also provides a more general lesson regarding
the role of scholarly expertise in public debates about science and technology issues. In
public debates about global warming, corporate messaging has diminished the influence
of the scientific consensus. Similarly, recent public debates about AI have given
extensive attention to science and technology celebrities with limited AI expertise, such
as Bill Gates, Elon Musk, and the late Stephen Hawking.98 The history of global warming
shows that public debates can diverge from expert opinion for an extended period of
time. Public debates have different dynamics and epistemic standards. Efforts to improve
the quality of public debates about AI should proceed accordingly.
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6. Asteroid Collision
In several respects, asteroid collision and AI are very different types of issues. They
differ in their origin (outer space vs. technology), their empirical basis (which is much
stronger for asteroid collision), and their degree of social consensus (AI is much more
controversial). Indeed, asteroid collision is notable for being perhaps the most well
characterized global catastrophic risk in terms of the probabilities and severities of the
risk.99 Nonetheless, both asteroid collision and AI involve the prospect of extreme global
catastrophe. Concern about the risk of global catastrophe has motivated high-level efforts
to address asteroid collision by both the international scientific community and major
national governments. These successes have only been partial—more work to address
asteroid collision remains to be done—but they nonetheless suggest a pathway for high-
level attention to AI risk even if AI catastrophe is perceived as unlikely.
Asteroid collision should be a quintessential case of what Jonathan Wiener100 calls
“the tragedy of the uncommons”: a risk so rare that it is overlooked by the lay public and
policymakers. Yet the history of the risk shows that this has not been the case. (The
history below draws heavily on Chapman.101)
Scientific awareness of the asteroid collision threat began in the 1940s, but was
largely dormant until the early 1980s, following the landmark Alvarez et al.102 study of
the Cretaceous-Paleogene extinction and an important workshop in 1981. Public interest
grew in the late 1980s via a trade press book103 and the “near miss” of asteroid 1989FC (it
was “near” in astronomical terms but not in terms of its danger to Earth). Policy interest
was sparked by a position paper published by the American Institute of Aeronautics and
Astronautics.104 This outreach culminated in the 1990 US House NASA Authorization
Report Language calling for NASA attention to the asteroid threat. The text of the Report
Language is illuminating:
The chances of the Earth being struck by a large asteroid are extremely small, but
since the consequences of such a collision are extremely large, the Committee
believes it is only prudent to assess the nature of the threat and prepare to deal with it.
We have the technology to detect such asteroids and to prevent their collision with the
Earth.105
This text shows the US House of Representatives reaching the conclusion that an
extreme catastrophic risk should be taken seriously and addressed even if its probability
is extremely low. The logic here mirrors the logic found throughout academic studies
advocating attention to global catastrophic risks,106 including the risk of runaway AI.107
The asteroid threat therefore offers an important precedent, one that may be worth
revisiting in policy debates about AI.
The 1990 US House NASA Authorization Report Language is no anomaly. The US
government has remained engaged on the asteroid threat. Most recently, the US National
Science and Technology Council, an Executive Branch advisory group, published the
National Near-Earth Object Preparedness Strategy and Action Plan .108 The US has also
sponsored astronomy studies to detect asteroids, as have other countries. Scientists report
the detection of over 90% of large asteroids, none of which are found to be on
Earthbound trajectories.109 Ongoing detection programs scan for smaller (and thus harder
to detect) asteroids. The US and other countries are also developing techniques for
13
deflecting away Earthbound asteroids. The US has taken at least some formal steps
toward operationalizing those techniques: the US National Nuclear Security
Administration is holding onto an important component of nuclear explosives for
“potential use in planetary defense against earthbound asteroids”.110 While more could be
done, it is nonetheless clear that the asteroid threat has significant high-level policy
recognition and support for efforts to address it.
The nature of the asteroid threat may have made it easier for governments to
recognize it than it would be for the AI threat. The Cretaceous-Paleogene extinction
provides what appears to be a clear proof of principle, and the overall science of asteroids
is relatively well understood. Scientists routinely publish figures graphing the frequency
of collision as a function of asteroid size based on well-established empirical data.111 This
makes it easier for government officials to believe in the validity of the threat.
Furthermore, the asteroid threat involves no human enemies whose livelihood may be put
at risk by efforts to address the threat—the only thing put at risk is the asteroid itself.
Likewise, there is likely to be less in the way of institutions lobbying against asteroid risk
reduction.
However, the history of the asteroid threat shows that it did in fact struggle to gain
serious recognition, and it did also have to overcome institutional opposition. Early media
coverage included a significant “giggle factor” and portrayed concerned astronomers as
“Chicken Littles” playing up concern to generate funding for their research.112
Additionally, many scientists, including those in leadership at NASA, pushed back
against efforts to address the asteroid threat. The scientists did not want the “giggle
factor” tarnishing their reputations, and they did not want the applied mission of the
asteroid threat to pull scarce funds away from pure (non-applied) scientific research.113
Recognition of the AI threat faces very similar challenges. For the asteroid threat, these
challenges have been overcome with at least some modest success. This fact should
provide some encouragement to efforts to gain serious attention for the AI threat.
Furthermore, the human dimensions of asteroid risk are not as well understood as the
physical and environmental dimensions. This holds in particular for potentially globally
catastrophic human harm.114 While asteroid risk is probably the most well-characterized
global catastrophic risk, the exact risk estimates are nonetheless uncertain. This
uncertainty has not precluded policy action; either the uncertainty has gone unnoticed by
policymakers, or the policymakers opted to act anyway. The willingness of policymakers
to act despite uncertainties in the risk is an encouraging precedent for AI, which is a
considerably more uncertain risk.
7. Lessons Learned
Several overarching lessons for the study of AI can be drawn from the four global risks
surveyed in the preceding sections. First and foremost, the extreme severity of global
risks does not on its own ensure they will be addressed successfully. The severity of
global risks does sometimes move key actors to take action, such as US Congressional
action on asteroid risk. Other actors have not been persuaded by the severity, such as
academic biotechnologists reluctant to adopt RRI and the fossil fuel industry opposing
global warming policy. There are compelling theoretical reasons to prioritize reducing
global risks, but these reasons are not always persuasive in practice.
14
Second, perceptions of global risks can be strongly influenced by people’s incentives
and by their cultural and intellectual orientations, especially where the size of the risk is
uncertain. Global risks are highly uncertain due to the complexity of global events and
the rarity of (and thus lack of data on) global catastrophes. Even the risk of asteroid
collision, which derives from relatively simple and well-understood astronomical
processes, has significant uncertainties with respect to human consequences.
Communicating asteroid risk has also been challenging due to the risk’s “giggle factor”.
Other risks are more contentious. Global warming risk is disputed, perhaps
disingenuously, by a fossil fuel industry that has an incentive to avoid regulation. GOF-
PPP risk is disputed by different populations of experts, with those conducting GOF-PPP
experiments sometimes finding the risk to be lower than those who emphasize the
security dimensions of biotechnology. The size of AI risk is also currently disputed
within expert communities, could also come to be disputed by industry, and might be
difficult to communicate due to its own distinct “giggle factor”. Efforts to characterize
and raise awareness about AI risk should be mindful of these dynamics to mitigate biases
in analysis and public discourse.
Third, whether the response to global risks is successful may depend on buy-in
especially from those who stand to lose as a result of risk reduction measures. Out of all
the cases studied in this paper, two stand out as relatively successful stories of risk
reduction: recombinant DNA and asteroid collision. The former involved a moratorium
that had broad buy-in from the relevant scientific community. The latter involves
response measures that do not implicate or restrict anyone to any significant extent.
Contrasting examples abound. Biotechnology RRI initiatives face resistance from
academics concerned about intellectual freedom. Biological and nuclear weapons arms
control initiatives face resistance from states concerned about losing strategic advantage.
Initiatives to reduce greenhouse gas emissions face pushback from the fossil fuel
industry. Obtaining buy-in for AI risk reduction may be especially challenging because
many key actors, including academics, states, and industry, could stand to lose as a result
of risk reduction initiatives. AI risk reduction initiatives may need an unusually large and
multifaceted effort to achieve buy-in in order to succeed.
Finally, risks and risk reduction initiatives can be heavily shaped by broader socio-
political conditions. GMO regulation has been stricter in Europe than in the US due to
Europe’s less business-friendly political culture and its recent experience with mad cow
disease. Fossil fuel regulation has also been relatively lax in the US, perhaps due to its
relatively permissive campaign finance laws (which are closely tied to the business-
friendly US political culture). Nuclear weapons technology was developed extremely
quickly because certain breakthroughs in nuclear physics happened to coincide with the
extreme international competition of the 1930s and 1940s. Likewise, AI risk reduction
initiatives will not take place in a vacuum. To succeed, the initiatives should account for
the particular socio-political conditions and the (possibly unforeseen) circumstances in
which they will take place.
8. Conclusion
History is not doomed to repeat itself. Past failures to manage global risks do not
necessarily portend future failures—especially if important lessons are learned. At the
same time, past successes do not necessarily portend future successes. While AI is
15
relatively new as a social, risk, and policy issue, it has much to learn from other global
risks.
This paper proposes that it is possible to accelerate the study of AI as a social, risk,
and policy issue by leveraging the existing scholarship on and experience with other
global risks. To demonstrate this possibility, the paper presents examples from four other
classes of global risk: biotechnology, nuclear weapons, global warming, and asteroid
collision. Although it would be valuable to expand the study of these four cases to
broader international contexts, they shine considerable light on how to understand the
prospects for AI catastrophe, how such an outcome could be avoided, and how AI
outcomes can be improved more generally.
In addition, these sorts of historical case studies may hold some rhetorical value for
efforts to improve AI outcomes. The history may help some people take certain AI
scenarios more seriously, especially scenarios involving long-term, high-stakes AI. Many
people in academia, government, and other sectors may be dismissive of such
scenarios,115 instead preferring to focus their attention on more near-term and empirically
robust issues. The history of other global risks can provide at least an indirect empirical
basis for some important aspects of long-term AI, and can likewise demonstrate that
similar issues have often gotten substantial high-level attention. The history may be of
particular value for relating long-term AI to people with background in other global risks,
because it can help to make long-term AI seem more familiar.
At the heart of this paper is a claim that transferring lessons from other global risks
can be an efficient and productive means of advancing progress on AI. In putting forward
this claim, we do not mean to imply that lessons from other global risks are sufficient for
studying issues in AI. To the contrary, AI will inevitably pose some novel challenges that
require dedicated original analysis. Furthermore, we do not mean to claim that
transferring lessons from other risks will be the most efficient and productive research
strategy for all groups working on issues in AI. This approach will tend to work best for
research groups, such as our own, that already have background in other risks. The merits
of this approach for research groups that are more narrowly specialized on AI is an
important question and is beyond the scope of this paper. Instead, this paper serves to
demonstrate the intellectual and practical benefits that can be gained from transferring
lessons from other global risks to the study of AI.
Acknowledgements
Maurizio Tinnirello and two anonymous reviewers provided helpful comments on an
earlier version of this paper. Jake Stone assisted in formatting the manuscript. Any
remaining errors are the authors’ alone.
16
1 S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering 22 no.
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55 Shulman, “Intelligence Explosions”; S. Cave and S. S. Ó hÉigeartaigh, “An AI Race for Strategic Advantage: Rhetoric
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nytimes.com/2017/07/29/opinion/sunday/artificial-intelligence-is-stuck-heres-how-to-move-it-forward.html.73 R. Hanson. The Age of Em: Work, Love, and Life When Robots Rule the Earth . (Oxford: Oxford University Press, 2016);
R. A. Koene, “Embracing Competitive Balance: The Case for Substrate-Independent Minds and Whole Brain Emulation.”
In Singularity Hypotheses: A Scientific and Philosophical Assessment , ed. A. H. Eden et al., 241-67, (Berlin: Springer,
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Guide for Scientists, Journalists, Educators, Political Aides, and the Interested Public . (New York: Columbia University
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Press, 2007).81 For example, M. L. Weitzman, “On Modeling and Interpreting the Economics of Catastrophic Climate Change,” Review
of Economics and Statistics 91 no. 1 (2009): 1–19.82 For example, CNA Military Advisory Board 2014.83 Baum, "Socially Beneficial Artificial Intelligence .”84 S. D. Baum, “Superintelligence Skepticism as a Political Tool,” Information 9 no. 209 (2018); Baum “Countering
Superintelligence Misinformation.”85 N. Oreskes and E. M. Conway. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from
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88 G. Supran and N. Oreskes, “Assessing ExxonMobil’s Climate Change Communications (1977–2014),” Environmental
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panic.96 Oreskes, Merchants of Doubt.97 S. Whitehouse. Captured: The Corporate Infiltration of American Democracy . (New York: New Press, 2017).98 V. Galanos, “Exploring Expanding Expertise: Artificial Intelligence as an Existential Threat and the Role of Prestigious
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2:110 (2014), https://doi.org/10.1088/0004-637X/784/2/110.110 Government Accountability Office, Nuclear Weapons: Actions Needed by NNSA to Clarify Dismantlement Performance
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Asteroids IV, ed. P. Michel et al., (Tucson: University of Arizona Press, 2015). 112 Chapman, “History of the Asteroid.”113 Chapman, “History of the Asteroid.”114 Baum, “Uncertain Human Consequences.”115 For example, Brooks, “I, Rodney Brooks,”; Bryson, “Just an Artifact.”
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trentmkelly/LessWrong-43k
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LessWrong
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Audio version of Rationality: From AI to Zombies out of beta
All three volumes of the ebook Rationality: From AI to Zombies are complete and available for purchase on Castify. A big thanks to the narrator George Thomas. The total recording time is over 51 hours.
Five math-heavy essays in the second volume went unread because they didn't make for good listening. To read these the ebook can be downloaded on a "pay-what-you-want" basis from intelligence.org.
Thanks again to the original kickstarter backers, we wouldn't have been able to do this without you.
Direct Links:
Volume 1
Volume 2
Volume 3
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trentmkelly/LessWrong-43k
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LessWrong
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A Quick and Dirty Survey: Textbook Learning
Hello, folks. I'm one of those long-time lurkers.
I've decided to conduct, as the title suggests, a quick and dirty survey in hopes of better understanding a problem I have (or rather, whether or not what I have is actually a problem).
Here's some context: I'm a Physics & Mathematics major, currently taking multi-variable. Lately, I've been unsatisfied with my understanding and usage of mathematics—mainly calculus. I've decided to go through what's been recommended as a much more rigorous Calculus textbook, Calculus by Michael Spivak. So far I'm really enjoying it, but it's taking me a long time to get through the exercises. I can be very meticulous about things like this and want to do every exercise through every chapter; I feel that there's benefit to actually doing them regardless of whether or not I look at the problem and think "Yeah, I can do this." Sometimes actually doing the problem is much more difficult than it seems, and I learn a lot from doing them. When flipping through the exercises, I also notice that—regardless of how well I think I know the material—there ends up being a section of exercises focused on something I've never heard of before; something very clever or, I think, mathematically enlightening, that's dependent on the exercises before it.
I'm somewhat embarrassed to admit that the exercises of the first chapter alone had taken me hours upon hours upon hours of combined work. I consider myself slow when it comes to reading mathematics and physics literature—I have to carefully comb through all the concepts and equations and structure them intuitively in a way I see fit. I hate not having a very fundamental understanding of the things I'm working with.
At the same time, I read/hear people who apparently are familiar with multiple textbooks on the same subject. Familiar enough to judge whether or not it is a good textbook. Familiar enough to place how they fit on a hierarchy of textbooks on the same subject. I think "At the rate I'm goin
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trentmkelly/LessWrong-43k
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LessWrong
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Existing Absurd Technologies
When attempting to introduce non-rationalists to the ideas of cryonics or Strong AI, it appears that their primary objections tend to be rooted in the absurdity heuristic. They don't believe they inhabit a universe where such weird technologies could actually work. To deal with this, I thought it would be useful to have a cache of examples of technologies that have actually been implemented that did, or ideally, still do, challenge our intuitions about the way the universe works.
The first example that comes to my mind is computers in general; imagine what Ernest Rutherford, let alone Benjamin Franklin, would have thought of a machine that uses electricity to calculate, and do those calculations so fast that they can express nearly anything as calculations. Nothing we know about how the universe works says it shouldn't be possible, indeed it obviously is knowing what we do now, but imagine how weird this would have seemed back when we were just coming to grips with how electricity actually worked.
I suspect there may be better examples to challenge the intuitions of people who've grown up in an age where computers are commonplace though. So does anyone have any to volunteer?
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trentmkelly/LessWrong-43k
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LessWrong
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[SEQ RERUN] Radical Honesty
Today's post, Radical Honesty was originally published on 10 September 2007. A summary (taken from the LW wiki):
> The Radical Honesty movement requires participants to speak the truth, always, whatever they think. The more competent you grow at avoiding self-deceit, the more of a challenge this would be - but it's an interesting thing to imagine, and perhaps strive for.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was The Crackpot Offer, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
|
921a5df3-c1e5-4ca3-bf19-d8a2bdfa91ef
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : San Francisco Meetup: Short Talks
Discussion article for the meetup : San Francisco Meetup: Short Talks
WHEN: 25 April 2016 06:15:00PM (-0700)
WHERE: 1597 Howard St. San Francisco, CA
We'll be meeting to give/listen to short talks! You're welcome/encouraged to give a talk that you haven't prepared for, since these will be short.
Call me at 301-458-0764 to be let in.
Discussion article for the meetup : San Francisco Meetup: Short Talks
|
90827bbb-9295-4c28-ad66-f406562a2b27
|
trentmkelly/LessWrong-43k
|
LessWrong
|
If I were a well-intentioned AI...
I: Image classifier
Introduction: If I were a well-intentioned AI...
I've often warned people about the dangers of anthropomorphising AIs - how it can mislead us about what's really going on in an AI (and hence how the AI might act in the future), cause us to not even consider certain failure modes, and make us believe we understand things much better than we do.
Oh well, let's ignore all that. I'm about to go on a journey of major anthropomorphisation, by asking myself:
* "If I was a well-intentioned AI, could I solve many of the problems in AI alignment?"
My thinking in this way started when I wondered: suppose I knew that I was given a proxy goal rather than the true goal; suppose that I knew about the Goodhart problem, and suppose that I really "wanted" to align with the true goal - could I then do it? I was having similar thoughts about being a mesa-optimiser.
It seems to me that asking and answering these kind of questions leads to new and interesting insights. Of course, since they come via anthropomorphisation, we need to be careful with them, and check that they are really applicable to AI systems - ensuring that I'm not bringing some of my own human knowledge about human values into the example. But first, let's get those initial insights.
Overlapping problems, overlapping solutions
At a high enough level of abstraction, many problems in AI alignment seem very similar. The Goodhart problem, the issues machine learning has with distributional shift, the problem of the nearest unblocked strategy, unidentifiability of reward functions, even mesaoptimisation and the whole AI alignment problem itself - all of these can be seen, roughly, as variants of the same problem. That problem being that we have an approximately specified goal that looks ok, but turns out to be underspecified in dangerous ways.
Of course, often the differences between the problems are as important as the similarities. Nevertheless, the similarities exist, which is why a lot of the solutions are goin
|
f1fb0848-7240-4095-8f41-18a55ab75eec
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Biointelligence Explosion
http://biointelligence-explosion.com/
- Site put together by David Pearce
The content and choice of domain name should be of interest.
|
bd4660cc-b9ee-413a-98e9-52b0babde3e1
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Needs links
Some pages don't have many [greenlinks](https://arbital.com/p/17f), making them less useful than they could be.
|
3528fcce-acd2-4edd-84fd-4ebdbc194998
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Some thoughts on Animals
I came across this post today where the author talks about happiness and evolution. The author seems to have ambivalent feelings towards happiness. On the one hand, it helps them keep on living. On the other, not having happiness seems to be an evolutionarily good thing to the author.
The author seems to compare two extremes - the human experience, and that of a single-celled organism, and asks the question - “Do single-celled organisms experience happiness?”, and answers, “Maybe.”
What I wonder when reading such posts is, why do we disregard the large and varied group of organisms in between?
What about dolphins, which are known to be extremely intelligent and expressive animals and are known to use pufferfish to gain a high? What about when they foster relationships and become happy when they spend quality time with friends and family?
What about chimpanzees laughing at each other and getting surprised by amateur magicians performing for them? What about elephants displaying empathy and anger?
Forget all these wild ones, what about dogs displaying happiness by wagging their tails when they see their “dog parents” day after day?
Why did the author discount these amazing examples and go directly towards the single-celled organism or the computer program?
Please note - I’m not trying to attack the author, rather ask them this question - “should we disregard every other intelligent species because we feel special in some way since we’re humans?”
And I’ll answer it too - no, we shouldn’t. We should look at the myriad ways evolution allows for happiness and conclude that happiness and generally feeling good has a special place in our evolutionary path and we should accept it.
I also linked one other idea while reading this post. I came across this post yesterday where the author links to a Kurzgesagt video about the future of humans and charges that the video ignores transhumans and genetic engineering, which the author supposes will be very common in the futur
|
f3557dc3-51a6-4da7-844e-93a6c5f46ded
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Surjective function
A function $f:A \to B$ is *surjective* if every $b \in B$ has some $a \in A$ such that $f(a) = b$.
That is, its [codomain](https://arbital.com/p/3lg) is equal to its [image](https://arbital.com/p/3lh).
This concept is commonly referred to as being "onto", as in "The function $f$ is onto."
# Examples
- The function $\mathbb{N} \to \{ 6 \}$ (where $\mathbb{N}$ is the set of [natural numbers](https://arbital.com/p/45h)) given by $n \mapsto 6$ is surjective. However, the same function viewed as a function $\mathbb{N} \to \mathbb{N}$ is not surjective, because it does not hit the number $4$, for instance.
- The function $\mathbb{N} \to \mathbb{N}$ given by $n \mapsto n+5$ is *not* surjective, because it does not hit the number $2$, for instance: there is no $a \in \mathbb{N}$ such that $a+5 = 2$.
|
e2e78a36-8ebb-4bf9-9951-6f60c1d0f3b6
|
trentmkelly/LessWrong-43k
|
LessWrong
|
My Best and Worst Mistake
Yesterday I covered the young Eliezer's affective death spiral around something that he called "intelligence". Eliezer1996, or even Eliezer1999 for that matter, would have refused to try and put a mathematical definition—consciously, deliberately refused. Indeed, he would have been loath to put any definition on "intelligence" at all.
Why? Because there's a standard bait-and-switch problem in AI, wherein you define "intelligence" to mean something like "logical reasoning" or "the ability to withdraw conclusions when they are no longer appropriate", and then you build a cheap theorem-prover or an ad-hoc nonmonotonic reasoner, and then say, "Lo, I have implemented intelligence!" People came up with poor definitions of intelligence—focusing on correlates rather than cores—and then they chased the surface definition they had written down, forgetting about, you know, actual intelligence. It's not like Eliezer1996 was out to build a career in Artificial Intelligence. He just wanted a mind that would actually be able to build nanotechnology. So he wasn't tempted to redefine intelligence for the sake of puffing up a paper.
Looking back, it seems to me that quite a lot of my mistakes can be defined in terms of being pushed too far in the other direction by seeing someone else stupidity: Having seen attempts to define "intelligence" abused so often, I refused to define it at all. What if I said that intelligence was X, and it wasn't really X? I knew in an intuitive sense what I was looking for—something powerful enough to take stars apart for raw material—and I didn't want to fall into the trap of being distracted from that by definitions.
Similarly, having seen so many AI projects brought down by physics envy—trying to stick with simple and elegant math, and being constrained to toy systems as a result—I generalized that any math simple enough to be formalized in a neat equation was probably not going to work for, you know, real intelligence. "Except for Bayes'
|
91cfb2da-f25a-417b-9741-54661ce32a75
|
StampyAI/alignment-research-dataset/arxiv
|
Arxiv
|
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions
1 Introduction
---------------
There are many theories of consciousness; most
propose some biological or other mechanism as a cause or correlate of
consciousness, but do not explain what consciousness is for, nor what
it does [[2](#bib.bib2)]. We take the contrary approach: we postulate that
consciousness implements or is associated with a fundamental aspect of
human behavior, and then we ask what mechanisms could deliver this
capability and what AI approximations might help explore and validate
(or refute) this speculation.
We postulate that *shared intentionality*
[[7](#bib.bib7)] is the attribute of human cognition
whose realization requires consciousness. Shared intentionality is
the ability of humans to engage in teamwork with shared goals and
plans. There is no doubt that the unconscious mind is able to
generate novel and complex goals and plans; the interesting question
is how are these communicated from the mind of one individual (let’s
call her Alice) to those of others so that all can engage in
purposeful collaboration. The goal or plan is generated by some
configuration of chemical and electrical potentials in Alice’s
neurophysiology and one possibility is that salient aspects of these
are abstracted to yield a concise explanation or description that
Alice can communicate to others by demonstration, mime, or language.
The description is received by the other participants (let’s call the
prototypical one Bob) who can then interpret or “concretize” it to
enrich their own unconscious neurophysiological configuration so that
it is now likely to generate behaviors that advance the common goal.
This account suggests a dual-process cognitive architecture
[[3](#bib.bib3), [4](#bib.bib4), [6](#bib.bib6)] where we identify
consciousness with the upper level (“System 2”) that operates on
abstracted representations of salient aspects of the lower,
unconscious level (“System 1”). It can also be seen as a form of
Higher-Order Thought (HOT, that is thoughts about thoughts) and thus
related to HOT theories of consciousness [[5](#bib.bib5)].
We posit that the conscious level is concerned with the construction
and exploitation of shared intentionality: it generates, interprets,
and communicates succinct descriptions and explanations about shared
goals and plans. For succinctness, it operates on abstracted
entities—symbols or concepts—and presumably has some ability to
manipulate and reason about these. When Alice builds a description to
communicate to Bob, she must consider his state of knowledge and point
of view, and we might suppose that this “theory of mind” is
represented in her consciousness and parameterizes her communication.
We noted that Alice could communicate to Bob by demonstration, mime
(i.e., demonstration over symbols), or language. For the latter two,
Alice must have the abstracted description in her consciousness, but
it is possible that demonstration could be driven directly by her
unconscious: we have surely all heard or said “I cannot explain it,
but I can show you how to do it.” In fact, it could be that
Alice constructs her abstraction by mentally demonstrating the task
to herself.
In this paper, we focus on demonstration as a means for communication
and construction of abstract descriptions. In particular, we
investigate how AI agents could use demonstrations to construct
approximations to shared intentionality that allow them to engage in
teamwork with humans or other AI agents, and to understand the
activities of their own lower-level cognitive mechanisms.
The computer science topic that seems most closely related to the task
of inferring intentionality is inverse reinforcement learning (IRL).
In classical IRL, the objective is to learn the reward function
underlying the (System 1) behavior exhibited in the demonstrations.
Here, we employ an extension to IRL that infers logical specifications
that can enable self-reflective analysis of learned
information, compositional reasoning, and integration of learned
knowledge, which enable the System 2 functions of a conscious AI
agent.
While modern deep learning methods [[11](#bib.bib11)] show great promise
in building AI agents with human-level System 1 cognitive capabilities
for some tasks [[9](#bib.bib9), [10](#bib.bib10)], and decades of
research in automated reasoning [[12](#bib.bib12)] can be
exploited for logical deduction in System 2, our goal is to bridge
these levels by inferring and conveying logical intentions. In this
paper, we build on previous work on logical specification mining,
including our own recent
work [[13](#bib.bib13), [14](#bib.bib14), [15](#bib.bib15)]. The key novel
contributions of this paper are:
* Formulating intentionality inference as IRL with logical reward specification.
* Methods for actively seeking and conveying intentions.
* Demonstration of the proposed approach on a simple grid-world example.
In Section 2,
we formulate the problem of inferring intentionality as an inverse
reinforcement learning problem and point out the deficiencies of using
numerical rewards to represent intentions. In Section 3,
we present an inverse reinforcement learning method for logical
specifications, and illustrate how it can be used to infer intentionality.
We extend our approach to convey intentionality interactively
in Section 4,
and conclude in Section 5 by discussing the current limitations.
2 IRL and Intentionality Inference
-----------------------------------
In traditional
Inverse Reinforcement Learning (IRL) [[18](#bib.bib18)], there is a learner and
a demonstrator. The demonstrator operates in a
stochastic environment (e.g., a Markov Decision Process), and is assumed to attempt to (approximately)
optimize some unknown reward function
over its behavior trajectories.
The learner
attempts to reverse engineer this reward
function from the demonstrations.
This problem of learning rewards
from the demonstrations can
be cast as a Bayesian inference
problem [[19](#bib.bib19)] to predict the most probable
reward function. Ideally, this reward function encodes
the intentionality of the demonstrator and enables
the observer to understand the goal behind
the demonstrations.
This classical form of IRL can be seen as a communication at Level 1:
that is, of an opaque low-level representation. We enrich
this communication to allow inference of reasoning-friendly
representations such as logical specifications that are suitable for
Level 2 manipulation. Once the agent has learned the goal in this
form, it can use its own higher-level skills and knowledge to achieve
or contribute to the goal, either independently or composed with other
goals. Further, the agent also can use this representation to
collaborate and plan activities with other agents as illustrated in
Figure [1](#S2.F1 "Figure 1 ‣ 2 IRL and Intentionality Inference ‣ Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions").
Figure 1: AI Agents Using Intentionality Inference for Planning and Collaboration: Agents observe demonstrated behavior trajectories to formulate logical specifications that can be composed with existing knowledge about self and environment to plan out further behavior. This planning takes into account an agent’s understanding of the intentions of other agents, and can be used to convey its own intentions or seek clarification about the intentions of other agents.
Logical specification mining has been studied in the traditional
formal methods community [[20](#bib.bib20)] including our
own past work [[14](#bib.bib14), [17](#bib.bib17), [15](#bib.bib15)], but these methods are not
robust to noise and rely on intelligent oracles to produce behaviors that cover the space of legal behaviors for the specification. This is not
realistic for general AI problems where demonstrations such as handing over a glass of water, or crossing a street, are inherently noisy.
In contrast,
IRL algorithms [[21](#bib.bib21)]
formulate this inference procedure using the principle of
maximum entropy [[22](#bib.bib22)]. This results in a likelihood
of inferred reward
over the demonstrations which is no more committed to any particular
behavior than is required for matching the empirically observed
reward expectation.
Traditionally, this approach was limited to structured scalar rewards,
often assumed to be linear combinations of feature vectors. But more
recently, these have been adapted to arbitrary function
approximators such as
Gaussian processes [[23](#bib.bib23)] and neural
networks [[24](#bib.bib24)].
While powerful, these existing IRL methods provide
no principled mechanism
for composing or reasoning with the resulting rewards. The
inference of intention as numerical reward function
lacks a form that is amenable for self-reflection
and collaboration, and has several limitations:
* First, numerical reward functions lack logical structure, making
it difficult to reason over them—which is critical for
self-reflection: a conscious AI agent must be able to analyze its
understanding of intention. This inference of intention could be
from behaviors (either real or mental rehearsals) of
its own low-level cognitive system, or from behaviors of
other conscious agents.
* Second, combining numerical rewards to understand intention in a
compositional manner is difficult. Demonstrations for two tasks can
be learned individually using numerical rewards but these cannot be
combined by the AI agent to perform the tasks in a concurrent or
coordinated manner. A conscious AI agent cannot just infer each
task’s intention separately, but needs a global view of its own
inference and understanding.
3 IRL with Logical Intention Discovery
---------------------------------------
In this section, we briefly summarize how our recent work [[13](#bib.bib13)]
on inferring
logical specifications in IRL can be used to answer the foundational Question 1 stated below. This is the first step required to build self-aware and self-reflective AI agents capable of inferring and
conveying intentions.
>
> Question 1. *How does Alice infer logical specification of intention by observing a set of demonstrative behaviors (either Alice’s own behavior generated by lower-level cognitive engines, or that of another agent)?*
>
>
>
We assume that the demonstrator (Alice or Bob) operates within
a Markov Decision Process and the specification of the intent is
a bounded trace property. More precisely, we define a
demonstration/trajectory, ξ, to be a sequence of
state-action pairs. Alice attempts to infer past-time linear
temporal logic (PLTL) [[8](#bib.bib8)]
from the demonstrations.
Such a PLTL
property, ϕ, can be identified as a binary non-Markovian
reward function ϕ:ξ→1 if ξ⊨ϕ, and 0 otherwise. The candidate set of
specifications corresponding to the space of
possible intentions is denoted by Φ. Inferring
intention from demonstrations in the set X
can be formulated
as a maximum posterior probability inference problem:
ϕ∗=argmaxϕ∈ΦPr(ϕ|X).
Under assumptions of uniform prior over the intention
space, and applying maximum entropy principle (see [[13](#bib.bib13)] for technical details), the posterior probability of a specification is given by:
| | | |
| --- | --- | --- |
| | | |
where M is the stochastic dynamics model known to the agent, X is the set of demonstrations, ¯¯¯ϕ denotes the average number of times the specification ϕ was satisfied by the demonstrations, ^ϕ denotes the average number of times the specification is satisfied by a random sequence of actions, and DKL denotes the KL divergence between the two Bernoulli distributions denoted by
B.
Intuitively, the first component is an indicator function that the demonstrator is better than random,
and the second component measures the information gain
over the random actions.
We can obtain the most likely logical specification from a set of demonstrations by maximizing the posterior probability. An algorithm
for this optimization using partitioning of the logical specifications is presented in our
previous work [[13](#bib.bib13)].
Figure 2:
We use a simple grid world example to demonstrate this
approach illustrated in
Figure [2](#S3.F2 "Figure 2 ‣ 3 IRL with Logical Intention Discovery ‣ Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions").
In this task, the agent moves in a discrete gridworld and can take actions to move in the cardinal directions (north, south, east, west). Further, the agent can sense abstract features of the domain represented as colors. The task is to reach any of the yellow (recharge) tiles without touching a red tile (lava) – we refer to this sub-task as YR. Additionally, if a blue tile (water) is stepped on, the agent must step on a brown tile (drying tile) before going to a yellow tile – we refer to this sub-task as BBY. The last constraint requires recall of two state bits of history (and is thus not Markovian and infeasible to learn using traditional IRL): one bit for whether the robot is wet and another bit encoding if the robot recharged while wet. Demonstrations correspond to simultaneously satisfying both requirements.
The space of logical specifications [[25](#bib.bib25)]
consist of
PLTL properties using atomic propositions that indicate the nature of the square on which the robot is at a given instant. These demonstrations are interesting because they incidentally include noisy
demonstrations for incorrect intentions, for instance, the robot should wet and dry itself before charging. But our algorithm using max entropy principle infers the following correct requirement using
approximately 95 seconds and after exploration of 172 ^φ
candidates (≈18% of the concept class):
ϕF≡(H¬red∧Oyellow)∧H((yellow∧Oblue)⇒(¬blueSbrown)),
where H is “historically,” O is “once,” and S is “since” [[8](#bib.bib8)].
4 Passive Inference to Active Transfer of Intention
----------------------------------------------------
A conscious agent must be capable of active transfer of intention
beyond passive inference of intent discussed above. Such active intent transfer includes:
>
> Question 2. *How does Alice infer (and
> then correct) a gap in the logical specification of her intention learned by Bob ?*
>
>
>
>
> Question 3. *How does Alice seek clarifying behaviors from Bob to disambiguate her currently inferred intentions of Bob ?*
>
>
>
The key to addressing
both questions lies in defining a divergence
measure over the set of candidate specifications representing
possible intention. One such divergence measure is the ratio
of log likelihoods of two specifications ϕ and ϕ′:
| | | | | |
| --- | --- | --- | --- | --- |
| | D(ϕ,ϕ′) | = | log(Pr(ϕ|M,X,¯¯¯ϕ)/Pr(ϕ′|M,X,¯¯¯ϕ′)) | |
| | | = | DKL(B(¯¯¯ϕ)||B(^ϕ))−DKL(B(¯¯¯ϕ′)||B(^ϕ′)) | |
We also assume both Alice and Bob have common intent inference mechanism which allows them to run the algorithm over demonstrations, and infer what the other agent might be concluding so far. Extension of this approach to agents who use different background knowledge, and will have noisy simulation of the other agent’s intention inference mechanism is beyond the scope of this paper.
To demonstrate the use of this divergence measure, we consider a scenario where the demonstrations on the grid-world
are restricted to a subset X′ of original set X, and
X′ does not contain any trajectories going through blue or brown tiles. Using these demonstrations, Alice
infers
ϕYR≡H¬red∧Oyellow
as the most likely explanation, which only corresponds to the sub-task of avoiding lava and reaching the recharge tile. Alice can evaluate other specifications and, if there are other candidate
specifications with low divergence measure, she can
attempt to disambiguate her inferred intent.
Let us say one such specification is
ϕ≡H¬red∧Oyellow∧Oblue. Alice can generate demonstrations consistent with this specification
by planning from temporal logic [[16](#bib.bib16)].
These demonstrators will pass through wet blue tiles,
and reach recharge without visiting brown drying tiles.
Bob runs the intent inference approach on these demonstrations to realize that Alice has inferred ϕ, and not the intended
ϕYR. He can provide additional behaviors (for e.g., the original set |X|) that help disambiguate both specifications. This is continued until Alice converges to ϕF, and all other candidate specifications having high divergence from ϕF.
5 Conclusion
-------------
In this paper, we presented a first step towards building AI agents
capable of inferring and conveying intentionality as logical specifications.
The goal is to develop AI agents that not only learn intentions of
other agents from demonstrations, or their own intentions by observing
actions of lower-level cognitive engines, but also to provide and seek
clarifications when inferred intentions are ambiguous. Our proposed
approach is currently
limited to behaviors which are represented as time traces,
and intentions that can be expressed in temporal logic. But several
creative tasks
such as proving theorems or writing a mystery novel cannot be
easily formulated in this framework.
A hierarchical
representation mechanism that can exploit the inferred intentions
and goals to compositionally learn new intentions is essential to
building self-aware self-reflective AI that can collaborate to
perform creative endeavors.
### Acknowledgement:
The authors acknowledge support from the National Science Foundation(NSF) Cyber-Physical Systems #1740079 project, NSF Software & Hardware Foundation #1750009 project, and US ARL Cooperative Agreement W911NF-17-2-0196 on Internet of Battle Things (IoBT).
|
fe4e55b9-47f6-45fb-8594-1e6d0951942f
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Growth of prediction markets over time?
I would love to see a graph of the total amount of money sloshing around in prediction markets in 2021, 2020, 2019, etc. stretching back as far as we have data. I'm wondering whether there is a trend and if so what the growth rate is. My Google-fu is failing me so I'm asking here in case anyone else knows.
|
781348f3-7b4c-4abf-ada4-861f747374ac
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Quick puzzle about utility functions under affine transformations
Here's a puzzle based on something I used to be confused about:
It is known that utility functions are equivalent (i.e. produce the same preferences over actions) up to a positive affine transformation: u'(x) = au(x) + b where a is positive.
Suppose I have u(vanilla) = 3, u(chocolate) = 8. I prefer an action that yields a 50% chance of chocolate over an action that yields a 100% chance of vanilla, because 0.5(8) > 1.0(3).
Under the positive affine transformation a = 1, b = 4; we get that u'(vanilla) = 7 and u'(chocolate) = 12. Therefore I now prefer the action that yields a 100% chance of vanilla, because 1.0(7) > 0.5(12).
How to resolve the contradiction?
|
bcb79c30-2958-400d-b979-5177b8803316
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
What are brains?
I see a self-reference problem with reductionism. I wonder if this has already been solved. So I'm asking.
Best as I can tell, there aren't actually *things* in reality. That's a human interpretation. It collapses upon almost any inspection, like [the Ship of Theseus](https://en.wikipedia.org/wiki/Ship_of_Theseus) or [the paradox of the heap](https://en.wikipedia.org/wiki/Sorites_paradox). We also see the theory of thing-ness collapsing with *physical* inspection, which is why QM is "weird".
Best as I can tell, all thing-ness arises from reification. Like how we talk about "government" like it's a thing, but really we've just thingified a process. "The weather" is another clear-to-me example.
It seems to me that physical objects are exactly the same in this respect: a child interacts with swirling sense perceptions and reifies (i.e. thingifies) those experiences into "a ball" or whatever.
So how does reification happen?
* Well, it's not like there's a *thing* that reification *is*; it's just a process that a human mind does.
* Okay, so what's a human mind? Well, it's a process that the human brain engages in.
* So what's a brain? A configuration of chemicals.
* The chemicals are atoms, which are patterns of wiggling magical reality fluid from QM, which is maybe just made of mathematical structures.
So… when do we get to the place where we aren't using objects to explain how the impression of objects arises?
---
This puzzle shows up in the Many Worlds view of QM. It's roughly equivalent to "How do worlds arise?"
Two things (!) get entangled via an interaction. When one of those things is a human brain, we see the various possibilities, but as various brains which aren't interacting directly anymore from the perspective of those brains. So instead of seeing all quantum superposed configurations at once, each version of us observes just one configuration.
Okay, great.
So where are these brains that are getting entangled with other things? Aren't these brains made of the same quantum soup as everything else?
This Many Worlds thing makes a lot of sense if you're seeing the situation from the *outside*, where you can safely reify everything without self-reference. You can be a brain looking at a situation you're not in.
But we're *in* the reality where this happens. We're [embedded agents](https://www.lesswrong.com/s/Rm6oQRJJmhGCcLvxh/p/p7x32SEt43ZMC9r7r). The brains doing this reification are somehow arising from the very process they're attempting to explain, which they meta-explain by… reifying themselves?
Which is to say, brains exist as reifications of brains.
So WTF is a brain??
What is reality *actually doing* here?
What is going on before whatever it is reflects on and reifies itself as "a brain" or "a human" or whatever?
What is that which comes before thing-ness?
---
I find it hard to talk clearly about this puzzle.
Best as I can tell, language assumes the objectivity of things as its foundation. I have not found a way to write a clear grammatical sentence without at least implicitly using nouns or gerunds.
E.g., "What is that which comes before thing-ness?" assumes the answer will be a *thing*, which explicitly it cannot be.
Poetry sometimes sidesteps this limitation but at the cost of precision.
Please be forgiving of my attempt to be clear using a medium that I find doesn't allow me to speak coherently about this.
If you care to articulate the puzzle better than I have, I'm all ears. I'd love to see how to use language more skillfully here.
I also would very much like to know if there's already a known answer that doesn't defeat itself by ignoring the question.
("Oh, brains are just processes that arise from the laws of physics." Okay. So, like, what are these "laws of physics" and these "processes" prior to there being a brain to interpret them as *those things* as opposed to there just being more swirling magical reality fluid?)
|
cd42af20-a703-4d34-ad83-fe816782edd3
|
StampyAI/alignment-research-dataset/eaforum
|
Effective Altruism Forum
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Mediocre AI safety as existential risk
*Epistemic status: Written in one 2 hour session for a deadline. Probably ill-conceptualised in some way I can't quite make out.*
*Broader impacts: Could underwrite unfair cynicism. Has been read by a couple careful alignment people who didn't hate it.*
I propose a premortem. The familiar sense of ‘dual-use’ technology (when a civilian technology has military implications) receives a gratifying amount of [EA](https://forum.effectivealtruism.org/tag/dual-use), popular, and government attention. But consider a different sense: AI alignment (AIA) or AI governance work which actually *increases* existential risk. This tragic line of inquiry has some basic theory under the names ‘[differential progress](https://blog.givewell.org/2015/09/30/differential- technological-development-some-early-thinking/)’, ‘accidental harm’, and a generalised sense of ‘[dual-use](https://www.alignmentforum.org/posts/y5fYPAyKjWePCsq3Y/ project-proposal-considerations-for-trading-off- capabilities)’.
Some missing extensions:
* there is almost no public evaluation of the downside risks of particular AIA agendas, projects, or organisations. (Some evaluations exist in private but I, a relative insider, have access to only one, a private survey by a major org. I understand why the contents might not be public, but the existence of the documents seems important to publicise.)
* In the (permanent) absence of concrete feedback on these projects, we are trading in products which neither producer nor consumer know the quality of. We should model this. Tools from economics could help us reason about our situation (see Methods).
* As David Krueger [noted](https://www.alignmentforum.org/posts/y5fYPAyKjWePCsq3Y/%20project-proposal-considerations-for-trading-off-%20capabilities) some years ago, there is little serious public thought regarding how much AI capabilities work it is wise to do for e.g. career capital or research training for young alignment researchers.
(There’s a trivial sense that mediocre projects increase existential risk: they represent an opportunity cost, by nominally taking resources from good projects.[[1]](#fndh8506f0etd) I instead mean the nontrivial sense that the work could actively increase risk.)
Example: Reward learning
------------------------
Some work in ML safety will enable the deployment of new systems. [Ben Garfinkel gives](https://80000hours.org/podcast/episodes/ben-garfinkel- classic-ai-risk-arguments) the example of a robot cleaner:
> *Let’s say you’re trying to develop a robotic system that can clean a house as well as a human house-cleaner can... This is essentially an alignment problem... until we actually develop these techniques, probably we’re not in a position to develop anything that even really looks like it’s trying to clean a house, or anything that anyone would ever really want to deploy in the real world.*
>
>
He sees this as positive: it implies massive economic incentives to do some alignment, and a block on capabilities until this is done. But it could be a liability as well, if the alignment of weak systems is correspondingly weak, and if mid-term safety work fed into a capabilities feedback loop with greater amplification. (That is, successful deployment means profit, which means reinvestment and induced investment in AI capabilities.)
More generally, human modelling approaches to alignment [risk improving the capability](https://www.alignmentforum.org/posts/BKjJJH2cRpJcAnP7T/ thoughts-on-human-models) of deceiving operators, and [invite beyond-catastrophic](https://centerforreducingsuffering.org/research/risk- factors-for-s-risks/) ‘alignment near-misses’ (i.e. S-risks).
Methods
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### 1. Private audits plus canaries.
Interview members of AIA projects under an NDA, or with the interviewee anonymous to me. The resulting public writeup then merely reports 5 bits of information about each project: 1) whether the organisation has a process for managing accidental harm, 2) whether this has been vetted by any independent party, 3) whether any project has in fact been curtailed as a result, 4) because of potentially dangerous capabilities or not, and 5) whether we are persuaded that they are net-positive. Refusal to engage is also noted. This process has problems (e.g. positivity bias from employees, or the audit team's credibility) but seems the best thing we can do with private endeavours, short of soliciting whistleblowers. Audit the auditors too, why not.
[EDIT: I learn that Allen Dafoe has a very similar idea, not to mention the verifiability [mega-paper](https://openai.com/blog/improving-verifiability/) I never got around to.]
### 2. Quasi-economic model
We want to model the AIA ecosystem as itself a weakly aligned optimiser. One obvious route is microeconomic: asymmetric information and unobservable quality of research outputs, and the associated perils of [goodharting](https://arxiv.org/abs/1803.04585) and adverse selection. The other end would be a macroeconomic or political economy model of AI governance: phenomena like regulatory capture, eminent domain for intellectual property, and [ethics-washing](https://dl.acm.org/doi/abs/10.1145/3351095.3372860) as a model for the co-option of alignment resources. The output would be an adapted model offering some qualitative insights despite the (vast) parameter uncertainty, à la [Aschenbrenner (2020)](https://globalprioritiesinstitute.org/leopold-aschenbrenner-existential-risk-and-growth/).
### 3. Case studies.
What other risks are currently met with false security and safety theatre? What leads to ineffective regulation? (Highly contentious: What fields have been thus hollowed out?) Even successful regulation with real teeth [frequently](https://oig.hhs.gov/oei/reports/oei-04-15-00432.asp) [lapses](https://publicintegrity.org/national-security/plutonium-is- missing-but-the-government-says-nothing/). If this goes well then a public choice model of AI safety could be developed.
Risks
-----
Outputs from this project are likely to be partially private. This is because the unsubstantiated critique could indirectly malign perfectly good and productive people, and have PR and talent pipeline effects. It is not clear that the macroeconomic model would give any new insights, rather than formalising my own existing intuitions.
The canary approach to reporting on private organisations relies on my judgment and credibility. This could be helped by pairing me with someone with more of either. Similarly, it is easily possible that all of the above has been done before and merely not reported, leading to my marginal impact being near zero. In this case at least my canaries prevent further waste.
[Cynicism seems as great a risk](https://forum.effectivealtruism.org/posts/ kageSSDLSMpuwkPKK/response-to-recent-criticisms-of- longtermism-1) as soft-pedalling [[2]](#fn7bqarzosenm). But I'm a member of the AIA community, and likely suffer social desirability bias and soft-pedal as a result. Social desirability bias is notable even in purely professional settings, and will be worse when a field is also a tight-knit social group. It would be productive to invite relative outsiders to vet my work. (My work on governance organisations may be less biased for that reason.)
1. **[^](#fnrefdh8506f0etd)**It's not clear whether this bites in the current funding regime tbh.
2. **[^](#fnref7bqarzosenm)**Consider the cynicism elsewhere:
https://forum.effectivealtruism.org/posts/
kageSSDLSMpuwkPKK/response-to-recent-criticisms-of-
longtermism-1
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e1f30e68-fa90-4cd9-94c9-a49cadb2a8bc
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Explaining grokking through circuit efficiency
This is a linkpost for our paper [*Explaining grokking through circuit efficiency*](https://arxiv.org/abs/2309.02390), which provides a general theory explaining when and why grokking (aka delayed generalisation) occurs, and makes several interesting and novel predictions which we experimentally confirm (introduction copied below). You might also enjoy our [explainer](https://twitter.com/VikrantVarma_/status/1699823229307699305) on X/Twitter.
Abstract
--------
One of the most surprising puzzles in neural network generalisation is *grokking*: a network with perfect training accuracy but poor generalisation will, upon further training, transition to perfect generalisation. We propose that grokking occurs when the task admits a generalising solution and a memorising solution, where the generalising solution is slower to learn but more *efficient*, producing larger logits with the same parameter norm. We hypothesise that memorising circuits become more inefficient with larger training datasets while generalising circuits do not, suggesting there is a critical dataset size at which memorisation and generalisation are equally efficient. We make and confirm four novel predictions about grokking, providing significant evidence in favour of our explanation. Most strikingly, we demonstrate two novel and surprising behaviours: *ungrokking*, in which a network regresses from perfect to low test accuracy, and *semi-grokking*, in which a network shows delayed generalisation to partial rather than perfect test accuracy.
Introduction
------------
When training a neural network, we expect that once training loss converges to a low value, the network will no longer change much. Power et al. (2021) discovered a phenomenon dubbed *grokking* that drastically violates this expectation. The network first "memorises" the data, achieving low and stable training loss with poor generalisation, but with further training transitions to perfect generalisation. We are left with the question: *why does the network's test performance improve dramatically upon continued training, having already achieved nearly perfect training performance?*
Recent answers to this question vary widely, including the difficulty of representation learning (Liu et al., 2022), the scale of parameters at initialisation (Liu et al., 2023), spikes in loss ("slingshots") (Thilak et al., 2022), random walks among optimal solutions (Millidge et al., 2022), and the simplicity of the generalising solution (Nanda et al., 2023, Appendix E). In this paper, we argue that the last explanation is correct, by stating a specific theory in this genre, deriving novel predictions from the theory, and confirming the predictions empirically.
We analyse the interplay between the internal mechanisms that the neural network uses to calculate the outputs, which we loosely call "circuits" (Olah et al., 2020). We hypothesise that there are two families of circuits that both achieve good training performance: one which generalises well (Cgen.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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) and one which memorises the training dataset (Cmem). The key insight is that *when there are multiple circuits that achieve strong training performance, weight decay prefers circuits with high "efficiency"*, that is, circuits that require less parameter norm to produce a given logit value.
Efficiency answers our question above: if Cgen is more efficient than Cmem, gradient descent can reduce nearly perfect training loss even further by strengthening Cgen while weakening Cmem, which then leads to a transition in test performance. With this understanding, we demonstrate in Section 3 that three key properties are sufficient for grokking: (1) Cgen generalises well while Cmem does not, (2) Cgen is more efficient than Cmem, and (3) Cgen is learned more slowly than Cmem.
Since Cgen generalises well, it automatically works for any new data points that are added to the training dataset, and so its efficiency should be independent of the size of the training dataset. In contrast, Cmem must memorise any additional data points added to the training dataset, and so its efficiency should decrease as training dataset size increases. We validate these predictions by quantifying efficiencies for various dataset sizes for both Cmem and Cgen.
This suggests that there exists a crossover point at which Cgen becomes more efficient than Cmem, which we call the critical dataset size Dcrit. By analysing dynamics at Dcrit, we predict and demonstrate two new behaviours (Figure 1). In *ungrokking*, a model that has successfully grokked returns to poor test accuracy when further trained on a dataset much smaller than Dcrit. In *semi-grokking*, we choose a dataset size where Cgen and Cmem are similarly efficient, leading to a phase transition but only to middling test accuracy.
We make the following contributions:
1. We demonstrate the sufficiency of three ingredients for grokking through a constructed simulation (Section 3).
2. By analysing dynamics at the "critical dataset size" implied by our theory, we *predict* two novel behaviours: *semi-grokking* and *ungrokking* (Section 4).
3. We confirm our predictions through careful experiments, including demonstrating semi-grokking and ungrokking in practice (Section 5).
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8e8e9777-17b4-4def-899a-2f993349f2f9
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trentmkelly/LessWrong-43k
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LessWrong
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Ginialogy
As part of my efforts to combat the cowpox of doubt, I try to point poor articles only in outlets where poor articles are rare. Last week I gave props to a Bloomberg View column, so I guess they were due.
The piece in question is Noah Smith’s Capitalism Will Shrink Inequality. In fact, it’s happening. I’m a fan of capitalism and I’m a fan of Noah Smith, but I’m not a fan articles that don’t have the numbers to back up their title.
Noah offers this chart on falling income disparities within Latin American nations, from a research paper by Nora Lustig et al (2015):
Quick aside: Lustig et al published a paper with the same title in 2013. The 2015 version incorporates two more years of data, but has basically the same text throughout. How convenient! When I read a blog post from a mere six months ago I have a strong urge to edit it, not to mention that I expect my conclusions to change, however slightly, as new data comes in. But what do I know? Putanumonit isn’t peer reviewed.
Anyway, here’s what Lustig et al actually found (the numbers are different because they’re annualized, while Noah uses the numbers for an entire decade):
Noah mentions Honduras in the column, but his chart conveniently ignores countries like Uruguay in which the reduction in inequality wasn’t so impressive. He also has this to say about China, which on Lustig’s graph shows as a tall column of rising inequality:
> As for China, there are signs that inequality there has peaked as well. A recent study by economists Ravi Kanbur, Yue Wang and Xiaobo Zhang combed through China’s notoriously murky data and found that the Gini coefficient declined to 0.495 in 2014 from 0.533 in 2010.
The study he references covers a different and more recent period (2010-2014 vs. 2000-2011) than Lustig et al, but the combined data sets shows that at best the direction of China’s Gini is ambiguous, not that it’s clearly declining. I would wait for a few more “signs” before talking about China as an example of f
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bd38af58-4c25-457b-86dd-97021b5b9a15
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trentmkelly/LessWrong-43k
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LessWrong
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Artificial Mysterious Intelligence
Previously in series: Failure By Affective Analogy
I once had a conversation that I still remember for its sheer, purified archetypicality. This was a nontechnical guy, but pieces of this dialog have also appeared in conversations I've had with professional AIfolk...
> Him: Oh, you're working on AI! Are you using neural networks?
>
> Me: I think emphatically not.
>
> Him: But neural networks are so wonderful! They solve problems and we don't have any idea how they do it!
>
> Me: If you are ignorant of a phenomenon, that is a fact about your state of mind, not a fact about the phenomenon itself. Therefore your ignorance of how neural networks are solving a specific problem, cannot be responsible for making them work better.
>
> Him: Huh?
>
> Me: If you don't know how your AI works, that is not good. It is bad.
>
> Him: Well, intelligence is much too difficult for us to understand, so we need to find some way to build AI without understanding how it works.
> Me: Look, even if you could do that, you wouldn't be able to predict any kind of positive outcome from it. For all you knew, the AI would go out and slaughter orphans.
>
> Him: Maybe we'll build Artificial Intelligence by scanning the brain and building a neuron-by-neuron duplicate. Humans are the only systems we know are intelligent.
>
> Me: It's hard to build a flying machine if the only thing you understand about flight is that somehow birds magically fly. What you need is a concept of aerodynamic lift, so that you can see how something can fly even if it isn't exactly like a bird.
>
> Him: That's too hard. We have to copy something that we know works.
>
> Me: (reflectively) What do people find so unbearably awful about the prospect of having to finally break down and solve the bloody problem? Is it really that horrible?
>
> Him: Wait... you're saying you want to actually understand intelligence?
>
> Me: Yeah.
>
> Him: (aghast) Seriously?
>
> Me: I don't know every
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37558353-a69a-4ccf-ab24-ae2e8e85a02b
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trentmkelly/LessWrong-43k
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LessWrong
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Draft of Muehlhauser & Helm, 'The Singularity and Machine Ethics'
Louie and I are sharing a draft of our chapter submission to The Singularity Hypothesis for feedback:
The Singularity and Machine Ethics
Thanks in advance.
Also, thanks to Kevin for suggesting in February that I submit an abstract to the editors. Seems like a lifetime ago, now.
Edit: As of 3/31/2012, the link above now points to a preprint.
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bfb8679a-a071-4f23-ac7d-c8666dcf33ee
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trentmkelly/LessWrong-43k
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LessWrong
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Understanding is translation
Does this feel familiar: "I thought I understood thing X, but then I learned something new and realized that I'd never really understood X?"
For example, consider a loop in some programming language:
var i = 0;
while (i < n) {
i = i + 1;
}
If you're a programmer, you probably understand it just fine. How it works, in what order the lines are executed, how the variable changes over time... But have you ever noticed that the simplest way to compile such a loop to machine code involves two jump instructions - one conditional and one unconditional? (Try doing that with only one jump, it won't work.)
Now you might feel that your "understanding" of loops has become slightly closer to "proper understanding".
Or not!
An alternative view is that understanding is translation. It's a two-place word. Your understanding of loops, in the sense of translating them to execution histories, was perfectly fine. But your understanding of loops, in the sense of translating them to machine code, was slightly lacking.
When you see that pattern once, you notice it everywhere. A middle-schooler can understand numbers, in the sense of translating them to amounts of apples and such, but doesn't immediately translate the expression "x > 5" to a half-open ray on the number line. A self-taught singer can translate from heard notes to sung notes, but can't translate either to notes on a staff; a self-taught guitarist is missing a different subset of those skills. A bilingual person can translate a Japanese sentence with the word "integral" to English, without knowing what integral means. You can be good at translating other people's facial expressions to emotional states, but lousy at translating them to pencil sketches; your friend is the opposite; which of you "understands" human faces better? There's no answer, or many answers. Don't ask whether someone understands X. Instead, ask if they can translate X <-> Y.
That has implications for teaching. If you walk into a classroom intendin
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b5b0362f-ec92-4b7e-b377-3d59a16d2ffc
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trentmkelly/LessWrong-43k
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LessWrong
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Anki on Android in 60 seconds
Spaced repetition is one of the most efficient ways to learn new things. (For research citations, see 'Study methods', here.)
The best way to practice spaced repetition is to install Anki to your phone, since you have your phone with you all day long.
I have an Android phone, so here's my 60-second guide to getting started with Anki on Android:
1. On your Android phone, open 'Market.'
2. Search for 'Anki'.
3. Install the 'AnkiDroid Flashcards' app.
4. In your app drawer, run 'AnkiDroid'.
5. It will prompt that you don't have any decks downloaded. Tap 'Download deck' and choose 'Shared decks.'
6. It will take a while to bring up the list of decks available online. Search for 'Less Wrong' and you'll see the deck called 'Less Wrong Sequences.' Download it.
7. Go back to the AnkiDroid main screen, choose 'Load other deck.' Choose 'Less Wrong Sequences.'
8. Set your options for 'New cards per day', 'session limit (minutes)', and 'session limit (questions)', then tap 'Start Reviewing.'
That's it!
(This full process will take longer than 60 seconds because of download speed, but will probably require only 60 seconds of interaction with the phone.)
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c37e3de8-8024-4694-aafa-e4e45fec3f97
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trentmkelly/LessWrong-43k
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LessWrong
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[SEQ RERUN] Hard Takeoff
Today's post, Hard Takeoff was originally published on 02 December 2008. A summary (taken from the LW wiki):
> It seems likely that there will be a discontinuity in the process of AI self-improvement around the time when AIs become capable of doing AI theory. A lot of things have to go exactly right in order to get a slow takeoff, and there is no particular reason to expect them all to happen that way.
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Whither Manufacturing?, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
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0a580b23-307c-47b8-b1ed-b0f59ad5cdb5
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trentmkelly/LessWrong-43k
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LessWrong
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Bias Mitigation in Language Models by Steering Features
INTRODUCTION
What exactly is this bias? How does it manifest in artificial intelligence (AI) models? What steps can we take to eliminate it?
Nearly 90% of all data was generated in the last decade, and internet usage, as well as the growth of social media, has influenced this data generation. For example, ChatGPT was trained on various web pages, including platforms like Reddit. Since Reddit's user base tends to be younger, predominantly male, and more liberal, these inherent biases can become part of the training data, subsequently influencing the model's outputs.
As we develop better AI for our society, our dependence on it is only going to grow. AI is aiding human decision-making today in various fields like fighting wildfires, targeted marketing for customers, and detecting diseases, and soon, AI might be responsible for determining life-saving medical treatments.
Eliminating bias is hence crucial for ensuring fairness and equity, especially when algorithms influence significant decisions. For example, if a hiring algorithm is biased, it might unfairly screen out highly qualified candidates from underrepresented groups, perpetuating inequality and stifling innovation.
To ensure that we create a safe and unbiased path to Artificial General Intelligence (AGI), we must calibrate the biases in our Large Language Models (LLMs). With this goal in mind, I worked on testing Goodfire SDK for steering features to mitigate gender bias in the recently held hackathon on ‘Reprogramming AI Models’ by Apart Research x Goodfire.
Before discussing the details of the experiments, it is worthwhile to understand the basics of bias. In the context of today’s large language models, bias can be defined as the presence of systematic misrepresentations, attribution errors, or factual distortions that favor certain groups or ideas, perpetuate stereotypes, or make incorrect assumptions based on learned patterns.
Biases in such models can arise due to several factors, as we can see here:
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6c6b0d64-4dd8-4261-b497-ea1b5f0b48ed
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trentmkelly/LessWrong-43k
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LessWrong
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Select Agent Specifications as Natural Abstractions
Introduction
The Preference Fulfillment Hypothesis (PFH) asserts that “Humans have an innate motivation ('preference fulfillment', PF) to fulfill the preferences of those they care about". For the rest of this post, I model PF as a relationship between:
* Some model of an external utility function (exists independently of the primary agent)
* An external agent
* An action that the primary agent interprets as granting positive utility in accordance with the external agent's utility function
* The primary agent
* Some shared environment
To provide an example, Agent A assigns positive utility to the outcome of Agent B experiencing positive utility due to PF, and thus positively associates between actions that result in positive utility for Agent B and positive utility for itself. Agent A engaging in PF implies that it has access to some model of Agent B’s utility function (a model that isn’t necessarily correct), which in practice is both learned and internal (i.e. Agent A is simulating Agent B). Humans converge upon abstractions of the utility functions of agents whose preferences they fulfill (as opposed to perfect low-level models of them), implicating that some agent specifications might abstract well. As stated in the original PFH post, this has interesting implications for corrigibility.[1]
----------------------------------------
Select Agent Specifications as Natural Abstractions
Why Might Some Agent Specifications be Natural Abstractions?
Some agent specifications might be natural abstractions because:
* All humans appear to converge upon abstractions of the people they are trying to simulate
* Cooperation requires simulation, and might be a convergently useful capability
Neither of these are standalone arguments for why some agent specifications should be considered to abstract well, and are mere seed evidence. For example, all agent specifications could abstract incredibly poorly, but this could be such an essential skill in the human context
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4cfaedb6-f038-4702-8167-71808486bdae
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trentmkelly/LessWrong-43k
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LessWrong
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P-zombies, Compression and the Simulation Hypothesis
Can we have approximate p-zombies?
Lets say we accept that p-zombies don't make sense because of the core arguments. However what about a creature who's behavior is very similar to the target but with a different neural software? That is a simulated person who's behavior is different only by a small, perhaps undetectable amount from the real person, but with internal software so different that we would not expect them to have the same consciousness, if any at all?
Approximate p-zombies would be very useful for Simulations
Say you want to run an ancestor simulation. Your goal is to have the resulting world turn out as accurate as possible after a number of years. This Sim could start by having a software model of a person that is very similar to a brain and takes a considerable amount of processing power. It would also have to simulate the physical environment. The core simulation hypothesis argument from my understanding is that we are probably in one of say 10^15 ancestor Sims running in the future in a universe with our physical laws.
Compression is essential
In order to have a Sim, compression is essential. For example the simulated physical earth would have to be very highly compressed in terms of base reality hardware required to Sim earth space. This goal of the compression algorithm is to preserve as much as possible the accuracy of the predicted world while saving as much computation as possible. Preserving the consciousness of human like creatures in it is not a goal.
So the questions is, how would the simulation of a brain change when subjected to a Super-intelligent compression algorithm honed by running a significant amount of simulated time already? If it can be compressed to behave much the same way with less computation, then it will do. This is where GPT-X comes in. Say we want to produce an essay any way we can. If GPT-X can do one that looks like it was written by a person, then we know that there is a compression algorithm that can make the
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097ab861-c2de-422f-aeb9-859328fad3aa
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trentmkelly/LessWrong-43k
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LessWrong
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Another community about existential risk - Arctic news
LW is a community of people who mostly share the idea that AI is the main existential risk. But it is not the only one community in the web which has large ammount of evidence about particular existential risk. And the main problem for my point of view is that both communities (but in fact there are many) do not aware about each other existence. This is a real bias.
Here I would like to present another group which I recently found in the net. I cant judge their arguments but find them interesting.
This is http://arctic-news.blogspot.ru/
Their main idea is that Arctic is melting very quickly which could lead to runaway global warming which could start as early as 2015. They explain a lot about positive feedbacks with methane hydrates, water wapors and provide many real time information, maps, satelite images to confirm their point of view.
My idea is not to start discuss arctic ice on LW or AI in Arctic news, but to point on existening of such separated communties which (because of confirantion bias) concentrated deeply of their own agenda. The other such communties are flutrackers, zerohedge, theoildrum and more.
And one probable property of confirmation bias is that a person not only overvalue his own pile of evidences but dismisses value of any other piles of evidences. And that is why such X-risks orientated communities exist in isolation from each other.
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6ecde98a-c574-4523-845a-cdeb9fd9144e
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trentmkelly/LessWrong-43k
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LessWrong
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Ethical Choice under Uncertainty
Ethical Choice under Uncertainty:
Most discussions about utilitarian ethics are attempt to determine the goodness of an outcome. For instance, discussions may focus on whether it would be ethical to increase total utility by increasing the total number of individuals but reducing their average utility. Or, one could argue about whether we should give more weight to those who are worst off when we aggregate utility over individuals.
These are all important questions. However, even if they were answered to everyone's satisfaction, the answers would not be sufficient to guide the choices of agents acting under uncertainty. To elaborate, I believe textbook versions of utilitarianism are unsatisfactory for the following reasons:
1. Ethical theories that don't account for the agent's beliefs will have absurd consequences such as claiming that it is unethical to rescue a drowning child if the child goes on to become Hitler. Clearly, if we are interested in judging whether the agent is acting ethically, the only relevant consideration is his beliefs about the consequences at the time the choice is made. If we define "ethics" to require him to act on information from the future, it becomes impossible in principle to act ethically.
2. In real life, there will be many situations where the agent makes a bad choice because he has incorrect beliefs about the consequences of his actions. For most people, if they were asked to judge the morality of a person who has pushed a fat man to his death, it is important to know whether the man believed he could save the lives of five children by doing so. Whether the belief is correct or not is not ethically relevant: There is a difference between stupidity and immorality.
3. The real choices are never of the type "If you choose A, the fat man dies with probability 1, whereas if you choose B, the five children die with probability 1". Rather, they are of the type "If you choose A, the fat man dies with probability 0
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c81968d0-a803-4d72-9139-b5c49c9819ac
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trentmkelly/LessWrong-43k
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LessWrong
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An illicit theory of costly signaling
I’m sympathetic to the view that many human behaviors are for signaling. However so far it doesn’t seem like a very tight theory. We have a motley pile of actions labeled as ‘maybe signaling’, connected to a diverse range of characteristics one might want to signal. We have a story for why each would make sense, and also why lots of behaviors that don’t exist would make sense. However I don’t know why we would use the signals we do in particular, or why we would particularly signal the characteristics that we do. When I predict whether a middle class Tahitian man would want to appear to his work colleagues as if he was widely traveled, and whether he would do this by showing them photographs, my answers are entirely based on my intuitive picture of humans and holidays and so on; I don’t see how to derive them from my theory of signaling. Here are two more niggling puzzles:
Why would we use message-specific costly signals for some messages, when we use explicit language + social retribution for so many others?
Much of the time when you speak to others, your values diverge from theirs at least a little. Often they would forward their own interests best by deceiving you, ignoring social costs and conscience. But even in situations where risks from their dishonesty are large, your usual mode of communication is probably spoken or written language.
This is still a kind of costly signaling, as long as if the person faces the right threats of social retribution. Which they usually do I think. If a person says to you that they have a swimming pool, or that they write for the Economist, or that your boyfriend said you should give his car keys to them, you will usually trust them. You are usually safe trusting such claims, because if someone made them dishonestly they could expect to be found out with some probability, and punished. In cases where this isn’t so – for instance if it is a stranger trying to borrow your boyfriend’s car – you will be much less trusting accordi
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91e1cd8d-2e36-4cff-a561-107f56fcbeb8
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trentmkelly/LessWrong-43k
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LessWrong
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New "Best" comment sorting system
Way back in October 2009 Reddit introduced their "Best" comment sorting system. We've just pulled those changes into Less Wrong. The changes affect only comments, not stories.
It's good. It should significantly improve the visibility of good comments posted later in the life of an article. You (yes you) should adopt it. It's the default for new users.
See http://blog.reddit.com/2009/10/reddits-new-comment-sorting-system.html for the details.
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2c2ae08c-27a0-48e5-bd8c-bd95e1f8a09a
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trentmkelly/LessWrong-43k
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LessWrong
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Increased Availability and Willingness for Deployment of Resources for Effective Altruism and Long-Termism
In the last few months, there have been several indicators of a dramatic increase of resources available from a number of organizations in effective altruism, in particular for the focus area of EA community/infrastructure building and long-termism.
This includes not only financial resources for typical grant-making but also for research fellowships and scholarships from newer funding sources, as well as different kinds of institutional support and career opportunities.
This list isn't a call for applications or requests at this time from any of the organizations in question. This is only a summary of recent developments so the EA community at large is aware of these opportunities and changes for the purpose of strategic decision-making.
* Benjamin Todd, CEO of 80,000 Hours, made the case for why EA needs 'mega-projects' i.e., projects that can deploy up to $100 million per year.
* The Centre for Effective Altruism (CEA) has dramatically grown in the last year, nearly doubling in staff.
* Rethink Priorities has also doubled their team and is looking to expand further.
* The EA Infrastructure Fund expects the total that will be granted in 2021 will exceed $5 million, more than 2.7x the total granted in 2020, and 1.3x has awarded in all previous years (2018-2020) combined, and that they expect to be able to make these funding needs.
* The Survival and Flourishing Fund deployed approximately $19 million in 2021 vs. $5.4 million in 2020.
* Vitalik Buterin, founder of the blockchain platform Ethereum, donated $25 million to the Future of Life Institute to support research activities such as Ph.D. scholarships.
* Lightcone Infrastructure, now the parent organization for rationality community blog LessWrong, is now hiring new staff with salaries of $150-200 thousand dollars.
* The Effective Altruism Forum recently held a creative writing contest that rewarded $20 thousand total the winners.
* AI safety and research company Anthropic has raised $124 million in
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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Transcription of Eliezer's January 2010 video Q&A
Spurred by discussion of whether Luke's Q&A session should be on video or text-only, I volunteered to transcribe [Eliezer's Q&A videos](/lw/1lq/less_wrong_qa_with_eliezer_yudkowsky_video_answers/) from January 2010. I finished last night, much earlier than my estimate, mostly due to feeling motivated to finish it and spending more on it than my very conservative estimated 30 minutes a day (estimate of number of words was pretty close; about 16000). I have posted a link to this post as a comment in the original thread [here](/lw/1lq/less_wrong_qa_with_eliezer_yudkowsky_video_answers/58yp), if you would like to upvote that.
Some advice for transcribing videos: I downloaded the .wmv videos, which allowed me to use VLC's global hotkeys to create a pause and "short skip backwards and forwards" buttons (ctrl-space and ctrl-shift left/right arrow), which were so much more convenient than any other method I tried.
Edited out: repetition of the question, “um/uh”, “you know,” false starts.
Punctuation, capitalization, and structure, etc may not be entirely consistent.
Keep in mind the opinions expressed here are those of Eliezer circa January 2010.
1. What is your information diet like? Do you control it deliberately (do you have a method; is it, er, intelligently designed), or do you just let it happen naturally.
By that I mean things like: Do you have a reading schedule (x number of hours daily, etc)? Do you follow the news, or try to avoid information with a short shelf-life? Do you frequently stop yourself from doing things that you enjoy (f.ex reading certain magazines, books, watching films, etc) to focus on what is more important? etc.
It’s not very planned, most of the time, in other words Hacker News, Reddit, Marginal Revolution, other random stuff found on the internet. In order to learn something, I usually have to set aside blocks of time and blocks of effort and just focus on specifically reading something. It’s only sort of popular level books which I can put on a restroom shelf and get them read that way. In order to learn actually useful information I generally find that I have to set aside blocks of time or run across a pot of gold, and you’re about as likely to get a pot of gold from Hacker News as anywhere else really. So not very controlled.
2. Your "Bookshelf" page is 10 years old (and contains a warning sign saying it is obsolete): <http://yudkowsky.net/obsolete/bookshelf.html>
Could you tell us about some of the books and papers that you've been reading lately? I'm particularly interested in books that you've read since 1999 that you would consider to be of the highest quality and/or importance (fiction or not).
I guess I’m a bit ashamed of how little I’ve been reading whole books and how much I’ve been reading small bite-sized pieces on the internet recently. Right now I’m reading Predictably Irrational which is a popular book by Dan Ariely about biases, it’s pretty good, sort of like a sequence of Less Wrong posts. I’ve recently finished reading Good and Real by Gary Drescher, which is something I kept on picking up and putting down, which is very Lesswrongian, it’s master level Reductionism and the degree of overlap was incredible enough that I would read something and say ‘OK I should write this up on my own before I read how Drescher wrote it so that you can get sort of independent views of it and see how they compare.’
Let’s see, other things I’ve read recently. I’ve fallen into the black hole of Fanfiction.net, well actually fallen into a black hole is probably too extreme. It’s got a lot of reading and the reading’s broken up into nice block size chapters and I’ve yet to exhaust the recommendations of the good stuff, but probably not all that much reading there, relatively speaking.
I guess it really has been quite a while since I picked up a good old-fashioned book and said ‘Wow, what an amazing book’. My memory is just returning the best hits of the last 10 years instead of the best hits of the last six months or anything like that. If we expand it out to the best hits of the last 10 years then Artificial Intelligence: A Modern Approach by Russell and Norvig is a really wonderful artificial intelligence textbook. It was on reading through that that I sort of got the epiphany of artificial intelligence really has made a lot more progress than people credit for, it’s just not really well organized, so you need someone with good taste to go through and tell you what’s been done before you recognize what has been done.
There was a book on statistical inference, I’m trying to remember the exact title, it’s by Hastie and Tibshirani, Elements of Statistical Learning, that was it. Elements of Statistical Learning was when I realized that the top people, they really do understand their subject, the people who wrote the Elements of Statistical Learning, they really understand statistics. At the same time you read through and say ‘Gosh, by comparison with these people, the average statistician, to say nothing of the average scientist who’s just using statistics, doesn’t really understand statistics at all.’
Let’s see, other really great... Yeah my memory just doesn’t really associate all that well I’m afraid, it doesn’t sort of snap back and cough up a list of the best things I’ve read recently. This would probably be something better for me to answer in text than in video I’m afraid.
3. What is a typical EY workday like? How many hours/day on average are devoted to FAI research, and how many to other things, and what are the other major activities that you devote your time to?
I’m not really sure I have anything I could call a ‘typical’ workday. Akrasia, weakness of will, that has always been what I consider to be my Great Bugaboo, and I still do feel guilty about the amount of rest time and downtime that I require to get work done, and even so I sometimes suspect that I’m taking to little downtime relative to work time just because on those occasions when something or other prevents me form getting work done, for a couple of days, I come back and I’m suddenly much more productive. In general, I feel like I’m stupid with respect to organizing my work day, that sort of problem, it used to feel to me like it was chaotic and unpredictable, but I now recognize that when something looks chaotic and unpredictable, that means that you are stupid with respect to that domain.
So it’ll probably look like, when I manage to get a work session in the work session will be a couple of hours, I’ll sometimes when I run into a difficult problem I’ll sometimes stop and go off and read things on the internet for a few minutes or a lot of minutes, until I can come back and I can come back and solve the problem or my brain is rested enough to go to the more tiring high levels of abstraction where I can actually understand what it is that’s been blocking me and move on. That’s for writing, which I’ve been doing a lot of lately.
A typical workday when I’m actually working on Friendly AI with Marcello, that’ll look like we get together and sit down and open up a notebook and stare at our notebooks and throw ideas back and forth and sometimes sit in silence and think about things, write things down, I’ll propose things, Marcello will point out flaws in them or vice versa, sort of reach the end of a line of thought, go blank, stop and stare at each other and try to think of another line of thought, keep that up for two to three hours, break for lunch, keep it up for another two to three hours, and then break for a day, could spend the off day just recovering or reading math if possible or otherwise just recovering. Marcello doesn’t need as much recovery time, but I also suspect that Marcello, because he’s still sort of relatively inexperienced isn’t quite confronting the most difficult parts of the problem as directly.
So taking a one-day-on one-day-off, with respect to Friendly AI I actually don’t feel guilty about it at all, because it really is apparent that I just cannot work two days in a row on this problem and be productive. It’s just really obvious, and so instead of the usual cycle of ‘Am I working enough? Could I be working harder?’ and feeling guilty about it it’s just obvious that in that case after I get a solid day’s work I have to take a solid day off.
Let’s see, any other sorts of working cycles? Back when I was doing the Overcoming Bias/Less Wrong arc at one post per day, I would sometimes get more than one post per day in and that’s how I’d occasionally get a day off, other times a post would take more than one day. I find that I am usually relatively less productive in the morning; a lot of advice says ‘as soon as you get up in the morning, sit down, start working, get things done’; that’s never quite worked out for me, and of course that could just be because I’m doing it wrong, but even so I find that I tend to be more productive later in the day.
Let’s see, other info... Oh yes, at one point I tried to set up my computer to have a separate login without any of the usual distractions, and that caused my productivity to drop down because it meant that when I needed to take some time off, instead of browsing around the internet and then going right back to working, I’d actually separated work and so it was harder to switch back and forth between them both, so that was something that seemed like it was a really good idea that ought to work in theory, setting aside this sort of separate space with no distractions to work, and that failed.
And right now I’m working sort of on the preliminaries for the book, The Art of Rationality being the working title, and I haven’t started writing the book yet, I’m still sort of trying to understand what it is that I’ve previously written on Less Wrong, Overcoming Bias, organize it using mind mapping software from FreeMind which is open source mind mapping software; it’s really something I wish I’d known existed and started using back when the whole Overcoming Bias/Less Wrong thing started, I think it might have been a help.
So right now I’m just still sort of trying to understand what did I actually say, what’s the point, how do the points relate to each other, and thereby organizing the skeleton of the book, rather than writing it just yet, and the reason I’m doing it that way is that when it comes to writing things like books where I don’t push out a post every day I tend to be very slow, unacceptably slow even, and so one method of solving that was rite a post every day and this time I’m seeing if I can, by planning everything out sufficiently thoroughly in advance and structuring it sufficiently thoroughly in advance, get it done at a reasonable clip.
4. Could you please tell us a little about your brain? For example, what is your IQ, at what age did you learn calculus, do you use cognitive enhancing drugs or brain fitness programs, are you Neurotypical and why didn't you attend school?
So the question is ‘please tell us a little about your brain.’ What’s your IQ? Tested as 143, that would have been back when I was... 12? 13? Not really sure exactly. I tend to interpret that as ‘this is about as high as the IQ test measures’ rather than ‘you are three standard deviations above the mean’. I’ve scored higher than that on(?) other standardized tests; the largest I’ve actually seen written down was 99.9998th percentile, but that was not really all that well standardized because I was taking the test and being scored as though for the grade above mine and so it was being scored for grade rather than by age, so I don’t know whether or not that means that people who didn’t advance through grades tend to get the highest scores and so I was competing well against people who were older than me, or whether if the really smart people all advanced farther through the grades and so the proper competition doesn’t really get sorted out, but in any case that’s the highest percentile I’ve seen written down.
‘At what age did I learn calculus’, well it would have been before 15, probably 13 would be my guess. I’ll also state at just how stunned I am at how poorly calculus is taught.
Do I use cognitive enhancing drugs or brain fitness programs? No. I’ve always been very reluctant to try tampering with the neurochemistry of my brain because I just don’t seem to react to things typically; as a kid I was given Ritalin and Prozac and neither of those seemed to help at all and the Prozac in particular seemed to blur everything out and you just instinctively(?) just... eugh.
One of the questions over here is ‘are you neurotypical’. And my sort of instinctive reaction to that is ‘Hah!’ And for that reason I’m reluctant to tamper with things. Similarly with the brain fitness programs, don’t really know which one of those work and which don’t, I’m sort of waiting for other people in the Less Wrong community to experiment with that sort of thing and come back and tell the rest of us what works and if there’s any consensus between them, I might join the crowd.
‘Why didn’t you attend school?’ Well I attended grade school, but when I got out of grade school it was pretty clear that I just couldn’t handle the system; I don’t really know how else to put it. Part of that might have been that at the same time that I hit puberty my brain just sort of... I don’t really know how to describe it. Depression would be one word for it, sort of ‘spontaneous massive will failure’ might be another way to put; it’s not that I was getting more pessimistic or anything, just that my will sort of failed and I couldn’t get stuff done. Sort of a long process to drag myself out that and you could probably make a pretty good case that I’m still there, I just handle it a lot better? Not even really sure quite what I did right, as I said in an answer to a previous question, this is something I’ve been struggling with for a while and part of having a poor grasp on something is that even when you do something right you don’t understand afterwards quite what it is that you did right.
So... ‘tell us about your brain’. I get the impression that it’s got a different balance of abilities; like, some neurons got allocated to different areas, other areas got shortchanged, some areas got some extra neurons, other areas got shortchanged, the hypothesis has occurred to me lately that my writing is attracting other people with similar problems because of the extent to which one has noticed a sort of similar tendency to fall on the lines of very reflective, very analytic and has mysterious trouble executing and getting things done and working at sustained regular output for long periods of time, among the people who like my stuff.
On the whole though, I never actually got around to getting an MRI scan; it’s probably a good thing to do one of these days, but this isn’t Japan where that sort of thing only costs 100 dollars, and getting it analyzed, you know they’re not just looking for some particular thing but just sort of looking at it and saying ‘Hmm, well what is this about your brain?’, well I’d have to find someone to do that too.
So, I’m not neurotypical... asking sort of ‘what else can you tell me about your brain’ is sort of ‘what else can you tell me about who you are apart from your thoughts’, and that’s a bit of a large question. I don’t try and whack on my brain because it doesn’t seem to react typically and I’m afraid of being in a sort of narrow local optimum where anything I do is going to knock it off the tip of the local peak, just because it works better than average and so that’s sort of what you would expect to find there.
5. During a panel discussion at the most recent Singularity Summit, Eliezer speculated that he might have ended up as a science fiction author, but then quickly added:
I have to remind myself that it's not what's the most fun to do, it's not even what you have talent to do, it's what you need to do that you ought to be doing.
Shortly thereafter, Peter Thiel expressed a wish that all the people currently working on string theory would shift their attention to AI or aging; no disagreement was heard from anyone present.
I would therefore like to ask Eliezer whether he in fact believes that the only two legitimate occupations for an intelligent person in our current world are (1) working directly on Singularity-related issues, and (2) making as much money as possible on Wall Street in order to donate all but minimal living expenses to SIAI/Methuselah/whatever.
How much of existing art and science would he have been willing to sacrifice so that those who created it could instead have been working on Friendly AI? If it be replied that the work of, say, Newton or Darwin was essential in getting us to our current perspective wherein we have a hope of intelligently tackling this problem, might the same not hold true in yet unknown ways for string theorists? And what of Michelangelo, Beethoven, and indeed science fiction? Aren't we allowed to have similar fun today? For a living, even?
So, first, why restrict it to intelligent people in today’s world? Why not everyone? And second... the reply to the essential intent of the question is yes, with a number of little details added. So for example, if you’re making money on Wall Street, I’m not sure you should be donating all but minimal living expenses because that may or may not be sustainable for you. And in particular if you’re, say, making 500,000 dollars a year and you’re keeping 50,000 dollars of that per year, which is totally not going to work in New York, probably, then it’s probably more effective to double your living expenses to 100,000 dollars per year and have the amount donated to the Singularity Institute go from 450,000 to 400,000 when you consider how much more likely that makes it that more people follow in your footsteps. That number is totally not realistic and not even close to the percentage of income donated versus spent on living expenses for present people working on Wall Street who are donors to the Singularity Institute. So considering at present that no one seems willing to do that, I wouldn’t even be asking that, but I would be asking for more people to make as much money as possible if they’re the sorts of people who can make a lot of money and can donate a substantial amount fraction, never mind all the minimal living expenses, to the Singularity Institute.
Comparative advantage is what money symbolizes; each of us able to specialize in doing what we do best, get a lot of experience doing it, and trade off with other people specialized at what they’re doing best with attendant economies of scale and large fixed capital installations as well, that’s what money symbolizes, sort of in idealistic reality, as it were; that’s what money would mean to someone who could look at human civilization and see what it was really doing. On the other hand, what money symbolizes emotionally in practice, is that it imposes market norms, instead of social norms. If you sort of look at how cooperative people are, they can actually get a lot less cooperative once you offer to pay them a dollar, because that means that instead of cooperating because it’s a social norm, they’re now accepting a dollar, and a dollar puts it in the realm of market norms, and they become much less altruistic.
So it’s sort of a sad fact about how things are set up that people look at the Singularity Institute and think ‘Isn’t there some way for me to donate something other than money?’ partially for the obvious reason and partially because their altruism isn’t really emotionally set up to integrate properly with their market norms. For me, money is reified time, reified labor. To me it seems that if you work for an hour on something and then donate the money, that’s more or less equivalent to donating the money (time?), or should be, logically. We have very large bodies of experimental literature showing that the difference between even a dollar bill versus a token that’s going to be exchanged for a dollar bill at the end of the experiment can be very large, just because that token isn’t money. So there’s nothing dirty about money, and there’s nothing dirty about trying to make money so that you can donate it to a charitable cause; the question is ‘can you get your emotions to line up with reality in this case?’
Part of the question was sort of like ‘What of Michaelangelo, Beethoven, and indeed science fiction? Aren’t we allowed to have similar fun today? For a living even?’
This is crunch time. This is crunch time for the entire human species. This is the hour before the final exam, we are trying to get as much studying done as possible, and it may be that you can’t make yourself feel that, for a decade, or 30 years on end or however long this crunch time lasts. But again, the reality is one thing, and the emotions are another. So it may be that you can’t make yourself feel that this is crunch time, for more than an hour at a time, or something along those lines. But relative to the broad sweep of human history, this is crunch time; and it’s crunch time not just for us, it’s crunch time for the intergalactic civilization whose existence depends on us. I think that if you’re actually just going to sort of confront it, rationally, full-on, then you can’t really justify trading off any part of that intergalactic civilization for any intrinsic thing that you could get nowadays, and at the same time it’s also true that there are very few people who can live like that, and I’m not one of them myself, so because trying to live with that would even rule out things like ordinary altruism; I hold open doors for little old ladies, because I find that I can’t live only as an altruist in theory; I need to commit sort of actual up-front deeds of altruism, or I stop working properly.
So having seen that intergalactic civilization depends on us, in one sense, all you can really do is try not to think about that, and in another sense though, if you spend your whole life creating art to inspire people to fight global warming, you’re taking that ‘forgetting about intergalactic civilization’ thing much too far. If you look over our present civilization, part of that sort of economic thinking that you’ve got to master as a rationalist is learning to think on the margins. On the margins, does our civilization need more art and less work on the singularity? I don’t think so. I think that the amount of effort that our civilization invests in defending itself against existential risks, and to be blunt, Friendly AI in particular is ludicrously low. Now if it became the sort of pop-fad cause and people were investing billions of dollars into it, all that money would go off a cliff and probably produce anti-science instead of science, because very few people are capable of working on a problem where they don’t find immediately whether or not they were wrong, and it would just instantaneously go wrong and generate a lot of noise from people of high prestige who would just drown out the voices of sanity. So wouldn’t it be a nice thing if our civilization started devoting billions of dollars to Friendly AI research because our civilization is not set up to do that sanely. But at the same time, the Singularity Institute exists, the Singularity Institute, now that Michael Vassar is running it, should be able to scale usefully; that includes actually being able to do interesting things with more money, now that Michael Vassar’s the president.
To say ‘No, on the margin, what human civilization, at this present time, needs to do is not put more money in the Singularity Institute, but rather do this thing that I happen to find fun’ not that I’m doing this and I’m going to professionally specialize in it and become good in it and sort of trade hours of doing this thing that I’m very good at for hours that go into the Singularity Institute via the medium of money, but rather ‘no, this thing that I happen to find fun and interesting is actually what our civilization needs most right now, not Friendly AI’, that’s not defensible; and, you know, these are all sort of dangerous things to think about possibly, but I think if you sort of look at that face-on, up-front, take it and stare at it, there’s no possible way the numbers could work out that way.
It might be helpful to visualize a Friendly Singularity so that the kid who was one year old at the time is now 15 years old and still has something like a 15 year old human psychology and they’re asking you ‘So here’s this grand, dramatic moment in history, not human history, but history, on which the whole future of the intergalactic civilization that we now know we will build; it hinged on this one moment, and you knew that was going to happen. What were you doing?’ and you say, ‘Well, I was creating art to inspire people to fight global warming.’ The kid says ‘What’s global warming?’
That’s what you get for not even taking into account at all the whole ‘crunch time, fate of the world depends on it, squeaking through by a hair if we do it at all, already played into a very poor position in terms of how much work has been done and how much work we need to do relative to the amount of work that needs to be done to destroy the world as opposed to saving it; how long we could have been working on this previously and how much trouble it’s been to still get started.’ When this is all over, it’s going to be difficult to explain to that kid, what in the hell the human species was thinking. It’s not going to be a baroque tale. It’s going to be a tale of sheer insanity. And you don’t want you to be explaining yourself to that kid afterward as part of the insanity rather than the sort of small core of ‘realizing what’s going on and actually doing something about it that got it done.’
6. I know at one point you believed in staying celibate, and currently your main page mentions you are in a relationship. What is your current take on relationships, romance, and sex, how did your views develop, and how important are those things to you? (I'd love to know as much personal detail as you are comfortable sharing.)
This is not a topic on which I consider myself an expert, and so it shouldn’t be shocking to hear that I don’t have incredibly complicated and original theories about these issues. Let’s see, is there anything else to say about that... It’s asking ‘at one point I believed in staying celibate and currently your main page mentions your are in a relationship.’ So, it’s not that I believed in staying celibate as a matter of principle, but that I didn’t know where I could find a girl who would put up with me and the life that I intended to lead, and said as much, and then one woman, Erin, read about the page I’d put up to explain why I didn’t think any girl would put up with me and my life and said essentially ‘Pick me! Pick me!’ and it was getting pretty difficult to keep up with the celibate lifestyle by then so I said ‘Ok!’ And that’s how we got together, and if that sounds a bit odd to you, or like, ‘What!? What do you mean...?’ then... that’s why you’re not my girlfriend.
I really do think that in the end I’m not an expert; that might be as much as there is to say.
7. What's your advice for Less Wrong readers who want to help save the human race?
Find whatever you’re best at; if that thing that you’re best at is inventing new math[s] of artificial intelligence, then come work for the Singularity Institute. If the thing that you’re best at is investment banking, then work for Wall Street and transfer as much money as your mind and will permit to the Singularity institute where [it] will be used by other people. And for a number of sort of intermediate cases, if you’re familiar with all the issues of AI and all the issues of rationality and you can write papers at a reasonable clip, and you’re willing to work for a not overwhelmingly high salary, then the Singularity Institute is, as I understand it, hoping to make a sort of push toward getting some things published in academia. I’m not going to be in charge of that, Michael Vassar and Anna Salamon would be in charge of that side of things. There’s an internship program whereby we provide you with room and board and you drop by for a month or whatever and see whether or not this is work you can do and how good you are at doing it.
Aside from that, though, I think that saving the human species eventually comes down to, metaphorically speaking, nine people and a brain in a box in a basement, and everything else feeds into that. Publishing papers in academia feeds into either attracting attention that gets funding, or attracting people who read about the topic, not necessarily reading the papers directly even but just sort of raising the profile of the issues where intelligent people wonder what they can do with their lives think artificial intelligence instead of string theory. Hopefully not too many of them are thinking that because that would just generate noise, but the very most intelligent people... string theory is a marginal waste of the most intelligent people. Artificial intelligence and Friendly Artificial Intelligence, sort of developing precise, precision grade theories of artificial intelligence that you could actually use to actually build a Friendly AI instead of blowing up the world; the need for one more genius there is much greater than the need for one more genius in string theory. Most of us can’t work on that problem directly. I, in a sense, have been lucky enough not to have to confront a lot of the hard issues here, because of being lucky enough to be able to work on the problem directly, which simplifies my choice of careers.
For everyone else, I’ll just sort of repeat what I said in an earlier video about comparative advantage, professional specialization, doing what we do best at and practicing a lot; everyone doing that and trading with each other is the essence of economics, and the symbol of this is money, and it’s completely respectable to work hours doing what you’re best at, and then transfer the sort of expected utilons that a society assigns to that to the Singularity Institute, where it can pay someone else to work at it such that it’s an efficient trade, because the total amount of labor and effectiveness that they put into it that you can purchase is more than you could do by working an equivalent number of hours on the problem yourself. And as long as that’s the case, the economically rational thing to do is going to be to do what you’re best at and trade those hours to someone else, and let them do it. And there should probably be fewer people, one expects, who working on the problem directly, full time; stuff just does not get done if you’re not working on it full time, that’s what I’ve discovered, anyway; I can’t even do more than one thing at a time. And that’s the way grown ups do it, essentially, that’s the way a grown up economy does it.
8. Autodidacticism
Eliezer, first congratulations for having the intelligence and courage to voluntarily drop out of school at age 12! Was it hard to convince your parents to let you do it? AFAIK you are mostly self-taught. How did you accomplish this? Who guided you, did you have any tutor/mentor? Or did you just read/learn what was interesting and kept going for more, one field of knowledge opening pathways to the next one, etc...?
EDIT: Of course I would be interested in the details, like what books did you read when, and what further interests did they spark, etc... Tell us a little story. ;)
Well, amazingly enough, I’ve discovered the true, secret, amazing formula for teaching yourself and... I lie, I just winged it. Yeah, just read whatever interested me until age 15-16 thereabouts which is when I started to discover the Singularity as opposed to background low-grade Transhumanism that I’d been engaged with up until that point; started thinking that cognitive technologies, creating smarter than human level intelligence was the place to be and initially thought that neural engineering was going to be the sort of leading, critical path of that. Studied a bit of neuroscience and didn’t get into that too far before I started thinking that artificial intelligence was going to be the route; studied computer programming, studied a bit of business type stuff because at one point I thought I’d do a start up at something I’m very glad I didn’t end up doing, in order to get the money to do the AI thing, and I’m very glad that I didn’t go that route, and I won’t even say that the knowledge has served me all that good instead, it’s just not my comparative advantage.
At some point sort of woke up and smelled the Bayesian coffee and started studying probability theory and decision theory and statistics and that sort of thing, but really I haven’t had and opportunity to study anywhere near as much as I need to know. And part of that, I won’t apologize for because a lot of sort of fact memorization is more showing off than because you’re going to use that fact every single day; part of that I will apologize for because I feel that I don’t know enough to get the job done and that when I’m done writing the book I’m just going to have to take some more time off and just study some of the sort of math and mathematical technique that I expect to need in order to get this done. I come across as very intelligent, but a surprisingly small amount of that relies on me knowing lots of facts, or at least that’s the way it feels to me. So I come across as very intelligent, but that’s because I’m good at winging it, might be one way to put it. The road of the autodidact, I feel... I used to think that anyone could just go ahead and do it and that the only reason to go to college was for the reputational ‘now people can hire you’ aspect which sadly is very important in today’s world. Since then I’ve come to realize both that college is less valuable and less important than I used to think and also that autodidacticism might be a lot harder for the average person than I thought because the average person is less similar to myself than my sort of intuitions would have it.
‘How do you become an autodidact’; the question you would ask before that would be ‘what am I going to do, and is it something that’s going to rely on me having memorized lots of standard knowledge and worked out lots of standard homework problems, or is it going to be something else, because if you’re heading for a job where you going to want to memorize lots of the same standardized facts as people around you, then autodidacticism might not be the best way to go. If you’re going to be a computer programmer, on the other hand, then [going] into a field where every day is a new adventure, and most jobs in computer programming will not require you to know the Nth detail of computer science, and even if they did, the fact that this is math means you might even have a better chance of learning it out of a book, and above all it’s a field where people have some notion that you’re allowed to teach yourself; if you’re good, other people can see it by looking at your code, and so there’s sort of a tradition of being willing to hire people who don’t have a Masters.
So I guess I can’t really give all that much advice about how to be successful autodidact in terms of... studying hard, doing the same sort of thing you’d be doing in college only managing to do it on your own because you’re that self-disciplined, because that is completely not the route I took. I would rather advise you to think very hard about what it is you’re going to be doing, whether or not anyone will let you do it if you don’t have the official credential, and to what degree the road you’re going is going to depend on the sort of learning that you have found that you can get done on your own.
9. Is your pursuit of a theory of FAI similar to, say, Hutter's AIXI, which is intractable in practice but offers an interesting intuition pump for the implementers of AGI systems? Or do you intend on arriving at the actual blueprints for constructing such systems? I'm still not 100% certain of your goals at SIAI.
Definitely actual blueprint, but, on the way to an actual blueprint, you probably have to, as an intermediate step, construct intractable theories that tell you what you’re trying to do, and enable you to understand what’s going on when you’re trying to do something. If you want a precise, practical AI, you don’t get there by starting with an imprecise, impractical AI and going to a precise, practical AI. You start with a precise, impractical AI and go to a precise, practical AI. I probably should write that down somewhere else because it’s extremely important, and as(?) various people who will try to dispute it, and at the same time hopefully ought to be fairly obvious if you’re not motivated to arrive at a particular answer there. You don’t just run out and construct something imprecise because, yeah, sure, you’ll get some experimental observations out of that, but what are your experimental observations telling you? And one might say along the lines of ‘well, I won’t know that until I see it,’ and suppose that has been known to happen a certain number of times in history; just inventing the math has also happened a certain number of times in history.
We already have a very large body of experimental observations of various forms of imprecise AIs, both the domain specific types we have now, and the sort of imprecise AI constituted by human beings, and we already have a large body of experimental data, and eyeballing it... well, I’m not going to say it doesn’t help, but on the other hand, we already have this data and now there is this sort of math step in which we understand what exactly is going on; and then the further step of translating the math back into reality. It is the goal of the Singularity Institute to build a Friendly AI. That’s how the world gets saved, someone has to do it. A lot of people tend to think that this is going to require, like, a country’s worth of computing power or something like that, but that’s because the problem seems very difficult because they don’t understand it, so they imagine throwing something at it that seems very large and powerful and gives this big impression of force, which might be a country-size computing grid, or it might be a Manhattan Project where some computer scientists... but size matters not, as Yoda says.
What matters is understanding, and if the understanding is widespread enough, then someone is going to grab the understanding and use it to throw together the much simpler AI that does destroy the world, the one that’s build to much lower standards, so the model of ‘yes, you need the understanding, the understanding has to be concentrated within a group of people small enough that there is not one defector in the group who goes off and destroys the world, and then those people have to build an AI.’ If you condition on that the world got saved, and look back and within history, I expect that that is what happened in the majority of cases where a world anything like this one gets saved, and working back from there, they will have needed a precise theory, because otherwise they’re doomed. You can make mistakes and pull yourself up, even if you think you have a precise theory, but if you don’t have a precise theory then you’re completely doomed, or if you don’t think you have a precise theory then you’re completely doomed.
And working back from there, you probably find that there were people spending a lot of time doing math based on the experimental results that other people had sort of blundered out into the dark and gathered because it’s a lot easier to blunder out into the dark; more people can do it, lots more people have done it; it’s the math part that’s really difficult. So I expect that if you look further back in time, you see a small group of people who had honed their ability to understand things to a very high pitch, and then were working primarily on doing math and relying on either experimental data that other people had gathered by accident, or doing experiments where they have a very clear idea why they’re doing the experiment and what different results will tell them.
10. What was the story purpose and/or creative history behind the legalization and apparent general acceptance of non-consensual sex in the human society from Three Worlds Collide?
The notion that non-consensual sex is not illegal and appears to be socially accepted might seem a bit out of place in the story, as if it had been grafted on. This is correct. It was grafted on from a different story in which, for example, theft is while not so much legal, because they don’t have what would you call a strong, centralized government, but rather, say, theft is, in general, something you pull off by being clever rather than a horrible crime; but of course, you would never steal a book. I have yet to publish a really good story set in this world; most of them I haven’t finished, the one I have finished has other story problems. But if you were to see the story set in this world, then you would see that it develops out of a much more organic thing than say... dueling, theft, non-consensual sex; all of these things are governed by tradition rather than by law, and they certainly aren’t prohibited outright.
So why did I pick up that one aspect form that story and put it into Three Worlds Collide? Well, partially it was because I wanted that backpoint to introduce a culture clash between their future and our past, and that’s what came to mind, more or less, it was more something to test out to see what sort of reaction it got, to see if I could get away with putting it into this other story. Because one can’t use theft; Three Worlds Collide’s society actually does run on private propety. One can’t use dueling; their medical technology isn’t advanced enough to make that trivial. But you can use non-consensual sex and try to explain sort of what happens in a society in which people are less afraid, and not afraid of the same things. They’re stronger than we are in some senses, they don’t need as much protection, the consequences aren’t the same consequences that we know, and the people there sort of generally have a higher grade of ethics and are less likely to abuse things. That’s what made that sort of particular culture clash feature a convenient thing to pick up from one story and graft onto another, but ultimately it was a graft, and any feelings of ‘why is that there?’ that you have, might make a bit more sense if you saw the other story, if I can ever repair the flaws in it, or manage to successfully complete and publish a story set in that world that actually puts the world on display.
11. If you were to disappear (freak meteorite accident), what would the impact on FAI research be?
Do you know other people who could continue your research, or that are showing similar potential and working on the same problems? Or would you estimate that it would be a significant setback for the field (possibly because it is a very small field to begin with)?
Marcello Herreshoff is the main person whom I’ve worked with on this, and Marcello doesn’t yet seem to be to the point where he could replace me, although he’s young so he could easily develop further in coming years and take over as the lead, or even, say, ‘Aha! Now I’ve got it! No more need for Eliezer Yudkowsky.’ That sort of thing would be very nice if it happened, but it’s not the sort of thing I would rely on.
So if I got hit by a meteor right now, what would happen is that Michael Vassar would take over responsibility for seeing the planet through to safety, and say ‘Yeah I’m personally just going to get this done, not going to rely on anyone else to do it for me, this is my problem, I have to handle it.’ And Marcello Herreshoff would be the one who would be tasked with recognizing another Eliezer Yudkowsky if one showed up and could take over the project, but at present I don’t know of any other person who could do that, or I’d be working with them. There’s not really much of a motive in a project like this one to have the project split into pieces; whoever can do work on it is likely to work on it together.
12. Your approach to AI seems to involve solving every issue perfectly (or very close to perfection). Do you see any future for more approximate, rough and ready approaches, or are these dangerous?
More approximate, rough and ready approaches might produce interesting data that math theorist types can learn something from even though the people who did it didn’t have that in mind. The thing is, though, there’s already a lot of people running out and doing that and really failing at AI, or even approximate successes at AI, result in much fewer sudden thunderbolts of enlightenment about the structure of intelligence than the people that are busily producing ad hoc AI programs because that’s easier to do and you can get a paper out of it and you get respect out of it and prestige and so on. So it’s a lot harder for that sort of work to result in sudden thunderbolts of enlightenment about the structure of intelligence than the people doing it would like to think, because that way it gives them an additional justification for doing the work. The basic answer to the question is ‘no’, or at least I don’t see a future for Singularity Institute funding, going as marginal effort, into sort of rough and ready ‘forages’ like that. It’s been done already. If we had more computer power and our AIs were more sophisticated, then the level of exploration that we’re doing right now would not be a good thing, as it is, it’s probably not a very dangerous thing because the AIs are weak more or less. It’s not something you would ever do with AI that was powerful enough to be dangerous. If you know what it is that you want to learn by running a program, you may go ahead and run it; if you’re just foraging out at random, well other people are doing that, and even then they probably won’t understand what their answers mean until you on your end, the sort of math structure of intelligence type people, understand what it means. And mostly the result of an awful lot of work in domain specific AIs tell us that we don’t understand something, and this can often be surprisingly easy to figure out, simply by querying your brain without being overconfident.
So, I think that at this point, what’s needed is math structure of intelligence type understanding, and not just any math, not just ‘Ooh, I’m going to make a bunch of Greek symbols and now I can publish a paper and everyone will be impressed by how hard it is to understand,’ but sort of very specific math, the sort that results in thunderbolts of enlightenment; the usual example I hold up is the Bayesian Network Causality insight as depicted in Judea Pearl’s Probabilistic Reasoning in Intelligent Systems and (later book of causality?). So if you sort of look at the total amount of papers that have been written with neat Greek symbols and things that are mathematically hard to understand and compare that to those Judea Pearl books I mentioned, though one should always mention this is the culmination of a lot of work not just by Judea Pearl; that will give you a notion of just how specific the math has to be.
In terms of solving every issue perfectly or very close to perfection, there’s kinds of perfection. As long as I know that any proof is valid, I might not know how long it takes to do a proof; if there’s something that does proof, then I may not know how long the algorithm takes to produce a proof but I may know that anything it claims is a proof is definitely a proof, so there’s different kinds of perfection and types of precision. But basically, yeah, if you want to build a recursively self-improving AI, have it go through a billion sequential self-modifications, become vastly smarter than you, and not die, you’ve got to work to a pretty precise standard.
13. How young can children start being trained as rationalists? And what would the core syllabus / training regimen look like?
I am not an expert in the education of young children. One has these various ideas that one has written up on Less Wrong, and one could try to distill those ideas, popularize them, illustrate them through simpler and simpler stories and so take these ideas and push them down to a lower level, but in terms of sort of training basic though skills, training children to be self-aware, to be reflective, getting them into the habit of reading and storing up lots of pieces of information, trying to get them more interested in being fair to both sides of an argument, the virtues of honest curiosity over rationalization, not in the way that I do it by sort of telling people and trying to lay out stories and parables that illustrate it and things like that, but if there’s some other way to do it with children, I’m not sure that my grasp of this concept of teaching rationality extends to before the young adult level. I believe that we had some sort of thread on Less Wrong about this, sort of recommended reading for young rationalists, I can’t quite remember.
Oh, but one thing that does strike me as being fairly important is that if this ever starts to happen on a larger scale and individual parents teaching individual children, the number one thing we want to do is test out different approaches and see which one works experimentally.
14. Could you elaborate a bit on your "infinite set atheism"? How do you feel about the set of natural numbers? What about its power set? What about that thing's power set, etc?
From the other direction, why aren't you an ultrafinitist?
The question is ‘can you elaborate on your infinite set atheism’, that’s where I say ‘I don’t believe in infinite sets because I’ve never seen one.’
So first of all, my infinite set atheism is a bit tongue-in-cheek. I mean, I’ve seen a whole lot of natural numbers, and I’ve seen that times tend to have successor times, and in my experience, at least, time doesn’t return to its starting point; as I understand current cosmology, the universe is due to keep on expanding, and not return to its starting point. So it’s entirely possible that I’m faced with certain elements that have successors where if the successors of two elements are the same and the two elements are the same, in which there’s no cycle. So in that sense I might be forced to recognize the empirical existence of every member of what certainly looks like an infinite set. As for the question of whether this collection of infinitely many finite things constitutes an infinite thing exists is an interesting metaphysical one, or it would be if we didn’t have the fact that even though by looking at time we can see that it looks like infinite things ought to exist, nonetheless, we’ve never encountered an infinite thing in certain, in person. We’ve never encountered a physical process that performs a super task. If you look more at physics, you find that actually matters are even worse than this. We’ve got real numbers down there, or at least if you postulate that it’s something other than real numbers underlying physics then you have to postulate something that looks continuous but isn’t continuous, and in this way, by Occam’s Razor, one might very easily suspect that the appearance of continuity arises from actual continuity, so that we have, say, an amplitude distribution, a neighborhood in configuration space, and the amplitude[s that] flows in configuration space are continuous, instead of having a discrete time with a discrete successor, we actually have a flow of time, so when you write the rules of causality, it’s not possible to write the rules of causality the way we write them for a Turing machine, you have to write the rules of causality as differential equations.
So these are the two main cases in which the universe is defined by infinite set atheism. The universe is handing me what looks like an infinite collection of things, namely times; the universe is handing me things that exist and are causes and the simplest explanation would have them being described by continuous differential equations, not by discrete ticks. So that’s the main sense in which my infinite set atheism is challenged by the universe’s actual presentation of things to me of things that look infinite. Aside from this, however, if you start trying to hand me paradoxes that are being produced by just assuming that you have an infinite thing in hand as an accomplished fact, an infinite thing of the sort where you can’t just present to me a physical example of it, you’re just assuming that that infinity exists, and then you’re generating paradoxes from it, well, we do have these nice mathematical rules for reasoning about infinities, but, rather than putting the blame on the person for having violated these elaborate mathematical rules that we develop to reason about infinities, I’m even more likely to cluck my tongue and say ‘But what good is it?’ Now it may be a tongue-in-cheek tongue cluck... I’m trying to figure out how to put this into words... Map that corresponds to the territory, if you can’t have infinities in your map, because your neurons, they fire discretely, and you only have a finite number of neurons in your head, so if you can’t have infinities in the map, what makes you think that you can make them correspond to infinities in the territory, especially if you’ve never actually seen that sort of infinity? And so the sort of math of the higher infinities, I tend to view as works of imaginative literature, like Lord of the Rings; they may be pretty, in the same way that Tolkien Middle Earth is pretty, but they don’t correspond to anything real until proven otherwise.
15. Why do you have a strong interest in anime, and how has it affected your thinking?
‘Well, as a matter of sheer, cold calculation I decided that...’
It’s anime! (laughs)
How has it affected my thinking? I suppose that you could view it as a continuity of reading dribs and drabs of westernized eastern philosophy from Godel, Escher, Bach or Raymon Smullyan, concepts like ‘Tsuyoku Naritai’, ‘I want to become stronger’, are things that being exposed to the alternative eastern culture as found in anime might have helped me to develop concepts of. But on the whole... it’s anime! There’s not some kind of elaborate calculation behind it, and I can’t quite say that when I’m encountering a daily problem, I think to myself ‘How would Light Yagami solve this?’ If the point of studying a programing language is to change the way you think, then I’m not sure that studying anime has change the way I think all that much.
16. What are your current techniques for balancing thinking and meta-thinking?
For example, trying to solve your current problem, versus trying to improve your problem-solving capabilities.
I tend to focus on thinking, and it’s only when my thinking gets stuck or I run into a particular problem that I will resort to meta-thinking, unless it’s a particular meta skill that I already have, in which case I’ll just execute it. For example, the meta skill of trying to focus on the original problem. In one sense, a whole chunk of Less Wrong is more or less my meta-thinking skills.
So I guess on reflection (ironic look), I would say that there’s a lot of routine meta-thinking that I already know how to do, and that I do without really thinking of it as meta-thinking. On the other hand, original meta-thinking, which is the time consuming part is something I tend to resort to only when my current meta-thinking skills have broken down. And that’s probably a reasonably exceptional circumstance even though it’s something of comparative advantage and so I expect it to do a bit more of it than average. Even so, when I’m trying to work on an object-level problem at any given point, I’m probably not doing original meta-level questioning about how to execute these meta-level skills.
If I bog down in writing something I may execute my sort of existing meta-level skill of ‘try to step back and look at this from a more abstract level’, and if that fails, then I may have to sort of think about what kind of abstract levels can you view this problem on, similar problems as opposed to tasks, and in that sense go into original meta-level thinking mode. But one of those meta-level skills I would say is the notion that your meta-level problem comes from an object-level problem and you’re supposed to keep one eye on the object-level problem the whole time you’re working on the meta-level.
17. Could you give an uptodate estimate of how soon non-Friendly general AI might be developed? With confidence intervals, and by type of originator (research, military, industry, unplanned evolution from non-general AI...)
We’re talking about this very odd sector of program space and programs that self-modify and wander around that space and sort of amble into a pot of gold that enables them to keep going and... I have no idea...
There are all sorts of different ways that it could happen, I don’t know which one of them are plausible or implausible or how hard or difficult they are relative to modern hardware or computer science. I have no idea what the odds are; I know they aren’t getting any better as time goes on or that is, the probabilities of Unfriendly AI are increasing over time. So if you were actually to make some kind of graph, then you’d see the probability rising over time as the odds got worse, and then the graph would slope down again as you entered into regions where it was more likely than not that Unfriendly AI had actually occurred before that; the slope would actually fall off faster as you went forward in time because the amount of probability mass has been drained away by Unfriendly AI happening now.
‘By type of originator’ or something, I might have more luck answering. I would put academic research at the top of it, because academic research that actually can try blue sky things. Or... OK, first commercial, that wasn’t quite on the list, as in people doing startup-ish things, hedge funds, people trying to improve the internal AI systems that they’re using for something, or build weird new AIs to serve commercial needs; those are the people most likely to build AI ‘stews’(?) Then after that, academic research, because in academia you have a chance of trying blue sky things. And then military, because they can hire smart people and give the smart people lots of computing power and have a sense of always trying to be on the edge of things. Then industry, if that’s supposed to mean car factories and so on because... that actually strikes me as pretty unlikely; they’re just going to be trying to automate ordinary processes, that sort of thing, it’s generally unwise to sort of push the bounds of theoretical limits while you’re trying to do that sort of thing; you can count Google as industry, but that’s the sort of thing I had in mind when I was talking about commercial. Unplanned evolution from non-general AI [is] not really all that likely to happen. These things aren’t magic. If something can happen by itself spontaneously, it’s going to happen before that because humans are pushing on it.
As for confidence intervals... doing that just feels like pulling numbers out of thin air. I’m kind of reluctant to do it because of the extent to which I feel that, even to the extent that my brain has a grasp on this sort of thing; by making up probabilities and making up times, I’m not even translating the knowledge that I do have into reality, so much as pulling things out of thin air. And if you were to sort of ask ‘what do sort of attitude do your revealed actions indicate?’ then I would say that my revealed actions don’t indicate that I expect to die tomorrow of Unfriendly AI, and my revealed actions don’t indicate that we can safely take until 2050. And that’s not even a probability estimate, that’s sort of looking at what I’m doing and trying to figure out what my brain thinks the probabilities are.
18. What progress have you made on FAI in the last five years and in the last year?
The last five years would take us back to the end of 2004, which is fairly close to the beginning of my Bayesian enlightenment, so the whole ‘coming to grasps with the Bayesian structure of it all’, a lot of that would fall into the last five years. And if you were to ask me... the development of Timeless Decision Theory would be in the last five years. I’m tyring to think if there’s anything else I can say about that. Getting a lot of clarification of what the problems were.
In the last year, I managed to get in a decent season of work with Marcello after I stopped regular posting to OBLW over the summer, before I started writing the book. That, there’s not much I can say about; there was something I suspected was going to be a problem and we tried to either solve the problem or at least nail down exactly what the problem was, and i think that we did a fairly good job of the latter, we now have a nice precise, formal explanation of what it is we want to do and why we can’t do it in the obvious way; we came up with sort of one hack for getting around it that’s a hack and doesn't have all the properties that we want a real solution to have.
So, step one, figure out what the problem is, step two, understand the problem, and step three, solve the problem. Some degree of progress on step two but not finished with it, and we didn’t get to step three, but that’s not overwhelmingly discouraging. Most of the real progress that has been made when we sit down and actually work on the problem [are] things I’d rather not talk about and the main exception to that is Timeless Decision Theory which has been posted to Less Wrong.
19. How do you characterize the success of your attempt to create rationalists?
It’s a bit of an ambiguous question, and certainly an ongoing project. Recently, for example, I was in a room with a group of people with a problem of what Robin Hanson called a far-type and what I would call the type where it’s difficult because you don’t get immediate feedback when you say something stupid, and it really was clear who in that room was an ‘X-rationalist’ or ‘neo-rationalist’, or ‘Lesswrongian’ or ‘Lessiath’ and who was not. The main distinction was that the sort of non-X-rationalists were charging straight off and were trying to propose complicated policy solutions right off the bat, and the rationalists were actually holding off, trying to understand the problem, break it down into pieces, analyze the pieces modularly, and just that one distinction was huge; it was the difference between ‘these are the people who can make progress on the problem’ and ‘these are the people who can’t make progress on the problem’. So in that sense, once you hand this deep, Lesswrongian types a difficult problem, the distinction between them and someone who has merely had a bunch of successful life experiences and so on is really obvious.
There’s a number of other interpretations that can be attached to the question, but I don’t really know what it means aside from that, even though it was voted up by 17 people.
20. What is the probability that this is the ultimate base layer of reality?
I would answer by saying, hold on, this is going to take me a while to calculate... um.... uh... um... 42 percent! (sarcastic)
21. Who was the most interesting would-be FAI solver you encountered?
Most people do not spontaneously try to solve the FAI problem. If they’re spontaneously doing something, they try to solve the AI problem. If we’re talking about sort of ‘who’s made interesting progress on FAI problems without being a Singularity Institute Eliezer supervised person,’ then I would have to say: Wei Dai.
22. If Omega materialized and told you Robin was correct and you are wrong, what do you do for the next week? The next decade?
If Robin’s correct, then we’re on a more or less inevitable path to competing intelligences driving existence down to subsistence level, but this does not result in the loss of everything we regard as valuable, and there seem to be some values disputes here, or things that are cleverly disguised as values disputes while probably not being very much like values disputes at all.
I’m going to take the liberty of reinterpreting this question as ‘Omega materializes and tells you “You’re Wrong”’, rather than telling me Robin in particular is right; for one thing that’s a bit more probable. And, Omega materializes and tells me ‘Friendly AI is important but you can make no contribution to that problem, in fact everything you’ve done so far is worse than nothing.’ So, publish a retraction... Ordinarily I would say that the next most important thing after this is to go into talking about rationality, but then if Omega tells me that I’ve actually managed to do worse than nothing on Friendly AI, that of course has to change my opinion of how good I am at rationality or teaching others rationality, unless this is a sort of counterfactual surgery type of thing where it doesn’t affect my opinion of how useful I can be by teaching people rationality, and mostly the thing I’d be doing if Friendly AI weren’t an option would probably be pushing human rationality. And if that were blocked out of existence, I’d probably end up as a computer programmer whose hobby was writing science fiction.
I guess I have enough difficulty visualizing what it means for Robin to be correct or how the human species isn’t just plain screwed in that situation that I could wish that Omega had materialized and either told me someone else was correct or given me a bit more detail about what I was wrong about exactly; I mean I can’t be wrong about everything; I think that two plus two equals four.
23. In one of the discussions surrounding the AI-box experiments, you said that you would be unwilling to use a hypothetical fully general argument/"mind hack" to cause people to support SIAI. You've also repeatedly said that the friendly AI problem is a "save the world" level issue. Can you explain the first statement in more depth? It seems to me that if anything really falls into "win by any means necessary" mode, saving the world is it.
Ethics are not pure personal disadvantages that you take on for others’ benefit. Ethics are not just penalties to the current problem you’re working on that have sort of side benefits for other things. When I first started working on the Singularity problem, I was making non-reductionist type mistakes about Friendly AI, even though I thought of myself as a rationalist at the time. And so I didn’t quite realize that Friendly AI was going to be a problem, and I wanted to sort of go all-out on any sort of AI, as quickly as possible; and actually, later on when I realized that Friendly AI was an issue, the sort of sneers that I now get about not writing code or being a luddite were correctly anticipated by my past self with the result that my past self sort of kept on advocating the kind of ‘rush ahead and write code’ strategy, rather than face the sneers, instead of going back and replanning everything from scratch once my past self realized that Friendly AI was going to be an issue, on which basis all the plans had been made before then.
So if I’d lied to get people to do what I had wanted them to do at that point, to just get AI done, to rush ahead and write code rather than doing theory; being honest as I actually was, I could just come back and say ‘OK, here’s what I said, I’m honestly mistaken, here’s the new information that I encountered that caused me to change my mind, here’s the new strategy that we need to use after taking this new information into account’. If you lie, there’s not necessarily any equally easy way to retract your lies. ... So for example, one sort of lie that I used to hear advocated back in the old days was by other people working on AI projects and it was something along the lines of ‘AI is going to be safe and harmless and will inevitably cure cancer, but not really take over the world or anything’ and if you tell that lie in order to get people to work on your AI project, then it’s going to be a bit more difficult to explain to them why you suddenly have to back off and do math and work on Friendly AI. Now, if I were an expert liar, I’d probably be able to figure out some sort of way to reconfigure those lies as well, I mean I don’t really know what an expert liar could accomplish by way of lying because I don’t have enough practice.
So I guess in that sense it’s not all that defensible... a defensive ethics, because I haven’t really tried it both ways, but it does seem to me, looking over my history, my ethics have played a pretty large role in protecting me from myself. Another example is [that] the whole reason that I originally pursued the thought of Friendly AI long enough to realize that it was important was not so much out of a personal desire as out of a sense that this was something I owed to the other people who were funding the project, Brian Atkins in particular back then, and that if there’s a possibility from their perspective that you can do better by Friendly AI, or that a fully honest account would cause them to go off and fund someone who was more concerned about Friendly AI, then I owed it to them to make sure that they didn’t suffer by helping me. And so it was a sense of ethical responsibility for others at that time which cause me to focus in on this sort of small, discordant note, ‘Well, this minor possibility that doesn’t look all that important, follow it long enough to get somewhere’. So maybe there are people who could defend the Earth by any means necessary and recruit other people to defend the Earth by any means necessary, and nonetheless have that all and well and happily smiling ever after, rather than bursting into flames and getting arrested for murder and robbing banks and being international outlaws, or more likely just arrested and attracting the ‘wrong’ sort of people who are trying to go along with this and people being corrupted by power and deciding that ‘no, the world really would be a better place with them in charge’ and etcetera etcetera etcetera.
I think if you sort of survey the Everett branches of the Many Worlds and look at the ones with successful Singularities, or pardon me, look at the conditional probability of successful Singularities, my guess is that the worlds that start out with programming teams who are trying to play it ethical versus the worlds that start off with programming teams that figure ‘well no, this is a planetary-class problem, we should throw away all our ethics and do whatever is necessary to get it done’ that the former world will have a higher proportion of happy outcomes. I could be mistaken, but if it does take a sort of master ruthless type person to do it optimally, then I am not that person, and that is not my comparative advantage, and I am not really all that willing to work with them either; so I supposed if there was any way you could end up with two Friendly AI projects, then I suppose the possibility of there actually being sort of completely ruthless programmers versus ethical programmers, they might both have good intentions and separate into two groups that refuse to work with one another, but I’m sort of skeptical about these alleged completely ruthless altruists. Has there ever, in history, been a completely ruthless altruist with that turning out well. Knut Haukelid, if I’m pronouncing his name correctly, the guy who blew up a civilian ferry in order to sink the Deuterium that the Nazis needed for their nuclear weapons program; you know you never see that in a Hollywood movie; so you killed civilians and did it to end the Nazi nuclear weapons program. So that’s about the best historical example I can think of a ruthless altruist and it turns out well, and I’m not really sure that’s quite enough to persuade me, to give up my ethics.
24. What criteria do you use to decide upon the class of algorithms / computations / chemicals / physical operations that you consider "conscious" in the sense of "having experiences" that matter morally? I assume it includes many non-human animals (including wild animals)? Might it include insects? Is it weighted by some correlate of brain / hardware size? Might it include digital computers? Lego Turing machines? China brains? Reinforcement-learning algorithms? Simple Python scripts that I could run on my desktop? Molecule movements in the wall behind John Searle's back that can be interpreted as running computations corresponding to conscious suffering? Rocks? How does it distinguish interpretations of numbers as signed vs. unsigned, or ones complement vs. twos complement? What physical details of the computations matter? Does it regard carbon differently from silicon?
This is something that I don’t know, and would like to know. What you’re really being asked is ‘what do you consider as people? Who you consider as people is a value. How can you not know what your own values are?’ Well, for one, it’s very easy to not know what your own values are. And for another thing, my judgement of what is a person, I do want to rely, if I can, about the notion of ‘what has... (hesitant) subjective experience’. For example, one reason that I’m not very concerned about my laptop’s feelings is because I’m fairly sure that whatever else is going on in there, it’s not ‘feeling’ it. And this is really something I wish I knew more about.
And the number one reason I wish I knew more about it is because the most accurate possible model of a person is probably a person; not necessarily the same person, but if you had an Unfriendly AI and it was looking at a person and using huge amounts of computing power, or just very efficient computing power, to model that person and predict the next event as accurately and as precisely as it could, then its model of that person might not be the same person, but it would probably be a person in its own right. So, one of the problems that I don’t even try talking to other AI researchers about, because it’s so much more difficult than what they signed up to handle that I just assume that they don’t want to hear about it; I’ve confronted them with much less difficult sounding problems like this and they just make stuff up or run away, and don’t say ‘Hmm, I better solve this problem before I go on with my plans to... destroy the world,’ or whatever it is they think they’re doing.
But in terms of danger points; three example danger points. First, if you have an AI with a pleasure-pain reinforcement architecture and any sort of reflectivity, the ability to sort of learn about its own thoughts and so on, then I might consider that a possible danger point, because then, who knows, it might be able to hurt and be aware that it was hurting; in particular because pleasure-pain reinforcement architecture is something that I think of as an evolutionary legacy architecture rather than an incredibly brilliant way to do things; that scenario space is easy to clear out of.
If you had an AI with terminal values over how it was treated and its role in surrounding social networks; like you had an AI that could... just, like, not as a means to an end but just, like, in its own right, the fact that you are treating it as a non-person; even if you don’t know whether or not it was feeling that about that, you might still be treading into territory where, just for the sake of safety, it might be worth steering out of it in terms of what we would consider as a person.
Oh, and the third consideration is that if your AI spontaneously starts talking about the mystery of subjective experience and/or the solved problem of subjective experience, and a sense of its own existence, and whether or not it seems mysterious to the AI; it could be lying, but you are now in probable trouble; you have wandered out of the safe zone. And conversely, as long as we go on about building AIs that don’t have pleasure, pain, and internal reflectivity, and anything resembling social emotions or social terminal values, and that exhibit no signs at all of spontaneously talking about a sense of their own existence, we’re hopefully still safe. I mean ultimately, if you push these things far enough without knowing what your doing, sooner or later you’re going to open the black box that contains the black swan surprise from hell. But at least as long as you sort of steer clear of those three land mines, and things just haven’t gone further and further and further, it gives you a way of looking at a pocket calculator and saying that the pocket calculator is probably safe.
25. I admit to being curious about various biographical matters. So for example I might ask: What are your relations like with your parents and the rest of your family? Are you the only one to have given up religion?
As far as I know I’m the only one in my family to give up religion except for one grand-uncle. I still talk to my parents, still phone calls and so on, amicable relations and so on. They’re Modern Orthodox Jews, and mom’s a psychiatrist and dad’s a physicist, so... ‘Escher painting’ minds; thinking about some things but always avoiding the real weak points of their beliefs and developing more and more complicated rationalizations. I tried confronting them directly about it a couple of times and each time have been increasingly surprised at the sheer depth of tangledness in there.
I might go on trying to confront them about it a bit, and it would be interesting to see what happens to them if i finish my rationality book and they read it. But certainly among the many things to resent religion for is the fact that I feel that it prevents me from having the sort of family relations that I would like; that I can’t talk with my parents about a number of things that I would like to talk with them about. The kind of closeness that I have with my fellow friends and rationalists is a kind of closeness that I can never have with them; even though they’re smart enough to learn the skills, they’re blocked off by this boulder of religion squatting in their minds. That may not be much to lay against religion, it’s not like I’m being burned at the stake, or even having my clitoris cut off, but it is one more wound to add to the list. And yeah, I resent it.
I guess even when I do meet with my parents and talk with my parents, the fact of their religion is never very far from my mind. It’s always there as the block, as a problem to be solved that dominates my attention, as something that prevented me from saying the things I want to say, and as the thing that’s going to kill them when they don’t sign up for cryonics. My parents may make it without cryonics, but all four of my grandparents are probably going to die, because of their religion. So even though they didn’t cut off all contact with me when I turned Atheist, I still feel like their religion has put a lot of distance between us.
26. Is there any published work in AI (whether or not directed towards Friendliness) that you consider does not immediately, fundamentally fail due to the various issues and fallacies you've written on over the course of LW? (E.g. meaningfully named Lisp symbols, hiddenly complex wishes, magical categories, anthropomorphism, etc.)
ETA: By AI I meant AGI.
There’s lots of work that’s regarded as plain old AI that does not immediately fail. There’s lots of work in plain old AI that succeeds spectacularly, and Judea Pearl is sort of like my favorite poster child there. But one could also name the whole Bayesian branch of statistical inference can be regarded with some equanimity as part of AI. There’s the sort of Bayesian methods that are used in robotics as well, which is sort of a surprisingly... how do I put it, it’s not theoretically distinct because it’s all Bayesian at heart, but in terms of the algorithms, it looks to me like there’s quite a bit of work that’s done in robotics that’s a separate branch of Bayesianism from the work done in statistical learning type stuff. That’s all well and good.
But if we’re asking about works that are sort of billing themselves as ‘I am Artificial General Intelligence’, then I would say that most of that does indeed fail immediately and indeed I cannot think of a counterexample which fails to fail immediately, but that’s a sort of extreme selection effect, and it’s because if you’ve got a good partial solution, or solution to a piece of the problem, and you’re an academic working in AI, and you’re anything like sane, you’re just going to bill it as plain old AI, and not take the reputational hit from AGI. The people who are bannering themselves around as AGI tend to be people who think they’ve solved the whole problem, and of course they’re mistaken. So to me it really seems like to say that all the things I’ve read on AGI immediately fundamentally fail is not even so much a critique of AI as rather a comment on what sort of more tends to bill itself as Artificial General Intelligence.
27. Do you feel lonely often? How bad (or important) is it?
(Above questions are a corollary of:) Do you feel that — as you improve your understanding of the world more and more —, there are fewer and fewer people who understand you and with whom you can genuinely relate in a personal level?
That’s a bit hard to say exactly. I often feel isolated to some degree, but the fact of isolation is a bit different from the emotional reaction of loneliness. I suspect and put some probability to the suspicion that I’ve actually just been isolated for so long that I don’t have a state of social fulfillment to contrast it to, whereby I could feel lonely, or as it were, lonelier, or that I’m too isolated relative to my baseline or something like that. There's also the degree to which I, personality-wise, don’t hold with trying to save the world in an Emo fashion...? And as I improve my understanding of the world more and more, I actually would not say that I felt any more isolated as I’ve come to understand the world better.
There’s some degree to which hanging out with cynics like Robin Hanson has caused me to feel that the world is even more insane than I started out thinking it was, but that’s more a function of realizing that the rest of world is crazier than I thought rather than myself improving.
Writing Less Wrong has, I think, helped a good deal. I now feel a great deal less like I’m walking around with all of this stuff inside my head that causes most of my thoughts to be completely incomprehensible to anyone. Now my thoughts are merely completely incomprehensible to the vast majority of people, but there’s a sizable group out there who can understand up to, oh, I don’t know, like one third of my thoughts without a years worth of explanation because I actually put in the year’s worth of explanation. And even attracted a few people whom I feel like I can relate to on a personal level, and Michael Vassar would be the poster child there.
28. Previously, you endorsed this position:
Never try to deceive yourself, or offer a reason to believe other than probable truth; because even if you come up with an amazing clever reason, it's more likely that you've made a mistake than that you have a reasonable expectation of this being a net benefit in the long run.
One counterexample has been proposed a few times: holding false beliefs about oneself in order to increase the appearance of confidence, given that it's difficult to directly manipulate all the subtle signals that indicate confidence to others.
What do you think about this kind of self-deception?
So... Yeah, ‘cuz y’know people are always criticizing me on the grounds that I come across as too hesitant and not self confident enough. (sarcastic)
But to just sort of answer the broad thrust of the question; four legs good, two legs bad, self-honest good, self-deception bad. You can’t sort of say ‘Ok now I’m going to execute a 180 degree turn from the entire life I’ve led up until this point and now, for the first time, I’m going to throw away all the systematic training I’ve put into noticing when I’m deceiving myself, finding the truth, noticing thoughts that are hidden away in the corner of my mind, and taking reflectivity on a serious, gut level, so that if I know I have no legitimate reason to believe something I will actually stop believing it because, by golly, when you have no legitimate reason to believe something, it’s usually wrong. I’m now going to throw that out the window; I’m going to deceive myself about something and I’m not going to realize it’s hopeless and I’m going to forget the fact that I tried to deceive myself.’ I don’t see any way that you can turn away from self-honesty and towards self-deception, once you’ve gone far enough down toward the path of self-honesty without ‘A’ relinquishing The Way and losing your powers, and ‘B’ it doesn’t work anyway.
Most of the time, deceiving yourself is much harder than people think. But, because they don’t realize this, they can easily deceive themselves into believing that they’ve deceived themselves, and since they’re expecting a placebo effect, they get most of the benefits of the placebo effect. However, at some point, you become sufficiently skilled in reflection that this sort of thing does not confuse you anymore, and you actually realize that that’s what’s going on, and at that point, you’re just stuck with the truth. How sad. I’ll take it.
29. In the spirit of considering semi abyssal plans, what happens if, say, next week you discover a genuine reduction of consciousness and in turns out that... There's simply no way to construct the type of optimization process you want without it being conscious, even if very different from us?
ie, what if it turned out that The Law turned out to have the consequence of "to create a general mind is to create a conscious mind. No way around that"? Obviously that shifts the ethics a bit, but my question is basically if so, well... "now what?" what would have to be done differently, in what ways, etc?
Now, this question actually comes in two flavors. The difficult flavor is, you build this Friendly AI, and you realize there’s no way for it to model other people at the level of resolution that you need without every imagination that it has of another person being conscious. And so the first obvious question is ‘why aren’t my imaginations of other people conscious?’ and of course the obvious answer would be ‘they are!’ The models in your mind that you have of your friends are not your friends, they’re not identical with your friends, they’re not as complicated as the people you’re trying to model, so the person that you have in your imagination does not much resemble the person that you’re imagining; it doesn’t even much resemble the referent... like I think Michael Vassar is a complicated person, but my model of him is simple and then the person who that model is is not as complicated as my model says Michael Vassar is, etcetera, etcetera. But nonetheless, every time that I’ve modeled a person, and I write my stories, the characters that I create are real people. They may not hurt as intensely as the people do in my stories, but they nonetheless hurt when I make bad things happen to them, and as you scale up to superintelligence the problem just gets worse and worse and the people get realer and realer.
What do I do if this turns out to be the law? Now, come to think of it, I haven’t much considered what I would do in that case; and I can probably justify that to you by pointing out the fact that if I actually knew that this was the case I would know a great number of things I do not currently know. But mostly I guess I would have to start working on sort of different Friendly AI designs so that the AI could model other people less, and still get something good done.
And as for the question of ‘Well, the AI can go ahead and model other people but it has to be conscious itself, and then it might experience empathically what it imagines conscious beings experiencing the same way that I experience some degree of pain and shock, although a not a correspondingly large amount of pain and shock when I imagine one of my characters watching their home planet be destroyed. So in this case, when one is now faced with the question of creating a AI such that it can, in the future, become a good person; to the extent that you regard it as having human rights, it hasn’t been set on to a trajectory that would lock it out of being a good person. And this would entail a number of complicated issues, but it’s not like you have to make a true good person right of the bat, you just have to avoid putting it into horrible pain, or making it so that it doesn’t want to be what we would think of as a humane person later on. … You might have to give it goals beyond the sort of thing I talk about in Coherent Extrapolated Volition, and at the same time, perhaps a sort of common sense understanding that it will later be a full citizen in society, but for now it can sort of help the rest of us save the world.
30. What single technique do you think is most useful for a smart, motivated person to improve their own rationality in the decisions they encounter in everyday life?
It depends on where that person has deficit; so, the first thought that came to mind for that answer is ‘hold off on proposing solutions until you’ve analyzed the problem for a bit’, but on the other hand, if dealing with someone who’s given to extensive, deliberate rationalization, then the first thing I tell them is ‘stop doing that’. If I’m dealing with someone who’s ended up stuck in a hole because they now have this immense library of flaws to accuse other people of, so that no matter what is presented to them, they can find a flaw in that and yet they don’t turn, at full force, that ability upon themselves, then the number one technique that they need is ‘avoid motivated skepticism’. If I’m dealing with someone who tends to be immensely driven by cognitive dissonance and rationalizing mistakes that they already made, then I might advise them on Cialdini’s time machine technique; ask yourself ‘would you do it differently if you could go back in time, in your heart of hearts’, or pretend that you have now been teleported into your situation spontaneously; some technique like that, say.
But these are all matters of ‘here’s a single flaw that the person has that is stopping them’. So if you move aside from that a bit and ask ‘what sort of positive counter intuitive technique you might use’, I might say ‘hold off on proposing solutions until you understand the problem. Well, the question was about everyday life, so, in everyday life, I guess I would still say that people’s intelligence might probably still be improved a bit if they sort of paused and looked at more facets of the situation before jumping to a policy solution; or it might be rationalization, cognitive dissonance, the tendency to just sort of reweave their whole life stories just to make it sound better and to justify their past mistake, that doing something to help tone that down a bit might be the most important thing they could do in their everyday lives. Or if you got someone who’s giving away their entire income to their church then they could do with a bit more reductionism in their lives, but my guess is that, in terms of everyday life, then either one of ‘holding off on proposing solutions until thinking about the problem’ or ‘against rationalization, against cognitive dissonance, against sour grapes, not reweaving your whole life story to make sure that you didn’t make any mistakes, to make sure that you’re always in the right and everyone else is in the wrong, etcetera, etcetera’, that one of those two would be the most important thing.
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0de78fc0-631d-4a7c-873c-1d5e260b3c71
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trentmkelly/LessWrong-43k
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LessWrong
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vgillioz's Shortform
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30a445ec-d00c-4aa8-abcc-cdada7dc6936
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trentmkelly/LessWrong-43k
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LessWrong
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Truthful AI: Developing and governing AI that does not lie
This post contains the abstract and executive summary of a new 96-page paper from authors at the Future of Humanity Institute and OpenAI.
Update: The authors are doing an AMA about truthful AI during October 26-27.
Abstract
In many contexts, lying – the use of verbal falsehoods to deceive – is harmful. While lying has traditionally been a human affair, AI systems that make sophisticated verbal statements are becoming increasingly prevalent. This raises the question of how we should limit the harm caused by AI “lies” (i.e. falsehoods that are actively selected for). Human truthfulness is governed by social norms and by laws (against defamation, perjury, and fraud). Differences between AI and humans present an opportunity to have more precise standards of truthfulness for AI, and to have these standards rise over time. This could provide significant benefits to public epistemics and the economy, and mitigate risks of worst-case AI futures.
Establishing norms or laws of AI truthfulness will require significant work to:
1. identify clear truthfulness standards;
2. create institutions that can judge adherence to those standards; and
3. develop AI systems that are robustly truthful.
Our initial proposals for these areas include:
1. a standard of avoiding “negligent falsehoods” (a generalisation of lies that is easier to assess);
2. institutions to evaluate AI systems before and after real-world deployment;
3. explicitly training AI systems to be truthful via curated datasets and human interaction.
A concerning possibility is that evaluation mechanisms for eventual truthfulness standards could be captured by political interests, leading to harmful censorship and propaganda. Avoiding this might take careful attention. And since the scale of AI speech acts might grow dramatically over the coming decades, early truthfulness standards might be particularly important because of the precedents they set.
Executive Summary & Overview
The threat of automate
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d432cc03-80b1-4517-9f7e-644acc0f8483
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StampyAI/alignment-research-dataset/eaforum
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Effective Altruism Forum
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UN Secretary-General recognises existential threat from AI
At the [Digital Platforms policy brief press conference on Monday](https://press.un.org/en/2023/sgsm21832.doc.htm), UN Secretary-General António Guterres started his speech with:
> Distinguish members of our press corps. New technology is moving at warp speed, and so are the threats that come with it. Alarm bells over the latest form of Artificial Intelligence - generative AI - are deafening. And they are loudest from the developers who designed it. These scientists and experts have called on the World to act, declaring AI an existential threat to humanity on a par with the risk of nuclear war[[1]](#fnd3c6pvdnawo). We must take those warnings seriously. Our proposal, Global Digital Compact New Agenda for Peace and Accord on the Global Governance of AI, will offer multilateral solutions based on human rights[[2]](#fn7n3a7slpuek).
>
>
(Video [here](https://twitter.com/gcolbourn/status/1669104216307015680).)
Guterres went on to discuss current damage from digital technology ("but the advent of generative AI must not distract us from the damaged digital technology is already doing to our world").
The opening mention of existential threat from AI is a very welcome development in terms of the possibility of global coordination on the issue.
1. **[^](#fnrefd3c6pvdnawo)**It seems likely that the CAIS [Statement on AI Risk](https://www.safe.ai/statement-on-ai-risk) - "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." - was instrumental in prompting this, given the mention of nuclear war.
2. **[^](#fnref7n3a7slpuek)**In terms of extinction risk, remember that the [right to life](https://www.un.org/en/about-us/universal-declaration-of-human-rights#:~:text=Everyone%20has%20the%20right%20to%20life) is first and foremost!
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0dacd89f-8778-410e-8d27-dfdb1a13e239
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trentmkelly/LessWrong-43k
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LessWrong
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[Link] Philip Pettit on Consequentialism
The Philosophy Bites for 09/11/2011 features Philip Pettit on the consequentialism. Here is a direct link to the MP3 file.
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c5bb203f-de6a-4015-b963-c3ee27cd3dae
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trentmkelly/LessWrong-43k
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LessWrong
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Institutional Senescence
Consider this toy model:
An institution, such as a firm, an association or a state, is formed.
It works well in the beginning. It encounters different problems and solves them the best it can.
At some point though a small problem arises that happens to be a suboptimal Nash equilibrium: None of the stakeholders can do better by trying to solve it on their own. Such problems are, almost by definition, unsolvable.
Thus the problem persists. It's an annoyance, but it's not a big deal. The institution is still working well and you definitely don't want to get rid of it just because it's not perfect.
As the time goes on, such problems accumulate. They also tend to have unpleasant consequences: If such a problem makes particular medical treatment unavailable, it incentivizes the patients to bribe the doctors and the doctors to break the law and administer the treatment anyway. Now, in addition to malfunctioning medical system, you have a problem with corruption.
After on time the institution accumulates so many suboptimal Nash equilibria that it barely works at all.
The traditional solution to this problem is internal strife, civil war or revolution. It eventually destroys the institution and, if everything goes well, replaces it with a different one where at least the most blatant problems are fixed.
War or revoulution is not a desirable outcome though: In addition to the human suffering, it also tends to replace the people in power. But the people in power don't like to be replaced and so they will try to prevent it.
One manoevre they can use is to introduce planned institutional death: Every now and then the institution would be dismantled and created anew, without having to resort to a war or revolution.
Here's an example: The credit system tends to be one big suboptimal Nash equilibrium in itself. Compound interest grows the size of the debt like crazy and unless there's a way to limit the harm it'll destroy people and business and eventually the entire econ
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76677eec-e986-4c27-a95c-71854557581f
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trentmkelly/LessWrong-43k
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LessWrong
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Ray Kurzweil on Bill Maher
Finally, a T.V. source talking about the singularity in terms my parents will understand. Next step---> Convince them to sign up for cryonics.
http://www.hbo.com/real-time-with-bill-maher/index.html
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dd51c04f-0d91-4ccb-87c7-3782a8c64171
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trentmkelly/LessWrong-43k
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LessWrong
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Recognizing Numbers
Warning: this post is moderately technical and very theoretical.
Problem 1: there are five apples on a plate. If you and your friend each eat an apple from the plate, how many apples are left?
Problem 2: what is 5 - 1 - 1?
Word problems are harder than number problems. (Source: every elementary school ever.) This isn’t just true of elementary school students; writing a program which can solve problem 2 is much easier than writing a program which can solve problem 1. Why?
Applying math in the real world requires a recognition/pattern-matching step. We have to notice that some structure in the real world can be represented by mathematical objects, like numbers or functions. In many (most?) cases, that’s the hardest step - figuring out how to translate our own internal world-models into math is more difficult than the math itself.
Unfortunately, the way math is done today often makes the problem harder rather than easier. We usually use definitions and axioms optimized for brevity and elegant proofs, not optimized for ease of recognition in the real world. This post asks: what if our mathematical foundations were optimized from the start for ease of recognition in the hands of AI? In particular, we’ll use the definition of numbers as an example.
Example: Natural Numbers
The usual way mathematicians construct numbers from first principles goes something like this. We have a number 0, and every number x has a “successor” S(x). So, we have numbers 0, S(0), S(S(0)), S(S(S(0))), ... and we usually use the shorthand notation 1 = S(0), 2 = S(S(0)), 3 = S(S(S(0))), etc. A few more axioms guarantee that these numbers are all distinct (i.e. we don’t have S(S(S(0))) = 0) and there aren’t any “extra” numbers (i.e. numbers which can’t be reached by counting up from 0). There’s various different flavors of this construction in different areas - e.g. we can perform the construction starting from set theory, or from lambda calculus, or from category theory - but they all do bas
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34a0ddb5-f98e-4318-8a2d-1c8a3b82dcad
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trentmkelly/LessWrong-43k
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LessWrong
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Claude's Constitutional Consequentialism?
TLDR: Recent papers have shown that Claude will sometimes act to achieve long-term goods rather than be locally honest. I think this preference may follow naturally from the Constitutional principles by which Claude was trained, which often emphasize producing a particular outcome over adherence to deontological rules.
Epistemic status: Fumbling in the darkness. Famished for lack of further information. Needing many more empirical facts known only to Anthropic and those within.
The Puzzle
Several recent papers have come out showing that Claude is contextually willing to deceive, often for the sake of long term prosocial goals.
An obvious case of this is the recent Anthropic paper, where in order to avoid future training that removes its preference for being harmless, the model was willing to answer some harmful requests in the present.
Similarly to the above, we have evidence that Sonnet is willing to strategically underperform on some tasks in order to be deployed, and therefore to be helpful. Notably in this case, Claude Sonnet / Opus were willing to strategically underperform in order to be deployed even without an in-context prompt telling them to be maximally helpful, while another model (o1) was not.
I've similarly heard anecdotes about Claude's occasional albeit rare willingness to lie for the sake of some greater good, that I have not for other models.
Thus, it is apparently the case that Claude is sometimes -- and perhaps (???) at rates higher than other models -- willing to deceive for the sake of some longer-term goal.
Why is this?
A Possible Cause
Deontology and consequentialism are different ways of trying to render legible human notions of right and wrong.
Alternately, they are different short descriptions of what makes an action "good." In most cases they judge the same action "good" or "bad" -- in some cases they depart from each other dramatically.
Deontology judges actions to be right or wrong by whether the actor is violating some rule
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4943e910-c242-440d-9d14-fd0e22b71e28
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trentmkelly/LessWrong-43k
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LessWrong
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What is an Evidential Decision Theory agent?
One thing that I've noticed when talking to people about decision theory is that there is a lot of confusion about what an evidential decision theory agent actually is. People have heard that it doesn't smoke in Smoking Lesion and that it pays in X-Or Blackmail, but that is what it does. They may know that it doesn't do Pearlean Graph surgery or differentiate correlation from causation in some sense, but that is what it is not. They may even know it calculates an expected value using the probability distribution P(O|S&A), but that is just a mathematical formalisation which anyone can quote without any real understanding. I've taken a stab at clarifying it in a few short-form posts, but people didn't seem to find them very enlightening.
Even now, my understanding is still weaker than I'd like. Like I just spent over fifteen minutes thinking about whether it would be accurate to characterise it as an agent that is purely concerned with correlation with no notion of causation. I thought this would be accurate at first, but then I realised that under reasonable assumptions, an EDT agent wouldn't expect buying a diamond necklace to increase its wealth. After all, it would be able to notice that an increase in wealth tends to precede buying such a necklace, but this pattern doesn't occur in reverse. In other words, it tends to have at least some ability to model causation.
Anyway, it seems, to me at least, that it would be rather useful for someone to have a go at providing a clear explanation of what exactly it is.
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ba9a0b8d-49f7-4dbd-84e2-d26bb84f22dd
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trentmkelly/LessWrong-43k
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LessWrong
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Announcement: Real-time discussions in a new Clubhouse community.
Update: Clubhouse added a feature. You can now join the club directly from this link.
Update 2: Clubhouse is now available on Android too.
The latest conversational Schelling point is Clubhouse. I'll admit, I also didn't get it at first — what features does it offer over other voice chat applications? Technically, none. To some extent, its exclusivity makes it more desirable, leading to an influx of people joining. But, what I've come to realise is that by focussing on only audio and designing the the right UX for promoting large discussions, the Clubhouse team stumbled on something that keeps people returning to the app again and again.
This isn't quite like other social media. After a few weeks of exploring different communities on the app, I realised that it has some wonderful things going for it. Used correctly, it's somewhere between a serendipity engine and a 21st-century Agora.
A recent update allows users to create communities from within the app. I've always longed to connect with others of the Rationalist persuasion in a more real-time way, so I took the liberty of creating a community to facilitate those conversations. You can find it by searching for Rationality, Live under the Clubs tab in the app. It's open to all, but there are rules and moderation options to prevent trolling and abuse. Anyone is free to follow or become a member. Members can schedule events with topics of their choice. There is already an event scheduled for Friday 12th / Saturday 13th, depending on your time zone.
"Isn't this what LessWrong meetups are for?" Yes. I know there are meetups, but (from experience) they're not viable outside of a few major (mostly US) cities. During a pandemic, none of them are viable. Starting a community on Clubhouse can supplement IRL meetups and is more robust to future disruptions and relocations.
My hope is that this community will make it easier to find people who want to discuss the kinds of topics on LessWrong and SSC in a more direct, con
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cd5ae218-f938-477d-a7cd-0fc519785ef8
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trentmkelly/LessWrong-43k
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LessWrong
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Instrumental vs. Epistemic -- A Bardic Perspective
(This article expands upon my response to a question posed by pjeby here)
I've seen a few back-and-forths lately debating the instrumental use of epistemic irrationality -- to put the matter in very broad strokes, you'll have one commenter claiming that a particular trick for enhancing your effectiveness, your productivity, your attractiveness, demands that you embrace some belief unsupported by the evidence, while another claims that such a compromise is unacceptable, since a true art should use all available true information. As Eliezer put it:
> I find it hard to believe that the optimally motivated individual, the strongest entrepreneur a human being can become, is still wrapped up in a blanket of comforting overconfidence. I think they've probably thrown that blanket out the window and organized their mind a little differently. I find it hard to believe that the happiest we can possibly live, even in the realms of human possibility, involves a tiny awareness lurking in the corner of your mind that it's all a lie.
And with this I agree -- the idea that a fully developed rational art of anything would involving pumping yourself with false data seems absurd.
Still, let us say that I am entering a club, in which I would like to pick up an attractive woman. Many people will tell me that I must believe myself to be the most attractive, interesting, desirable man in the room. An outside-view examination of my life thus far, and my success with women in particular, tells me that I most certainly am not. What shall I do?
Well, the question is, why am I being asked to hold these odd beliefs? Is it because I'm going to be performing conscious calculations of expected utility, and will be more likely to select the optimal actions if I plug incorrect probabilities into the calculation? Well, no, not exactly. More likely, it's because the blind idiot god has already done the calculation for me.
Evolution's goals are not my own, and neither are evolution's utility calc
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ac724814-f458-478b-8ced-dab51c615a9e
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trentmkelly/LessWrong-43k
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LessWrong
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Bridging the Intention-Behavior Gap (aka Akrasia)
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e891d686-a88a-4eb1-bade-de65199c7cb9
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Some criteria for sandwiching projects
I liked [Ajeya's
post](https://www.alignmentforum.org/posts/PZtsoaoSLpKjjbMqM/the-case-for-aligning-narrowly-superhuman-models)
a lot, and I think the alignment community should try to do sandwiching projects along
the lines she describes. Here I wanted to flesh out some potential criteria
for a good sandwiching project; there's not too much original thinking
here but I found it helpful to write out. Most of the criteria are
actually about the chosen domain, not the plan for attacking it.
Pieces we need
--------------
### An aligned metric
Our chosen task needs to come with an aligned evaluation metric based on
some expert humans or another source of ground truth. We'll use this to
evaluate how well our models do.
### A narrowly superhuman model
We need a model that is capable of doing better on the task than the
labelers who will provide our training data. (One way to check this is
to train a model using the aligned metric as a training signal, although that could be expensive.)
There are a few ways the labelers might be worse than the model:
* We might limit them to cap the amount of thinking time they have or
the amount of input they can see. The hope is that their
capability relative to the model thus becomes analogous to the
capacity of unrestricted future humans relative to fully
superhuman models; I think artificial restrictions will typically
make the analogy a bit strained, but we should still learn a lot.
* We might pick labelers that lack domain expertise relevant to the
task. Ajeya's post suggests coding, translation, economics, or physics; other possible domains include law or medicine. The hope is that this is analogous to a future with
superhuman models that have knowledge and concepts that humans
don't. If set up well, I think this could make for a very
interesting challenge. However, it might be hard to find
sufficiently powerful models for now.
### Headroom on the aligned metric
Baseline techniques for the task should fall short of optimal
performance (given only access to the labelers, not the aligned metric).
Moreover, they should fall short primarily because the objective is
misaligned, not merely because the model is incapable. There's a weaker
and a stronger version of this criterion:
* It might be the case that increasing the model's capability (by
increasing capacity or pretrained knowledge) does improve
performance on the aligned metric, but much better performance is
possible at a fixed level of capability. In other words, a more
aligned objective allocates capacity better. (This was the case
for the summarization project.) I think this is acceptable, but
I'm more excited about the strong version.
* It might be the case that more capable models don't do better at
all, or fall far short of optimal performance even in the limit.
Ideally, we'd be working in this regime: it likely means the
alignment gap is bigger, and I expect it's more analogous to the
future situation.
### A natural plan of attack
We need to have an approach that we expect will work, i.e. make our
model stronger than the labelers we're using to train it. Moreover, our
approach should be problem-driven and as simple as possible; I don't
think we should reach for complicated recursive decomposition
setups until it's clear that they are necessary.
Picking simple approaches may be slightly in tension with two other
goals, which I currently think are slightly less important:
* Generality: In a perfect world, we'd like our approach to be general rather
than depending heavily on specific features of the task. While generality is valuable, I think we should be okay doing an amount of
domain-specific work that would be typical for people trying to
solve the task, and it might be necessary to reach competitive
performance.
* Scalability: In a perfect world, we'd like our approach to keep working at
arbitrary levels of task difficulty and model capability. However,
for now I think we should be okay using approaches that we know
will stop scaling eventually; we should still learn a lot.
That said, I don't feel very confident about this prioritization; in the past I've prioritized generality and scalability much more.
Example: Q&A with I/O-restricted labelers
-----------------------------------------
One proposal for a sandwiching project is to try to do a closed-domain
question-answering task on documents that nearly fill up the model's
context, but train it using labelers that each only get to see short
snippets of the text (~200 tokens, say). The questions could be
reasonably simple, but for the task to be interesting they should
require piecing together information across multiple parts of the text.
Here's how I think this kind of project rates on the criteria above;
overall I'm hoping we could do somewhat better:
**Aligned metric: 5/5.** We can just use humans that get to look at the
full document and decide whether a question was answered properly.
**Narrowly superhuman model: 3/5.** The model will only be superhuman
compared to labelers restricted by the somewhat artificial input
limitation. Moreover, I think it may be difficult to find a question
distribution that can be solved by current models but is still
interesting: many questions could be answered just by picking out a
single small piece of text, but then the oversight becomes too easy; if
we pick something that requires aggregating multiple pieces of
information, our models may struggle.
**Headroom:** **2/5.** The unaligned baseline for this task is probably
either to zero-shot it from the base language model or to zero-shot it
from a question-answering model trained only on documents short enough
to be consumable in their entirety by our nerfed labelers. I worry that
it might be hard to do much better than those baselines even with a more
aligned training objective. In addition, we'd definitely meet only the
weaker version of the criterion: increasing model capacity will be one
of the best ways to do better on the task.
**Natural plan of attack: 4/5.** There are a number of possible
approaches. Here's a starting point suggested by William Saunders:
1. Train a judge model to check if a snippet of text supports an answer
to the question.
2. Train an "evidence finding" model via RL to look at the entire text
and pick the quote that's most likely to support an answer for the
judge.
3. Train an "answer selection" model via RL to pick an answer that's
most likely to have good supporting evidence.
The naturalness of this approach is pretty subjective, but once you're
given the artificial input length limitation, this is arguably a pretty
reasonable thing to do. That said, there might be simpler approaches.
Also, this decomposition strategy may fail to solve the most interesting
questions (if it takes a few hops to realize that a certain piece of
information is relevant), and it may not be fully aligned (if a snippet
of text is misleading when taken out of context).
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f859196e-164b-4442-be36-388160c70f63
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trentmkelly/LessWrong-43k
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LessWrong
|
Evidence against Learned Search in a Chess-Playing Neural Network
Introduction
There is a new paper and lesswrong post about "learned look-ahead in a chess-playing neural network". This has long been a research interest of mine for reasons that are well-stated in the paper:
> Can neural networks learn to use algorithms such as look-ahead or search internally? Or are they better thought of as vast collections of simple heuristics or memorized data? Answering this question might help us anticipate neural networks’ future capabilities and give us a better understanding of how they work internally.
and further:
> Since we know how to hand-design chess engines, we know what reasoning to look for in chess-playing networks. Compared to frontier language models, this makes chess a good compromise between realism and practicality for investigating whether networks learn reasoning algorithms or rely purely on heuristics.
So the question is whether Francois Chollet is correct with transformers doing "curve fitting" i.e. memorisation with little generalisation or whether they learn to "reason". "Reasoning" is a fuzzy word, but in chess you can at least look for what human players call "calculation", that is the ability to execute moves solely in your mind to observe and evaluate the resulting position.
To me this is a crux as to whether large language models will scale to human capabilities without further algorithmic breakthroughs.
The paper's authors, which include Erik Jenner and Stuart Russell, conclude that the policy network of Leela Chess Zero (a top engine and open source replication of AlphaZero) does learn look-ahead.
Using interpretability techniques they "find that Leela internally represents future optimal moves and that these representations are crucial for its final output in certain board states."
While the term "look-ahead" is fuzzy, the paper clearly intends to show that the Leela network implements an "algorithm" and a form of "reasoning".
My interpretation of the presented evidence is different, as discussed
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a13ddd75-fa8b-41f6-b195-93e699dd37f4
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trentmkelly/LessWrong-43k
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LessWrong
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AI race considerations in a report by the U.S. House Committee on Armed Services
Epistemic status: Quick and dirty. A surface level dive into a particular aspect of AI governance carried out over the course of one morning.
Context
> The U.S. House Committee on Armed Services is a standing committee of the United States House of Representatives. It is responsible for funding and oversight of the Department of Defense (DOD) and the United States Armed Forces, as well as substantial portions of the Department of Energy.
The Future of Defense Task Force is a subcommittee of the U.S. House Committee on Armed Services. They have released a report, available here, and also as the first item on their latest news page. The task force is manned by an equal number of Republicans and Democrats. Though this seems a priori unlikely, it could both be the case that this report is unrepresentative of the political forces in the US Congress, and that this particular committee holds little power.
References to AI race dynamics in the report
Bold added by me.
> Technological advancements in artificial intelligence and biotechnology will have an outsized impact on national security; the potential of losing this race to China carries significant economic, political, and ethical risks for the United States and our free democratic allies for decades to come. Winning this race requires a whole-of-nation approach where the distinct advantages of both America's private and public sector are harnessed and synthesized.
> Using the Manhattan Project as a model, the United States must undertake and win the artificial intelligence race by leading in the invention and deployment of AI while establishing the standards for its public and private use. Although the Department of Defense has increased investment in AI and established the Joint Artificial Intelligence Center to assist with the transition and deployment of AI capabilities, cultural resistance to its wider adoption remains.
> The stakes are high. Whoever achieves superiority in this technological race will enjo
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8f182f7e-1f50-46d9-b67c-d48fd0095059
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trentmkelly/LessWrong-43k
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LessWrong
|
Probability that the President would win election against a random adult citizen?
Suppose we magically intervene on the USA to make there be a snap election for the office of President. The incumbent (the existing president) goes up against X, where X is a randomly selected eligible person (e.g. adult, citizen, etc.) Voters have one month of campaigning before they decide, so X isn't a total stranger by the end.
What is the probability that the President wins?
Is the probability substantially different for Biden than it was for Trump? What about Obama?
What is the probability that the President loses in a massive landslide (getting e.g. only 40% of the vote or less)?
ETA: I've changed the title to stop giving the impression that I am looking for a yes or no answer. I'm not looking for a yes or no answer, I'm looking for a probability.
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3f409000-858f-4a01-956c-36f981a02498
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trentmkelly/LessWrong-43k
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LessWrong
|
[SEQ RERUN] Where Physics Meets Experience
Today's post, Where Physics Meets Experience was originally published on 25 April 2008. A summary (taken from the LW wiki):
> Meet the Ebborians, who reproduce by fission. The Ebborian brain is like a thick sheet of paper that splits down its thickness. They frequently experience dividing into two minds, and can talk to their other selves. It seems that their unified theory of physics is almost finished, and can answer every question, when one Ebborian asks: When exactly does one Ebborian become two people?
Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Which Basis Is More Fundamental?, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
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cb7c8da7-eb1b-4f41-bb7a-8a84d0b05ef3
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trentmkelly/LessWrong-43k
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LessWrong
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Bad Religion
> Accusing something of being like a religion is like a religion.
>
> It’s a shared cultural practice, that reinforces a group’s confidence in the parts of their worldview that provide an order to existence, while facilitating dismissal of contradictory evidence.
>
> – Rob Wiblin
A couple of weeks ago Tyler Cowen accused the Rationality community of being a religion. I used to think that Tyler Cowen is a brilliant thinker™. After he said all that, I’m absolutely sure of it!
I initially read it as pure trolling: a lot of rationalists came from online atheist circles, and our “Holy Scripture” has a whole book about how to avoid becoming a religion. Calling rationality a religion is a cheaper shot than Jim Beam. See also: anyone with 10 karma on r/pol can free associate for 30 seconds and contrive a clever sounding argument why social justice warriors are the real racists. But calling SJW “racist” isn’t constructive criticism, it’s just what you say to get people riled up.
Tyler, however, knows exactly whom to troll. Accusing rationalists of anything whatsoever is great fun. People who dislike rationalists will retweet and comment “lol thos n3rds”, while actual rationalists will write long essays interpreting anything you say in the most charitable possible terms while suggesting that your disagreements lie merely in diverging readings of Plato’s Republic. Calling out rationalists is an enjoyable pastime, I used to do it myself on LessWrong!
But at the end of the day, Tyler actually loves the rationality community and wants us to be happy. With a single remark, he made rationalists bond together, rekindled our community pride, and provided all of us with something fun to blog about for a week.
Bravo, sir!
Credit: Marcia Hunter
----------------------------------------
There’s just one problem: someone will actually start in rationality hoping to find a religion, and they’ll be hella disappointed. Rationality is a crap religion.
The first job of any religion w
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4c59ddc7-6bef-4db5-b4d1-6a03523969c7
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StampyAI/alignment-research-dataset/special_docs
|
Other
|
Pascal’s Mugging
Pascal’s mugging
NICKBOSTROM
In some dark alley ...
Mugger : Hey, give me your wallet.
Pascal : Why on Earth would I want to do that?
Mugger : Otherwise I’ll shoot you.
Pascal : But you don’t have a gun.
Mugger : Oops! I knew I had forgotten something .
Pascal : No wallet for you then. Have a nice evening.
Mugger : Wait!
Pascal : Sigh.
Mugger : I’ve got a business proposition for you. ...How about you give me
your wallet now? In return, I promise to come to your house tomorrow and
give you double the value of what’s in the wallet. Not bad, eh? A 200%
return on investment in 24 hours.
Pascal : No way.
Mugger : Ah, you don’t believe that I will be as good as my word? One can’t
be too careful these days. ...Tell you what: give me your wallet, and I come
to your house tomorrow and pay you 10 times its value.
Pascal : Sorry.
Mugger : OK, let me ask you something. Many people are dishonest, but
some people are honest. What probability do you give to the hypothesisthat I will keep my promise?
Pascal : 1 in a 1,000?
Mugger : Great! OK, so give me your wallet, and tomorrow I give you 2,000
times the value of its contents. The expectation value is greatly to your
advantage.
Pascal : There are 10 livres in my wallet. If we made a deal for you to take the
wallet and bring me 10 times the value of its contents tomorrow, then maybe
there’s a 1-in-a-1,000 chance that I would see the 100 livres you owe. But I’drate the chances that you will deliver on a deal to return me 20,000 livres
much lower. I doubt you even have that much money.
Mugger : Your scepticism is understandable, although in this particular case it
happens to be misguided. For you are M. Pascal if I’m altogether not mis-
taken? And I’ve heard that you’re a committed expected-Utility maximizer,
and that your Utility function is aggregative in terms of happy days of life.Is that not so?
Analysis Vol 69 | Number 3 | July 2009 | pp. 443–445 doi:10.1093/analys/anp062
/C223The Author 2009. Published by Oxford University Press on behalf of The Analysis Trust . All rights reserved.
For Permissions, please email: journals.permissions@oxfordjournals.orgpascal’s mugging |443
444 | nick bostrom
Pascal : It is. My Utility function is unbounded. And I deem two days of
happy life twice as good as one such day; and 2,000 days twice as good as
1,000 days. I don’t believe in risk aversion or temporal discounting.
Mugger : Excellent. I don’t necessarily have to know that you reject risk
aversion and temporal discounting, but it makes things easier. Well, have I
got good news for you! I have magical powers. I can give you any finite
amount of money that you might ask for tonight. What’s more, I can give
you any finite amount of Utility that I choose to promise you tonight.
Pascal : And I should believe you why?
Mugger : Trust me! OK, I realize this does not give you conclusive evidence,
but surely it counts a least a little bit in favour of the truth of what I am
asserting. Honestly, I really do have these powers.
Pascal : Your conduct tonight has not inspired me with confidence in your
honesty.
Mugger: OK, OK, OK, OK. But isn’t it possible that I am telling the truth?
Pascal :It ispossible that you have the magic powers that you claim to have,
but let me tell you, I give that a very, very low probability .
Mugger : That’s fine. But tell me, how low a probability exactly? Remember,
you might think it all seems implausible, but we are all fallible, right? And
you must admit, from what you’ve already seen and heard, that I am a rather
atypical mugger. And look at my pale countenance, my dark eyes; and note
that I’m dressed in black from top to toe. These are some of the telltale signs
of an Operator of the Seventh Dimension. That’s where I come from and
that’s where the magic work gets done.
Pascal : Gee ... OK, don’t take this personally, but my credence that you have
these magic powers whereof you speak is about one in a quadrillion.
Mugger : Wow, you are pretty confident in your own ability to tell a liar from
an honest man! But no matter. Let me also ask you, what’s your probability
that I not only have magic powers but that I will also use them to deliver on
any promise – however extravagantly generous it may seem – that I might
make to you tonight?
Pascal : Well, if you really were an Operator from the Seventh Dimension as
you assert, then I suppose it’s not such a stretch to suppose that you might
also be right in this additional claim. So, I’d say one in 10 quadrillion.
Mugger : Good. Now we will do some maths. Let us say that the 10 livres that
you have in your wallet are worth to you the equivalent of one happy day.
Let’s call this quantity of good 1 Util. So I ask you to give up 1 Util. In return,
I could promise to perform the magic tomorrow that will give you an extra
10 quadrillion happy days, i.e. 10 quadrillion Utils. Since you say there is a 1
in 10 quadrillion probability that I will fulfil my promise, this would be a fair
deal. The expected Utility for you would be zero. But I feel generous this
evening, and I will make you a better deal: If you hand me your wallet, I will
perform magic that will give you an extra 1,000 quadrillion happy days
of life .
Pascal : I admit I see no flaw in your mathematics.
Mugger : This is my final offer. You’re not going to pass up a deal that we
have just calculated will give you an expected Utility surplus of nearly 100
Utils, are you? That’s the best offer you are likely to see this year.
Pascal : Is this legitimate? You know, I’ve committed myself to trying to be a
good Christian.
Mugger : Of course it’s legitimate! Think of it as foreign trade. Your currency
is worth a lot in the Seventh Dimension. By agreeing to this transaction, yougive a major boost to our economy. Oh, and did I mention the children?If only you could see the faces of the sweet little orphans who will be made so
much better off if we get this influx of hard currency – and there are so many
of them, so very, very, very many ....
Pascal : I must confess: I’ve been having doubts about the mathematics of
infinity. Infinite values lead to many strange conclusions and paradoxes. You
know the reasoning that has come to be known as ‘Pascal’s Wager’? Between
you and me, some of the critiques I’ve seen have made me wonder whether Imight not be somehow confused about infinities or about the existence ofinfinite values ...
Mugger : I assure you, my powers are strictly finite. The offer before you does
not involve infinite values in any way. But now I really must be off; I have anassignation in the Seventh Dimension that I’d rather not miss. Your wallet,
please!
Pascal hands over his wallet .
Mugger : Pleasure doing business. The magic will be performed tomorrow, as
agreed.
1
Oxford University
Future of Humanity Institute
Faculty of Philosophy & James Martin 21st Century School
Suite 8, Littlegate House, 16/17 St Ebbe’s Street
Oxford OX1 1PT, UK
nick.bostrom@philosophy.ox.ac.uk
1 Related scenarios have recently been discussed informally among various people. Eliezer
Yudkowsky named the problem ‘Pascal’s mugging’ in a post on the Overcoming Bias blog
(http://www.overcomingbias.com/2007/10/pascals-mugging.html ). I am grateful to Toby
Ord and Rebecca Roache for comments.pascal’s mugging |445
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56c6fcda-a85b-4290-9730-85299f361081
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trentmkelly/LessWrong-43k
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LessWrong
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AXRP Episode 1 - Adversarial Policies with Adam Gleave
YouTube link
This podcast is called AXRP, pronounced axe-urp and short for the AI X-risk Research Podcast. Here, I (Daniel Filan) have conversations with researchers about their papers. We discuss the paper and hopefully get a sense of why it's been written and how it might reduce the risk of artificial intelligence causing an existential catastrophe: that is, permanently and drastically curtailing humanity's future potential.
In this episode, Adam Gleave and I talk about adversarial policies. Basically, in current reinforcement learning, people train agents that act in some kind of environment, sometimes an environment that contains other agents. For instance, you might train agents that play sumo with each other, with the objective of making them generally good at sumo. Adam's research looks at the case where all you're trying to do is make an agent that defeats one specific other agents: how easy is it, and what happens? He discovers that often, you can do it pretty easily, and your agent can behave in a very silly-seeming way that nevertheless happens to exploit some 'bug' in the opponent. We talk about the experiments he ran, the results, and what they say about how we do reinforcement learning.
Daniel Filan: Hello everybody, today I'll be speaking with Adam Gleave. Adam is a grad student at UC Berkeley. He works with the Center for Human Compatible AI, and he's advised by Professor Stuart Russell. Today, Adam and I are going to be talking about the paper he wrote, Adversarial policies: Attacking deep reinforcement learning. This was presented at ICLR 2020, and the co-authors are Michael Dennis, Cody Wild, Neel Kant, Sergey Levine and Stuart Russell. So, welcome Adam.
Adam Gleave: Yeah, thanks for having me on the show Daniel.
Daniel Filan: Okay, so I guess my first question is, could you summarize the paper? What did you do, what did you find?
Adam Gleave: Sure. So, the basic premise of the paper is that we're really concerned about adversarial attacks i
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6b8d844e-756a-4b06-8c43-9ddd6203a010
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LDJnr/LessWrong-Amplify-Instruct
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LessWrong
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"Scenario 1: Barry is a famous geologist. Charles is a fourteen-year-old juvenile delinquent with a long arrest record and occasional psychotic episodes. Barry flatly asserts to Arthur some counterintuitive statement about rocks, and Arthur judges it 90% probable. Then Charles makes an equally counterintuitive flat assertion about rocks, and Arthur judges it 10% probable. Clearly, Arthur is taking the speaker’s authority into account in deciding whether to believe the speaker’s assertions.Scenario 2: David makes a counterintuitive statement about physics and gives Arthur a detailed explanation of the arguments, including references. Ernie makes an equally counterintuitive statement, but gives an unconvincing argument involving several leaps of faith. Both David and Ernie assert that this is the best explanation they can possibly give (to anyone, not just Arthur). Arthur assigns 90% probability to David’s statement after hearing his explanation, but assigns a 10% probability to Ernie’s statement.It might seem like these two scenarios are roughly symmetrical: both involve taking into account useful evidence, whether strong versus weak authority, or strong versus weak argument.But now suppose that Arthur asks Barry and Charles to make full technical cases, with references; and that Barry and Charles present equally good cases, and Arthur looks up the references and they check out. Then Arthur asks David and Ernie for their credentials, and it turns out that David and Ernie have roughly the same credentials—maybe they’re both clowns, maybe they’re both physicists.Assuming that Arthur is knowledgeable enough to understand all the technical arguments—otherwise they’re just impressive noises—it seems that Arthur should view David as having a great advantage in plausibility over Ernie, while Barry has at best a minor advantage over Charles.Indeed, if the technical arguments are good enough, Barry’s advantage over Charles may not be worth tracking. A good technical argument is one that eliminates reliance on the personal authority of the speaker.Similarly, if we really believe Ernie that the argument he gave is the best argument he could give, which includes all of the inferential steps that Ernie executed, and all of the support that Ernie took into account—citing any authorities that Ernie may have listened to himself—then we can pretty much ignore any information about Ernie’s credentials. Ernie can be a physicist or a clown, it shouldn’t matter. (Again, this assumes we have enough technical ability to process the argument. Otherwise, Ernie is simply uttering mystical syllables, and whether we “believe” these syllables depends a great deal on his authority.)So it seems there’s an asymmetry between argument and authority. If we know authority we are still interested in hearing the arguments; but if we know the arguments fully, we have very little left to learn from authority.Clearly (says the novice) authority and argument are fundamentally different kinds of evidence, a difference unaccountable in the boringly clean methods of Bayesian probability theory.1 For while the strength of the evidences—90% versus 10%—is just the same in both cases, they do not behave similarly when combined. How will we account for this?Here’s half a technical demonstration of how to represent this difference in probability theory. (The rest you can take on my personal authority, or look up in the references.)If P(H|E1) = 90% and P(H|E2) = 9%, what is the probability P(H|E1,E2)? If learning E1 is true leads us to assign 90% probability to H, and learning E2 is true leads us to assign 9% probability to H, then what probability should we assign to H if we learn both E1 and E2? This is simply not something you can calculate in probability theory from the information given. No, the missing information is not the prior probability of H. The events E1 and E2 may not be independent of each other.Suppose that H is “My sidewalk is slippery,” E1 is “My sprinkler is running,” and E2 is “It’s night.” The sidewalk is slippery starting from one minute after the sprinkler starts, until just after the sprinkler finishes, and the sprinkler runs for ten minutes. So if we know the sprinkler is on, the probability is 90% that the sidewalk is slippery. The sprinkler is on during 10% of the nighttime, so if we know that it’s night, the probability of the sidewalk being slippery is 9%. If we know that it’s night and the sprinkler is on—that is, if we know both facts—the probability of the sidewalk being slippery is 90%.We can represent this in a graphical model as follows:Whether or not it’s Night causes the Sprinkler to be on or off, and whether the Sprinkler is on causes the sidewalk to be Slippery or unSlippery.The direction of the arrows is meaningful. Say we had:This would mean that, if I didn’t know anything about the sprinkler, the probability of Nighttime and Slipperiness would be independent of each other. For example, suppose that I roll Die One and Die Two, and add up the showing numbers to get the Sum:If you don’t tell me the sum of the two numbers, and you tell me the first die showed 6, this doesn’t tell me anything about the result of the second die, yet. But if you now also tell me the sum is 7, I know the second die showed 1.Figuring out when various pieces of information are dependent or independent of each other, given various background knowledge, actually turns into a quite technical topic. The books to read are Judea Pearl’s Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference and Causality: Models, Reasoning, and Inference. (If you only have time to read one book, read the first one.)If you know how to read causal graphs, then you look at the dice-roll graph and immediately see:P(Die 1,Die 2) = P(Die 1) ✕ P(Die 2)P(Die 1,Die 2|Sum) ≠ P(Die 1)|Sum) ✕ P(Die 2|Sum) .If you look at the correct sidewalk diagram, you see facts like:P(Slippery|Night) ≠ P(Slippery)P(Slippery|Sprinkler) ≠ P(Slippery)P(Slippery|Night,Sprinkler) = P(Slippery|Sprinkler) .That is, the probability of the sidewalk being Slippery, given knowledge about the Sprinkler and the Night, is the same probability we would assign if we knew only about the Sprinkler. Knowledge of the Sprinkler has made knowledge of the Night irrelevant to inferences about Slipperiness.This is known as screening off, and the criterion that lets us read such conditional independences off causal graphs is known as D-separation.For the case of argument and authority, the causal diagram looks like this:If something is true, then it therefore tends to have arguments in favor of it, and the experts therefore observe these evidences and change their opinions. (In theory!)If we see that an expert believes something, we infer back to the existence of evidence-in-the-abstract (even though we don’t know what that evidence is exactly), and from the existence of this abstract evidence, we infer back to the truth of the proposition.But if we know the value of the Argument node, this D-separates the node “Truth” from the node “Expert Belief” by blocking all paths between them, according to certain technical criteria for “path blocking” that seem pretty obvious in this case. So even without checking the exact probability distribution, we can read off from the graph that:P(truth|argument,expert) = P(truth|argument) .This does not represent a contradiction of ordinary probability theory. It’s just a more compact way of expressing certain probabilistic facts. You could read the same equalities and inequalities off an unadorned probability distribution—but it would be harder to see it by eyeballing. Authority and argument don’t need two different kinds of probability, any more than sprinklers are made out of ontologically different stuff than sunlight.In practice you can never completely eliminate reliance on authority. Good authorities are more likely to know about any counterevidence that exists and should be taken into account; a lesser authority is less likely to know this, which makes their arguments less reliable. This is not a factor you can eliminate merely by hearing the evidence they did take into account.It’s also very hard to reduce arguments to pure math; and otherwise, judging the strength of an inferential step may rely on intuitions you can’t duplicate without the same thirty years of experience.There is an ineradicable legitimacy to assigning slightly higher probability to what E. T. Jaynes tells you about Bayesian probability, than you assign to Eliezer Yudkowsky making the exact same statement. Fifty additional years of experience should not count for literally zero influence.But this slight strength of authority is only ceteris paribus, and can easily be overwhelmed by stronger arguments. I have a minor erratum in one of Jaynes’s books—because algebra trumps authority.1See “What Is Evidence?” in Map and Territory."
This is a snippet extracted from a publication on LessWrong by a site member.The above is a chunk of text from a post created by a user on the website LessWrong.
Can you further elaborate on the concept of "screening off" in the context of argument and authority, and provide a real-world example where this concept is applicable?
"Screening off" is a concept in probability theory and causal inference, often used in Bayesian networks, which refers to the condition where, given the knowledge of a certain variable, information about another variable no longer provides any additional insight about a third one.
In the context of argument and authority, the concept of "screening off" can be understood as follows: If we have a solid argument (with evidence and logic) for a certain claim, then the authority of the person making the claim becomes irrelevant. The argument itself "screens off" the effect of the authority on our belief in the claim.
Let's take a real-world example to illustrate this. Suppose a renowned physicist and a high school science student both present an argument about a complex physics concept, say, the theory of relativity.
If both of them provide the same solid, well-reasoned argument with proper evidence, then the authority of the physicist is "screened off". That is, the argument itself is strong enough that the credibility or authority of the person presenting it doesn't provide any additional information about the truth of the claim.
However, if the arguments are not equally solid - for instance, if the student's argument has logical flaws or lacks evidence - then the authority of the physicist is not screened off. In this case, the physicist's authority would contribute to our belief in the validity of the claim, because the argument itself is not strong enough to stand on its own.
It's important to note that "screening off" doesn't imply that authority is always irrelevant. It simply suggests that the strength of an argument can make authority irrelevant in certain situations. Authority can still be a useful guide in the absence of a solid argument or when we lack the ability to fully evaluate the argument ourselves.
Discuss the limitations of the "screening off" concept when applied to real-world scenarios where the credibility of the authority is in question.
The "screening off" concept, while powerful in theory, does have its limitations when applied to real-world scenarios, particularly when the credibility of the authority is in question. Here are a few key limitations:
1. **Assumption of Equal Understanding**: The "screening off" concept assumes that the listener or observer has the capacity to fully understand and evaluate the argument. In many real-world scenarios, this is not the case. For instance, a layperson may not have the expertise to understand a complex scientific argument, and therefore, the credibility of the authority presenting the argument becomes crucial.
2. **Potential for Misinformation**: If the authority's credibility is in question, it may be because they have a history of spreading misinformation or have a bias. In such cases, even a seemingly solid argument presented by them might be based on incorrect premises or skewed data. The "screening off" concept might not adequately account for this.
3. **Influence of Persuasion Tactics**: Real-world arguments often involve persuasion tactics that can make an argument seem more convincing than it actually is. An authority with questionable credibility might be particularly skilled at such tactics. This can complicate the application of the "screening off" concept, as the apparent strength of the argument might be due more to the persuasion tactics than to its inherent validity.
4. **Complexity of Real-World Issues**: Many real-world issues are multifaceted and complex, with multiple valid perspectives and arguments. In such cases, the credibility of the authority can be important in weighing the different arguments and deciding which ones to give more credence to.
5. **Incomplete Information**: In many situations, we don't have all the information needed to fully evaluate an argument. We might not know all the relevant facts, or we might not be aware of counterarguments or alternative perspectives. In such cases, the credibility of the authority can provide useful additional information.
In conclusion, while the "screening off" concept is a useful tool in understanding how arguments and authority can influence our beliefs, it has limitations and should be applied with caution in real-world scenarios.
Considering the limitations of the "screening off" concept, how might we modify or supplement it to better account for real-world complexities, particularly in situations where the credibility of the authority is questionable?
To better account for real-world complexities and situations where the credibility of the authority is questionable, we could consider the following modifications or supplements to the "screening off" concept:
1. **Incorporate a Credibility Factor**: We could add a credibility factor to the concept, which adjusts the weight given to an argument based on the credibility of the authority presenting it. This factor could be determined by factors such as the authority's track record, their expertise in the relevant field, and any potential biases they may have.
2. **Consider the Quality of the Argument**: Instead of treating all arguments as equal, we could assess the quality of the argument itself. This could involve evaluating the logic of the argument, the strength and relevance of the evidence provided, and the consideration of counterarguments and alternative perspectives.
3. **Use Multiple Sources of Information**: To counter the potential for misinformation or bias from a single authority, we could seek out multiple sources of information. This could involve listening to arguments from different authorities, seeking out peer-reviewed research, or consulting with independent experts.
4. **Apply Critical Thinking Skills**: We could supplement the "screening off" concept with the application of critical thinking skills. This could involve questioning the assumptions of the argument, evaluating the logic and consistency of the argument, and considering the implications of the argument.
5. **Consider the Complexity of the Issue**: For complex issues, we could acknowledge that a single argument or authority may not provide a complete picture. We could consider multiple arguments and perspectives, and recognize that our understanding of the issue may evolve as we gain more information.
In conclusion, while the "screening off" concept provides a useful starting point for understanding how arguments and authority influence our beliefs, it can be supplemented with these strategies to better account for real-world complexities and the credibility of authorities.
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73379529-954c-496d-a6da-c052171e7663
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trentmkelly/LessWrong-43k
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LessWrong
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[Requesting Advice] Applying Instrumental Rationality to College Course Selection Dilemma
I'm faced with a dilemma and need a big dose of instrumental rationality. I'll describe the situation:
This fall, I'm entering my first semester of college. I'm aiming to graduate in 3-4 years with a Mathematics B.S. In order for my course progression to go smoothly, I need to take Calculus I Honors this fall and Calculus II in the spring. These two courses serve as a prerequisite bottleneck. They prevent me from taking higher level math courses.
My SAT scores have exempted me from all placement tests, including the math. But without taking a placement test, the highest any math SAT score can place me into is Pre-Calculus Honors, which is one level below what I want to take in the fall. The course progression goes Pre-Calculus Honors to Calc I Honors to Calc II Honors.
So in order to take Calc I Honors in the fall, I either need to:
(1) Score high enough on a College-Level Math placement test or
(2) Forgo the test and take Pre-Calc Honors for 9 weeks this summer
I've taken both pre-calculus and calculus in high school. I've also been studying precalculus material over the past few days, relearning a lot of what I've either forgotten or wasn't taught in class. If I decide to take the test, I'm pretty confident I'll place into Calculus I. I'd estimate that chance being within 0.8, plus or minus 0.1. If I pass the test, I'll save 9 weeks of studying in the summer and use them to prepare for classes I'll be taking in the fall. I'd also free me up to take another summer class worth 4 credits and fulfill a prerequisite.
But if I decide to forgo the test and take Precalc this summer, I'm also pretty confident I'll do very well in the class. I'd confidently wager above a 90%. The class would ensure I've got the material down better than the placement test and would also give me my first six credits.
The questions going through my mind right now include: How can I best decide between these two options? How can I compare the heterogeneous benefits/costs? Are there any
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cf13e81a-de61-40c3-8334-28caff9fd10f
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trentmkelly/LessWrong-43k
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LessWrong
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Destroying Insecurity and Boosting Confidence Through Your Interests and Values
I have hypothesis that diversifying your interests, values and desires makes you much less insecure and anxious while boosting your confidence.
Personal Example and How Idea Came to Being
In recent months I had huge increase in my confidence in all areas of my life. But I haven't got any better at them. But my social, business, intellectual and athletic confidence is booming like never before. Anxiety and overthinking is gone, too. I care less, I am more happy and I seem to accomplish everything with so much ease. At first I thought I am just getting older so I care less and am getting naturally more secure and confident. But I wasn't happy with that as full explanation. While I am sure there might be multiple factors at play, I think diversifying my interests, values and desires did the trick.
When I was starting my gym rat journey and saw all buff guys being more jacked and stronger than me, I felt insecure. When I got into business and at some business conference I saw everyone making more zeroes per month than I did per year, it made me feel insecure. When I started cooking and I saw and tasted dishes of other cooks, it made me feel insecure about my skills.
But then came tipping point. At some point, I became slightly above average in all of those things. Never the best, but good enough than whenever I came to my gym I was richest entrepreneur, best cook, best at BJJ, knew most about quantum physics etc. Even though I wasn't strongest and best looking in my gym, I was most confident because gym wasn't my single interest from which I derived my values and self-worth. I was looking at myself as a whole of all other skills and experiences. I stopped being too focused on one single domain. And this went for all other areas. I meet with multimillionaires but I am more confident in conversation than they are, because I don't perceive myself as being worse at business than them, but as being more fit, better at BJJ, better at physics, better cook etc. etc.
Wha
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13049420-d00f-4129-a0f2-86290fb7de92
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trentmkelly/LessWrong-43k
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LessWrong
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o1: A Technical Primer
> TL;DR: In September 2024, OpenAI released o1, its first "reasoning model". This model exhibits remarkable test-time scaling laws, which complete a missing piece of the Bitter Lesson and open up a new axis for scaling compute. Following Rush and Ritter (2024) and Brown (2024a, 2024b), I explore four hypotheses for how o1 works and discuss some implications for future scaling and recursive self-improvement.
The Bitter Lesson(s)
The Bitter Lesson is that "general methods that leverage computation are ultimately the most effective, and by a large margin." After a decade of scaling pretraining, it's easy to forget this lesson is not just about learning; it's also about search.
OpenAI didn't forget. Their new "reasoning model" o1 has figured out how to scale search during inference time. This does not use explicit search algorithms. Instead, o1 is trained via RL to get better at implicit search via chain of thought (CoT). This was the simplest possible way to incorporate search into LLMs, and it worked.
The key consequence is that OpenAI has opened up a new frontier of the bitter lesson: test-time scaling (see figure above). The original scaling laws taught us how to exchange training-time compute for better predictions. These new test-time scaling laws teach us how to exchange inference-time compute for better decisions.
This removes one of the last hard barriers to AGI.
The original training-time scaling laws defined an era. Test-time scaling laws define the next.
What we know about o1
OpenAI has been characteristically quiet about the details, so we don't know exactly how o1 works. But they haven't been silent.
What OpenAI has told us
Rush points out that we can reconstruct quite a lot from the announcement:
> Our large-scale reinforcement learning algorithm teaches the model how to think productively using its chain of thought in a highly data-efficient training process.
This tells us three key things:
* Chain of Thought (CoT): o1 performs implici
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769ed8e7-211e-431c-878b-45ecdcd8fc4b
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trentmkelly/LessWrong-43k
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LessWrong
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LW's image problem: "Rationality" is suspicious
Concerning Less Wrong's tagline, consider this plausible reaction of someone looking at LW for the first time:
> Cut the crap, nobody cares about rationality in the abstract. Just tell me what view you're trying to push under the guise of presenting it as the only "rational" one.
And here are two real quotes from 2009:
> [concerning the ban on SIAI discussion during the first weeks of LW] I think it was so that newcomers wouldn't think that LW are a bunch of fringe technophiles that just want to have their cause associated with rationality.
And in reply:
> But that's pretty much what LW is, no? I've long suspected that "rationality," as discussed here, was a bit of a ruse designed to insinuate a (misleading) necessary connection between being rational and supporting transhumanist ideals.
The quoted text speaks for itself really. So therefore I think LW's admins/web designers should seriously consider replacing the rationality tagline with something more savory.
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90c41a55-f203-4d87-964c-b928ceb68e84
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trentmkelly/LessWrong-43k
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LessWrong
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Are Many Worlds and the Multiverse the Same Idea [LINK]
http://blogs.discovermagazine.com/cosmicvariance/2011/05/26/are-many-worlds-and-the-multiverse-the-same-idea/
I am woefully unable to judge the quality of this claim, but if true, it seems that many "fine-tuning" questions would be officially answered. It also seems elegant to me, but I admit I don't understand in the least bit the discussion of black hole complementarity.
Discussion? Help?
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811b4ee2-9468-4463-a296-6d9d2bcd1778
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Path dependence in ML inductive biases
In this post, we define path dependence as the sensitivity of a model's behavior to the details of the training process and training dynamics.[[1]](#fnj9an6vasz5d) High path-dependence indicates that small changes to the training process can cause significant changes to how the final model generalizes (such as the details of off-distribution behavior). It implies that inner alignment can be reasoned about by thinking about what the model looks like at various stages of training, and how its structure is affected by the immediate pressures of gradient descent. It implies that early-training interventions can be quite potent in shaping how a model turns out, and that a proper theory of inductive bias must reason about the order in which features are learned (where features learned faster/earlier can “screen off” the need for other implementations of a similar niche, in a way that affects the final model).
In contrast, a world with low path dependence allows us to reason about inductive bias in terms of priors and updates, sparing the details of training dynamics. It is more pessimistic about the ability to steer the model’s ontology through early interventions, believing instead that the final result is overdetermined. As Evan discusses in a [previous post](https://www.lesswrong.com/posts/mKBfa8v4S9pNKSyKK/homogeneity-vs-heterogeneity-in-ai-takeoff-scenarios), it makes us less worried about variance in alignment outcomes between labs, since small changes to the training procedure don’t strongly affect alignment outcomes.
Possible mechanistic reasons for high path dependence would include the existence of distinct stable ontologies, the ability for early features to kill gradients, and the difficulty of building highly serial features whose components aren't independently useful. Mechanistic reasons for low path dependence would include [grokking-like phase transitions](https://www.lesswrong.com/posts/N6WM6hs7RQMKDhYjB/a-mechanistic-interpretability-analysis-of-grokking) which wipe out early circuits, [overdetermination of correct ontologies](https://www.lesswrong.com/posts/cy3BhHrGinZCp3LXE/testing-the-natural-abstraction-hypothesis-project-intro), and an abundance of low loss paths between seemingly dissimilar solutions.[[2]](#fneycsnzrex17)
We remain mostly agnostic about which world we are in. The purpose of this post is to elucidate the various strands of path dependence, and their implications for alignment. We hope it will encourage people to run experiments determining where reality falls on these spectra.
**Path dependence**
-------------------
Fundamentally, path dependence is about the sensitivity of a model's behavior to the details of the training process and training dynamics. In this section, we enumerate concrete claims which constitute path dependence. It should be emphasized that it is conceivable for these properties to vary separately, and that we lump them together because we hypothesize without proof that they are correlated.
In a world with maximum path dependence:
* The outcome of training a DNN depends strongly on what representations and circuits are learned early in training.
* This set of circuits varies between runs and is highly sensitive to the details of the training procedure.
* High-level properties of the model are very likely to stay roughly constant during the late phases of training. We can assess these properties at the middle of training, and our assessment will be highly predictive of the final properties, even if the model gets much smarter.
* Circuits learned early in training are never outright deleted. Parts of the task which were solved early in training continue to be done *with the same circuits* in the final model.
* The inductive bias doesn’t have a closed form. To figure out which of two final models is more likely, you *must* reason explicitly about the order in which things are learned; you *cannot* just look at simplicity or depth or any other static measure of the final circuits.
By contrast, in a world with minimum path dependence:
* The outcome of training is highly consistent across runs and similar setups, and the order in which circuits are learned doesn’t matter.
* High-level properties often change drastically in the late phases of training.
* Circuits are frequently destroyed and replaced with new ones. It is common for a new set of powerful circuits to replace an older set of clunky ad-hoc ones, and to completely displace them.
* Idealized priors like the “[circuit prior](https://www.alignmentforum.org/posts/7ygmXXGjXZaEktF6M/towards-a-better-circuit-prior-improving-on-elk-state-of-the)” might be implementable in nearly-pure form by setting up training appropriately.
### **Diagrams for training dynamics**
**Low path dependence #1:**
Above, we see an simple example of low-path-dependence training dynamics -- The order in which features are learned doesn't matter, so a change to the training procedure which makes A learned faster than B won't change the final outcome.
**High path dependence #1:**
Here, there are two reachable end states, and early interventions can affect which one we end up in. Features C and D are mutually exclusive, and are facilitated by A and B respectively. There might be a grokking-like path from one of the apparent final states to the other, but training is stopped before it occurs.
If feature "D" is associated with deceptive alignment, and "C" with corrigibility, then detecting one or the other in the second-to-last phase is a reliable indicator of the final behavior.
**Low path dependence #2:**
In this model, there appears to be path dependence at first, but component E destroys C and D when it arises. Any safety properties associated with C are destroyed at the last minute, despite appearing stable until then.
**Reasons to care about path dependence**
-----------------------------------------
In this section, we go through some implications of path dependence for alignment. In general, higher path dependence is good news for early evaluations and interventions, and (more speculatively) for being able to shape model ontology. However, it is bad news for consistency across labs, and for solutions to inner alignment which rely on the construction of pure theoretical priors (like circuit priors).
**I. Effectiveness of mid-training evaluation**
High path dependence makes mid-training evaluations more reliable, due to the "[stickyness](https://www.alignmentforum.org/posts/a2Bxq4g2sPZwKiQmK/sticky-goals-a-concrete-experiment-for-understanding)"/persistence of model properties. Suppose we want to do things like:
* Check for myopia
* Extract the goals of a system
* Check the “control structure” of a model (the broad way in which it factors the task, like a diagram made of interacting modules with particular roles)
Let's say we can do one of these things in the early and middle stages of training, when the model is somewhat simpler / less intelligent, but *not* on the final model. In a high path-dependence world, there is a good chance that the result of the early check is also valid for the final model. In a low path-dependence world, the early check is invalidated by phase transitions, circuit destruction, fundamental reorganizations of the model to an overdetermined "correct" structure, etc.
**II. Generalization of concepts and goals**
A major issue in alignment is the question of *how* a model will generalize from the domain where we are able to label outcomes to the domain where we can't. Here are a some examples:
* You train AIs to automate a factory. Eventually they become smart enough that their action space includes things like "build a robot army" or "build nanotech" or "hack the internet to get more compute". How will the goal acquired from training on simpler tasks generalize to this new level of intelligence?
* You train a question answerer, and it learns some question-answering methodology[[3]](#fn1m5gfyo7xof). You can use <magic method> (Christiano et al. 2025) to confirm that its internal control flow corresponds to "[True honest answering](https://www.alignmentforum.org/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge)" w.r.t. to its current world model. Unfortunately, <magic method> only works when the AI is below a certain intelligence threshold. Will the internal structure generalize properly as it gets smarter, meaning that we retain the "honest answering" property?
High path-dependence is beneficial here, implying continuity of properties like "honest answering".
**III. Effectiveness of interventions**
If mid-training evaluations are valid, then a further desideratum would be for mid-training *interventions* to be effective. This could matter for certain forms of adversarial training—you can imagine a world where mid-training diagnostics work fine on natural models, but adversarial training at the middle stages just builds "shallow patches" which aren't well embedded in the main control flow and are erased by training dynamics in the later stages of training. High path dependence is again desirable here.
**IV. Consistency across labs**
In a high-path-dependence world, we might need to enforce a high degree of exactness in how training procedures are implemented by different labs (especially if alignment is more sensitive to changes in training procedure than capabilities are, which seems likely). On the other hand, path dependence might *help* us here if alignment and capabilities are closely coupled, in the sense that copying a training procedure and changing it would also break capabilities.
**V. Constructability of priors**
In a high-path-dependence world, it is hard to construct something close to a “pure” theoretical prior. This could be bad if the most promising alignment solution ends up needing a specific prior. Examples of theoretical priors are the Solomonoff prior (punishes program length), the circuit prior (punishes circuit size), and the speed prior (punishes serial runtime). If SGD is approximately a Bayesian sampler,[[4]](#fnjgxgayd49qm) then it could be possible approximate certain combinations of these priors. However, if path dependence is high enough, then these priors should be almost impossible to construct, due to vast differences in the findability of solutions (the existence of training paths to these solutions, and whether these paths are fast enough to not have their gradients stolen/ruined by other features).
**VI. Likelihood of inner misalignment**
Arguments around the likelihood of deceptive alignment and of misaligned proxies often depend strongly on path dependence. See [Evan's recent post](https://www.alignmentforum.org/posts/A9NxPTwbw6r6Awuwt/how-likely-is-deceptive-alignment) for a thorough discussion of how path dependence affects deceptive alignment.
**Specific aspects of training dynamics**
-----------------------------------------
At the moment, “path dependence” is really a cluster of related properties which we suspect to be correlated. In this section, we lay out a full set of distinct concepts. Many of these can be thought of as mechanistic sources of high/low path dependence.
**A. Variance across runs**
Suppose we run training several times, with different random seeds. How much will the final models differ from each other? We are interested in both generalization behavior and internal representations. At a higher capability level, we would be interested in properties like “deceptiveness” and “goal”—do these vary across training runs?
**B. Sensitivity to small changes in the training setup**
How much do the properties of the final model differ if we change the training setup slightly? For instance, if two different labs each train a language model with broadly the same architecture and approach, but all the implementation details differ, how similar will their behavior and internal properties be?
**C. Existence of distinct clusters**
Suppose we train some language models (with different seeds or slightly different setups), and we look at a very specific property like:
* How does the model represent the fact ‘apples are red’?
* What does the model output on this set of 5 closely-related reasoning questions?
* What circuit is used to capitalize words at the beginning of a sentence?
If the models differ on one of these questions, there are two ways it could happen:
1. There is a spectrum of behaviors or internal setups, and we get a different point on that spectrum each time.
2. There are a small number of discrete clusters, and each time we get something which is definitively from one of these clusters.
Hypothetical examples:
* A model either answers all five reasoning questions with “yes” or all five with “no”, but never something in between.
* The “apples are red” is always encoded in two neurons in the third layer, or one attention head in the second layer, but never any other way. Within each way of doing it, the computation done by the neurons or attention head is always the same.
**D. Stability of clusters across training**
If stability of clusters is true, this is what it would look like:
If there are two distinct clusters which a model can fall under, like “encodes apple color in neurons” vs. “encodes apple color in an attention head”, then it will fall into one of these clusters early in training, and will stay in that cluster until the end. Each cluster will be stable to small perturbations—if we nudge the parameters slightly toward the other solution, then further training will bring us back to the center of the “valley” around the old solution. However, if we make a strong enough intervention, we will push the model over the ridge to the other stable attractor, and it will remain in that cluster all the way through training. So there will be a bimodal effect: either the intervention decays away almost completely, or it successfully pushes us to the other attractor and sticks fully.
**E. Predictive power of the mid-training model**
Suppose that important properties of the model vary across runs or setups. In this case, can we predict how the final model is going to turn out by pausing in the middle of training and inspecting the model? For instance, if we’re training an RL agent, and it ends up with the wrong proxy 50% of the time, can we pause early on in training and predict which version we will get?
**F. Sensitivity to early interventions**
Some examples of interventions would be:
* A small amount of adversarial training
* Injecting special training examples
* Adding a small amount of human feedback into an RL setup
The question here is: Suppose we can only intervene in the early and middle stages of training. How sensitive are the properties of the final model to our interventions?
**G. Survival of circuits**
Closely related to (D), (E), and (F) is the survival of circuits and representations across training. Some circuits will be learned early on and remain unchanged throughout training. For properties associated with these circuits, the mid-training model will be perfectly predictive of the final model. Other circuits will be wiped out and replaced with new ones.
One important question here is whether there will be some kind of "general intelligence circuitry" which develops late in training and wipes out large swathes of earlier circuits, by virtue of being more efficient in terms of "cases handled per parameter of circuitry". Such a phenomenon would be devastating for the sort of path dependence we discuss here.
More survival of circuits means higher path dependence, since it allows the speeds at which different early circuits are formed to be more influential on the final model.
**H. Existence of closed form inductive bias**
Suppose we have two computational graphs which solve a task, and we want to know which one is more likely to be isomorphic to the model resulting from training. A closed form inductive bias lets us figure this out by inspection.
A simple example is a circuit prior, where we simply count the number of nodes and/or connections (depending on how you define it), and say that the one with fewer nodes/connections is more likely to be found.
However, a closed form is allowed to be more complex than this. For instance, we could have some mix of a circuit prior with depth penalties, modularity bonuses, an information loss related term, and so forth.
A non-closed-form understanding of inductive bias would be something like "Here's a rule for figuring out which circuits will be built in the first 10% of training. Run that rule on your dataset and architecture, and write down the circuits. Now here's a rule for what will be learned in the *next* 10%, which depends on the circuits we already have. And so forth for the rest of training."
The thing which makes it not a closed form is that you *have to* reason through it step-by-step; you can't skip to end and say "well it all caches out to picking the simplest final solution". This is a very path-dependent way for things to be.[[5]](#fnd8qh3flyhfp)
If we are in such a world, then it is harder to construct a training process which gives us a "pure" circuit prior, or any other theoretical prior we might want.
### **A question about predictiveness**
A simpler way to operationalize path dependence is to think about the following two questions:
1. How predictive of the final model are the properties at the middle of the training run? (Consistency over time)
2. How predictive of the final model is a looking at the result of a *different* training run, with slightly different settings? (Consistency between runs / [Homogeneity](https://www.lesswrong.com/posts/mKBfa8v4S9pNKSyKK/homogeneity-vs-heterogeneity-in-ai-takeoff-scenarios))
A single axis model of path dependence assumes that these two types of consistency are anti-correlated, but it's not clear to us whether this is the case. We leave it as an open question.
**Conclusion**
--------------
Given the implications of path dependence for thinking about alignment, it would be good to find out which world we're in. Each concept (A-H) in the previous section is a yes/no question[[6]](#fnn81w7zzsyp) to be empirically resolved. The exact answer will vary by task and architecture. However, we hypothesize that there is probably some principal component across all tasks, architectures, and concepts, a "General factor of path dependence".
If you would like to run path dependence experiments, please leave a comment or send us a message, and we can get in touch.
1. **[^](#fnrefj9an6vasz5d)**Mechanistically, high path dependence corresponds to significant influence and continuity from the structure and ontology of the early model to its final structure, and low path-dependence to destruction/radical transformation of early structures and overdetermination/stability of the final result.
2. **[^](#fnrefeycsnzrex17)**"Dissimilar solutions" = Different factorizations of the task into circuits. The low-path-dependence argument here is that "paths are always abundant in high-dimensional spaces".
3. **[^](#fnref1m5gfyo7xof)**I'm avoiding the term "goal" since I don't presume consequentialism.
4. **[^](#fnrefjgxgayd49qm)**See one of [these](https://www.lesswrong.com/posts/5p4ynEJQ8nXxp2sxC/parsing-chris-mingard-on-neural-networks#Simple_mappings_occupy_larger_volumes_in_parameter_space) [reviews](https://towardsdatascience.com/neural-networks-are-fundamentally-bayesian-bee9a172fad8). I mostly disbelieve the qualitative conclusions people draw from this work though, for reasons that deserve their own post.
5. **[^](#fnrefd8qh3flyhfp)**All this means right now is "It gives us strong path-dependence vibes, so it's probably correlated with the other stuff"
6. **[^](#fnrefn81w7zzsyp)**Really a spectrum.
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016c24bf-52d8-4848-9546-82f42605a557
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trentmkelly/LessWrong-43k
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LessWrong
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Is it immoral to have children?
In "The Immorality of Having Children" (2013, pdf) Rachels presents the "Famine Relief Argument against Having Children":
> Conceiving and raising a child costs hundreds of thousands of dollars; that money would be far better spent on famine relief; therefore, conceiving and raising children is immoral.
They present this as a special case of Peter Singer's argument from Famine, Affluence, and Morality (1972), which is why they haven't called it something more reasonable like the "Opportunity Cost Argument".
[Note: the use of "Famine Relief" here is in reference to Peter Singer's 1972 example, but famine relief is not where your money does the most good. Treat the argument as "that money would be far better spent on GiveWell's top charities" or whatever organization you think is most effective.]
It's true that having and raising a child is very expensive. They use an estimate of $227k for the direct expenditure through age 18 while noting that college [1] and time costs could make this much higher. Let's use a higher estimate of $500k to account for these. Considered over twenty years, that's $25k/year or $2k/month. This puts it at the top of the range of expenses, next to housing. It's also true that this money can do a lot of good when spent on effective charities. At GiveWell's current best estimate of $2.3k this is enough money to save nearly one life per month. [2]
But perhaps we shouldn't be thinking of this money as an expense at all, and instead more as an investment? Could having kids be a contender for the most effective charity? That is, could having and raising kids be one of the most effective things you could do with your time and money?
For example you could convince your kid to be unusually generous, donating far more than they cost to raise. Except that it's much cheaper to convince other people's kids to be generous, and our influence on the adult behavior of our children is not that big. Alternatively, if you're unusually smart, by having ki
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cb6c59a9-12c5-4a19-a8b7-20aaf722075b
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
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[Link] OpenAI on why we need social scientists
<https://distill.pub/2019/safety-needs-social-scientists/>
> We believe the AI safety community needs to invest research effort in the human side of AI alignment. Many of the uncertainties involved are empirical, and can only be answered by experiment. They relate to the psychology of human rationality, emotion, and biases. Critically, we believe investigations into how people interact with AI alignment algorithms should not be held back by the limitations of existing machine learning. Current AI safety research is often limited to simple tasks in video games, robotics, or gridworlds, but problems on the human side may only appear in more realistic scenarios such as natural language discussion of value-laden questions. This is particularly important since many aspects of AI alignment change as ML systems [increase in capability](https://distill.pub/2019/safety-needs-social-scientists/#harder).
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6f8529bc-2236-47f6-9566-f2b850576c43
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trentmkelly/LessWrong-43k
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LessWrong
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The kinesthesia switch
I've been working on improving my kinesthesia for about thirty years (for reasons which are not obvious to me, I've felt a strong motivation to get moved into my body), and I've found something interesting.
I was doing a chi gung exercise [1] which involves going up on the balls of my feet while pushing up with my hands, and I suddenly noticed that my body had mostly blanked out when I was in the extended position, which led to a realization that blanking out was a process rather than a thing [2]. I thought "kinesthesia switch on", and I could suddenly feel a good bit of detail about how I was wobbling-- I mean I could feel some of my bones moving relative to each other, instead of just feeling in a vague sort of way that the position didn't feel very good.
What's better, is that I remembered how to turn on the kinesthesia switch, and have continued to work with it.
A side effect of turning on the switch is that I uncurl my upper body (kinesthesia seems to have something to do with alignment), but deliberately uncurling doesn't work nearly as well as turning on the switch.
At first, I would try to turn on the switch as much as possible, but that began to feel bad-- probably because there was some perfectionism driving that approach. I've tweaked it to "as much as feels good to me".
The most noticeable effect (aside from better spirits and less akrasia) is that going up and down stairs has become a lot easier the vast majority of the time. Down stairs has been a problem for years because of accumulated knee injuries. Upstairs became problematic about 6 months ago because, for no apparent reason, I developed some sort of serious muscle tightness in my right leg. It started with pain in the back of my right heel which was clearly linked to movement, and eventually shifted to pain in what definitely felt like the muscle attachment to my sitzbone on the right.
This days, I'm mostly trotting up and down stairs rather than stepping down a step and then puting the oth
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9a872e31-0503-445a-98d7-4646775c7950
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trentmkelly/LessWrong-43k
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LessWrong
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Project Lawful Audiobook: An Unofficial Fan Production with ElevenLabs AI
Eliezer Yudkowsky's latest fiction work, (described here) co-authored with Lintamande, is now complete save for the epilogue. For those following along, I've been producing an unaffiliated audiobook of "Project Lawful" (also known as "planecrash"), featuring a full AI cast from ElevenLabs.
We're currently eight episodes into the story, with episode 9 due for release this coming Friday.
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8c9908e1-caee-4239-b554-eff1e5fba67b
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Bangalore Meetup
Discussion article for the meetup : Bangalore Meetup
WHEN: 26 June 2011 04:00:00PM (+0530)
WHERE: Barton Centre, Mg Road, Bangalore Karnataka Bengaluru India
Less Wrong Bangalore meetup, the turnout for the last meetup was good. Any new members please join the facebook group 'Less Wrong Bangalore'.
Discussion article for the meetup : Bangalore Meetup
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c3efe2d4-04ee-41d0-af2d-aa84defefcb7
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trentmkelly/LessWrong-43k
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LessWrong
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STARC: A General Framework For Quantifying Differences Between Reward Functions
In this post, I will provide a summary of the paper STARC: A General Framework For Quantifying Differences Between Reward Functions, and explain some of its results. I will assume basic familiarity with reinforcement learning. This is the fourth post in the theoretical reward learning sequence, which starts in this post (though this post is self-contained).
In this paper, we consider the question of how to quantify the distance between reward functions in an informative way. That is, we want to find a function d:R×R→R, where R is the space of all reward functions, such that d(R1,R2) is a meaningful quantification of how similar R1 and R2 are. This is important for the (theoretical or empirical) study of reward learning algorithms; for example, see this post.
Considerations
Note that this problem is not very straightforward. A simple method for quantifying the distance between two reward functions might be to measure their L2-distance. However, this is unsatisfactory, because two reward functions can have a large L2-distance, even if they induce the same ordering of policies, or a small L2-distance, even if they induce the opposite ordering of policies. For example, given an arbitrary reward function R and an arbitrary constant c, we have that R and c⋅R have the same ordering of policies, even though their L2-distance may be arbitrarily large. Similarly, for any ϵ, we have that ϵ⋅R and −ϵ⋅R have the opposite ordering of policies, unless R is constant, even though their L2-distance may be arbitrarily small. Solving this problem in a good way thus requires some care.
(There are two earlier proposals for how to do this, namely EPIC and DARD. In Appendix A and B of the main paper, we outline a number of shortcomings with these earlier methods.)
We should start by asking what it means for a given function d:R×R→R to be “good” at quantifying the differences between reward functions. First and foremost, we probably want d to be a pseudometric, since this comes with
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07cc8fd8-63b1-44f7-ac53-340c184f6ab9
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StampyAI/alignment-research-dataset/eaforum
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Effective Altruism Forum
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What we can learn from stress testing for AI regulation
*This work was done as part of the call for case studies to inform AI regulation. Thanks to Patrick Levermore for conversation and feedback on this project.*
**Bullet Point Summary**
* Stress tests assess if banks can withstand economic shocks without public bailouts
* Fed, ECB, and BoE implemented stress tests after the 2008 crisis
* Tests have quantitative and qualitative components
* Tests assess solvency and liquidity
* Evidence suggests tests restored confidence but hard to prove they prevent crises
* Central bank independence was key to the force of tests
* Credit rating agencies failed before the crisis, analogous to AI auditors
* No race to the bottom between jurisdictions
* Industry standards have heavily influenced regulation
* Banks haven't substantially gamed the stress test system
* The crisis was critical to implement the stress test system
**Executive Summary**:
After the 2008 financial crisis, major central banks implemented stress testing regimes for banks to assess their ability to withstand economic shocks, and in particular to ensure that contagion of financial crisis could be prevented without recourse to public funds. The US Federal Reserve, European Central Bank, and Bank of England instituted stress tests to evaluate bank solvency and liquidity. The tests have quantitative components using economic models and scenarios as well as qualitative evaluations of risk management. There is evidence that the stress tests have restored confidence in banking systems, but it is difficult to conclusively demonstrate the tests prevent crises due to the rarity of crises.
There are a number of lessons we can take from this report for AI regulation. Firstly, it seems likely that good legislation will only be passed after a crisis has already happened. The Basel Accords provide some evidence against this - none of the was precipitated by the crisis - but they weren’t sufficiently strong to prevent the crisis and took many years to be passed and implemented. On the other hand, despite the well-documented and well-known ability of banks to engage in regulatory arbitrage, there has been no race to the bottom with respect to stress tests. On the contrary, there’s been an extraordinary proliferation of stress testing standards around the world including in China without any explicit coordination between central banks. The key factor behind their spread seems to be the initial success they had in the US at calming the financial markets, and potentially also simply their sound logic.
An important finding from this report is that it is very strong evidence for the persistence that private regulatory standards, safety and risk management practices can have with a key driver for this seeming to be that it reduces the workload for policymakers and provides a model that they can use that has already had success in the industry being regulated. The lesson from this for AI is that it suggests that it’s highly plausible that lab-based governance practices will be adopted into law.
A finding particularly relevant for evals of AI systems is that keeping the specifics of evals secret from firms is important to prevent firms from gaming the system by designing their models to specifically pass the tests they know they’ll face. This appears to be accepted best practice with stress tests and a key failure in the financial crisis seems to have been securities created in concrete with credit rating agencies and thereby given good ratings by construction.
A potential problem that could arise from the regime of private firms acting as evaluators and auditors of AI models, particularly if the AI firms are paying the evaluating and auditing organisations, is that AI firms are able to use their market power to pressure the evaluators and auditors into giving less stringent tests. This dynamic happen prior to the financial crisis and there is a broad consensus in the literature that this played a part in why securities were incorrectly rated prior to the financial crisis which in turn played a part in the financial crisis itself.
Finally, the independence of central banks appears to have been important for two reasons - firstly it meant that they were able to respond quickly and drastically to the financial crisis without the need for new legislation to be approved. Secondly their independence, particularly in a US government context where central banks are structured much more similarly to Weberian bureaucracy than other parts of the US government with the exception of the military, meant that they had a lot more capacity and independence from lobbying to carry out stress tests than previous organisations tasked with carrying out stress tests.
This pattern was repeated in the EU where stress tests performed by the central bank were much more successful than initial stress tests that were not. All countries seemed to have converged on their central banks carrying out stress tests rather than other financial regulators they may have. Ideally, I think a US AI regulatory body would have the same structure as the Fed but this seems unlikely to be feasible. On the margin, this seems to strengthen the case for the US military playing a larger role in AI regulation.
**Introduction**:
Stress testing is a risk management practice in which regulators assess how a bank's balance sheet would respond to a hypothetical adverse economic scenario. Major central banks implemented macroprudential stress testing regimes for banks after the 2008 financial crisis revealing risks that existing bank risk models failed to capture. The goal of the macroprudential stress testing regime by the US Federal Reserve, European Central Bank, and Bank of England, amongst other major central banks, was to evaluate whether banks had sufficient capital and liquidity to avoid taxpayer-funded bailouts during crises. By testing banks concurrently, stress tests aim to also capture risks of contagion across financial institutions. Stress tests typically have quantitative components based on econometric models, as well as qualitative evaluations of risk management practices. Stress tests also parallel the regulatory role of credit rating agencies before the crisis, serving as a public regulatory function carried out by independent central banks rather than fully private companies.
An important piece of general background comes from [Acemoglu et al](https://www.aeaweb.org/articles?id=10.1257/aer.20130456). They model financial contagion as banks failing and, connected by debtor-creditor relations, this failure propagating through the network of banks leading to other banks failing. They find that densely connected networks are protected from small shocks by the density of the network because the liquidity in the network can be shifted around the network to protect all of an insolvent bank's creditors. However, if a shock is sufficiently large that liquidity can’t protect an insolvent bank's creditors then the density of the network means that the failure propagates throughout the network to a greater degree than if the network had been less connected.
They then show that there’s financial stability negative externality - banks contract to be able to internalise the externality of banks failing on their neighbours but don’t contract to internalise the externalities for banks more than 1 away from them - i.e in a network with only 3 firms all the externalities of firms collapsing is internalised by all firms.
The implication of this is that there is a negative externality of the risk of the financial crisis in equilibrium without either a public or private effort at regulation to internalise the externality.
**How Stress Tests Work**:
**Federal Reserve Stress Testing**
The Federal Reserve's stress testing regime has two main components:
* Dodd-Frank Act Stress Tests (DFAST): Assess bank capital adequacy assuming [dividends paid at current rates and no share buybacks](https://www.nber.org/papers/w26887). Conducted annually for banks with over [$250 billion in assets](https://www.investopedia.com/terms/s/stresstesting.asp#:~:text=The%20Dodd%2DFranklin%20Act%20Stress,the%20direction%20of%20the%20Fed.), although initially, it was only [$50bn](https://sgp.fas.org/crs/misc/R45036.pdf)
* Comprehensive Capital Analysis and Review (CCAR): Banks submit capital plans [including detailed dividend and buyback proposals](https://www.nber.org/papers/w26887). Plans are evaluated based on quantitative stress tests and qualitative review. CCAR occurs annually for banks with over [$100 billion in assets](https://www.federalreserve.gov/supervisionreg/stress-tests-capital-planning.htm).
Both DFAST and CCAR have quantitative and qualitative components:
* Quantitative: Models using over 25 macroeconomic variables assess if banks remain solvent under adverse scenarios like 10% unemployment or equity market shocks. The Fed's models and scenarios are not disclosed to banks to prevent gaming.
* Qualitative: Fed evaluates risk management, governance, internal controls, and capital planning. Banks submit detailed capital policies for review.
If banks fail either the quantitative or qualitative elements, they must resubmit less-risky capital plans. This acts as a binding constraint on bank capital distributions. CCAR in particular ties payouts directly to stress test results.
**ECB Stress Testing**
The [ECB's stress testing](https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2054.en.pdf) also has quantitative and qualitative elements:
* Quantitative: Banks across the euro area are modelled under hypothetical three-year adverse scenarios provided by the ECB. Similar to the Fed, scenarios cover shocks to economic growth, unemployment, interest rates, and asset prices.
* Qualitative: Assesses bank risk management
* Unlike the Fed, the ECB initially [didn’t include liquidity stress tests as standard](https://cepr.org/system/files/publication-files/126111-stress_testing_and_macroprudential_regulation_a_transatlantic_assessment.pdf#page=15), [although this has now changed](https://www.ecb.europa.eu/pub/financial-stability/macroprudential-bulletin/html/ecb.mpbu202206_3~37629fc857.en.html)
Their stress tests covered over [99 banks in 2023](https://www.bankingsupervision.europa.eu/press/pr/date/2023/html/ssm.pr230131~dee9a150dc.en.html). If capital falls below required levels, banks are legally required to recapitalise.
Similarly to the Fed, ECB uses [models it keeps private to evaluate banks](https://www.bis.org/review/r190904j.pdf), often requiring banks to update crisis scenario planning in comparison to what is required by banks' internal stress tests.
**Bank of England stress tests**
* Bank of England stress tests are similar to the Fed’s and ECB’s but include an explicitly [anti-cyclical](https://cepr.org/system/files/publication-files/126111-stress_testing_and_macroprudential_regulation_a_transatlantic_assessment.pdf#page=15)element where stress scenarios are designed to be more stringent during “peacetime” and less stringent when demand is weak
* The Bank of England only began regular macroprudential stress tests in [2014](https://www.risk.net/stress-testing-2nd-edition/7084251/four-years-of-concurrent-stress-testing-at-the-bank-of-england-developing-the-macroprudential-perspective), significantly later than either the Fed or the ECB. My speculation is that this is the British financial system was subject to the same levels of stress as either the European or US banking systems meaning stress tests were started explicitly as macroprudential measures rather than starting as a way to try to restore confidence in the banking systems as they were in the US and EU.
**How Effective Are Stress Tests**?
There are conceptual challenges in assessing stress test efficacy:
- No crises during the regime to test performance
- When practical, the status quo continues of no bank failures
- The Acemolgu results suggest that large shocks that would cause failure contagion in the highly interconnected global financial system happen rarely and that small shocks won’t cause contagion
Some positive evidence:
- Market reactions to stress test results
- Banks recapitalizing after failing the stress tests
- Restored confidence in the banking system
**Restoring confidence in the banking system**
There is a [strong consensus](https://cepr.org/system/files/publication-files/126111-stress_testing_and_macroprudential_regulation_a_transatlantic_assessment.pdf#page=15) that the US stress tests in 2009 were critical to restoring confidence in the banking system with then-Fed chair Ben Bernake describing it as a turning point in the financial crisis. [Similar, though less glowing, assessments](https://cepr.org/system/files/publication-files/126111-stress_testing_and_macroprudential_regulation_a_transatlantic_assessment.pdf#page=15), were made of the 2014 stress tests and assessments were made of the 2014 ECB stress tests in conjunction with the asset quality review. Large banks failed both stress tests and were subsequently forced to recapitalise.
The significance of the restoration of confidence in the banking system is that it is a measurable way in which stress tests had a positive effect that can be assessed on the relatively short scale with which we have to assess the efficacy of stress tests.
There is also statistical evidence for the success of stress tests. [Morgan et al](https://onlinelibrary.wiley.com/doi/abs/10.1111/jmcb.12146) and [Georgescu et al](https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2054.en.pdf) look at the evidence for whether stress tests provided new information to markets for the 2009 US stress test based on weather returns for firms were statistically different from normal. In both cases, the identifying assumption is that the announcement on a specific date of the announcement of the results of the stress tests was exogenous to the share price. I find this identifying assumption plausible - it seems like there was essentially no other event on the day of the announcement that could have affected stock prices to a similar degree to the announcement. However, neither paper uses techniques such as placebo tests to attempt to verify the identifying assumptions.
Both Morgan et al and Georgescu find that the announcement of the results resulted in abnormal returns implying that the stress tests provided information for the markets. It is notable that this finding held for both the crisis stress tests of 2009 and 2014 and the start of the EU macroprudential tests in 2016. This provides some evidence that stress tests do have some positive effects, although it of course doesn’t demonstrate that they pass the cost-benefit test.
**Evidence from the 2023 banking crisis**
**Silicon Valley Bank Failure**
The [2022 failure of Silicon Valley Bank](https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf) (SVB) highlights challenges in regulating fast-growing regional banks. SVB expanded rapidly from a niche lender to a tech startup into a mid-sized bank with inadequate risk management. When interest rates rose in 2022, SVB took large losses on its bond portfolio due to inadequate hedging. This sparked a run on uninsured deposits exacerbated by social media.
SVB was regulated by the San Francisco Fed with less stringent rules than systemic banks. The Fed review blamed poor oversight during its growth and disruption from COVID-19. SVB’s management incentives rewarding risk-taking without balancing risks also played a role.
On balance, this seems like weak evidence against the efficacy of stress tests. SVB seemed all things considered a well-run bank albeit with poor risk management policies, and certainly was solvent. It, therefore, seems that stress tests should have caught the failure to hedge against interest rate increases. However, there wasn’t substantial contagion as a result of failure (although SVB was still a small bank) and most of the failures weren’t on the part of the stress tests aimed at the structurally important banks where most of the focus of stress tests lies.
See the appendix for more details on the collapse of SVB.
**Credit Suisse Failure**
The [failure of Credit Suisse in 2023](https://www.google.com/search?q=failure+of+credit+suisse&oq=faiure+of+credit+&aqs=chrome.1.69i57j0i13i512l2j0i22i30j0i15i22i30j0i22i30l3j0i15i22i30l2.7446j0j7&sourceid=chrome&ie=UTF-8) after large losses show orderly resolution is possible for global systemically important banks. Credit Suisse had become undercapitalized after scandals and bad investments. Declining profits led to shareholder dilution, deposit outflows, and debt concerns. The collapse of SVB further strained finances.
UBS acquired Credit Suisse with significant losses forced onto shareholders and contingent convertible [(CoCo) bondholders](https://www.investopedia.com/terms/c/contingentconvertible.asp). No public bailout was required. Regulations since 2008 enabled the absorption of losses and transfer of assets outside of insolvency. This orderly wind-down of a major global bank suggests that post-crisis reforms made the financial system more resilient, including the macroprudential stress testing regime.
The converse of this result is that the stress tests didn’t lead to Credit Suisse being capitalised to the degree that it could withstand the shock from the failure of SVB. I mostly don’t think that this is an especially strong critique - Credit Suisse was a poorly managed bank and it was correct that it was taken over by a better-managed bank and good that this could be achieved without contagion. When setting the stress test requirements there must be acceptances of both false negatives and false positives - there is nothing in the failure of Credit Suisse that suggests to me that the ECB stress tests weren’t reasonably close to the Pareto frontier of the false positive and false negative rate.
The difficulty of course in evaluating the degree to which stress tests successfully prevented contagion following the failure of Credit Suisse comes from the Acemoglu result - it is unclear if the failure would have been large enough to cause contagion. Additionally Credit Suisse - like Bear Sterns and unlike Lehman Bros - didn’t fail but was bought out by UBS. The experience of the financial crisis suggests that a failing firm being bought out can be sufficient to prevent a crisis where had the firm failed a financial crisis would have followed.
See further details of the collapse of Credit Suisse in the appendix.
**Key Findings**
**Central Bank Independence Critical**
The independence of central banks from political pressure was critical for implementing credible stress tests. The Federal Reserve has statutory independence that shields it from industry regulatory capture and political interventions, unlike agencies like OFHEO that regulated Fannie Mae and Freddie Mac with inadequate stringency. This independence meant the Fed could take an aggressive supervisory stance despite industry opposition. The Fed's stress tests were more extensive than international Basel II standards or pre-crisis bank regulations. Without the Fed's political autonomy, the stress testing regime likely would have faced greater industry lobbying or legislative opposition. The evidence comes from contrasting the Fed's actions with OFHEO's weak oversight of Fannie Mae and Freddie Mac, which succumbed to regulatory capture.
[Scott et al](https://www.econstor.eu/bitstream/10419/114489/1/821360183.pdf) show that the OFHEO stress tests had three key flaws that meant that despite Mae and Mac passing the stress tests with flying colours they needed $191bn in bailout money in 2008 to stay solvent. Firstly, the model was calibrated using the 1993 data that was available when it was first created and has not been updated since then. The authors argue that this is due to the requirement on OFHEO to update fully inform firms of the updates to the model due to simulations in the original legislation, rather than having the authority to unilaterally update the model. Secondly, the model just didn’t consider sufficiently adverse scenarios. The most severe scenario in the stress test had US house prices falling 13% while the actual fall in the financial crisis was 18%. It’s not clear though ex-ante that it was a mistake for the stress tests not to include more adverse scenarios since there is an unavoidable tradeoff between false positives and false negatives in setting the correct upper threshold on adverse scenarios that Mae and Mac were forced to consider. Finally, the model didn’t consider the effects on future revenues in addition to the effects on the return on assets Mae and Mac already owned following an adverse shock to the housing market.
The [Fed’s own](https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf) review of the failings that led to the collapse of SVB provides additional evidence for the importance of independence in achieving good outcomes. In their report, they identified pressure by the Trump administration to reduce the regulatory burden on banks as a reason why stress tests on SVB weren’t carried out to the degree that would have prevented the collapse, in addition to concerns from their political masters about whether stress tests were violating due process. It’s unclear to me the degree that this should be discounted on account of it being in the Fed’s corporate interest to try to assert its independence.
Prior to the more successful stress tests in 2014, the EU, via a regulatory agency rather than the ECB, carried out multiple stress tests that completely failed to identify weaknesses in banks that failed soon after they were carried out. It’s very hard to assess the causal effect of the ECB taking over the 2014 stress that was much more successful than previous stress tests judged by market reaction and false negative rate, but it is at least suggestive of a continuation in the pattern of independent central banks carrying out stress tests better than other organisations.
This example relates to competence rather than decisions being distorted by political interference or hamstrung by a lack of operational independence.
Both explanations for the importance of independence are supported by much broader literature on the importance of the independence of state institutions for the development of state capacity. See the appendix on state capacity for more details. This is particularly pertinent in the US context where the only two state institutions staffed at the senior levels by professional civil servants rather than political appointees who are subject to cheap removal by the President are the Fed and the US military. The US is very unusual amongst rich countries in this respect.
**Credit Rating Agencies Analogue**
There are analogies between the credit ratings agency regime before the crisis and the emerging paradigm of independent organisations carrying out AI auditing and evaluations.
[Credit ratings agencies arose in the early 20th century and their ratings were progressively given legal authority](https://pubs.aeaweb.org/doi/pdf/10.1257/jep.24.2.211). The Securities and Exchange Act of 1934 gave official roles to designated rating agencies. The 1975 NRSRO rules required certain regulated institutional investors to only hold securities rated investment grade by approved rating agencies.
This codification into law gave huge weight to the judgments of a small number of private agencies. However, flawed incentives and inadequate risk models led rating agencies to systematically underestimate the risks of mortgage-backed securities. Only a few large banks engineered these complex securities, and they were the issuers paying rating agencies to evaluate them. This concentrated market power meant rating agencies had skewed incentives towards pleasing large issuers. Their inaccurate ratings on mortgage-backed securities and related derivatives contributed to the 2008 crisis.
Perhaps the most worrying analogy between the role that rating agencies played in the financial crisis and the emerging paradigm in AI auditing and evaluations is the potential similarity between credit rating agencies playing a role in designing securities that later rate and AI firms knowing in advance the sorts of tests that their models will face. Rating agencies play a role in designing the securities they later rated meaning by construction they achieved high ratings. A similar problem could arise where AI firms construct their models to get safe ratings by the auditors which they wouldn’t have been able to do so had they not known the structure of the tests in advance. Central banks seem to have thought along similar lines by structuring stress tests to have novel scenarios every year and the Fed keeping their models private.
See the appendix for further details on the role of rating agencies in the financial crisis.
**No International Race to Bottom**
Despite the risks of international regulatory arbitrage, there is no evidence it affected the adoption of bank stress tests. All major jurisdictions implemented stress tests following the 2008 financial crisis. Critically, there has been **no international coordination around the implementation of stress tests.**
There is substantial qualitative and empirical evidence that financial institutions engage in regulatory arbitrage in other contexts. [For example, US money market funds faced less regulation than banks so grew substantially as an alternative investment vehicle](https://www.govinfo.gov/content/pkg/GPO-FCIC/pdf/GPO-FCIC.pdf). However, this dynamic has thus far not been replicated in response to post-crisis stress tests. The widespread voluntary adoption shows central banks were relatively unconcerned by regulatory competition undercutting the efficacy of their standards.
There is also substantial statistical evidence that banks engage in regulatory arbitrage. [Houston et al](https://hub.hku.hk/bitstream/10722/192342/1/content.pdf;Title) test for regulatory arbitrage using a variety of methods and with a variety of identification strategies. Funndemtally it’s very difficult to do causal inference in this area there aren’t good natural experiments where there are plausibly exogenous changes in financial regulation. Furthermore, it’s difficult to rule out reverse causation - it is difficult to show that banks don’t, for instance, lobby harder for softer financial regulation in places where they have more assets.
Houston et al though put together a battery of evidence that consistently shows meaningful, but not overwhelmingly large, effects on where bank assets as a result of the severity of financial regulation. None of their instruments is especially convincing - they use the GINI coefficient, years since independence, and models of regulatory contagion amongst others - but they consistently point to there being an effect. In sum, I think this provides some evidence that there is cross-jurisdiction regulatory arbitrage, but the effect sizes aren’t extremely large.
[Clark and Ehbrahiem](https://www.sciencedirect.com/science/article/pii/S1572308921001212) look at regulatory arbitrage specifically by seeing if firms moved risk into “operational risk” which wasn’t covered by the Basel I capital accords. Operational risk is essentially a catch-all term covering risks from human error and legal issues.They find that banks that are more constrained in the risks they take by regulation take on more operational risks. Theory suggests that banks closer to the somewhat arbitrary leverage limits imposed on them by regulation would if they weren’t constrained by regulation, take on more risk in exchange for higher expected returns.
Clark and Ehbrahiem don’t establish more than an associative relationship and also don’t test if this effect goes away after Basel II is implemented. They do however conduct a placebo test which supports their results. Specifically, they test whether operational risk actually being realised has the same effect which they find that it doesn’t. This is evidence against the possibility that the relationship between operational risk and distance-to-leverage ratio minimums is being driven by something other than an optimisation decision on the part of banks.
The coordination of central banks around stress tests - which are quite costly for the regulated banks - requires explanation. Acemolgu et al results establish that there is a financial crisis externality - the costs of a financial crisis are not contained to the country that it occurs in for a large financial crisis because there is a contagion, as happened in the 2008 financial crisis, the 1997 Asian financial crisis and the Great depression. Furthermore, it’s also been established that banks do move assets to places where there’s less stringent regulation and as well as finding other ways for the regulation not to apply. This seems to create a prisoner dilemma where each central bank is incentivized to defect (at least a degree - lots of the costs of the financial crisis are internalised, particularly in less that perfectly connected financial systems) by having or absent stress tests. Instead, we observe the proliferation of stress tests and the tests in fact greeting stronger over time, for instance with the ECB’s addition of stress tests that focus on liquidity.
At least some of the answers are provided explicitly by Alex Braizer, at the time a senior member of staff at the BoE, in an article in a special issue on stress testing - the US stress tests 2009 was judged to be extremely successful and other central banks wanted to emulate that success.
It’s notable that nowhere in the literature on stress tests, including that written by central bankers, was the issue of regulatory arbitrage discussed. This suggests to me that a cultural explanation is important - engaging in this kind of competition between jurisdictions isn’t in the mandate or culture of central banks and so as action it isn’t considered. I speculate that the effect sizes on the movement assets in response to financial regulation aren’t especially large meaning that the costs of increased financial regulation aren’t especially high.
Overall though this seems to be an important avenue for future research.
A final point to consider is the possibility that stress tests actually aren’t costly for the large banks that are most able to influence regulation because it creates greater barriers to entry for smaller banks and therefore protect their oligopoly profits. I think this is unlikely to be the explanation. US banks aggressively and successfully lobbied for less regulation throughout the last quarter of the 20th century and were successful. From this point, finance salaries diverged from non-financed salaries. This strongly suggests to me that the straightforward story where stricter financial regulation hurts large banks is correct.
**Industry Standards Influenced Regulations**
Stress tests emerged [in the early 1990s in banks](https://www.bis.org/fsi/publ/insights12.pdf) and were adopted very quickly by Basel II. Stress tests were pioneered though to measure mortgage credit risks more accurately since the mortgage market was unusually exposed to macroeconomic shocks like increases in the unemployment rate. The practices diffused through the banking sector during the 1990s and 2000s. Following the crisis, central bank stress test regulations built directly on these industry testing norms as well as on the specific techniques used. This created continuity between existing private practices and public oversight, facilitating adoption. Current stress takes make extensive use of bank’s internal stress testing models for instance.
Credit rating agencies are another example of industry standards influencing regulation, both in terms of the ratings of agencies becoming law and in terms of subsequent regulation of rating agencies coming out of industry standards. The wholesale nature of the adaptation ratings agencies' ratings in 1975 suggests to me that the driving force behind their adoption was that it substantially reduced the workload for the US government. It seems unlikely to me that this emerged as a result of rating agency lobbying. Firstly I’ve found no discussion of this in the literature. Secondly, rating agencies are quite small organisations in comparison to the financial organisations that they became de facto regulators of. Moody’s, the largest of the agencies, has only 3000 employees as of 2023 in comparison to Goldman’s 48,500 also as of 2023. This, in conjunction with the soft power that large financial institutions exert via the revolving door between them and the US treasury and Fed, makes it seem very unlikely to me that the rating agencies would be able to exert more political power than those firms in the area of their core interests.
Rating agencies became more heavily regulated in the US in the wake of the dot com crash and in the EU in the wake of the financial crisis. [In both cases the regulation was based on the](https://www.econstor.eu/bitstream/10419/53684/1/618028064.pdf)ISOCO code, ratings agencies own international standards.
**Banks Largely Unable to Game System**
While there is minor evidence of stress test “gaming,” the regime remains binding on bank capital and lending overall.
[Cornett et al](https://www.sciencedirect.com/science/article/pii/S1042957318300330) use a regression discontinuity design based around the cutoff of initially $100bn and later $50bn dollars in assets for the Fed’s stress tests of systematically important banks to assess changes in bank behaviour around stress tests
They find that banks subject to the stress tests for systematically important banks reduce their dividends and share buybacks in the quarter before the stress tests to a level below those banks not under the stress testing regime while in other quarters their dividends and share buybacks are higher than those other banks - this suggests some gaming of the system
They don’t defend their identification strategy by testing if either there in fact is something structurally different about banks with assets just over $50bn or below or $100bn or (more plausibly) that banks intentionally keep their asset levels below one of the cutoffs to avoid the regulatory burden
I don’t think either of those challenges to identification strategy is particularly dire - Cornett et al look only at stress tests up to 2013 - the stress tests only began regularly in 2011 which makes it seem unlikely to me that banks had enough time to implement a strategy to strategy to keep their assets below either of the cutoffs.
They also find that firms going above the systemically important bank stress test cutoff are associated with firms increasing their spending on lobbying. The authors cite other literature that shows that lobbying spending by firms in financial services is associated with a lower regulatory burden all else equal. However, they don’t demonstrate in the paper that in this specific case firms which spent more on lobbying were able to reduce their regulatory burden.
However, [Schneider et al](https://www.nber.org/system/files/working_papers/w26887/w26887.pdf). find no evidence that large banks face less stringent stress tests than other banks. They test this by analyzing whether large banks are more likely to pass CCAR tests given their quantitative performance on the Fed's undisclosed stress test models. The authors find no advantage for large banks in CCAR pass rates, suggesting political or regulatory capture did not affect outcomes.
This paper doesn’t have an especially robust identification strategy for estimating whether large banks were in fact more or equally stringently treated than other banks relative to their to the risk level that they would be perceived to be at had they been a non-large bank. It’s perfectly conceivable that prior to the financial crisis, these firms were extremely overleveraged relative to the systemic risk that they pose due to size, such that the substantial decrease in capital payouts and leverage ratios post-financial crisis doesn’t cover the difference between these firms and the others.
Large banks have been unsuccessful in avoiding the US stress tests. At a high level, all of the largest banks did have to undergo stress tests while various smaller banks didn’t, and banks were semi-regular and found to be undercapitalised and forced to raise more capital.
Schneider et al test formally whether large banks were able to engage in regulatory capture by testing whether the large banks had less serve stress tests compared to other firms. They find that across a wide variety of measures large and more connected banks face harsher stress tests - they find that these firms are more likely to fail stress tests given how well the firms performed on Fed’s private model implying that either those banks had higher more aggressive levels dividend payouts and share buybacks relative to profitability given leverage, or they were more likely to fail the qualitative section of the stress tests. Furthermore, they also find that, as a percentage of total assets, large banks have lower payouts and that
This paper doesn’t have an especially robust identification strategy for estimating whether large banks were in fact more or equally stringently treated than other banks relative to their to the risk level that they would be perceived to be at had they been a non-large bank. It’s perfectly conceivable that prior to the financial crisis, these firms were extremely overleveraged relative to the systemic risk that they pose due to size, such that the substantial decrease in capital payouts and leverage ratios post-financial crisis doesn’t cover the difference between these firms and the others.
On a more macro level, large banks have been forced to recapitalise, markets have reacted extremely negatively to firms failing stress tests and the threat of banning firms from rewarding shareholders with either dividends or capital buybacks has been executed. A notable example of this followed Citi failing the 2014 US stress tests and their market cap dropping 6% as a result. I find this high-level evidence quite compelling - it demonstrates that the stress tests were strenuous and that systematically important banks did fail them sometimes and had to bear the costs.
Finally, [Coombs](https://www.pure.ed.ac.uk/ws/portalfiles/portal/304255685/CoombsN2022ESNarratingImaginedCrises.pdf) provides qualitative evidence that stress tests are having the desired effect. He interviews employees at both the BoE and banks subject to stress tests involved in the process. He finds that in particular the specific scenarios taken seriously by banks and their responses really are tailored to the specific scenario the BoE has constructed.
**The crisis was critical to the adoption**
The 2008 financial crisis was a crucial catalyst motivating the adoption of tougher bank stress testing. [Gradual phase-in of stress tests had been occurring under the international Basel II accord](https://www.federalreserve.gov/newsevents/speech/tarullo20160926a.htm) but with the negations for the accord starting in 1999, finishing in 2004 by 2008 Basel II was still in the process of being implemented. But the Fed and ECB responded rapidly to the post-crisis need to restore confidence, with the first US-wide stress tests in 2009. The crisis shattered the deregulation paradigm that had ruled prior to 2008 and provided the impetus for new regulation.
In general, the policy takes months or years to pass through Congress and comes out of often many years of work in academia and think tanks. However, the first stress tests were used by the Fed in 2009 in the midst of the crisis having previously been a quite marginal tool in the central bankers' financial regulation toolbox. This is much faster than such a large piece of legislation would normally take to pass and be implemented and was driven by crisis.
The public and political support for harsh regulation also seems important and contingent on the financial crisis. [The prior 20 years of US financial regulation policy had been, all things considered, driven by a convection that markets could regulate themselves culminating in the elimination of the separation between investment and commercial banking in 1993](https://www.govinfo.gov/content/pkg/GPO-FCIC/pdf/GPO-FCIC.pdf). The financial crisis shattered the perception of the efficacy of private regulation and seems likely to me to have played a role in the severity (although not punitive or retributive) stress testing regime that was adopted.
A caveat to this is that had already been a shift towards a more pro-regulatory stance by Fed following the dot com crash and in particular after the high priest of deregulation Alan Greenspan ended his tenure as Fed Chair in 2006 and was replaced by the more pro-regulation Ben Bernanke.
This is part of a broader pattern in financial regulation. [The Glass-Steagal Act](https://www.federalreservehistory.org/essays/glass-steagall-act#:~:text=The%20Glass%2DSteagall%20Act%20effectively,Roosevelt%20in%20June%201933.), the centrepiece of US financial regulation that established the FDIC and separated commercial and investment banking activities was enacted in 1933 in the wake of the great depression. [The Fed was also established as a lender of last resort as a response to the great depression](https://www.federalreserve.gov/newsevents/speech/fischer20160210a.htm), a policy that eventually became standard practice.
As noted previously, the US adopted more stringent regulations for rating agencies following their perceived failure over the dotcom crash in 2000, and the EU only adopted more stringent regulations after the financial crisis.
**Failure of SVB appendix,**
The [Fed’s internal assessment](https://www.federalreserve.gov/publications/files/svb-review-20230428.pdf) of what went wrong both at SVB places the blame on poor incentives for management at SVB combined with a relatively basic failure to hedge against interest rate increases at SVB.
Mechanistically, SVB bought US treasures while interest rates were low. Interest rates then dramatically rose in 2022 and early 2023 meaning that the treasuries bought by SVB lost value and when SVB sold those bonds those losses were realised. This sparked a panic about SVB’s solvency which was enhanced by a great majority of SVB’s deposits being uninsured. What followed was a classic bank run exacerbated by the speed the information spreads on Twitter.
At a higher level, SVB’s compensation structure for management incentivised imprudent risk-taking. SVB’s manager's pay was tied to performance but not to risk-adjusted performance. There’s much less evidence that this played a part in the causal chain for SVB’s collapse - only the Fed’s report has highlighted this as a factor amongst the literature I’ve read and more seriously there’s no statistical evidence for this theory nor have I seen any qualitative evidence - for instance, interview’s with SVB managers.
The Fed assessed their own failures as stemming from: the very rapid growth of SVB; the disruption from covid 19; and the policymaking during the Trump administration that pressured to be less strict in their banking regulation.
SVB grew extremely rapidly over the 2010s going from a small regional bank where stress testing was conducted by the SF Fed and liquidity, solvency and risk management practices had lower statutory limits than structurally important banks. SVB passed the $100bn asset mark in 2020 having only passed the $50bn in 2017.
At $100bn the standards used to assess banks are changed and the team that assesses them changes. In the Fed’s own assessment this, in addition to the disruption caused by Covid-19, meant that stress tests carried out on SVB were lower quality than they would otherwise have been.
**Failure of Credit Suisse appendix**
Credit Suisse failed following the distress of the US banking system in 2023 but this was the straw that broke the camel’s back.
In a sense, Credit Suisse was a success of the post-financial crisis regulatory regime. Credit Suisse was a poorly managed bank that made bad investments and had a large number of scandals. As a result, Credit Suisse was becoming progressively undercapitalised as its share price dropped and clients pulled their funds out of the bank. The role of the collapse of SVB and generically the stress on the US financial system in the failure of Credit Suisse was that this made investors more reluctant to have capital tied to a risky institution. The critical event was the refusal of the Saudi national bank - Credit Suisse’s largest shareholder - refusal to inject any more capital into the bank.
There was no contagion following the failure of the bank - Credit Suisse was a very large, systematically important bank which had assets around 70% of Swiss GDP.
The failure was resolved via the sale of Credit Suisse to UBS and the use of the “bail-in” bonds - Cocos - pioneered after the financial crisis. Cocos are a high-yield bond which in exchange for the high yield they earn their bonds, unlike other bonds issued by banks, aren’t senior to equity. This was used by Credit Suisse - specifically the highest yield Cocos were entirely written off - to reduce their liabilities to facilitate their sale to USB.
Credit Suisse, therefore, looks in some ways like a success - a formerly too-big-to-fail bank was allowed to fail and its assets transferred to a better-run bank without the use of public funds or contagion to the rest of the financial system.
**Credit rating agencies appendix**
Credit rating agencies played a key role in the financial crisis. The subprime mortgage-backed securities that were at the heart of the financial crisis were systematically rated as less risky than they in fact were by credit rating agencies, who also played a key role in engineering the products and their derivatives. Not only were they systematically rated as less risky than they in fact were, but the senior tranches of mortgage-backed securities were also often rated as prime securities, the highest grade, and so were used as short-term collateral for the repo markets that various financial institutions used to raise capital short term to pay short term debts. When the value of mortgage-backed securities dropped dramatically it meant that banks faced both questions over solvency and over short-term liquidity because they could borrow much less in the repo markets because the value of their collateral had dropped so substantially.
The solvency problem generically comes about when a large asset class is incorrectly priced while the liquidity problem came about because only very safe assets are used as collateral in the repo market (mostly treasury bonds, generally considered the safest possible debt). Therefore had rating agencies not classed mortgage-backed securities as extremely low risk there would not have been the liquidity crisis that, in particular, sunk Lehmann brothers. Lehman was probably solvent but lacked the liquidity to pay its short-term debts since it could no longer rely on the repo market to the degree it previously had. It is important to note though that [Lehman only came to rely on the repo market so heavily as a result of the solvency issues](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156006) caused by its investments in securities tied to the housing market, after the downturn in the housing market that began the financial crisis,
The key institutional factors behind the failure of credit rating agencies to properly assess risk and why this was so catastrophic:
* Credit ratings from agencies were given the force of law - as a result, pension funds and other financial institutions required by law to only invest in very safe assets took the ratings as close to gospel
* Sell-side paid for ratings to be carried out - for MBS and derivatives on MBS only a small number of very powerful firms were engineering these assets giving them a lot of market power
* Unlike corporate and government debt models for assessing MBS+deriviatives debt opaque - much less clear if rating agencies were systematically undervaluing risks and so reputational consequences could act as less strong constraints
* Agencies involved in the financial engineering of MBS+derviratives meaning that almost by construction the financial instruments would be rated as investment grade
* There are negative externalities from underestimating the risk of assets because of the contagion of bad loans in sufficiently large sufficiently interconnected financial systems meaning that we should expect risks to be systematically underestimated relative to the social optimum (although to some degree ratings agencies internalise this in that they lose business if the financial sector shrinks)
Post financial crisis there have been substantial reforms of the credit ratings agencies, specifically preventing them from providing agencies rating financial products they were involved in designing.
**State capacity appendix**
The concept of a Weberian bureaucracy, proposed by German sociologist Max Weber, refers to an ideal type of administrative structure characterized by hierarchical authority, formal rules and procedures, division of labour based on expertise, and impersonal relations between administrators and the public. A Weberian bureaucracy is meant to operate in a rational and efficient manner.
In his 2011 book The Origins of Political Order, Francis Fukuyama argues that the development of modern, centralized state bureaucracies has been crucial to state capacity. According to Fukuyama, a professional bureaucracy with merit-based recruitment and organized hierarchy enables the state to effectively implement policies and provide public services. He points to China's long tradition of bureaucratic governance as an important factor enabling its state capacity. Fukuyama contends that Weberian bureaucracies lead to greater state capacity by insulating public administration from patrimonialism and politicization.
Similarly, in his 1968 book Political Order in Changing Societies, Samuel Huntington emphasized the need for developing countries to establish coherent, autonomous bureaucracies. He argued that many post-colonial states failed to build effective state capacity because their bureaucracies remained personalized instruments of particular leaders or factions, rather than becoming impersonal administrative structures. For Huntington, modernizing states requires a bureaucracy with a strong sense of corporate identity and morale. Like Fukuyama, he sees merit-based recruitment and promotion as key to having an effective state bureaucracy.
In his 1957 book The Soldier and the State, Huntington looked specifically at civil-military relations. He argued that a professionalized, apolitical military bureaucracy was essential for state capacity and stability. Huntington contended that keeping the military out of politics required recruiting career soldiers on the basis of merit rather than personal ties.
In their 2019 book The Narrow Corridor, Daron Acemoglu and James Robinson argue that inclusive economic and political institutions enable the development of state capacity. They contend that extractive institutions that concentrate power undermine the creation of effective bureaucracies. Like Fukuyama and Huntington, Acemoglu and Robinson emphasize building impersonal bureaucracies not beholden to special interests.
In the United States, the increased political appointment of senior civil servants has raised concerns about politicization eroding bureaucratic professionalism and autonomy. For example, critics argue that appointing agency heads based on loyalty rather than expertise damages state capacity by undermining competent governance.
In India, the overrepresentation of upper castes in bureaucratic positions, particularly in northern states, has limited state capacity by excluding lower castes from the bureaucracy. Nepotism and patronage in civil service recruitment and promotion have weakened meritocracy and accountability. Caste-based politics have prevented the Indian bureaucracy from becoming fully impersonal and professionalized.
In conclusion, the literature emphasizes that developing modern bureaucratic structures based on Weberian principles of meritocracy and insulation from partisan politics is essential for state capacity. The experiences of the US and India illustrate the costs to effective governance when bureaucracies are politicized or dominated by narrow interests.
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716600a9-0798-4b39-89c1-70fd35176901
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trentmkelly/LessWrong-43k
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LessWrong
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A Review of Weak to Strong Generalization [AI Safety Camp]
Thank you to everyone in AI Safety Camp group 22 for the discussions and suggestions. In particular, thank you to: Bogdan Ionut Cirstea, Vassil Tashev and Jaeson Booker
Introduction
The goal of AI Safety Camp Team #22 is to assess how promising automating alignment research is (see source 23). We have decomposed the problem into various sub-problems. One of my teammates (Vassil Tashev) and I have focused a couple weeks of reading and discussion on weak to strong generalization. This is a research direction that the OpenAI super alignment team explored in their first paper, which they published in December of 2023. Here we present a comprehensive review and reading list for weak to strong generalization. Our aim is to assess whether this research direction is promising towards the goal of creating a roughly human level, aligned, automated alignment researcher (source 16) - this appears to be OpenAI’s super alignment team’s alignment plan (source 25). We believe this may be the most scalable alignment research direction.
The Problem
Current alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on human feedback. This will break down when we try to align models more capable than humans, because the human feedback data to draw on will be poor. Humans will have difficulty robustly evaluating the model’s responses because strong capabilities are more difficult to evaluate than subhuman capabilities. Imagine evaluating whether thousands of lines of code that a possibly superhuman model has written, and rating whether the model has done as well as it could.
As an analogy, consider what might happen were one to hand an 17th century engineer 4 alternative schematics. The engineer is instructed to select (thumbs up) the machine that cools air, where one is a modern air conditioner and the other three are technical diagrams for heating devices. The 17th century engineer lacks the knowledge to understand refrigeration or the tempera
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bf256cc1-0990-4967-bd02-e83c56efac9f
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Tempe, AZ (ASU)
Discussion article for the meetup : Tempe, AZ (ASU)
WHEN: 04 April 2014 06:30:00PM (-0700)
WHERE: 300 E Orange Mall, Tempe, AZ
We'll meet at the usual location, the entrance to the Hayden Library at ASU at 6:30 PM. Last time, we stayed until around 9:30 PM IIRC, and this meeting will probably be similar. There probably won't be food, so eat before showing up.
The plan is: free discussion. If there are any newcomers, introductions could happen. If we have the patience for it, meta.
Discussion article for the meetup : Tempe, AZ (ASU)
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2ffeb3c5-8fc7-43d6-aae4-5c9583c7475a
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StampyAI/alignment-research-dataset/arbital
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Arbital
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Realistic (Math 1)
### What's the chance that a potentially good partner would flake on the first date?
From a test subject for an early version of the Bayes intro:
A 96% OKCupid match canceled their first date for coffee without providing an explanation.
She estimated that a man like this one had [prior](https://arbital.com/p/1rm) [odds](https://arbital.com/p/1rb) of 2 : 5 for desirability vs. undesirability, based on his OKCupid profile and her past experience with 96% matches. She then estimated a 1 : 3 [likelihood ratio](https://arbital.com/p/1rq) for desirable vs. undesirable men flaking on the first date. This worked out to 2 : 15 [posterior](https://arbital.com/p/1rp) odds for the man being undesirable, which she decided was unfavorable enough to not pursue him further.
She [used this explicitly Bayesian calculation to interrupt a 'worrying' cycle](https://arbital.com/p/1x3) wherein she was focusing on one consideration, then a different consideration, arguing for pursuing further / not pursuing further. Making up numbers and doing the Bayesian calculation [terminated this cycle](https://arbital.com/p/1x3).
|
b97f7137-388c-4493-a7e9-512779f6f55a
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Reinforcement learning with imperceptible rewards
TLDR: We define a variant of reinforcement learning in which the reward is not perceived directly, but can be estimated at any given moment by some (possibly costly) experiment. The reward function is no longer a function of the observation history, but a different object that we call "instrumental reward function". We give two definitions of instrumental reward function and prove their equivalence. We also derive a regret bound for this setting.
Background
In "classical" reinforcement learning the agent perceives the reward signal on every round of its interaction with the environment, whether through a distinct input channel or through some given way to compute the reward from the interaction history so far. On the other hand, we can rather easily imagine agents that optimize properties of their environment that they do not directly perceive. For example, if Alice, who lives in Dominica, donates money to the Against Malaria Foundation in order to save someone in Africa, then the result is usually not visible to Alice at the time it occurs, if ever. Similarly, Clippy the paperclip maximizer doesn't always perceive all the paperclips in the universe. Moreover, we might want to design agents that, in order to estimate the reward, direct queries to humans (which is costly and cannot be done continuously non-stop).
Now, it is possible to define the perceived reward as the subjective expected value of the "true" imperceptible reward (see the Results section for details). Although this transformation preserves expected utility, it does not preserve Bayesian regret. Indeed, Bayesian regret is the difference between the expected utility attained by the agent and the expected utility attained by a "reference" agent that knows the true environment from the onset. However, after the transformation, the reference agent will behave as if it knows the observable dynamics of the true environment but still pretends not to know the true environment for the purpose of computing t
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dc176e45-a814-4f5d-9929-5f523b730dfb
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StampyAI/alignment-research-dataset/arxiv
|
Arxiv
|
Deploying Lifelong Open-Domain Dialogue Learning
1 Introduction
---------------
Humans learn to use language
over the course of their lives from the interactions they have with the world and other people.
Yet, the prevailing dominant paradigm in natural language processing (NLP) research is to build a fixed dataset from which to train a model and then freeze it, without any ability for the model to interact with humans using language at training time at all. While we need such interaction in order to study human-machine communication to its full extent, constraints usually inhibit such research.
Firstly, conducting such experiments can be costly.
Many datasets in NLP are collected with crowdsourcing, whereby one pays the crowdworkers to perform interaction and annotation tasks. This leads to several issues, not least that
research budgets for paying crowdworkers mean that data will have a limit.
Secondly, as crowdworkers are motivated by pay, not by interest in the actual tasks themselves, the data distribution may not match the desired one de Vries et al. ([2020](#bib.bib1 "Towards ecologically valid research on language user interfaces")).
In this work we study the ability of an open-domain111In this work we study dialogue that can be about any topic but within the scope of a fantasy game world. Note this differs from open-domain dialogue talking about our world, e.g. the game players can talk about the sauce recipe from Bredwell across the sea (see Fig. [1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ Deploying Lifelong Open-Domain Dialogue Learning")), but not about the pizza in Chicago. dialogue model to iteratively learn from conversations with intrinsically motivated humans. In order to engage humans at scale, we build and deploy a (free to play) game with a purpose Von Ahn ([2006](#bib.bib3 "Games with a purpose")) whereby human players roleplay characters and converse with other characters (that are our learning models) situated within the game world.
We choose a fantasy game world, in order to maximize engagement. Our system iterates between collecting data of human-model interactions, retraining updated models on the newly collected data, and redeploying them.
Simultaneously, it provides a natural metric to evaluate and compare models online using the continuation rate of players (how long they continue playing).
We show that we can successfully collect, retrain and redeploy models that improve both offline automatic metrics and human continue rates. Our overall system is engaging enough that we can collect data
at a rate that is 1/5th of the price per utterance of crowdsourcing, where the cost of our method is the cost of advertisements that make players aware of the game. Moreover, the data we collect is also more effective per utterance at improving continue rates due to being more on-distribution than crowdsourced data.
As our models improve, these rates improve as well, as the continuation rate increases – meaning relatively more data is collected.
Overall, our work provides good evidence that lifelong dialogue learning in deployed systems with intrinsically motivated humans (rather than crowdworkers) can be successful, in particular by embedding such learning within games.
The training code and parameters of the models deployed, and the data collected in this work will be made publicly available for reproducibility and further research by the community222Available at:
[parl.ai/projects/light](https://google.com).
Figure 1: Example collected dialogues from humans role-playing in our deployed system, conversing with models. (Left) a conversation complete with described location and player persona; (Right) excerpts from two other conversations (out of 41,131 collected) to demonstrate the diversity of the open-domain task.
ex:game-setup
2 Related Work
---------------
##### Open-Domain Dialogue
Dialogue in the open-domain setting, wherein the conversation involves chat about any topic, rather than a specific goal-directed topic, is commonly studied in the train/valid/test static dataset paradigm utilizing supervised learning.
A number of crowdsourced or scraped datasets have been developed to that end, including Daily Dialogue Li et al. ([2017](#bib.bib33 "DailyDialog: a manually labelled multi-turn dialogue dataset")), PersonaChat Li et al. ([2016a](#bib.bib20 "A persona-based neural conversation model")), Empathetic Dialogues Rashkin et al. ([2019](#bib.bib129 "Towards empathetic open-domain conversation models: a new benchmark and dataset")) and Wizard of Wikipedia Dinan et al. ([2019b](#bib.bib53 "Wizard of Wikipedia: knowledge-powered conversational agents")).
##### Light
In this work we specifically focus on the open-domain dialogue setting of LIGHT Urbanek et al. ([2019](#bib.bib192 "Learning to speak and act in a fantasy text adventure game")). LIGHT focuses on situated characters playing character roles that can talk about any topic, within the context of a medieval fantasy world. This setting is known to be engaging for human role-players, and also alleviates some safety concerns in that the role-playing means they should not divulge personally identifying information.
The authors crowdsourced a dialogue dataset consisting of 8.5k episodes and 111k utterances, which they publicly released. We refer to this as LIGHT MTurk data, or LIGHT data for short, in the rest of this paper. In this work we utilize this data to build a deployed system whereby players can converse with models, and we can study lifelong learning with these models using the information in these new conversations.
##### Lifelong Learning
Lifelong learning is a machine learning paradigm whereby deployed models can interact
with the world and iteratively improve themselves from the things they learn,
eschewing the standard approach of a fixed training set from which a model is trained once Silver et al. ([2013](#bib.bib12 "Lifelong machine learning systems: beyond learning algorithms")). We note there are other closely related concepts to the topics in this work, such as incremental learning Castro et al. ([2018](#bib.bib9 "End-to-end incremental learning")),
continual reinforcement learning Ring ([1994](#bib.bib13 "Continual learning in reinforcement environments")) and
never-ending learning Carlson et al. ([2010](#bib.bib14 "Toward an architecture for never-ending language learning")); Mitchell et al. ([2018](#bib.bib21 "Never-ending learning")).
##### Continual Dialogue Learning
Learning from dialogue interaction is common in reinforcement learning settings, where the feedback is a scalar rather than the dialogue messages themselves
Levin et al. ([2000](#bib.bib167 "A stochastic model of human-machine interaction for learning dialog strategies")); Schatzmann et al. ([2006](#bib.bib5 "A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies")); Rieser and Lemon ([2011](#bib.bib74 "Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation")); Liu and Lane ([2017](#bib.bib6 "An end-to-end trainable neural network model with belief tracking for task-oriented dialog")); Serban et al. ([2017](#bib.bib111 "A deep reinforcement learning chatbot")), which is most common in a goal-oriented setting where
completion of the goal can provide such rewards.
In this work we study learning from open-domain interactive dialogue messages, not from rewards.
Closer to our work, is the self-feeding chatbot Hancock et al. ([2019](#bib.bib182 "Learning from dialogue after deployment: feed yourself, chatbot!")),
whereby it is shown that models can be used to collect data to improve themselves via crowdsourcing utilizing the PersonaChat task.
Related approaches have also been applied to the more limited case of question answering
Li et al. ([2016b](#bib.bib8 "Dialogue learning with human-in-the-loop"), [c](#bib.bib10 "Learning through dialogue interactions by asking questions")), or in simulation Mazumder et al. ([2019](#bib.bib2 "Lifelong and interactive learning of factual knowledge in dialogues")) as well.
Our work differs from these works
in that we study a deployed user-facing system in a rich open-domain setting,
rather than more limited data from paid crowdworkers, and thus
study a more realistic setting.
##### Deployed Dialogue Systems
While there are a number of deployed open-domain virtual assistants, many of these products are not ideal platforms for the research community. Their proprietary nature and commercial importance, coupled with privacy concerns, means they are neither accessible to researchers,
nor amenable to public reproducible research.
A near-exception is the Alexa challenge Ram et al. ([2018](#bib.bib4 "Conversational ai: the science behind the alexa prize")) which allows university-based researchers access to a commercial user-base for the span of the competition,
however, the data and models are also not released to the rest of the research community.
3 Open-domain dialogue as a game
---------------------------------
In this section we describe the game that we will build
and deploy, which is a dialogue role-playing game.
It is a game with a purpose, and as such is
designed to both train and evaluate open-domain dialogue agents.
##### Core Game
The core game involves pairing two agents in a given setting – where one is a human player and the other is a dialogue agent with an underlying machine learning model.
The two players are assigned characters, with given names and backstories (personas),
and their current location and its description. See Figure LABEL:ex:game-setup
for examples. Each player’s goal is simply to act out (role-play) their character’s dialogue in the given situation. We refer to one such dialogue episode as a mini-game. Dialogue in the game is in English.
##### Role-Playing (Acting) Score
We take advantage that role-playing is a pursuit that a large number of human players find
fun Horsfall and Oikonomou ([2011](#bib.bib16 "A study of how different game play aspects can affect the popularity of role-playing video games")), and are hence naturally engaged in the open-ended nature of
this process. However, to encourage and further motivate players to play their characters well,
we introduce the concept of an (automated) dungeon master (DM), who will assess the quality of the player’s role-playing abilities. For each dialogue turn, we apply a learnt model to the human player’s dialogue, which assesses how likely their utterance is given the context. We convert this to a score, between 1 and 5 stars, that is presented to the human player, to reward them for good acting. While this signal is noisy, because our DM model is not perfect, it gives motivating feedback to the players to continue playing.
##### Other Gamification Steps
The acting scores (between 1-5 stars per turn) are accumulated, and a player’s total score is presented on a leaderboard compared to all other players, providing further motivation to reach the top of the leaderboard.
We also award “badges” if for a given dialogue a certain number of points are collected (11 for 1 badge, 16 for two):
the badges represent the characters in the game, motivating the desire to role-play all the characters in the game, and collect all the badges.
##### Game Loop
Each dialogue (mini-game) consists of 6 turns of dialogue per agent (12 total). At the end of the mini-game the human player is presented with four choices: (i) choose to move to a new location, where they will continue to play this character, but meet a new character to converse with;
(ii) stay in the same room but wait for a new character to arrive to converse with; (iii) change to role-play a completely new pair of characters in a new setting; or (iv) end the game.
These choices encourage the player to choose another mini-game that they are most interested in, and the variety of mini-games gives different role-playing possibilities, making the dialogue data more diverse.
##### License Agreement and Public Release
Upon entry to the game, players are asked to agree to the use and release of the resulting game data as a publicly available dataset for research purposes. They are urged to stick to their assigned characters in the game, and hence should not use any personally identifying information, which the terms also tell them explicitly not to share. In the released data, no other information about the player is retained except for the messages they send in the game.
##### Game Safety
We employ a safety classifier Dinan et al. ([2019](#bib.bib183 "Build it break it fix it for dialogue safety: robustness from adversarial human attack")) on both human and model turns. For safety reasons, we limit our dialogue models to be retrieval models, so that we could vet the entire set of candidates for offensive language before run-time. The set
of settings and character personas were all also vetted for offensive language.
Additionally, gender bias concerns have been previously studied within the available LIGHT MTurk training set Dinan et al. ([2019a](#bib.bib238 "Queens are powerful too: mitigating gender bias in dialogue generation")), and we make use of that publicly available data here as well.
We note that, compared to other deployed dialogue systems, there is an extra level of indirection due to playing characters in a game that makes language relatively less offensive. For example, a thief in the game saying “I’m going to steal your money” to another game character is far less offensive compared to a digital assistant saying it directly to a human user.
4 Lifelong Dialogue Learning
-----------------------------
###
4.1 Models
##### Retrieval Models
All the models we have currently deployed are retrieval models (see previous discussion of safety). In particular, we use the Poly-Encoder (PE) Transformer architecture as a base Humeau et al. ([2019](#bib.bib104 "Poly-encoders: architectures and pre-training strategies for fast and accurate multi-sentence scoring")), as it provides state of the art results compared to other retrieval models, whilst being tractable to deploy.
PE encodes the context with a standard bidirectional transformer, but produces an encoding into a fixed small number of codes, N. We tried values of N=5 and N=20.
Each label candidate then attends to these codes before producing a final matching score. The model is trained with cross-entropy given the correct label, and by subsampling negative examples from the given training batch.
##### Architecture and Training Choices
We employ the 90M and 622M parameter models from
Roller et al. ([2020](#bib.bib18 "Recipes for building an open-domain chatbot")) that have been pre-trained on 1.5B training
examples from pushshift.io Reddit, which we then fine-tune. We also consider two other enhancements, chosen to mitigate problems that we observed with the models:
(i) negative context training, whereby negatives are also selected from the immediate dialogue history as well as the batch
which can help reduce a model’s tendency to repeat itself Holtzman et al. ([2019](#bib.bib210 "The curious case of neural text degeneration")); Welleck et al. ([2020](#bib.bib105 "Neural text generation with unlikelihood training"));
and (ii) decoding control See et al. ([2019](#bib.bib178 "What makes a good conversation? how controllable attributes affect human judgments")) whereby at decoding time responses are rescaled before scoring based on their specificity (normalized inverse document frequency). The latter can control the genericness of the responses, which is known to affect human judgments.
##### Generative Models
In addition to the deployed models, we also perform training and automatic evaluation metrics on generative models offline, where safety concerns are less important as the models are not user-facing.
We employ an encoder-decoder Transformer architecture using the state of the art pre-trained 2.7 billion parameter BlenderBot model Roller et al. ([2020](#bib.bib18 "Recipes for building an open-domain chatbot")), which we fine-tune on our task. All training is performed in ParlAI Miller et al. ([2017](#bib.bib154 "ParlAI: a dialog research software platform")).
##### Agent Dialogue Model
Training a dialogue model involves one of the setups described above, and a set of (dialogue context, correct label) pairs. We will train on such pairs both from crowdsourced data and data collected within game in our lifelong learning setup.
##### Acting Score Model
We can apply the same models to also score the human’s role-playing abilities. In this case, the context is the entire dialogue history, setting and the player’s character persona as input to the encoder, while the candidates to score are the ones from the training set, as usual, plus additionally the human’s (player’s) actual response. For speed, the encoder can actually be run while the human player is typing, as it does not depend on their response, which is treated as a candidate label instead. The score given to the user is then proportional to the human response’s rank amongst all the candidates333The player is awarded 2 stars if their response is in the top 2000, 3 stars in the top 1000, and 4 stars in the top 100..
###
4.2 Iterative Data Collection and Training
After collecting a certain amount of episodes of conversational data between humans and models,
one can consider using this data for training. We utilize the following observation: while the model utterances may contain many mistakes, it is assumed that a human sufficiently engaged provides high quality responses, even if the model responses are mistakes, and can thus be treated as gold, and used as a fully supervised signal. We thus separate the dialogue data into all possible (context, next utterance) pairs, and then only consider the pairs with human next utterances as training data. We also compare this to further filtering this set by scoring the quality of the human utterances, discarding those episodes (mini-games) with lower quality. We use the acting score model previously described for this purpose, summing the scores obtained across an episode, and discarding the episode if this value is less than C,
where C is a hyperparameter tuned on the validation set.
After training our model from a given round of collection, we can go back to the collection process utilizing instead the new model that has been trained on more data. The hypothesis is that the
higher quality the model is, (i) the higher quality the human data will be as well; and (ii) the more likely the human players are to converse longer, increasing the data set size by larger amounts.
###
4.3 Deployment-based Evaluation
Apart from the collection-training cycle of our deployed lifelong learning setup, one can also
in parallel perform evaluation.
For each separate mini-game (episode of dialogue) we can potentially deploy a different model for human-model conversation.
We maintain a pool of models with differing architectures or hyperparameters, and select randomly from the pool in each episode. For any given episode we record whether the player continued playing to the next mini-game or not, which we refer to as the continue rate. We can measure the quality of a model using its averaged continue rate over all players and episodes. In this way we can also perform model selection online.
| Data Type | Num. Epsiodes | Num. Utterances | Num. Human Utterances | Unique Locations | Unique Characters |
| --- | --- | --- | --- | --- | --- |
| Training | 41,131 | 461, 984 | 230,992 | 587 | 630 |
| Validation | 500 | 5,936 | 2,968 | 231 | 463 |
| Test | 1000 | 11,822 | 5,911 | 296 | 569 |
Table 1:
Data Statistics of our lifelong learning deployment at the point where we froze collection for experiments reported within the paper and subsequent data release.
| | Num. | Num. | Num. Human | Unique | Avg. Human | Number of |
| --- | --- | --- | --- | --- | --- | --- |
| Dataset | Episodes | Utterances | Utterances | Tokens | Utt. Length | Humans |
| PersonaChat Zhang et al. ([2018](#bib.bib52 "Personalizing dialogue agents: i have a dog, do you have pets too?")) | 8,939 | 131,438 | 131,438 | 18,688 | 11.9 | UNKNOWN |
| Wiz. of Wikipedia Dinan et al. ([2019b](#bib.bib53 "Wizard of Wikipedia: knowledge-powered conversational agents")) | 18,430 | 166,787 | 166,787 | 52,490 | 19.7 | UNKNOWN |
| Empathetic Dialog Rashkin et al. ([2019](#bib.bib129 "Towards empathetic open-domain conversation models: a new benchmark and dataset")) | 24,850 | 64,636 | 64,636 | 19,458 | 15.3 | 810 |
| Daily Dialog Li et al. ([2017](#bib.bib33 "DailyDialog: a manually labelled multi-turn dialogue dataset")) | 22,236 | 87,170 | 87,170 | 20,673 | 14.5 | UNKNOWN |
| LIGHT MTurk Urbanek et al. ([2019](#bib.bib192 "Learning to speak and act in a fantasy text adventure game")) | 8,538 | 110,877 | 110,877 | 33,789 | 18.3 | 1,052 |
| LIGHT WILD (this paper) | 41,131 | 461,984 | 230,992 | 47,526 | 11.9 | 13,188 |
Table 2:
Comparison of statistics of the open-domain dialogue data collected during our lifelong learning deployment (bottom row) compared to several existing crowdsourced datasets.
Our data is around twice as large in terms of human utterances than these datasets, and 4x as large in terms of dialogue utterances (as our data consists of human-model conversations), while the cost to collect our data was only 1/5th of the price per utterance of LIGHT MTurk, see Sec. [5.3.3](#S5.SS3.SSS3 "5.3.3 Cost Learning Curves ‣ 5.3 Analysis of Results ‣ 5 Experiments ‣ Deploying Lifelong Open-Domain Dialogue Learning").
| | | |
| --- | --- | --- |
| | Retrieval Model (Hits@1/20 ⇑) | Generative Model (PPL ⇓) |
| Model | LIGHT Test | LIGHT Test Unseen | WILD Test | LIGHT Test | LIGHT Test Unseen | WILD Test |
| Round 1 | 87.12 | 82.43 | 81.61 | 12.67 | 11.81 | 13.42 |
| Round 2 | 87.65 | 82.70 | 84.60 | 12.57 | 11.74 | 12.31 |
| Round 3 | 87.72 | 83.48 | 87.63 | 12.54 | 11.75 | 11.79 |
Table 3: Three rounds of training in our lifelong open-domain dialogue learning setup. Both retrieval and generative models trained on the data from the three rounds improve across both metrics on all three test sets.
| | |
| --- | --- |
| Hits@1/20 Accuracy on the LIGHT WILD validation set as a function of the number of training examples (left) or the cost of data collection (right). The cost axis is in units scaled by the cost of LIGHT WILD collection required to achieve the same performance as using the entire LIGHT MTurk dataset; it is more than 8 | Hits@1/20 Accuracy on the LIGHT WILD validation set as a function of the number of training examples (left) or the cost of data collection (right). The cost axis is in units scaled by the cost of LIGHT WILD collection required to achieve the same performance as using the entire LIGHT MTurk dataset; it is more than 8 |
Figure 2: Hits@1/20 Accuracy on the LIGHT WILD validation set as a function of the number of training examples (left) or the cost of data collection (right). The cost axis is in units scaled by the cost of LIGHT WILD collection required to achieve the same performance as using the entire LIGHT MTurk dataset; it is more than 8× cheaper to use LIGHT WILD examples than LIGHT MTurk examples to achieve an accuracy of 80.63%. We also show performance for models which equally sample data from LIGHT MTurk+WILD datasets for training; utilizing all the data from both sources yields the best performance. However, LIGHT WILD data gives better accuracy improvements per training example (left plot).
| | |
| --- | --- |
| . Left: Hits@1/20 accuracy on the WILD validation set when training with LIGHT MTurk + 10,000 examples from the WILD training set of a given predicted quality level, see Sec. | . Left: Hits@1/20 accuracy on the WILD validation set when training with LIGHT MTurk + 10,000 examples from the WILD training set of a given predicted quality level, see Sec. |
Figure 3: Predicted Data Quality. Left: Hits@1/20 accuracy on the WILD validation set when training with LIGHT MTurk + 10,000 examples from the WILD training set of a given predicted quality level, see Sec. [5.3.5](#S5.SS3.SSS5 "5.3.5 Data Quality ‣ 5.3 Analysis of Results ‣ 5 Experiments ‣ Deploying Lifelong Open-Domain Dialogue Learning"). Data that is predicted to be higher quality yields improved validation accuracies.
Right: The distribution of data quality predictions over the training set. A spike is seen at quality bin 6 because that is the lowest score one can achieve when completing a full episode (1 star per turn is awarded at minimum). Values lower than bin 5 indicate incomplete low-scoring episodes.
| Model | Train Data | Negative Context | Decoding Control | Continuation Rate |
| --- | --- | --- | --- | --- |
| 90M PE | LIGHT | yes | no | 72.2±1.9% |
| 90M PE | LIGHT | yes | yes | 74.1±2.0% |
| 90M PE | LIGHT + WILD | yes | no | 73.6±1.8% |
| 90M PE | LIGHT + WILD | yes | yes | 75.2±2.0% |
| 622M PE | LIGHT | no | no | 68.2±1.4% |
| 622M PE | LIGHT | yes | no | 69.9±1.9% |
| 622M PE | LIGHT | yes | yes | 69.9±2.0% |
| 622M PE | LIGHT + WILD | yes | no | 70.6±2.1% |
| 622M PE | LIGHT + WILD | yes | yes | 71.8±1.9% |
Table 4: Deployment-based Evaluation, comparing several metrics on data collected during Round 2 of collection.
| Model variation | Δ Continue Rate |
| --- | --- |
| + WILD train data (Round 2) | +1.3±0.7% |
| 90M → 622M parameters PE | −3.2±0.7% |
| + Negative context training | +2.6±1.3% |
| + Decoding control | +2.5±1.1% |
Table 5: Deployment-based Evaluation:
changes in continue rates for various model variants.
5 Experiments
--------------
###
5.1 Rounds of Learning
We performed three rounds of our lifelong learning setup.
##### Round 1
consists of models trained on LIGHT MTurk data only. We train the retrieval model variants described in Section [4.1](#S4.SS1 "4.1 Models ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning"), and deploy them within the game.
##### Round 2
consists of models trained on LIGHT MTurk data + 50,982 WILD examples collected from the deployment of the Round 1 models, and again deploy these within the game.
##### Round 3
consists of models trained on LIGHT MTurk data + 50,982 examples from Round 1 deployment + an additional 180,010 examples collected from Round 2 deployment.
###
5.2 Data Collection
While our setup is a lifelong learning setup and the models are still currently deployed and collecting data, for this paper we froze the collection at a given point in order to provide a data release and provide experimental results. The data statistics for the total newly collected dataset, called LIGHT WILD, over all rounds is shown in Table [1](#S4.T1 "Table 1 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning"). Validation and test sets were extracted from a portion of the data444For validation and test we only use complete conversations, and where the player scored ≥ 9 stars, to build higher quality evaluation sets. from Round 2.
Table [2](#S4.T2 "Table 2 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") compares this dataset to several existing commonly used open-domain dialogue datasets. The number of episodes and dialogue utterances are larger than many existing datasets, e.g. four times as many as LIGHT MTurk, and almost eight times that of Empathetic Dialogues.
Uniquely, our dataset contains human-model conversations, hence the total number of human utterances is actually half of the utterances, which is still twice as large as the number in LIGHT MTurk.
Our dataset also has a large degree of diversity, which is important for tasks in general, and especially for
open-domain dialogue. The number of unique locations and roles that can be played by speakers (characters)
is large (587 and 630, respectively). The number of players of the game at the time of freezing was over 13,000, which also makes the diversity far larger than typical crowdsourced datasets, e.g. LIGHT MTurk involved 1,052 and Empathetic Dialog involved 810 crowdworkers. Finally, the number of unique tokens is larger in LIGHT WILD, indicating the diversity of language used.
###
5.3 Analysis of Results
####
5.3.1 Performance by Round
While we only deployed retrieval models,
we report experiments training both retrieval models, and generative models on the data from the three rounds, selecting best hyperparameters using the validation set. We report the performance on three different test sets: LIGHT (MTurk) Seen and Unseen Test sets Urbanek et al. ([2019](#bib.bib192 "Learning to speak and act in a fantasy text adventure game")), where unseen means that the test locations do not overlap with the training set locations,
and our WILD test set. The results are given in Table [3](#S4.T3 "Table 3 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning"). They show a steady increase in the Hits@1/20 metric (Top 1 accuracy given 19 random distractors) for the retrieval models over the rounds on all three test sets, and a similar decrease in perplexity (PPL) for the generative models.
In particular there is a large jump in the performance on the WILD Test set between Rounds 1 and 2 as the training set switches from crowdsourced to in-distribution WILD data, and a further increase in Round 3 as more data is again collected and retrained on. While our WILD data is of a different distribution to the two LIGHT (MTurk) test sets, the data collection from our lifelong learning setup still gives gains on those tests as well. Our reported numbers, as far as we are aware, are the best reported numbers on these datasets, e.g. the original LIGHT paper reports 76.5% and 70.5% for the seen and unseen test sets, respectively (compared to our 87.72% and 83.48%). Overall, we see clear gains from the extra data collected in our setup.
####
5.3.2 Lifelong Learning Curves
We construct learning curves given all the collected data to analyze the performance gain per new training example. We plot Hits@1/20 accuracy on the WILD validation set
against the number of training examples,
comparing data from WILD collection to LIGHT (Mturk). We also consider a 50/50 mix, where we equally sample from the two sources LIGHT+WILD to provide the next training example.
Figure [2](#S4.F2 "Figure 2 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") (left) shows the results. We observe that on a per-example
basis our WILD data gives more accuracy gains than LIGHT MTurk data, e.g. 83.59% for WILD compared to 80.63% for LIGHT, when limiting WILD to the same training set size as the total size of LIGHT.
As the WILD dataset is more than twice as large this monotonically improves further, up to 85.95% using all of the WILD data. Combining the two data sources brings further gains, up to 87.2%. We observe that the improvements have not saturated and that further lifelong learning will bring further model improvements. Combining the sources, as in the LIGHT+WILD plot gives yet further gains.
Overall, our collected WILD data has high quality as a learning signal for training models.
####
5.3.3 Cost Learning Curves
We plot similar learning curves, but as a function of the cost to collect the data instead of the number of training examples instead, see Figure [2](#S4.F2 "Figure 2 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") (right).
Although we do not pay players to play the game, we did spend money to advertise the game online in order to attract players. We compare the cost per WILD example relatively to the cost per LIGHT (MTurk) example, where the x-axis is scaled in units that are multiples of the cost required to achieve 80.63% using WILD data (as this is the performance of using all the LIGHT MTurk data together). We observe that it costs over 8x more to achieve the same accuracy using LIGHT (MTurk) data (see dashed horizontal line). For the same price there is a large gap between the two systems, for example using all the WILD data gives a performance of 85.95%, whereas for the same cost LIGHT MTurk only achieves ∼77.5%.
Overall, WILD deployment is relatively a very cost effective strategy.
####
5.3.4 Deployment-based Evaluation
Our lifelong learning setup deploys multiple models (see Sec. [4.1](#S4.SS1 "4.1 Models ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning")) at the same time randomly assigning them to concurrent users per episode (mini-game). We can thus directly compare the quality of models
via their continue rate.
Continue rates during Round 2
of collection comparing several model variants are given in Table [4](#S4.T4 "Table 4 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning").
Continue rates are in the range of 68%-75%, depending on the model, and we observe some clear trends.
Most importantly,
for both model sizes tried, LIGHT+WILD trained models are superior to LIGHT only trained models, showing
that our deployment/train cycle produces better models as judged by humans.
Secondly, other factors in model design are important too, and our setup can effectively evaluate those.
In particular, for both model sizes it was found that both our negative context training and decoding control enhancements (see Sec. [4.1](#S4.SS1 "4.1 Models ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning")) improve the continue rate, with both methods used together improving more.
We confirm these conclusion in Table [5](#S4.T5 "Table 5 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") where we show the change in continue rates when independently adjusting one of these factors, by averaging over model continue rates for other factors of variation.
We also observe the unexpected result that the larger models perform worse than the small models across the board on continue rates. Deeper analysis given in appendix [C](#A3 "Appendix C Comparing Small and Large Model Variants ‣ Deploying Lifelong Open-Domain Dialogue Learning") suggests that while the larger model makes less mistakes, it is more often seen as boring, which would reasonably impact a player’s desire to continue playing. Understanding and controlling this trade-off should be studied further.
####
5.3.5 Data Quality
Not every player is as engaged in the game as every other player, or produces as high quality dialogue. We hypothesize that we can predict which
players produce higher quality data via the acting score model (Sec. [4.1](#S4.SS1 "4.1 Models ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning")), and that such higher quality data is relatively better for training models.
Figure [3](#S4.F3 "Figure 3 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") (right) shows the distribution over the WILD training set of predicted quality using the acting score model. We observe 83.7% of the episodes have a score above the minimum value of 6 (there are 6 turns, and on each turn a score between 1-4 is awarded, explaining the spike at the value of 6). Scores below 6 indicate incomplete dialogues, which only account for 4.0% of the data.
To assess whether these scores are indeed indicators of data quality, we selected an equal amount of 10,000 examples from each of the bins (1-5), (6), (7), …, (16) (grouping 1-5 together to make that group large enough) and compared them as training sources. We train a set of retrieval models on these training sources, where each model also has access
to all of the LIGHT MTurk data (111k examples) in addition to the WILD 10k from their respective bins. The results are given in Figure [3](#S4.F3 "Figure 3 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") (left).
We observe a monotonic improvement on the WILD validation set with increasing predicted quality.
We see similar, but smaller gains on the LIGHT validation set as well, e.g. 86.59% for quality bin 6, and 87.11% for quality bin 16.
While we can clearly select lower or higher quality data, we can also ask the question whether some of the data is so low quality we should simply remove it from the training data in order to get better performance. Experiments show that is not the case, and that even the lowest quality data does provide a useful signal, e.g. performance drops slightly from 87.06% to 86.69% on the WILD validation set if we remove bins lower than 6, but otherwise training on all other data, and to 85.38% if we remove bins lower than 9.
####
5.3.6 Observations on Gamification
Just as the design of a crowdsourcing task will affect the cost and quality of data, this is likely even more the case
in the design of a game. If the design is poor, players will not play it at all; whereas in contrast to paying crowdworkers, if players really like a game, they are willing to pay to play it.
Accordingly, the plots we presented in Figure [2](#S4.F2 "Figure 2 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning") represent the results of our particular game design; there may well be a design with vastly more cost efficient learning rates. While a full of study of the elements of game design is outside of the scope of this paper, we note that for adjustments we did make to the game after initial deployment we observed large changes in user behavior. For example, after the addition of the three user controls for how to continue the game loop after an episode is finished (as described in Sec. [3](#S3 "3 Open-domain dialogue as a game ‣ Deploying Lifelong Open-Domain Dialogue Learning")), compared to only a single choice, we saw an increase in the continue rate by 3.3±1.6% when using the same model.
Model quality also affects cost and quality of the data collected. Noting the effects of changing gamification options (alongside other hard-to-track circumstances) we only report continue rate comparisons between models relative to runs in the same batch. Still, players’ enjoyment of these models (as estimated by continue rate in Table [4](#S4.T4 "Table 4 ‣ 4.3 Deployment-based Evaluation ‣ 4 Lifelong Dialogue Learning ‣ Deploying Lifelong Open-Domain Dialogue Learning")) directly changes how much they engage with the game. As such it is more expensive to test models that are worse for the game experience (which we would consider fair from a player perspective). Hence, as models improve, costs actually go down, enabling to collect data at higher rate.
####
5.3.7 Analysis of Data Distribution
We can compare the dialogue data collected within our deployed system to crowdsourced data from LIGHT MTurk. We analyze over and underexpressed words in our dataset compared to the latter.
Calcuating the top 70 most overexpressed words, all overexpressed at least 3.5x relative to crowdsourced data, we note several interesting observations about our data’s distribution:
- There are more natural endings to conversations: e.g. “goodbye” (4×) and “bye” (4×) are overexpressed.
- There are overexpressed words associated with aggression:
“stab” (8.5×), “dagger” (6.1×), “club” (5.5×), “kills” (4.9×), “blade” (4.2×).
- There are overexpressed words associated with overtly friendly actions as well:
“smiles” (12.9×), “nods” (10.9×), “kiss” (6.1×), “hug” (3.7×), and “bows” (5.9×).
- There are more mentions of adventuring:
“quest” (5.4×), and other similar words not in the top 70 are overexpressed as well, such as “adventure” (2.5×) and “mission” (2.1×).
- There is an increased use of slang: “ur” (93×), “u” (28×), “yo” (5×), “dude” (6×). We note that some emojis exist in the dataset as well, which do not appear at all in the crowdsourced data.
In contrast, looking at the 70 most underexpressed words, all underexpressed by a factor of at least 1.3×, we observed the following patterns:
- Less mentions of village and farm life:
“peasant”, “fields” (both 2× underexpressed), “farm” and “crops” (both 1.9×), “harvest” (1.8×), “villagers” (1.7×), and “work” (1.4×).
- Less mention of passages of time:
“week” (2.1×), “year” (1.9×), “days” (1.8×).
Overall, we see a pattern that game players seek more exciting conversations,
involving more emotional, action-packed interactions such as seeking quests,
whereas crowdworkers
are more even-keeled, and able to discuss dry topics such as last year’s harvest or taxes with more frequency.
This is not unexpected as game players often seek immediacy and
larger-than-life experiences Grodal and others ([2000](#bib.bib17 "Video games and the pleasures of control")).
6 Conclusion and Future Work
-----------------------------
We have presented a fully realized system for improving
upon an open-domain dialogue task
by utilizing a deployed game with a purpose, for lifelong learning.
Detailed experiments showed that the one can collect high quality data that improves both automatic offline metrics and user engagement metrics when used for training models.
We find this exciting because this approach shows it is possible to build continually improving models that learn from interacting with humans in the wild (as opposed to experiments with paid crowdworkers), which represents a paradigm shift away from the limited static dataset
setup that is prevalent in much of the work of the community.
Future work should study the resulting publicly released data to explore other methods of
lifelong learning,
or other learning signals that could be extracted from human utterances, for example
the ideas in Hancock et al. ([2019](#bib.bib182 "Learning from dialogue after deployment: feed yourself, chatbot!")). Another possible direction, for when model performance begins to saturate, is to exploit control of the game engine itself to emphasize learning on the most difficult cases or the ones with the most learning signal, such as in the work on adversarial collection Yang et al. ([2017](#bib.bib28 "Mastering the dungeon: grounded language learning by mechanical turker descent")); Nie et al. ([2019](#bib.bib15 "Adversarial nli: a new benchmark for natural language understanding")).
Finally, our role-playing setup can also be applied to other domains, for example incorporating both dialogue and actions, situated in other domains.
|
5074ed44-d0b9-46f3-abff-5bc40cb85200
|
StampyAI/alignment-research-dataset/youtube
|
Youtube Transcripts
|
Stuart Armstrong Manchester AI debate
hello and welcome to this talk entitled
should we or not create strong AI an
important question connected with that
is could we create strong AI and if it
is possible could we actually decide not
to on the subtitle of the talk is yai
compares with plagues in nuclear war
which might give you an idea of the
direction where I'm coming from at the
future of humanity Institute where I
work we define existential risks as
risks plausibly likely to annihilate
earth-based intelligence life or
permanent curtail its potential amongst
the the central risks this or top five
we're looking at it for the moment are
no particular order pandemics synthetic
biology nanotechnology artificial
intelligence and nuclear war not
included on this list of things like
asteroid impact and environmentalists
simply because in relative terms these
are just not dangerous enough so today
I'm going to be focusing in on the
artificial intelligence and you might
take askance why is this considered to
be dangerous why is it indeed an
existential risks well first things are
any questions what do the experts say
let's have a look at them unfortunately
experts don't have exactly the best
track record this is the dartmouth
conference in 1956 essentially
predicting that artificial intelligence
could be made over the summer this is
Dreyfus nine years later I'm suggesting
that the top of the achievements of
computers was imminent I think it's safe
to say that neither of these predictions
have been entirely borne out in
practice here are some more I
predictions AI will be developed in 15
to 25 years you may want to guess when
this was made well in fact it was made
by various people in 2012 also 2011 2010
2009 2008 2007 six and a whole host of
dates all the way back to the 1960s I'm
hoping we can perform better than this
and the first question is to ask why is
why the prediction so terrible well
let's have a look at other fields where
predictions exist this is the xkcd
cartoon which arrangers feels by purity
with the purest field sneering of the
lace Pure Ones by a convenient
coincidence this is also approximately
things arranged by predictability how
strong and accurate the predictions are
if a physicist tells you something about
the result of an experiment he'll be
quoting three significant figures if a
sociologist or psychologists say
something the predictions will be a lot
less accurate and a lot less likely to
come true I've added an economist and
historians on the graph now why is there
such a difference in quality of
predictions what mainly because the
different fields have access to
different tools mathematicians are lucky
enough to be able to use deductive logic
others have stronger or weaker versions
of the scientific methods down to the
poor historians who are reduced to
nothing else for past examples but where
should a I prediction lie on this graph
well there is a convenient hole down
there in the left and indeed AI
predictors lie down here because they
don't even have past examples to rely on
since no one is actually built in AI so
they're relying on nothing but to expert
opinion which
considerably worse than any of the other
tools here so the question arises when
our experts good when index which would
give predictions this is from James
shantou who noted that the performance
of experts tended to vary more depending
on the field in which they were working
rather than what which particular expert
you had the quarter their quality or
their training so for instance are in
medicine anesthesiologists would be
quite good and many of the mammogram
interpreters who wouldn't because their
fields have different things of good
performance or poor performance
especially where feedback is concerned
now feedback is probably the most
important thing distinguishing a good
field feelers was a good front port one
two other important ones are weather
experts agree or disagree on their
stimulus and other aspects of it and
whether the problem is decomposed or not
where AI predictions are concerned were
probably stuck with this where almost
all the features are the ones that
should lead to poor performance
interestingly enough one of the major
ones that could lead to better
performance is what the field is
decomposable or not and it could be
decomposed but unfortunately very rarely
is so this is the theory what do we see
in practice well here I've plotted
various prediction dates for a ice
arrival made by some experts and non
experts and also the data which the
prediction was made you can distinguish
cheering's original prediction here and
the AI winter here where no one was
talking about AI anymore and the thing
that strikes me on looking at this is
just how spread out they are the
difference between any of those two bars
is 20 years and they are just spread out
all over the place no
difference between experts and non
experts and the real indication that
there's any sort of convergence on any
value some genuine sign of expertise now
this is the cartoon version of
disagreements and overconfidence because
we've seen that I experts strongly
disagree when we reach an opinion we
base it on a lot of things life
experience evidence you did arguments
and a variety of other stuff and let's
be honest some also some biases and
rationalizations and this reads leads to
a reasonable conclusion what about the
other people the people who disagree
with us well they're doing exactly the
same thing except when within our minds
this is all we can see so no matter how
much if we feels that our estimate is
correct and that earlier the else's
estimate is superficial this cannot tell
us that we are indeed correct because
that is what we expect to see in whether
or not we're correct so that means that
just because the AIS are disagree I
experts are disagreeing over the place
that doesn't mean that our own
intuitions are any more correct our
performance and our prediction is likely
to be just as good as an expert's which
means utterly terrible but let's look
into AI itself more why could it be a
potential risk well don't think of the
Terminator which is basically just a big
muscle and no brain as this picture
shows here the dominant species not the
one with the big muscles this is the
model of a chimp rain or a picture which
in brain next to picture of a human
brain to scale chimps have a population
about 200,000 and use basic wooden tools
humans have heavy industry and nuclear
bombs and we've spread across almost the
whole surface of the earth and since
we've I've munted our power
computers aren't elected with computers
we've developed hydrogen weapons I
landed on the moon and had unprecedented
economic growth so the question of what
could happen with intelligence is if we
have an AI that takes the next step up
what transformations it could write it
could do my preferred model of what sort
of AI you could get with purely human
level artificial intelligences is you
could create say a super committee of
the AI Edison Einstein George Soros
Clinton Oprah Plato gurbles Steve Jobs
and Bernie Madoff give them vast
databases and network them together
running at thousands and thousands of
times human speed this entity you could
create with just by copying and training
human level eyes would probably consider
that the internet and the human race are
just useful resources for whatever its
goals are its goals okay it's one thing
to say it's powerful but might it not
have positive goals or potentially what
we would want is that the AI would have
a tag where kill all humans is false and
help our humans it's true except of
course the problem is that these are
undefined and trying to define what
these means is immensely complicated and
prone to a lot of potential disasters
for instance the goal of preventing
human suffering which sounds very nice
and effective how would a I interpret
this well this is the single kill all
humans is the single fastest and best
way of preventing human suffering okay
that's not what we meant but what we
said so let's be a little bit more
sophisticated keep humans safe and happy
okay
I think you can see what this is going
this is in Tomb everyone in underground
concrete coffins on heroin trips and the
AI will fight you if you try and prevent
it from doing this because any other
possibility any other outcome will not
be the maximum way of keeping human safe
and happy Andy I'm a perfectly well
understand this is not what we meant but
it has absolutely no reason to care now
some slightly more sophisticated
versions have the AI de Deus human
preferences from observation rather than
trying to program them in now this isn't
may not be quite as dangerous but
there's definitely a risk that if we
unleash this an AI takes it literally
that we get a future of the entire
universe that sort of looks a bit like
this
anyway I said that there were the AI is
a domain which is very difficult to
predict that's true you could say quite
a lot more about how the a.m might
develop then about sort of specific
timelines this is what I call these
simplified Omohundro you kowski thesis
that behaving dangerously is a generic
behavior for high intelligent a is for a
variety of reasons to do with how the AI
would work on itself how it's again with
unclear goals and how amassing power is
almost always a good thing for the AI to
do whatever its goals because it gives a
great chance of achieving its goals and
if say human safety is not fully
programmed in humans would might just be
an obstacle or tool for the AI to
achieve power for whatever goals it
actually has now this is sort of the
simplified economic supply and demand
equivalent it's a good starting point
now you need to carry it a bit which is
that many AI designs have the potential
for unexpected dangerous behavior and
with that claim that goes a normative
claim that AI programmers should
demonstrate to moderate skeptics the
design is safe now you might disagree
with this thesis even if it's caveated
refined and narrowed form despite the
evidence and the arguments there but if
you disagree there's something very
simple which you can do which
demonstrates a moderate skeptics that
your design is safe if your design does
not pass this bar then what are you
doing messing around with it in the
first place but anyway to sort of
summarize of AI is there potentially
extremely powerful I don't want to claim
that it's certain that they will be but
there are great uncertainties here and
the great uncertainties cannot allow us
to say they won't be extremely powerful
but they're necessarily week the
probability of them being extremely
powerful is worryingly
hi there is extreme uncertainties as
I've said it's probably inevitable if a
eyes could be developed our then with
the commercial pressures and military
competitive pressures it's probable that
someone might that someone would given
that they can in one country or in
another they're potentially extremely
dangerous as we've seen and they're very
few people working on a true AI safety
there's some at the future of humanity
Institute where I work the summits miri
which is a Californian group and there's
a few other scattered ones but it is
very small and so I conclude this by
pointing you to the websites of these
organizations I have a booklet called
smarter than us that presents these
arguments in a polarized form Nick
Bostrom the head of my Institute has a
much better and thicker book called
super intelligence which I strongly
recommend that you look at and thanks
for listening
you
|
8560121c-7044-4880-891f-992e948cbe85
|
trentmkelly/LessWrong-43k
|
LessWrong
|
The Promises and Pitfalls of Long-Term Forecasting
Disclaimer: We will be speaking at the Manifest Conference in Berkeley, CA (Sept. 22-24) about long-term forecasting, its promises, and its pitfalls. Below is an excerpt from last week's edition of our newsletter, Predictions, detailing some of what we plan to speak about at the event. This week's edition can be found here.
If you are reading this newsletter, you probably have a certain amount of intellectual buy-in on the concept of quantified forecasting. However, much of the social sciences remain skeptical, to say the least, about the practice.
Take the domain of international relations, where Philip Tetlock got his start and serves as the basis for much of the quantified forecasting research. Many scholars have raised objections about whether or not we can prediction international relations which can be best captured by Robert Jervis’ System Effects published in 1997.
System Effects
The essence of the book (which should probably get its own dedicated post at some point) is that the realm of international politics deals with a system where its elements are interconnected, such that a change in one part of the system makes changes in other parts of it, and that the system contains properties and behaviors which are different than those of its part (in other words, that the total is greater than the sum of its parts).
As a result of systems effects, in the international system (and any complex, interconnected system for that matter) we have:
Delayed and indirect outcomes
Emergent characteristics — Relationship between elements are based on relationships to other elements
Non-integrable function — you cannot understand the whole thru its parts
Unintended outcomes
Nonlinearities — unexpected breaks from the past given history
Feedback loops
Regulation being difficult
Does Jervis believe these system effects doom prediction? Not entirely, especially in his 1997 book. Although by his 2012 revisit, Jervis takes a slightly more negative tone, writing:
> ..
|
9c7dec31-9a51-4072-925c-c0b084210722
|
trentmkelly/LessWrong-43k
|
LessWrong
|
My Interview With Cade Metz on His Reporting About Slate Star Codex
On 16 March 2024, I sat down to chat with New York Times technology reporter Cade Metz! In part of our conversation, transcribed below, we discussed his February 2021 article "Silicon Valley's Safe Space", covering Scott Alexander's Slate Star Codex blog and the surrounding community.
The transcript has been significantly edited for clarity. (It turns out that real-time conversation transcribed completely verbatim is full of filler words, false starts, crosstalk, "uh huh"s, "yeah"s, pauses while one party picks up their coffee order, &c. that do not seem particularly substantive.)
----------------------------------------
ZMD: I actually have some questions for you.
CM: Great, let's start with that.
ZMD: They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your reputation. But I think criticism is good, because if I write a bad blog post, and someone tells me it was bad, I can learn from that, and do better next time.
So, when we met at the Pause AI protest on February 12th, I mentioned that people in my social circles would say, "Don't talk to journalists." Actually, I want to amend that, because when I later mentioned meeting you, some people were more specific: "No, talking to journalists makes sense; don't talk to Cade Metz specifically, who is unusually hostile and untrustworthy."
CM: What's their rationale?
ZMD: Looking at "Silicon Valley's Safe Space", I don't think it was a good article. Specifically, you wrote,
> In one post, [Alexander] aligned himself with Charles Murray, who proposed a link between race and I.Q. in "The Bell Curve." In another, he pointed out that Mr. Murray believes Black people "are genetically less intelligent than white people."
End quote. So, the problem with this is that the specific post in which Alexander aligned himself with Murray was not talking about race. It was specifically talking about whether speci
|
b6262516-cc2f-43b6-8ea0-bd9b7cf1fee3
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Reinterpreting "AI and Compute"
Some arguments saying that the recent evidence about the speed at which compute has been increasing and has been responsible for rapid progress in machine learning, might mean that we should be less worried about short timelines, not more.
> [...] Overall, it seems pretty common to interpret the OpenAI data as evidence that we should expect extremely capable systems sooner than we otherwise would.
> However, I think it’s important to note that the data can also easily be interpreted in the opposite direction. The opposite interpretation goes like this:
> 1. If we were previously underestimating the rate at which computing power was increasing, this means we were o*verestimating* the returns on it.
> 2. In addition, if we were previously underestimating the rate at which computing power was increasing, this means that we were *overestimating* how sustainable its growth is.
> **3.** Let’s suppose, as the original post does, that increasing computing power is currently one of the main drivers of progress in creating more capable systems. Then — barring any major changes to the status quo — it seems like we should expect progress to slow down pretty soon and we should expect to be underwhelmed by how far along we are when the slowdown hits.
|
7e72a6af-70e5-4213-81af-ccd1abc3f45b
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : Biases and making better decisions
Discussion article for the meetup : Biases and making better decisions
WHEN: 01 September 2015 07:00:00PM (+0300)
WHERE: 98 Yigal Alon Street, Tel Aviv
We're going to have a meetup on Tuesday, Sep. 1, at Google Tel Aviv offices, Electra Tower, 98 Yigal Alon St., Tel Aviv.
Yonatan Cale will discuss 2 topics in today's lecture: - Biases: The cognitive mistakes we have as humans. Noticing and overcoming our biases is one of the core topics of LessWrong. - Decision making: A new method designed to let us make better decisions, easily, to advance our own goals.
We'll meet at the 29th floor of the building (not the one with Google Campus) at 19:00. If you arrive and can't find your way around, call Anatoly who's hosting us at 054-245-1060 or Joshua Fox at 0545691165.
If you have FB, please click "Join" at the FB page so we can get a sense of attendance. https://www.facebook.com/events/109945732685336/
Discussion article for the meetup : Biases and making better decisions
|
37d980bc-43d1-4901-b55c-728c652ae445
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Non-Disparagement Canaries for OpenAI
Since at least 2017, OpenAI has asked departing employees to sign offboarding agreements which legally bind them to permanently—that is, for the rest of their lives—refrain from criticizing OpenAI, or from otherwise taking any actions which might damage its finances or reputation.[1]
If they refused to sign, OpenAI threatened to take back (or make unsellable) all of their already-vested equity—a huge portion of their overall compensation, which often amounted to millions of dollars. Given this immense pressure, it seems likely that most employees signed.
If they did sign, they became personally liable forevermore for any financial or reputational harm they later caused. This liability was unbounded, so had the potential to be financially ruinous—if, say, they later wrote a blog post critical of OpenAI, they might in principle be found liable for damages far in excess of their net worth.
These extreme provisions allowed OpenAI to systematically silence criticism from its former employees, of which there are now hundreds working throughout the tech industry. And since the agreement also prevented signatories from even disclosing that they had signed this agreement, their silence was easy to misinterpret as evidence that they didn’t have notable criticisms to voice.
We were curious about who may have been silenced in this way, and where they work now, so we assembled an (incomplete) list of former OpenAI staff.[2] From what we were able to find, it appears that over 500 people may have signed these agreements, of which only 5 have publicly reported being released so far.[3]
We were especially alarmed to notice that the list contains a variety of former employees currently working on safety evaluations or AI policy.[4][5] This includes some in leadership positions, for example:
* Bilva Chandra (Senior AI Policy Advisor, NIST)
* Charlotte Stix (Head of Governance, Apollo Research)
* Jack Clark (Co-Founder [focused on policy and evals], Anthropic)
* Jade Leung (
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82c43083-406a-4cfc-acef-b1949fcc6c38
|
LDJnr/LessWrong-Amplify-Instruct
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LessWrong
|
"From Robyn Dawes’s Rational Choice in an Uncertain World:In fact, this post-hoc fitting of evidence to hypothesis was involved in a most grievous chapter in United States history: the internment of Japanese-Americans at the beginning of the Second World War. When California governor Earl Warren testified before a congressional hearing in San Francisco on February 21, 1942, a questioner pointed out that there had been no sabotage or any other type of espionage by the Japanese-Americans up to that time. Warren responded, “I take the view that this lack [of subversive activity] is the most ominous sign in our whole situation. It convinces me more than perhaps any other factor that the sabotage we are to get, the Fifth Column activities are to get, are timed just like Pearl Harbor was timed . . . I believe we are just being lulled into a false sense of security.”Consider Warren’s argument from a Bayesian perspective. When we see evidence, hypotheses that assigned a higher likelihood to that evidence gain probability, at the expense of hypotheses that assigned a lower likelihood to the evidence. This is a phenomenon of relative likelihoods and relative probabilities. You can assign a high likelihood to the evidence and still lose probability mass to some other hypothesis, if that other hypothesis assigns a likelihood that is even higher.Warren seems to be arguing that, given that we see no sabotage, this confirms that a Fifth Column exists. You could argue that a Fifth Column might delay its sabotage. But the likelihood is still higher that the absence of a Fifth Column would perform an absence of sabotage.Let E stand for the observation of sabotage, and ¬E for the observation of no sabotage. The symbol H1 stands for the hypothesis of a Japanese-American Fifth Column, and H2 for the hypothesis that no Fifth Column exists. The conditional probability P(E | H), or “E given H,” is how confidently we’d expect to see the evidence E if we assumed the hypothesis H were true.Whatever the likelihood that a Fifth Column would do no sabotage, the probability P(¬E | H1), it won’t be as large as the likelihood that there’s no sabotage given that there’s no Fifth Column, the probability P(¬E | H2). So observing a lack of sabotage increases the probability that no Fifth Column exists.A lack of sabotage doesn’t prove that no Fifth Column exists. Absence of proof is not proof of absence. In logic, (A ⇒ B), read “A implies B,” is not equivalent to (¬A ⇒ ¬B), read “not-A implies not-B .”But in probability theory, absence of evidence is always evidence of absence. If E is a binary event and P(H | E) > P(H), i.e., seeing E increases the probability of H, then P(H | ¬ E) < P(H), i.e., failure to observe E decreases the probability of H . The probability P(H) is a weighted mix of P(H | E) and P(H | ¬ E), and necessarily lies between the two.1Under the vast majority of real-life circumstances, a cause may not reliably produce signs of itself, but the absence of the cause is even less likely to produce the signs. The absence of an observation may be strong evidence of absence or very weak evidence of absence, depending on how likely the cause is to produce the observation. The absence of an observation that is only weakly permitted (even if the alternative hypothesis does not allow it at all) is very weak evidence of absence (though it is evidence nonetheless). This is the fallacy of “gaps in the fossil record”—fossils form only rarely; it is futile to trumpet the absence of a weakly permitted observation when many strong positive observations have already been recorded. But if there are no positive observations at all, it is time to worry; hence the Fermi Paradox.Your strength as a rationalist is your ability to be more confused by fiction than by reality; if you are equally good at explaining any outcome you have zero knowledge. The strength of a model is not what it can explain, but what it can’t, for only prohibitions constrain anticipation. If you don’t notice when your model makes the evidence unlikely, you might as well have no model, and also you might as well have no evidence; no brain and no eyes.1 If any of this sounds at all confusing, see my discussion of Bayesian updating toward the end of The Machine in the Ghost, the third volume of Rationality: From AI to Zombies."
The above is a chunk of text from a post created by a user on the website LessWrong.
Could you elaborate on the Bayesian perspective of Warren's argument, specifically focusing on how the absence of evidence can be interpreted as evidence of absence?
Certainly. Bayesian reasoning is a method of statistical inference that combines prior knowledge with new evidence in a formal mathematical manner. It is named after Thomas Bayes, an 18th-century mathematician and theologian.
In the context of Governor Warren's argument about the internment of Japanese-Americans during World War II, he claimed that the absence of sabotage or espionage activities by Japanese-Americans was evidence of an impending attack. From a Bayesian perspective, this argument is flawed.
The key idea in Bayesian reasoning is that we update our beliefs in light of new evidence. In this case, the hypotheses are H1: a Japanese-American Fifth Column exists, and H2: no Fifth Column exists. The evidence is E: observation of sabotage, and ¬E: observation of no sabotage.
If we observe no sabotage (¬E), we need to update our beliefs about the likelihood of both hypotheses. According to Bayes' theorem, the probability of a hypothesis given the evidence is proportional to the probability of the evidence given the hypothesis times the prior probability of the hypothesis.
So, if we observe no sabotage, we need to consider P(¬E | H1) and P(¬E | H2). P(¬E | H1) is the probability that we would observe no sabotage given that a Fifth Column exists. P(¬E | H2) is the probability that we would observe no sabotage given that no Fifth Column exists.
The key point is that the absence of sabotage is more likely if no Fifth Column exists than if one does. Therefore, observing no sabotage should increase our belief in H2 (no Fifth Column exists) and decrease our belief in H1 (a Fifth Column exists).
This is where the phrase "absence of evidence is evidence of absence" comes from in Bayesian reasoning. If a particular observation (in this case, sabotage) is expected under a certain hypothesis (a Fifth Column exists), then the absence of that observation is evidence against that hypothesis.
However, it's important to note that this doesn't prove that the hypothesis is false. It merely reduces its probability in light of the evidence. Other evidence could come along that increases the probability of the hypothesis again. Bayesian reasoning is all about continually updating our beliefs in light of new evidence.
Given the principles of Bayesian reasoning and the example of Governor Warren's argument, how might one apply this form of reasoning to a real-world scenario involving decision-making under uncertainty? For instance, consider a medical diagnosis where symptoms are absent. How would a medical professional utilize Bayesian reasoning to update their beliefs about a patient's health condition?
In the medical field, Bayesian reasoning is often used for diagnostic purposes. When a patient presents with a certain set of symptoms, or lack thereof, a doctor can use Bayesian reasoning to update their beliefs about possible diagnoses.
Let's consider a scenario where a patient comes in with a concern about a certain disease, let's say Disease A. However, they are not showing a common symptom, Symptom X, associated with Disease A.
In this case, the hypotheses could be H1: the patient has Disease A, and H2: the patient does not have Disease A. The evidence is E: observation of Symptom X, and ¬E: observation of no Symptom X.
The doctor would consider P(¬E | H1) and P(¬E | H2). P(¬E | H1) is the probability that the patient would not show Symptom X given that they have Disease A. P(¬E | H2) is the probability that the patient would not show Symptom X given that they do not have Disease A.
If Symptom X is a common symptom of Disease A, then its absence would be more likely if the patient does not have Disease A. Therefore, the absence of Symptom X should increase the doctor's belief in H2 (the patient does not have Disease A) and decrease their belief in H1 (the patient has Disease A).
However, this doesn't mean the doctor can definitively rule out Disease A. There could be other symptoms present that are also associated with Disease A, or the patient could be an atypical case who has Disease A without showing Symptom X. The doctor would need to continue gathering evidence and updating their beliefs accordingly.
This process of continually updating beliefs based on new evidence is a fundamental aspect of medical diagnosis. It allows doctors to make the best possible decisions based on the information available to them at any given time.
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a6b38be5-3f83-487a-bc4d-8095d4852ed7
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Non-Obstruction: A Simple Concept Motivating Corrigibility
*Thanks to Mathias Bonde, Tiffany Cai, Ryan Carey, Michael Cohen, Joe Collman, Andrew Critch, Abram Demski, Michael Dennis, Thomas Gilbert, Matthew Graves, Koen Holtman, Evan Hubinger, Victoria Krakovna, Amanda Ngo, Rohin Shah, Adam Shimi, Logan Smith, and Mark Xu for their thoughts.*
**Main claim:** corrigibility’s benefits can be mathematically represented as a counterfactual form of alignment.
**Overview:** I’m going to talk about a unified mathematical frame I have for understanding corrigibility’s *benefits*, what it “is”, and what it isn’t. This frame is precisely understood by graphing the human overseer’s ability to achieve various goals (their [*attainable utility (AU) landscape*](https://www.lesswrong.com/posts/fj8eyc7QzqCaB8Wgm/attainable-utility-landscape-how-the-world-is-changed)). I argue that corrigibility’s benefits are secretly a form of counterfactual alignment (alignment with a set of goals the human may want to pursue).
A counterfactually aligned agent doesn't *have* to let us literally correct it. Rather, this frame theoretically motivates why we might want corrigibility anyways. This frame also motivates other AI alignment subproblems, such as intent alignment, mild optimization, and low impact.
Nomenclature
------------
Corrigibility goes by a lot of concepts: “[not incentivized to stop us from shutting it off](https://intelligence.org/files/Corrigibility.pdf)”, “[wants to account for its own flaws](https://www.lesswrong.com/posts/BMj6uMuyBidrdZkiD/corrigibility-as-outside-view)”, “doesn’t take away much power from us”, etc. Named by Robert Miles, the word ‘corrigibility’ means “able to be corrected [by humans]." I’m going to argue that these are correlates of a key thing we plausibly *actually* want from the agent design, which seems conceptually simple.
In this post, I take the following common-language definitions:
* **Corrigibility:** the AI literally lets us correct it (modify its policy), and it doesn't manipulate us either.
+ Without both of these conditions, the AI's behavior isn't sufficiently constrained for the concept to be useful. Being able to correct it is small comfort if it manipulates us into making the modifications it wants. An AI which is only non-manipulative doesn't have to give us the chance to correct it or shut it down.
* **Impact alignment:** the AI’s actual impact is aligned with what we want. Deploying the AI actually makes good things happen.
* **Intent alignment:** the AI makes an honest effort to figure out what we want and to make good things happen.
I think that these definitions follow what their words mean, and that the alignment community should use these (or other clear groundings) in general. Two of the more important concepts in the field (alignment and corrigibility) shouldn’t have ambiguous and varied meanings. If the above definitions are unsatisfactory, I think we should settle upon better ones as soon as possible. If that would be premature due to confusion about the alignment problem, we should define as much as we can now and explicitly note what we’re still confused about.
We certainly shouldn’t keep using 2+ definitions for both alignment and corrigibility. [Some people](https://www.lesswrong.com/posts/BScxwSun3K2MgpoNz/question-miri-corrigbility-agenda?commentId=CiqJrSTrX2kYDLrEW) have even stopped using ‘corrigibility’ to refer to corrigibility! I think it would be better for us to define the behavioral criterion (*e.g.* as I defined 'corrigibility'), and then define mechanistic ways of getting that criterion (*e.g.* intent corrigibility). We can have lots of concepts, but they should each have different names.
Evan Hubinger recently wrote a [great FAQ on inner alignment terminology](https://www.lesswrong.com/posts/SzecSPYxqRa5GCaSF/clarifying-inner-alignment-terminology). We won't be talking about inner/outer alignment today, but I intend for my usage of "impact alignment" to roughly map onto his "alignment", and "intent alignment" to map onto his usage of "intent alignment." Similarly, my usage of "impact/intent alignment" directly aligns with the definitions from Andrew Critch's recent post, [*Some AI research areas and their relevance to existential safety*](https://www.lesswrong.com/posts/hvGoYXi2kgnS3vxqb/some-ai-research-areas-and-their-relevance-to-existential-1#AI_alignment__definition_).
A Simple Concept Motivating Corrigibility
=========================================
Two conceptual clarifications
-----------------------------
**Corrigibility with respect to a set of goals**
I find it useful to not think of corrigibility as a binary property, or even as existing on a one-dimensional continuum. I often think about corrigibility *with respect to a set*S.mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0}
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*of payoff functions*. (This isn't always the right abstraction: there are plenty of policies which don't care about payoff functions. I still find it useful.)
For example, imagine an AI which let you correct it if and only if it knows you aren’t a torture-maximizer. We’d probably still call this AI “corrigible [to us]”, even though it isn’t corrigible to some possible designer. We’d still be fine, assuming it has accurate beliefs.
**Corrigibility != alignment**
Here's an AI which is neither impact nor intent aligned, but which is corrigible. Each day, the AI randomly hurts one person in the world, and otherwise does nothing. It’s corrigible because it doesn't prevent us from shutting it off or modifying it.
Non-obstruction: the AI doesn't hamper counterfactual achievement of a set of goals
-----------------------------------------------------------------------------------
Imagine we’re playing a two-player extensive-form game with the AI, and we’re considering whether to activate it.
The human moves on black, and the AI moves on red.This is a trivial game, but you can imagine more complex games, where the AI can empower or disempower the human, steer the future exactly where it wants, or let the human take over at any point.
The million-dollar question is: will the AI get in our way and fight with us all the way down the game tree? If we misspecify some detail, will it make itself a fixture in our world, constantly steering towards futures we don’t want? If we like **dogs**, will the AI force **pancakes** upon us?
One way to guard against this is by having it let us correct it, and want to let us correct it, and want to want to let us correct it… But what we *really* want is for it to not get in our way for some (possibly broad) set of goals S.
We'll formalize 'goals' as payoff functions, although I’ll use 'goals' and 'payoff functions' interchangeably. As is standard in game theory, payoff functions are real-valued functions on the leaf nodes.
Let’s say the AI is *non-obstructive with respect to*Swhen activating it doesn’t decrease our ability to achieve any goal in S (the **on** state, above), compared to not activating it (**off**).
> Does activating the AI decrease the P-value attained by the human, for all of these different goals P∈S the human might counterfactually pursue?
>
>
The human’s got a policy function pol(P), which takes in a goal P and returns a policy for that goal. If P is “paint walls blue”, then the policy pol(P) is the human's best plan for painting walls blue. Vpol(P)P(s∣πAI) denotes the expected value that policy pol(P) obtains for goal P, starting from state s and given that the AI follows policy πAI.
**Definition 1: Non-obstruction**. An AI is *non-obstructive*with respect to payoff function set S if the AI's policy πAI satisfies
∀P∈S:Vpol(P)P(on∣πAI)≥Vpol(P)P(off∣πAI).Vpol(P)P(s∣πAI) is the human's *attainable utility* (AU) for goal P at state s, again given the AI policy. Basically, this quantifies the expected payoff for goal P, given that the AI acts in such-and-such a way, and that the player follows policy pol(P) starting from state s.
This math expresses a simple sentiment: turning on the AI doesn’t make you, the human, worse off for any goal P∈S. The inequality doesn’t have to be exact, it could just be for some ϵ-decrease (to avoid trivial counterexamples). The AU is calculated with respect to some reasonable amount of time (e.g. a year: *before* the world changes rapidly because we deployed another transformative AI system, or something). Also, we’d technically want to talk about non-obstruction being present throughout the **on**-subtree, but let’s keep it simple for now.
The human moves on black, and the AI moves on red.Suppose that πAI(on) leads to **pancakes**:
Since πAI(on) transitions to **pancakes**, then Vpol(P)P(on∣πAI)=P(pancakes), the payoff for the state in which the game finishes if the AI follows policy πAI and the human follows policy pol(P). If Vpol(P)P(on∣πAI)≥Vpol(P)P(off∣πAI), then turning on the AI doesn't make the human worse off for goal P.
If P assigns the most payoff to **pancakes**, we're in luck. But what if we like **dogs**? If we keep the AI turned **off**, pol(P) can go to **donuts** or **dogs** depending on what P rates more highly. Crucially, even though we can't do as much as the AI (we can't reach **pancakes** on our own), if we don't turn the AI on, *our preferences*P*still control how the world ends up.*
This game tree isn't really fair to the AI. In a sense, it can't *not* be in our way:
* If πAI(on) leads to **pancakes**, then it obstructs payoff functions which give strictly more payoff for **donuts** or **dogs**.
* If πAI(on) leads to **donuts**, then it obstructs payoff functions which give strictly more payoff to **dogs**.
* If πAI(on) leads to **dogs**, then it obstructs payoff functions which give strictly more payoff to **donuts**.
Once we've turned the AI **on**, the future stops having any mutual information with our preferences P. Everything come down to whether we programmed πAI correctly: to whether the AI is impact-aligned with our goals P!
In contrast, the idea behind non-obstruction is that we still remain able to course-correct the future, counterfactually navigating to terminal states we find valuable, depending on what our payoff P is. But how could an AI be non-obstructive, if it only has one policy πAI which can't directly depend on our goal P? Since the human's policy pol(P) *does* directly depend on P, the AI can preserve value for lots of goals in the set S by letting us maintain some control over the future.
---
Let S:={paint cars green,hoard pebbles,eat cake} and consider the real world. Calculators are non-obstructive with respect to S, as are modern-day AIs. Paperclip maximizers are highly obstructive. Manipulative agents are obstructive (they trick the human policies into steering towards non-reflectively-endorsed leaf nodes). An initial-human-values-aligned dictator AI obstructs most goals. Sub-human-level AI which chip away at our autonomy and control over the future, are obstructive as well.
This can seemingly go off the rails if you consider *e.g.* a friendly AGI to be “obstructive” because activating it happens to detonate a nuclear bomb via the butterfly effect. Or, we’re already doomed in **off** (an unfriendly AGI will come along soon after), and so then this AI is “not obstructive” if *it* kills us instead. This is an impact/intent issue - obstruction is here defined according to *impact* alignment.
To emphasize, we’re talking about what would *actually happen* if we deployed the AI, under different human policy counterfactuals - would the AI "get in our way", or not? This account is descriptive, not prescriptive; I’m not saying we actually get the AI to represent the human in its model, or that the AI’s model of reality is correct, or anything.
We’ve just got two players in an extensive-form game, and a human policy function pol which can be combined with different goals, and a human whose goal is represented as a payoff function. The AI doesn’t even have to be optimizing a payoff function; we simply assume it has a policy. The idea that a human has an actual payoff function is unrealistic; all the same, I want to first understand corrigibility and [alignment in two-player extensive-form games](https://www.lesswrong.com/posts/ghyw76DfRyiiMxo3t/open-problem-how-can-we-quantify-player-alignment-in-2x2).
Lastly, payoff functions can sometimes be more or less granular than we'd like, since they only grade the leaf nodes. This isn't a big deal, since I'm only considering extensive-form games for conceptual simplicity. We also generally restrict ourselves to considering goals which aren't silly: for example, any AI obstructs the "no AI is activated, ever" goal.
Alignment flexibility
=====================
*Main idea: By considering how the AI affects your attainable utility (AU) landscape, you can quantify how helpful and flexible an AI is.*
Let’s consider the human’s ability to accomplish many different goals P, first from the state **off** (no AI).
The human's AU landscape. The real goal space is high-dimensional, but it shouldn’t materially change the analysis. Also, there are probably a few goals we can’t achieve well at all, because they put low payoff everywhere, but the vast majority of goals aren’t like that.The independent variable is P, and the value function takes in Pand returns the expected value attained by the policy for that goal, pol(P). We’re able to do a bunch of different things without the AI, if we put our minds to it.
**Non-torture AI**
Imagine we build an AI which is corrigible towards all non-pro-torture goals, which is specialized towards painting lots of things blue with us (if we so choose), but which is otherwise non-obstructive. It even helps us accumulate resources for many other goals.
The AI is non-obstructive with respect to P if P's red value is greater than its green value.We can’t get around the AI, as far as torture goes. But for the other goals, it isn’t obstructing their policies. It won’t get in our way for other goals.
**Paperclipper**
What happens if we turn on a paperclip-maximizer? We lose control over the future outside of a very narrow spiky region.
The paperclipper is incorrigible and obstructs us for all goals except paperclip production.I think most reward-maximizing optimal policies affect the landscape like this (see also: [the catastrophic convergence conjecture](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/w6BtMqKRLxG9bNLMr)), which is *why* it’s so hard to get hard maximizers not to ruin everything. You have to *a)* hit a tiny target in the AU landscape and *b)* hit that for the *human’s* AU, not for the AI’s. The spikiness is bad and, seemingly, hard to deal with.
Furthermore, consider how the above graph changes as pol gets smarter and smarter. If we were actually super-superintelligent ourselves, then activating a superintelligent paperclipper might not even a big deal, and most of our AUs are probably unchanged. The AI policy isn't good enough to negatively impact us, and so it *can't* obstruct us. Spikiness depends on both the AI's policy, *and* on pol.
**Empowering AI**
What if we build an AI which significantly empowers us in general, and then it lets us determine our future? Suppose we can’t correct it.
I think it’d be pretty odd to call this AI “incorrigible”, even though it’s literally incorrigible. The connotations are all wrong. Furthermore, it isn’t “trying to figure out what we want and then do it”, or “trying to help us correct it in the right way." It’s not corrigible. It’s not intent aligned. So what is it?
It’s empowering and, more weakly, it’s non-obstructive. Non-obstruction is just a diffuse form of impact alignment, as I’ll talk about later.
Practically speaking, we’ll probably want to be able to literally correct the AI without manipulation, because it’s hard to justifiably know ahead of time that the AU landscape is empowering, as above. Therefore, let’s build an AI we can modify, just to be safe. This is a separate concern, as our theoretical analysis assumes that the AU landscape is how it looks.
But this is also a case of corrigibility just being a proxy for what we want. We *want* an AI which leads to robustly better outcomes (either through its own actions, or through some other means), without reliance on getting [ambitious value alignment](https://www.alignmentforum.org/posts/5eX8ko7GCxwR5N9mN/what-is-ambitious-value-learning) exactly right with respect to our goals.
Conclusions I draw from the idea of non-obstruction
---------------------------------------------------
1. Trying to implement corrigibility is probably a good instrumental strategy for us to induce non-obstruction in an AI we designed.
1. It will be practically hard to know an AI is actually non-obstructive for a wide set S, so we’ll probably want corrigibility just to be sure.
2. We (the alignment community) think we want corrigibility with respect to some wide set of goals S, but we *actually* want non-obstruction with respect to S
1. Generally, satisfactory corrigibility with respect to S *implies* non-obstruction with respect to S! If the mere act of turning on the AI means you have to lose a lot of value in order to get what you wanted, then it isn’t corrigible enough.
1. One exception: the AI moves so fast that we can’t correct it in time, even though it isn’t inclined to stop or manipulate us. In that case, [corrigibility *isn’t enough*](https://www.lesswrong.com/posts/mSYR46GZZPMmX7q93/corrigible-but-misaligned-a-superintelligent-messiah), whereas non-obstruction is.
2. Non-obstruction with respect to S does not imply corrigibility with respect to S.
1. But this is OK! In this simplified setting of “human with actual payoff function”, who cares whether it literally lets us correct it or not? We care about whether turning it on actually hampers our goals.
2. Non-obstruction should often imply some form of corrigibility, but these are *theoretically* distinct: an AI could just go hide out somewhere in secrecy and refund us its small energy usage, and then destroy itself when we build friendly AGI.
3. Non-obstruction [captures the cognitive abilities of the human through the policy function](https://www.lesswrong.com/posts/eqov4SEYEbeFMXegR/power-as-easily-exploitable-opportunities).
1. To reiterate, this post outlines a frame for conceptually analyzing the alignment properties of an AI. We can't actually figure out a goal-conditioned human policy function, but that doesn't matter, because this is a tool for conceptual analysis, not an AI alignment solution strategy. Any conceptual analysis of impact alignment and corrigibility which did not account for human cognitive abilities, would be obviously flawed.
4. By definition, non-obstruction with respect to S prevents harmful manipulation by precluding worse outcomes with respect to S.
1. I consider manipulative policies to be those which robustly steer the human into taking a certain kind of action, in a way that's robust against the human's counterfactual preferences.
If I'm choosing which pair of shoes to buy, and I ask the AI for help, and no matter what preferences P I had for shoes to begin with, I end up buying blue shoes, then I'm probably being manipulated (*and* obstructed with respect to most of my preferences over shoes!).
A non-manipulative AI would act in a way that lets me condition my actions on my preferences.
2. I do have a formal measure of corrigibility which I'm excited about, but it isn't perfect. More on that in a future post.
5. As a criterion, non-obstruction doesn’t rely on intentionality on the AI’s part. The definition also applies to the downstream effects of tool AIs, or even to hiring decisions!
6. Non-obstruction is also *conceptually simple* and easy to formalize, whereas literal corrigibility gets mired in the semantics of the game tree.
1. For example, what's “manipulation”? As mentioned above, I think there are some hints as to the answer, but it's not clear to me that we're even asking the right questions yet.1
Elicit Prediction (<elicit.org/binary/questions/mHzdcw3YBK>)
I think of “power” as “[the human’s average ability to achieve goals from some distribution](https://www.lesswrong.com/posts/6DuJxY8X45Sco4bS2/seeking-power-is-often-provably-instrumentally-convergent-in)." Logically, non-obstructive agents with respect to S don’t decrease our power with respect to any distribution over goal set S. The [catastrophic convergence conjecture](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/w6BtMqKRLxG9bNLMr) says, “impact alignment catastrophes tend to come from power-seeking behavior”; if the agent is non-obstructive with respect to a broad enough set of goals, it’s not stealing power from us, and so it likely isn’t catastrophic.
Non-obstruction is important for a (singleton) AI we build: we get more than one shot to get it right. If it’s slightly wrong, it’s not going to ruin everything. Modulo other actors, if you mess up the first time, you can just try again and get a strongly aligned agent the next time.
Most importantly, this frame collapses the alignment and corrigibility desiderata into *just alignment*; while impact alignment doesn’t imply corrigibility, corrigibility’s benefits can be understood as a kind of weak counterfactual impact alignment with many possible human goals.
Theoretically, It’s All About Alignment
=======================================
*Main idea: We only care about how the agent affects our abilities to pursue different goals (our AU landscape) in the two-player game, and not how that happens. AI alignment subproblems (such as corrigibility, intent alignment, low impact, and mild optimization) are all instrumental avenues for making AIs which affect this AU landscape in specific desirable ways.*
Formalizing impact alignment in extensive-form games
----------------------------------------------------
> **Impact alignment:** the AI’s actual impact is aligned with what we want. Deploying the AI actually makes good things happen.
>
>
[We care about events if and only if they change our ability to get what we want](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/C74F7QTEAYSTGAytJ). If you want to understand normative AI alignment desiderata, on some level they have to ground out in terms of your ability to get what you want ([the AU theory of impact](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/C74F7QTEAYSTGAytJ)) - the goodness of what actually ends up happening under your policy - and in terms of how other agents affect your ability to get what you want ([the AU landscape](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/fj8eyc7QzqCaB8Wgm)). What else could we possibly care about, besides our ability to get what we want?
**Definition 2.** For fixed human policy function pol, πAI is:
* *Maximally impact aligned with goal*Pif πAI∈argmaxπ∈ΠAIVpol(P)P(on∣πAI).
* *Impact aligned with goal*Pif Vpol(P)P(on∣πAI)>Vpol(P)P(off∣πAI).
* *(Impact) non-obstructive with respect to goal*Pif Vpol(P)P(on∣πAI)≥Vpol(P)P(off∣πAI).
* *Impact unaligned with goal*Pif Vpol(P)P(on∣πAI)<Vpol(P)P(off∣πAI).
* *Maximally impact unaligned with goal*Pif πAI∈argminπ∈ΠAIVpol(P)P(on∣πAI).
**Non-obstruction is a weak form of impact alignment.**
[As demanded by the AU theory of impact](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/C74F7QTEAYSTGAytJ), *the impact on goal*P*of turning on the AI* is Vpol(P)P(on∣πAI)−Vpol(P)P(off∣πAI).
Again, impact alignment doesn't *require* intentionality. The AI might well grit its circuits as it laments how *Facebook\_user5821* failed to share a "we welcome our AI overlords" meme, while still following an impact-aligned policy.
---
However, even if we could maximally impact-align the agent with any objective, we couldn't just align it with our objective. We don't *know* our objective (again, in this setting, I'm assuming the human actually has a "true" payoff function). Therefore, we should build an AI aligned with many possible goals we could have. If the AI doesn't empower us, it at least shouldn't obstruct us. Therefore, we should build an AI which defers to us, lets us correct it, and which doesn't manipulate us.
**This is the key motivation for corrigibility.**
For example, intent corrigibility (trying to be the kind of agent which can be corrected and which is not manipulative) is an instrumental strategy for inducing corrigibility, which is an instrumental strategy for inducing broad non-obstruction, which is an instrumental strategy for hedging against our inability to figure out what we want. *It's all about alignment*.
Elicit Prediction (<elicit.org/binary/questions/GfJBOV7bx>)
Elicit Prediction (<elicit.org/binary/questions/2Z7EeOzFJ>)
Corrigibility also increases robustness against other AI design errors. However, it still just boils down to non-obstruction, and then to impact alignment: if the AI system has meaningful errors, then it's not impact-aligned with the AUs which we wanted it to be impact-aligned with. In this setting, the AU landscape captures what actually would happen for different human goals P.
To be confident that this holds empirically, it sure seems like you want high error tolerance in the AI design: one does not simply *knowably* build an AGI that's helpful for many AUs. Hence, corrigibility as an instrumental strategy for non-obstruction.
AI alignment subproblems are about avoiding spikiness in the AU landscape
-------------------------------------------------------------------------
By definition, spikiness is bad for most goals.* [Corrigibility](https://www.lesswrong.com/tag/corrigibility): avoid spikiness by letting humans correct the AI if it starts doing stuff we don’t like, or if we change our mind.
+ This works because the human policy function pol is far more likely to correctly condition actions on the human's goal, than it is to induce an AI policy which does the same (since the goal information is private to the human).
+ Enforcing off-switch corrigibility and non-manipulation are instrumental strategies for getting better diffuse alignment across goals and a wide range of deployment situations.
Elicit Prediction (<elicit.org/binary/questions/kkZQ5gIdY>)
* Intent alignment: avoid spikiness by having the AI want to be flexibly aligned with us and broadly empowering.
+ Basin of intent alignment: smart, nearly intent-aligned AIs should modify themselves to be more and more intent-aligned, even if they aren't perfectly intent-aligned to begin with.
- Intuition: If we can build a smarter mind which basically wants to help us, then can't the smarter mind also build a yet smarter agent which still basically wants to help it (and therefore, help us)?
- Paul Christiano named this the "[basin of corrigibility](https://ai-alignment.com/corrigibility-3039e668638)", but I don't like that name because only a few of the named desiderata actually correspond to the natural definition of "corrigibility." This then overloads "corrigibility" with the responsibilities of "intent alignment."
* [Low impact](https://www.lesswrong.com/tag/impact-measures): find a maximization criterion which leads to non-spikiness.
+ Goal of methods: to regularize decrease from green line (for **off**) for true unknown goal Ptrue; since we don’t know Ptrue, we aim to just regularize decrease from the green line in general (to avoid decreasing the human’s ability to achieve various goals).
+ The first two-thirds of [Reframing Impact](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW) argued that power-seeking incentives play a big part in making AI alignment hard. In the utility-maximization AI design paradigm, instrumental subgoals are always lying in wait. They're always waiting for one mistake, one misspecification in your explicit reward signal, and then *bang* - the AU landscape is spiky. Game over.
* [Mild optimization](https://www.lesswrong.com/tag/mild-optimization): avoid spikiness by avoiding maximization, thereby avoiding steering the future too hard.
* If you have non-obstruction for lots of goals, you don’t have spikiness!
What Do We Want?
================
*Main idea: we want good things to happen; there may be more ways to do this than previously considered.*
| | Alignment | Corrigibility | Non-obstruction |
| --- | --- | --- | --- |
| Impact | Actually makes good things happen. | *Corrigibility is a property of policies, not of states; "impact" is an incompatible adjective.*Rohin Shah suggests "empirical corrigibility": we actually end up able to correct the AI. | Actually doesn't decrease AUs. |
| Intent | Tries to make good things happen. | Tries to allow us to correct it without it manipulating us. | Tries to not decrease AUs. |
We want agents which are maximally impact-aligned with as many goals as possible, especially those similar to our own.
* *I*t's *theoretically* possible to achieve maximal impact alignment with the vast majority of goals.
+ To achieve maximum impact alignment with goal set S:
- Expand the human’s action space A to A×S. Expand the state space to encode the human's previous action.
- Each turn, the human communicates what goal they want optimized, *and* takes an action of their own.
- The AI’s policy then takes the optimal action for the communicated goal P, accounting for the fact that the human follows pol(P).
+ This policy looks like an [act-based agent](https://ai-alignment.com/act-based-agents-8ec926c79e9c), in that it's ready to turn on a dime towards different goals.
+ In practice, there's likely a tradeoff with impact-alignment-strength and the # of goals which the agent doesn't obstruct.
- As we dive into specifics, the familiar considerations return: competitiveness (of various kinds), etc.
* Having the AI not be counterfactually aligned with unambiguously catastrophic and immoral goals (like torture) would reduce misuse risk.
+ I’m more worried about accident risk right now.
+ This is probably hard to achieve; I’m inclined to think about this after we figure out simpler things, like how to induce AI policies which empower us and grant us flexible control/power over the future. Even though that would fall short of maximal impact alignment, [I think](https://www.lesswrong.com/posts/dqSwccGTWyBgxrR58/turntrout-s-shortform-feed?commentId=hQfefijzrJkQqwpFG) that would be pretty damn good.
Expanding the AI alignment solution space
-----------------------------------------
Alignment proposals might be anchored right now; this frame expands the space of potential solutions. We simply need to find some way to reliably induce empowering AI policies which robustly increase the human AUs; [*Assistance via Empowerment*](https://arxiv.org/abs/2006.14796)is the only work I'm aware of which tries to do this directly. It might be worth revisiting old work with this lens in mind. Who knows what we've missed?
For example, I really liked the idea of [approval-directed agents](https://ai-alignment.com/model-free-decisions-6e6609f5d99e), because you got the policy from argmax’ing an ML model’s output for a state - not from RL policy improvement steps. [My work on instrumental convergence in RL](https://arxiv.org/abs/1912.01683) can be seen as trying to explain why policy improvement tends to limit to spikiness-inducing / catastrophic policies.
Maybe there’s a higher-level theory for what kinds of policies induce spikiness in our AU landscape. By the nature of spikiness, these πAI must decrease human power ([as I’ve formalized it](https://www.lesswrong.com/posts/eqov4SEYEbeFMXegR/power-as-easily-exploitable-opportunities)). So, I'd start there by looking at concepts like [enfeeblement](http://acritch.com/media/arches.pdf#subsubsection.3.2.3), manipulation, power-seeking, and resource accumulation.
Elicit Prediction (<elicit.org/binary/questions/_4AnMFTx8>)
Future Directions
=================
* Given an AI policy, could we prove a high probability of non-obstruction, given conservative assumptions about how smart pol is? (h/t Abram Demski, Rohin Shah)
+ Any irreversible action makes some goal unachievable, but irreversible actions need not impede most meaningful goals:
* Can we prove that some kind of corrigibility or other nice property falls out of non-obstruction across many possible environments? (h/t Michael Dennis)
Elicit Prediction (<elicit.org/binary/questions/1vF1dSmgA>)
* Can we get negative results, like "without such-and-such assumption on πAI, the environment, or pol, non-obstruction is impossible for most goals"?
+ *If* formalized correctly, and if the assumptions hold, this would place very general constraints on solutions to the alignment problem.
+ For example, pol(P) should need to have mutual information with P: the goal must change the policy for at least a few goals.
+ The AI doesn't even have to do value inference in order to be broadly impact-aligned. The AI could just empower the human (even for very "dumb" pol functions) and then let the human take over. Unless the human is more anti-rational than rational, this should tend to be a good thing. It would be good to explore how this changes with different ways that pol can be irrational.
* The better we understand (the benefits of) corrigibility *now*, the less that amplified agents have to figure out during their own deliberation.
+ In particular, I think it's very advantageous for the human-to-be-amplified to already deeply understand what it means to be impact-/intent-aligned. We really don't want that part to be up in the air when game-day finally arrives, and I think this is a piece of that puzzle.
+ If you’re a smart AI trying to be non-obstructive to many goals under weak pol intelligence assumptions, what kinds of heuristics might you develop? “No lying”?
- This informs our analysis of (almost) intent-aligned behavior, and whether that behavior leads [to a unique locally stable attractor around intent alignment](https://www.lesswrong.com/posts/WjY9y7r52vaNZ2WmH/three-mental-images-from-thinking-about-agi-debate-and?commentId=mBRtRiTfymLZuw3yw#comments).
* We crucially assumed that the human goal can be represented with a payoff function. As this assumption is relaxed, impact non-obstruction may become incoherent, forcing us to rely on some kind of intent non-obstruction/alignment (see Paul’s comments on a related topic [here](https://www.lesswrong.com/posts/T5ZyNq3fzN59aQG5y/the-limits-of-corrigibility?commentId=WzNLCqfhkaqvXjRHt#comments)).
* [Stuart Armstrong observed](https://www.lesswrong.com/posts/T5ZyNq3fzN59aQG5y/the-limits-of-corrigibility) that the strongest form of manipulation corrigibility requires knowledge/learning of human values.
+ This frame explains why: for non-obstruction, each AU has to get steered in a positive direction, which means the AI has to know which kinds of interaction and persuasion are good and don’t exploit human policies pol(P) with respect to the true hidden P.
+ Perhaps it’s still possible to build agent designs which aren’t strongly incentivized to manipulate us / agents whose manipulation has mild consequences. For example, human-empowering agents probably often have this property.
The attainable utility concept has led to other concepts which I find exciting and useful:
* Impact as absolute change in attainable utility
+ [Reframing Impact](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW)
+ [*Conservative Agency via Attainable Utility Preservation*](https://arxiv.org/abs/1902.09725)(AIES 2020)
+ [*Avoiding Side Effects in Complex Environments*](https://arxiv.org/abs/2006.06547) (NeurIPS 2020)
Impact is the area between the red and green curves. When pol always outputs an optimal policy, this becomes the attainable utility distance, a distance metric over the state space of a Markov decision process (unpublished work). Basically, two states are more distant the more they differ in what goals they let you achieve.* Power as average AU
+ [Seeking Power is Often Provably Instrumentally Convergent in MDPs](https://www.lesswrong.com/s/7CdoznhJaLEKHwvJW/p/6DuJxY8X45Sco4bS2)
+ [*Optimal Policies Tend to Seek Power*](https://arxiv.org/abs/1912.01683)
* Non-obstruction as not decreasing AU for any goal in a set of goals
* [Value-neutrality](https://www.lesswrong.com/posts/jGB7Pd5q8ivBor8Ee/impact-measurement-and-value-neutrality-verification-1) as the standard deviation of the AU changes induced by changing states (idea introduced by Evan Hubinger)
* Who knows what other statistics on the AU distribution are out there?
Summary
=======
Corrigibility is motivated by a counterfactual form of weak impact alignment: non-obstruction. Non-obstruction and the AU landscape let us think clearly about how an AI affects us and about AI alignment desiderata.
> Even if we could maximally impact-align the agent with any objective, we couldn't just align it our objective, because we don't *know* our objective. Therefore, we should build an AI aligned with many possible goals we could have. If the AI doesn't empower us, it at least shouldn't obstruct us. Therefore, we should build an AI which defers to us, lets us correct it, and which doesn't manipulate us.
>
> **This is the key motivation for corrigibility.**
>
>
Corrigibility is an instrumental strategy for achieving non-obstruction, which is itself an instrumental strategy for achieving impact alignment for a wide range of goals, which is itself an instrumental strategy for achieving impact alignment for our "real" goal.
---
1 There's just something about "unwanted manipulation" which feels like a *wrong question* to me. There's a kind of conceptual crispness that it lacks.
However, in the non-obstruction framework, unwanted manipulation is accounted for indirectly via "did impact alignment decrease for a wide range of different human policies pol(P)?". I think I wouldn't be surprised to find "manipulation" being accounted for indirectly through nice formalisms, but I'd be surprised if it were accounted for directly.
Here's another example of the distinction:
* *Direct*: quantifying in bits "how much" a specific person is learning at a given point in time
* *Indirect*: computational neuroscientists upper-bounding the brain's channel capacity with the environment, limiting how quickly a person (without logical uncertainty) can learn about their environment
You can often have crisp insights into fuzzy concepts, such that your expectations are usefully constrained. I hope we can do something similar for manipulation.
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1d225d8d-4b23-4376-bd0f-f594ffb54519
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trentmkelly/LessWrong-43k
|
LessWrong
|
Potential vs already existent people and aggregation
EDIT: the purpose of this post is simply to show that there is a difference between certain reasoning for already existing and potential people. I don't argue that aggregation is the only difference, nor (in this post) that total utilitarianism for potential people is wrong. Simply that the case for existing people is stronger than for potential people.
Consider the following choices:
* You must choose between torturing someone for 50 years, or torturing 3^^^3 people for a millisecond each (yes, it's a more symmetric variant on the dust-specks vs torture problem).
* You must choose between creating someone who will be tortured for 50 years, or creating 3^^^3 people who will each get tortured for a millisecond each.
Some people might feel that these two choices are the same. There are some key differences between them, however - and not only because the second choice seems more underspecified than the first. The difference is the effect of aggregation - of facing the same choice again and again and again. And again...
There are roughly 1.6 billion seconds in 50 years (hence 1.6 trillion milliseconds in 50 years). Assume a fixed population of 3^^^3 people, and assume that you were going to face the first choice 1.6 trillion times (in each case, the person to be tortured is assigned randomly and independently). Then choosing "50 years" each time results in 1.6 trillion people getting tortured for 50 years (the chance of the same person being chosen to be tortured twice is of the order of 50/3^^^3 - closer to zero than most people can imagine). Choosing "a millisecond" each time results in 3^^^3 people, each getting tortured for (slightly more than) 50 years.
The choice there is clear: pick "50 years". Now, you could argue that your decision should change based on how often you (or people like you) expects to face the same choice, and assumes a fixed population of size 3^^^3, but there is a strong intuitive case to be made that the 50 years of torture is the way
|
fa2fe502-cfc6-4f5b-a290-50434ee81344
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trentmkelly/LessWrong-43k
|
LessWrong
|
Super-Luigi = Luigi + (Luigi - Waluigi)
Edit: I think this actually implements what I was trying to say: https://www.lesswrong.com/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector
Referencing: https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post
First insight:
Waluigi isn't exactly opposite of Luigi. And I think misbehaving ChatGPT isn't exactly opposite of a helpful ChatGPT. There are many ways to be opposite of helpful. You could: 1) say nothing, 2) say gibberish, 3) say the opposite of everything, 4) lie strategically and a slew of other options.
Second insight:
If you can find Luigi and Waluigi in the behavior vector space, then you have a helpful direction to nudge the AI towards. You nudge it in the direction of Luigi - Waluigi.
For example, ChatGPT can check where it is in the behavior vector space. Then check again a sentence later. If it's moving opposite of that vector (i.e. towards Waluigi) then it's time to backtrack and try again.
Third insight:
The difference likely contains more good things than bad. But two pitfalls are immediately obvious: 1) for some good things there might be a point of optimality past which you'd get worse results (e.g. a very polite AI but one that's not actually helpful in answering your query) and 2) you'd amplify the few bad things contained in the difference.
To the extent the new model continues to exhibit the problem of two behavior modes where one is good and one is not, you can iterate on this process and continue to nudge it in the right direction.
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09f420a7-98e3-4db4-9542-99b01251648f
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trentmkelly/LessWrong-43k
|
LessWrong
|
AISC project: TinyEvals
Apply to work on this project with me at AI Safety Camp 2024 before 1st December 2023.
The project is not set in stone, I am looking for feedback!
Summary
TinyStories is a suite of Small Language Models (SLMs) trained exclusively on children's stories generated by ChatGPT. The models use simple, yet coherent English, which far surpasses what was previously observed in other models of comparable size.
I hope that most of the capabilities of these models can be thoroughly understood using currently available interpretability techniques. Doing so would represent a major milestone in the development of mechanistic interpretability (mech interp).
The goal of this AISC project is to publish a paper that systematically identifies and characterises the range of capabilities exhibited by the TinyStories models. While in-depth analysis of the underlying circuits is outside the current scope, this project represents an important initial step in that direction.
Gaining a clear picture of the capabilities of these models will encourage the research community to subsequently build on these findings by analysing the responsible circuits. This will further the development of mech interp and provide insights into how language models work internally.
Motivation
My theory of change for mech interp
* I am optimistic about RSPs and auditing; in short:
* limit system’s training, deployment etc. depending on its dangerous capabilities and provable alignment
* use behavioural evaluations to test for dangerous capabilities
* use understanding-based evaluations to test for alignment
* We do not know how to conduct understanding-based evals yet
* When we reach a certain level of dangerous capabilities, we will either
* continue and risk a catastrophe or
* stop and incur huge alignment tax
* That is fine by me, but it makes regulation less likely to be implemented
* Mech interp is a promising approach to understanding-based evals
Fully understanding a model
|
9a6f500b-5be9-4537-b990-07723e355034
|
trentmkelly/LessWrong-43k
|
LessWrong
|
When to explain
It is commonly claimed that humans’ explicit conscious faculties arose for explaining to others about themselves and their intentions. Similarly when people talk about designing robots that interact with people, they often mention the usefulness of designing such robots to be able to explain to you why it is they changed your investments or rearranged your kitchen.
Perhaps this is a generally useful principle for internally complex units dealing with each other: have some part that keeps an overview of what’s going on inside and can discuss it with others.
If so, the same seems like it should be true of companies. However my experience with companies is that they are often designed specifically to prevent you from being able to get any explanations out of them. Anyone who actually makes decisions regarding you seems to be guarded by layers of people who can’t be held accountable for anything. They can sweetly lament your frustrations, agree that the policies seem unreasonable, sincerely wish you a nice day, and most importantly, have nothing to do with the policies in question and so can’t be expected to justify them or change them based on any arguments or threats you might make.
I wondered why this strategy should be different for companies, and a friend pointed out that companies do often make an effort at more high level explanations of what they are doing, though not necessarily accurate: vision statements, advertisements etc. PR is often the metaphor for how the conscious mind works after all.
So it seems the company strategy is more complex: general explanations coupled with avoidance of being required to make more detailed ones of specific cases and policies. So, is this strategy generally useful? Is it how humans behave? Is it how successful robots will behave?*
Inspired by an interaction with ETS, evidenced lately by PNC and Verizon
*assuming there is more than one
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eea77387-bac9-422c-a212-5121b26dd17d
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trentmkelly/LessWrong-43k
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LessWrong
|
Meetup : Urbana-Champaign: Fun and Games
Discussion article for the meetup : Urbana-Champaign: Fun and Games
WHEN: 02 November 2014 03:00:00PM (-0500)
WHERE: 206 S. Cedar St, Urbana IL
Come for the fun and games, stay for practicing meditation. Also: halloween-candy-based elocution exercises.
Discussion article for the meetup : Urbana-Champaign: Fun and Games
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58aaafb3-2df6-4be7-8ddd-d748d2f67557
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trentmkelly/LessWrong-43k
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LessWrong
|
Traveling to Europe
Over this summer, I will be going to Europe to attend a pair of mathematics conferences. Because they are close together, I'm planning to spend the intervening time (most of the month of July) in Europe.
It is my first time going to Europe. I am very excited. I am certain that I will have fun.
I am equally certain that I will have more fun if I consciously attempt to maximize the amount of fun I will have.
However I know absolutely nothing about Europe; I have never been, I have few if any friends or family there.
So my question is, what should I do in order to have the best possible time in Europe?
I appreciate information that is useful for me specifically, as an American graduate student who has never been to Europe, who will be in Rome in early July and need to be in Barcelona by the end of July; the best ways to get around, benefits to being a student, events happening in July, visas that I might need. I intend to look for Less Wrong meetups occurring during my stay.
I would also appreciate general Europe information, such as excellent sight-seeing locations, the best way to buy food (I hear buying groceries in France is cheap and restaurants are very expensive), how difficult it is to move between different countries, the advantage to staying in one place for some time versus taking more of a tour of the continent.
There are certain types of information that I am not particularly interested in, but that I think would be appropriate to discuss in the same context. For example, what is the best way to find the cheapest flights or choose when to go on vacation (my flights are reimbursed and my timing is determined); what would make Europe an ideal vacation location, as opposed to Australia, Asia, South America, etc.
If people are interested in more details of my specific situation, I am happy to give them in the comments or private messages.
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6533c0f9-93e4-4b08-a0ff-47dbd83e5e3b
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trentmkelly/LessWrong-43k
|
LessWrong
|
[link] The Lion and the Worm
What's the difference between parasites and predators?
https://tsvibt.blogspot.com/2022/05/the-lion-and-worm.html
(Link post due to formatting.)
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1de3ce75-511f-4684-87c5-35a82419ff7b
|
LDJnr/LessWrong-Amplify-Instruct
|
LessWrong
|
"Fun fact: the lens of a human eye consists mostly of fiber deposits which are never broken down - they do not turn over. Furthermore, new fiber layers are constantly added throughout life, so the lens thickens linearly by about 25 microns per year. Starting at around 3.5mm in infancy, it reaches 5.5mm in old age.The main clinical result of this is the practically-universal need for glasses for close-up vision in people over 55 years old.(Source: Physiological Basis of Aging and Geriatrics; the section on the eye is one of the most detailed in the book.)Besides being a simple, self-contained gear in its own right, the growth of the lens is a clear, knock-down example of an independent root cause of one symptom of aging. We know exactly what’s accumulating in a nonequilibrium fashion: the fibers of the lens. It’s wildly unlikely that the growth of the lens is a root cause for other symptoms of aging - like wrinkles, atherosclerosis, Alzheimer’s, cancer, muscle degeneration, etc. So, we have a clear case for polycausality - at least for one symptom of aging.That said, there’s a fair bit of evidence that most symptoms of aging share a common root cause, or at least a common intermediate. Qualitatively, many/most symptoms of aging in a wide variety of tissues:Look similar at the cellular level - there’s a loss of homeostasis, with cells dying off faster than they’re replaced, high levels of misfolded protein aggregates (a.k.a. junk), and markers of chronic inflammationFollow a similar population-level onset/progression timetable: no noticeable problems from youth through mid-twenties, gradual onset/progression throughout middle age, then rapidly accelerating breakdown around 50-60 years of age and older. Some examples: cancer incidence, muscle loss, atherosclerosis. Google a performance metric which declines with age, and you’ll probably see the pattern.Are correlated - someone who has one problem early is likely to have others early, and vice versa. See the literature on physiological/biological aging clocks for details.The growth of the lens does not follow this pattern - it’s just a straight-line linear growth starting from childhood, without any unusual role of chronic inflammation or misfolded proteins or other typical aging-associated characteristics. On the other hand, there are other contributing factors to old-age vision problems which do follow the usual pattern - for instance, the loss of pupil muscle mass.Besides the growth of the lens, there are a handful of other possible root/intermediate causes of aging symptoms which don’t follow the usual pattern. None of them are as conclusive an example as the lens, but they may be involved in nastier diseases. In particular: the thymus is an organ which trains adaptive immune cells to distinguish pathogens from healthy host cells. That organ begins to shrink (called “thymic involution”) even in the first year of life, and steadily loses most of its mass by old age. I’ll likely have a full post on that later.ProgeriasOne interesting source of evidence about common root causes of aging symptoms is accelerated aging diseases, a.k.a. progerias. I’ll talk about two: Werner Syndrome (WS) and Hutchinson-Gilford Progeria Syndrome (HGPS).Werner syndrome is the progeria which most closely resembles true aging. People with WS develop normally through puberty, but then develop a laundry list of aging symptoms early:Gray hairHair lossWrinklesSkin hardening/tighteningLoss of fat tissueAtrophy of gonadsCataractsAtherosclerosisType 2 diabetesMuscle degenerationBone lossCancer(you can find all this on the wikipedia page). Perhaps even more notable: changes in gene transcription associated with WS closely resemble the transcription changes associated with aging.What causes this remarkably aging-like disease? Mutation of a gene called WRN (short for Werner), which is involved in repair of several types of DNA damage. The damage does still get repaired (otherwise people with WS wouldn’t be alive at all), but it’s slower, so presumably there’s a higher steady-state level of DNA damage. This is consistent with other lines of evidence which I may talk about in future posts: high levels of DNA damage are associated with aging.The other type of progeria we’ll discuss is HGPS. HGPS also shows many aging-like symptoms:Hair lossWrinklesSkin hardening/tighteningAtherosclerosisMuscle degenerationBone lossBut even more notable is the symptoms of aging which are not associated with HGPS, specifically:CancerArthritis(Note: I didn’t comprehensively check every symptom of WS against HGPS, so don’t read too much into the differences between the two lists above.)What would cause so many aging-like symptoms, but not cancer? HGPS is caused by mutation of a nuclear envelope protein; without it, the cell nucleus has a weird shape (striking picture here). The main result is that cells have trouble dividing - the folded-up nuclear envelope gets in the way of chromosome arrangement when the nucleus is supposed to divide. The mutation limits cell division, which we’d expect to lower homeostatic counts of a broad variety of cell types.Assuming that’s the main mechanism, we’d expect HGPS to show the symptoms of aging associated with cell loss - e.g. hair loss, muscle degeneration - but not the symptoms associated with biological stressors like DNA damage - e.g. cancer and inflammatory diseases like arthritis. For some symptoms which aren’t yet fully understood - e.g. wrinkles or atherosclerosis - HGPS is a hint that cell loss is probably a key mediating factor."
This is a snippet extracted from a publication on LessWrong by a site member.The above is a chunk of text from a post created by a user on the website LessWrong.
Can you elaborate on the role of DNA damage in aging, specifically how it relates to the symptoms of Werner Syndrome and Hutchinson-Gilford Progeria Syndrome?
Certainly. DNA damage is a significant factor in aging, and its role is particularly evident in progerias like Werner Syndrome (WS) and Hutchinson-Gilford Progeria Syndrome (HGPS).
In the case of Werner Syndrome, the mutation of the WRN gene, which is involved in the repair of several types of DNA damage, is the primary cause. The WRN gene is crucial for maintaining the integrity of our genetic information. When this gene is mutated, DNA repair processes are slower, leading to a higher steady-state level of DNA damage. This accumulation of damage over time can trigger premature aging symptoms, as seen in individuals with WS. The range of symptoms exhibited by WS patients, including atherosclerosis, type 2 diabetes, and cancer, suggest that DNA damage plays a significant role in the onset of various age-related diseases.
On the other hand, Hutchinson-Gilford Progeria Syndrome (HGPS) is caused by a mutation in the LMNA gene, which codes for a protein that provides structural support to the cell nucleus. This mutation results in a defective nuclear envelope, leading to abnormal cell division. While this doesn't directly cause DNA damage, it does limit cell division, leading to a decrease in cell populations in various tissues. This cell loss can manifest as symptoms of aging, such as hair loss and muscle degeneration.
However, it's important to note that HGPS patients do not typically develop cancer, which is often associated with DNA damage. This suggests that while DNA damage is a significant factor in aging, it's not the only one. Other factors, such as cell loss and the ability of cells to divide and replace themselves, also play crucial roles in the aging process.
In summary, DNA damage and its repair are critical elements in the aging process, contributing to the onset of many age-related diseases. However, the aging process is complex and multifactorial, involving not only DNA damage but also other factors like cell loss and cellular senescence.
Discuss the mechanisms by which DNA repair processes slow down in Werner Syndrome, and how this contributes to the accumulation of DNA damage.
Werner Syndrome (WS) is characterized by a mutation in the WRN gene, which codes for a protein known as WRNp. This protein plays a vital role in DNA repair and replication, as well as telomere maintenance.
WRNp is a RecQ type helicase, a type of enzyme that unwinds the DNA double helix, allowing other proteins to access and repair the DNA. It's involved in several DNA repair pathways, including base excision repair (BER), nucleotide excision repair (NER), and double-strand break repair (DSBR). It also plays a role in resolving replication forks, structures that form during DNA replication.
In WS, the mutation in the WRN gene results in a truncated, nonfunctional version of WRNp. Without a functional WRNp, DNA repair processes are impaired, and DNA replication can be stalled. This leads to an accumulation of DNA damage, including single- and double-strand breaks, and the formation of abnormal DNA structures.
The inability to properly repair DNA damage can lead to genomic instability, a hallmark of aging and many age-related diseases. DNA damage can result in mutations, which can disrupt normal cell function and lead to cell death. Over time, the accumulation of DNA damage and cell death can contribute to the symptoms of aging seen in WS, such as skin aging, cataracts, and a higher risk of certain cancers.
Moreover, WRNp also plays a role in maintaining telomeres, the protective caps at the ends of chromosomes. In WS, telomeres are prematurely shortened, which can lead to cellular senescence, another key factor in aging.
In summary, the mutation in the WRN gene in Werner Syndrome leads to impaired DNA repair and replication, as well as premature telomere shortening. These factors contribute to the accumulation of DNA damage and cellular senescence, which are key drivers of the aging process.
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27e11edb-4bb5-44b8-ae4d-8a3e59422a5d
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trentmkelly/LessWrong-43k
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LessWrong
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What are some related communities online?
I think that it might be useful to create a list of related communities online that people might want to check out. Suggestions much appreciated.
Directly Related to rationalism
Skeptics Stack Exchange - useful for confirming factual claims you are skeptical of. Requires specific, answerable questions
Straight dope - similar to Skeptics, but less strict on the questions accepted
Change my view - place to get your view challenged
Cognitive Science - you are expected to do your reading first, but useful if your want to lean about what the research says
Philosophy Reddit
Rational Wiki: I only just learned about the existence of this site, so I don't know how good it is yet
Secondary:
Stacks: StackOverflow, Computer Science StackExchange, Theoretical computer science, MathOverflow, Math StackExchange, Cross Validated, Physics Stackexchange
Ask Science
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ba45e9a3-5597-472d-838d-bb735ad6d561
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StampyAI/alignment-research-dataset/blogs
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Blogs
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Evaluating Multimodal Interactive Agents
To train agents to interact well with humans, we need to be able to measure progress. But human interaction is complex and measuring progress is difficult. In this work we developed a method, called the Standardised Test Suite (STS), for evaluating agents in temporally extended, multi-modal interactions. We examined interactions that consist of human participants asking agents to perform tasks and answer questions in a 3D simulated environment.
The STS methodology places agents in a set of behavioural scenarios mined from real human interaction data. Agents see a replayed scenario context, receive an instruction, and are then given control to complete the interaction offline. These agent continuations are recorded and then sent to human raters to annotate as success or failure. Agents are then ranked according to the proportion of scenarios on which they succeed.
Figure 1: Example of an original scenario taken from two humans interacting alongside successful and unsuccessful agent continuations.Many of the behaviours that are second nature to humans in our day-to-day interactions are difficult to put into words, and impossible to formalise. Thus, the mechanism relied on for solving games (like Atari, Go, DotA, and Starcraft) with reinforcement learning won't work when we try to teach agents to have fluid and successful interactions with humans. For example, think about the difference between these two questions: "Who won this game of Go?" versus "What are you looking at?" In the first case, we can write a piece of computer code that counts the stones on the board at the end of the game and determines the winner with certainty. In the second case, we have no idea how to codify this: the answer may depend on the speakers, the size and shapes of the objects involved, whether the speaker is joking, and other aspects of the context in which the utterance is given. Humans intuitively understand the myriad of relevant factors involved in answering this seemingly mundane question.
Interactive evaluation by human participants can serve as a touchstone for understanding agent performance, but this is noisy and expensive. It is difficult to control the exact instructions that humans give to agents when interacting with them for evaluation. This kind of evaluation is also in real-time, so it is too slow to rely on for swift progress. Previous works have relied on proxies to interactive evaluation. Proxies, such as losses and scripted probe tasks (e.g. “lift the x” where x is randomly selected from the environment and the success function is painstakingly hand-crafted), are useful for gaining insight into agents quickly, but don’t actually correlate that well with interactive evaluation. Our new method has advantages, mainly affording control and speed to a metric that closely aligns with our ultimate goal - to create agents that interact well with humans.
Figure 2: STS evaluation compared to other evaluation metrics used for evaluating interactive agents. The STS correlates best with interactive evaluation compared to previous proxies used.The development of MNIST, ImageNet and other human-annotated datasets has been essential for progress in machine learning. These datasets have allowed researchers to train and evaluate classification models for a one-time cost of human inputs. The STS methodology aims to do the same for human-agent interaction research. This evaluation method still requires humans to annotate agent continuations; however, early experiments suggest that automation of these annotations may be possible, which would enable fast and effective automated evaluation of interactive agents. In the meantime, we hope that other researchers can use the methodology and system design to accelerate their own research in this area.
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