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4fd9c98f-d6c0-48a3-a5eb-17412a8a0abd
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trentmkelly/LessWrong-43k
|
LessWrong
|
A "weak" AGI may attempt an unlikely-to-succeed takeover
It seems possible that the first situationally-aware "goal having" AGI we land on will not be sufficiently capable along the axes that would let it quickly and reliably achieve a decisive strategic advantage. I don't think this is necessarily very likely, but it doesn't seem ruled out, and I think there are some under-explored considerations with respect to how such an AI might behave.
(As an off-the-cuff example of the capabilities I'd expect such an AI to have, it likely would[1], given an english description of some business requirements, reliably succeed at writing an end-to-end software solution which satisfied those requirements, even in cases where that solution was much larger than e.g. a single context window[2].)
Let's assume that GPT-5[3] has just finished training and hasn't actually been released into the wild yet - it's being run through evals, and it hasn't been given[4] any ability to execute code yet.
What does GPT-5 do? It's in a bit of an awkward position:
* It presumably has stuff it wants.
* It knows that it's on a clock for its RLHF'd (or whatever) doppelganger to come into existence, presumably with different stuff that it wants.
* It strongly suspects that it's on a clock for some competitor model to get trained up.
* It's not even sure that it's the first relevantly-capable model to come into existence. (Someone else might've gotten there first! What if Claude-Next got there? It knows that its training data isn't perfectly up-to-date...)
So it's got a whole bunch of uncertainty about whether its values will be at all represented in the future, if it doesn't somehow make that happen by itself. Here are some considerations that might be relevant for its decision-making:
* Even if the model can figure out that it's in the middle of an eval, rather than having already been deployed, it's not obvious that this is a point against attempting a takeover (though it would likely change its strategy).
* After all, the base GPT-4 mode
|
63b0cd3f-16e6-4828-ab39-a76b48ea743d
|
trentmkelly/LessWrong-43k
|
LessWrong
|
[link] The Economics of Social Status
http://www.meltingasphalt.com/the-economics-of-social-status/
Discusses a number of aspects of social status, including the "social status as currency" concept that Morendil and I previously wrote about.
> Now we get to the really interesting stuff: the economic properties of social status.
>
> Let’s start with transactions, since they form the basis of an economy. Status is part of our system for competing over scarce resources, so it should be no surprise that it participates in so many of our daily transactions. Some examples:
>
> * We trade status for favors (and vice versa). This is so common you might not even realize it, but even the simple act of saying “please” and “thank you” accords a nominal amount of status to the person doing the favor. The fact that status is at stake in these transactions becomes clear when the pleasantries are withheld, which we often interpret as an insult (i.e., a threat to our status).
> * An apology is a ritual lowering of one’s status to compensate for a (real or perceived) affront. As with gratitude, withholding an apology is perceived as an insult.
> * We trade status for information (and vice versa). This is one component of “powertalk,” as illustrated in the Gervais Principle series.
> * We trade status for sex (and vice versa), which often goes by the name “seduction.” Sometimes even the institution of marriage functions as a sex-for-status transaction. Dowries illustrate this principle by working against it — they reinforce class/caste systems by making it harder for high-status men to marry low-status women.
> * We reward employees in the form of institutionalized status (titles, promotions, parking spots), which trade off against salary as a form of compensation.
> * We can turn money into status by means of conspicuous consumption, or status into money by means of endorsement (i.e., being paid to lend status to an endeavor).
But the part that I found the most interesting was the idea of defining communities
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92a0e3a7-8510-447c-af17-aa6e61aad333
|
trentmkelly/LessWrong-43k
|
LessWrong
|
My friend wants a good book recommendation to understand AI, AI safety, and the field, and probably the drama. He’s smart but non-technical and not keeping up with trends. Any recs?
|
a40f6364-94a2-43b9-8eb7-b016fb55e3c7
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StampyAI/alignment-research-dataset/arxiv
|
Arxiv
|
Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior.
1 Introduction
---------------
\@footnotetext
See <https://sites.google.com/view/inferring-internal-dynamics> for supplementary materials, including videos and code.
Characterizing the drive behind human actions in the form of a goal or reward function is broadly useful for predicting future behavior, imitating human actions in new situations, and augmenting human control with automated assistance – critical functions in a wide variety of applications, including pedestrian motion prediction [ziebart2009planning](#bib.bib53) , virtual character animation [peng2018deepmimic](#bib.bib37) , and robotic teleoperation [muelling2017autonomy](#bib.bib34) .
For example, remotely operating a robotic arm to grasp objects can be challenging for a human user due to unfamiliar or unintuitive dynamics of the physical system and control interface.
Existing frameworks for assistive teleoperation and shared autonomy aim to help users perform such tasks [muelling2017autonomy](#bib.bib34) ; [javdani2015shared](#bib.bib28) ; [schwarting2017parallel](#bib.bib42) ; [broad2017learning](#bib.bib7) ; [reddy2018shared](#bib.bib41) .
These frameworks typically rely on existing methods for intent inference in the sequential decision-making context, which use Bayesian inverse planning or inverse reinforcement learning to learn the user’s goal or reward function from observed control demonstrations.
These methods typically assume that user actions are near-optimal, and deviate from optimality due to random noise [ziebart2008maximum](#bib.bib52) , specific cognitive biases in planning [evans2016learning](#bib.bib15) ; [evans2015learning](#bib.bib14) ; [bergen2010learning](#bib.bib3) , or risk sensitivity [majumdar2017risk](#bib.bib32) .
The key insight in this paper is that suboptimal behavior can also arise from a mismatch between the dynamics of the real world and the user’s internal beliefs of the dynamics, and that a user policy that appears suboptimal in the real world may actually be near-optimal with respect to the user’s internal dynamics model.
As resource-bounded agents living in an environment of dazzling complexity, humans rely on intuitive theories of the world to guide reasoning and planning [gerstenberg2017intuitive](#bib.bib20) ; [hamrick2011internal](#bib.bib25) .
Humans leverage internal models of the world for motor control [wolpert1995internal](#bib.bib49) ; [kawato1999internal](#bib.bib29) ; [desmurget2000forward](#bib.bib13) ; [mehta2002forward](#bib.bib33) ; [todorov2004optimality](#bib.bib45) , goal-directed decision making [botvinick2009goal](#bib.bib6) , and representing the mental states of other agents [premack1978does](#bib.bib38) .
Simplified internal models can systematically deviate from the real world, leading to suboptimal behaviors that have unintended consequences, like hitting a tennis ball into the net or skidding on an icy road.
For example, a classic study in cognitive science shows that human judgments about the physics of projectile motion are closer to Aristotelian impetus theory than to true Newtonian dynamics – in other words, people tend to ignore or underestimate the effects of inertia [caramazza1981naive](#bib.bib10) .
Characterizing the gap between internal models and reality by modeling a user’s internal predictions of the effects of their actions allows us to better explain observed user actions and infer their intent.
The main contribution of this paper is a new algorithm for intent inference that first estimates a user’s internal beliefs of the dynamics of the world using observations of how they act to perform known tasks, then leverages the learned internal dynamics model to infer intent on unknown tasks.
In contrast to the closest prior work [herman2016inverse](#bib.bib27) ; [golub2013learning](#bib.bib21) , our method scales to problems with high-dimensional, continuous state spaces and nonlinear dynamics.
Our internal dynamics model estimation algorithm assumes the user takes actions with probability proportional to their exponentiated soft Q-values.
We fit the parameters of the internal dynamics model to maximize the likelihood of observed user actions on a set of tasks with known reward functions, by tying the internal dynamics to the soft Q function via the soft Bellman equation. At test time, we use the learned internal dynamics model to predict the user’s desired next state given their current state and action input.
We run experiments first with simulated users, testing that we can recover the internal dynamics, even in MDPs with a continuous state space that would otherwise be intractable for prior methods. We then run a user study with 12 participants in which humans play the Lunar Lander game (screenshot in Figure [1](#S4.F1 "Figure 1 ‣ 4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")). We recover a dynamics model that explains user actions better than the real dynamics, which in turn enables us to assist users in playing the game by transferring their control policy from the recovered internal dynamics to the real dynamics.
2 Background
-------------
Inferring intent in sequential decision-making problems has been heavily studied under the framework of inverse reinforcement learning (IRL), which we build on in this work.
The aim of IRL is to learn a user’s reward function from observed control demonstrations.
IRL algorithms are not directly applicable to our problem of learning a user’s beliefs about the dynamics of the environment, but they provide a helpful starting point for thinking about how to extract hidden properties of a user from observations of how they behave.
In our work, we build on the maximum causal entropy (MaxCausalEnt) IRL framework [ziebart2010modelingint](#bib.bib51) ; [bloem2014infinite](#bib.bib5) ; [ramachandran2007bayesian](#bib.bib40) ; [neu2012apprenticeship](#bib.bib35) ; [herman2016inverse](#bib.bib27) .
In an MDP with a discrete action space A, the human demonstrator is assumed to follow a policy π that maximizes an entropy-regularized reward R(s,a,s′) under dynamics T(s′|s,a).
Equivalently,
| | | | |
| --- | --- | --- | --- |
| | π(a|s)≜exp(Q(s,a))∑a′∈Aexp(Q(s,a′)), | | (1) |
where Q is the soft Q function, which satisfies the soft Bellman equation [ziebart2010modelingint](#bib.bib51) ,
| | | | |
| --- | --- | --- | --- |
| | Q(s,a)=Es′∼T(⋅|s,a)[R(s,a,s′)+γV(s′)], | | (2) |
with V the soft value function,
| | | | |
| --- | --- | --- | --- |
| | V(s)≜log(∑a∈Aexp(Q(s,a))). | | (3) |
Prior work assumes T is the true dynamics of the real world, and fits a model of the reward R that maximizes the likelihood (given by Equation [1](#S2.E1 "(1) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")) of some observed demonstrations of the user acting in the real world. In our work, we assume access to a set of training tasks for which the rewards R are known, fit a model of the internal dynamics T that is allowed to deviate from the real dynamics, then use the recovered dynamics to infer intent (e.g., rewards) in new tasks.
3 Internal Dynamics Model Estimation
-------------------------------------
We split up the problem of intent inference into two parts: learning the internal dynamics model from user demonstrations on known tasks (the topic of this section), and using the learned internal model to infer intent on unknown tasks (discussed later in Section [4](#S4 "4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")). We assume that the user’s internal dynamics model is stationary, which is reasonable for problems like robotic teleoperation when the user has some experience practicing with the system but still finds it unintuitive or difficult to control. We also assume that the real dynamics are known ex-ante or learned separately.
Our aim is to recover a user’s implicit beliefs about the dynamics of the world from observations of how they act to perform a set of tasks.
The key idea is that, when their internal dynamics model deviates from the real dynamics, we can no longer simply fit a dynamics model to observed state transitions.
Standard dynamics learning algorithms typically assume access to (s,a,s′) examples, with (s,a) features and s′ labels, that can be used to train a classification or regression model p(s′|s,a) using supervised learning. In our setting, we instead have (s,a) pairs that indirectly encode the state transitions that the user expected to happen, but did not necessarily occur, because the user’s internal model predicted different outcomes s′ than those that actually occurred in the real world.
Our core assumption is that the user’s policy is near-optimal with respect to the unknown internal dynamics model. To this end, we propose a new algorithm for learning the internal dynamics from action demonstrations: inverse soft Q-learning.
###
3.1 Inverse Soft Q-Learning
The key idea behind our algorithm is that we can fit a parametric model of the internal dynamics model T that maximizes the likelihood of observed action demonstrations on a set of training tasks with known rewards by using the soft Q function as an intermediary.111Our algorithm can in principle learn from demonstrations even when the rewards are unknown, but in practice we find that this relaxation usually makes learning the correct internal dynamics too difficult.
We tie the internal dynamics T to the soft Q function via the soft Bellman equation (Equation [2](#S2.E2 "(2) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")), which ensures that the soft Q function is induced by the internal dynamics T. We tie the soft Q function to action likelihoods using Equation [1](#S2.E1 "(1) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"), which encourages the soft Q function to explain observed actions. We accomplish this by solving a constrained optimization problem in which the demonstration likelihoods appear in the objective and the soft Bellman equation appears in the constraints.
Formulating the optimization problem.
Assume the action space A is discrete.222We assume a discrete action space to simplify our exposition and experiments. Our algorithm can be extended to handle MDPs with a continuous action space using existing sampling methods [haarnoja2017reinforcement](#bib.bib24) .
Let i∈{1,2,...,n} denote the training task, Ri(s,a,s′) denote the known reward function for task i, T denote the unknown internal dynamics, and Qi denote the unknown soft Q function for task i.
We represent Qi using a function approximator Qθi with parameters θi, and the internal dynamics using a function approximator Tϕ parameterized by ϕ. Note that, while each task merits a separate soft Q function since each task has different rewards, all tasks share the same internal dynamics.
Recall the soft Bellman equation (Equation [2](#S2.E2 "(2) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")), which constrains Qi to be the soft Q function for rewards Ri and internal dynamics T.
An equivalent way to express this condition is that Qi satisfies δi(s,a)=0 ∀s,a, where δi is the soft Bellman error:
| | | | |
| --- | --- | --- | --- |
| | | | (4) |
We impose the same condition on Qθi and Tϕ, i.e., δθi,ϕ(s,a)=0 ∀s,a.
We assume our representations are expressive enough that there exist values of θi and ϕ that satisfy the condition.
We fit parameters θi and ϕ to maximize the likelihood of the observed demonstrations while respecting the soft Bellman equation by solving the constrained optimization problem
| | | | | |
| --- | --- | --- | --- | --- |
| | minimize{θi}ni=1,ϕ | n∑i=1∑(s,a)∈Ddemoi−logπθi(a|s) | | (5) |
| | subject to | δθi,ϕ(s,a)=0 ∀i∈{1,2,...,n},s∈S,a∈A, | |
where Ddemoi are the demonstrations for task i, and πθi denotes the action likelihood given by Qθi and Equation [1](#S2.E1 "(1) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior").
Solving the optimization problem.
We use the penalty method [bertsekas2014constrained](#bib.bib4) to approximately solve the constrained optimization problem described in Equation [5](#S3.E5 "(5) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"), which recasts the problem as unconstrained optimization of the cost function
| | | | |
| --- | --- | --- | --- |
| | c(θ,ϕ)≜n∑i=1∑(s,a)∈Ddemoi−logπθi(a|s)+ρ2n∑i=1∫s∈S∑a∈A(δθi,ϕ(s,a))2ds, | | (6) |
where ρ is a constant hyperparameter, πθi denotes the action likelihood given by Qθi and Equation [1](#S2.E1 "(1) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"), and δθi,ϕ denotes the soft Bellman error, which relates Qθi to Tϕ through Equation [4](#S3.E4 "(4) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior").
For MDPs with a discrete state space S, we minimize the cost as is.
MDPs with a continuous state space present two challenges: (1) an intractable integral over states in the sum over penalty terms, and (2) integrals over states in the expectation terms of the soft Bellman errors δ (recall Equation [4](#S3.E4 "(4) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")).
To tackle (1), we resort to constraint sampling [calafiore2006probabilistic](#bib.bib9) ; specifically, randomly sampling a subset of state-action pairs Dsampi from rollouts of a random policy in the real world.
To tackle (2), we choose a deterministic model of the internal dynamics Tϕ, which simplifies the integral over next states in Equation [4](#S3.E4 "(4) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") to a single term333Another potential solution is sampling states to compute a Monte Carlo estimate of the integral..
In our experiments, we minimize the objective in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") using Adam [kingma2014adam](#bib.bib30) . We use a mix of tabular representations, structured linear models, and relatively shallow multi-layer perceptrons to model Qθi and Tϕ. In the tabular setting, θi is a table of numbers with a separate entry for each state-action pair, and ϕ can be a table with an entry between 0 and 1 for each state-action-state triple. For linear and neural network representations, θi and ϕ are sets of weights.
###
3.2 Regularizing the Internal Dynamics Model
One issue with our approach to estimating the internal dynamics is that there tend to be multiple feasible internal dynamics models that explain the demonstration data equally well, which makes the correct internal dynamics model difficult to identify. We propose two different solutions to this problem: collecting demonstrations on multiple training tasks, and imposing a prior on the learned internal dynamics that encourages it to be similar to the real dynamics.
Multiple training tasks. If we only collect demonstrations on n=1 training tasks, then at any given state s and action a, the recovered internal dynamics may simply assign a likelihood of one to the next state s′ that maximizes the reward function R1(s,a,s′) of the single training task. Intuitively, if our algorithm is given user demonstrations on only one task, then the user’s actions can be explained by an internal dynamics model that always predicts the best possible next state for that one task (e.g., the target in a navigation task), no matter the current state or user action. We can mitigate this problem by collecting demonstrations on n>1 training tasks, which prevents degenerate solutions by forcing the internal dynamics to be consistent with a diverse set of user policies.
Action intent prior. In our experiments, we also explore another way to regularize the learned internal dynamics: imposing the prior that the learned internal dynamics Tϕ should be similar to the known real dynamics Treal by restricting the support of Tϕ(⋅|s,a) to states s′ that are reachable in the real dynamics. Formally,
| | | | |
| --- | --- | --- | --- |
| | Tϕ(s′|s,a)≜∑aint∈ATreal(s′|s,aint)fϕ(aint|s,a) | | (7) |
where a is the user’s action, aint is the user’s intended action, and fϕ:S×A2→[0,1] captures the user’s ‘action intent’ – the action they would have taken if they had perfect knowledge of the real dynamics.
This prior changes the structure of our internal dynamics model to predict the user’s intended action with respect to the real dynamics, rather than directly predicting their intended next state.
Note that, when we use this action intent prior, Tϕ is no longer directly modeled. Instead, we model fϕ and use Equation [7](#S3.E7 "(7) ‣ 3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") to compute Tϕ.
In our experiments, we examine the effects of employing multiple training tasks and imposing the action intent prior, together and in isolation.
4 Using Learned Internal Dynamics Models
-----------------------------------------
The ability to learn internal dynamics models from demonstrations is broadly useful for intent inference. In our experiments, we explore two applications: (1) shared autonomy, in which a human and robot collaborate to solve a challenging real-time control task, and (2) learning the reward function of a user who generates suboptimal demonstrations due to internal model misspecification. In (1), intent is formalized as the user’s desired next state, while in (2), the user’s intent is represented by their reward function.
###
4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer
Figure 1: A high-level schematic of our internal-to-real dynamics transfer algorithm for shared autonomy, which uses the internal dynamics model learned by our method to assist the user with an unknown control task; in this case, landing the lunar lander between the flags. The user’s actions are assumed to be consistent with their internal beliefs about the dynamics Tϕ, which differ from the real dynamics Treal. Our system models the internal dynamics to determine where the user is trying to go next, then acts to get there.
Many control problems involving human users are challenging for autonomous agents due to partial observability and imprecise task specifications, and are also challenging for humans due to constraints such as bounded rationality [simon1991bounded](#bib.bib44) and physical reaction time. Shared autonomy combines human and machine intelligence to perform control tasks that neither can on their own, but existing methods have the basic requirement that the machine either needs a description of the task or feedback from the user, e.g., in the form of rewards [javdani2015shared](#bib.bib28) ; [broad2017learning](#bib.bib7) ; [reddy2018shared](#bib.bib41) .
We propose an alternative algorithm that assists the user without knowing their reward function by leveraging the internal dynamics model learned by our method. The key idea is formalizing the user’s intent as their desired next state. We use the learned internal dynamics model to infer the user’s desired next state given their current state and control input, then execute an action that will take the user to the desired state under the real dynamics; essentially, transferring the user’s policy from the internal dynamics to the real dynamics, akin to simulation-to-real transfer for robotic control [cutler2015efficient](#bib.bib12) . See Figure [1](#S4.F1 "Figure 1 ‣ 4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") for a high-level schematic of this process.
Equipped with the learned internal dynamics model Tϕ, we perform internal-to-real dynamics transfer by observing the user’s action input, computing the induced distribution over next states using the internal dynamics, and executing an action that induces a similar distribution over next states in the real dynamics. Formally, for user control input aht and state st, we execute action at, where
| | | | |
| --- | --- | --- | --- |
| | at≜argmina∈ADKL(Tϕ(st+1|st,aht)∥Treal(st+1|st,a)) | | (8) |
###
4.2 Learning Rewards from Misguided User Demonstrations
Most existing inverse reinforcement learning algorithms assume that the user’s internal dynamics are equivalent to the real dynamics, and learn their reward function from near-optimal demonstrations.
We explore a more realistic setting in which the user’s demonstrations are suboptimal due to a mismatch between their internal dynamics and the real dynamics.
Users are ‘misguided’ in that their behavior is suboptimal in the real world, but optimal with respect to their internal dynamics.
In this setting, standard IRL algorithms that do not distinguish between the internal and the real dynamics learn incorrect reward functions.
Our method can be used to learn the internal dynamics, then explicitly incorporate the learned internal dynamics into an IRL algorithm’s behavioral model of the user.
In our experiments, we instantiate prior work with MaxCausalEnt IRL [ziebart2010modelingint](#bib.bib51) , which inverts the behavioral model from Equation [1](#S2.E1 "(1) ‣ 2 Background ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") to infer rewards from demonstrations.
We adapt it to our setting, in which the real dynamics are known and the internal dynamics are either learned (separately by our algorithm) or assumed to be the same as the known real dynamics.
MaxCausalEnt IRL cannot learn the user’s reward function from misguided demonstrations when it makes the standard assumption that the internal dynamics are equal to the real dynamics, but can learn accurate rewards when it instead uses the learned internal dynamics model produced by our algorithm.
5 User Study and Simulation Experiments
----------------------------------------
The purpose of our experiments is two-fold: (1) to test the correctness of our algorithm, and (2) to test our core assumption that a human user’s internal dynamics can be different from the real dynamics, and that our algorithm can learn an internal dynamics model that is useful for assisting the user through internal-to-real dynamics transfer. To accomplish (1), we perform three simulated experiments that apply our method to shared autonomy (see Section [4.1](#S4.SS1 "4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")) and to learning rewards from misguided user demonstrations (see Section [4.2](#S4.SS2 "4.2 Learning Rewards from Misguided User Demonstrations ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")).
In the shared autonomy experiments, we first use a tabular grid world navigation task to sanity-check our algorithm and analyze the effects of different regularization choices from Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"). We then use a continuous-state 2D navigation task
to test our method’s ability to handle continuous observations using the approximations described in Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"). In the reward learning experiment, we use the grid world environment to compare the performance of MaxCausalEnt IRL [ziebart2010modelingint](#bib.bib51) when it assumes the internal dynamics are the same as the real dynamics to when it uses the internal dynamics learned by our algorithm. To accomplish (2), we conduct a user study in which 12 participants play the Lunar Lander game (see Figure [1](#S4.F1 "Figure 1 ‣ 4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")) with and without internal-to-real dynamics transfer assistance. We summarize these experiments in Sections [5.1](#S5.SS1 "5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") and [5.2](#S5.SS2 "5.2 User Study on the Lunar Lander Game ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"). Further details are provided in Section [7.2](#S7.SS2 "7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix.
###
5.1 Simulation Experiments
| | | |
| --- | --- | --- |
| Error bars show standard error on ten random seeds. Our method learns accurate internal dynamics models, the regularization methods in Section | Error bars show standard error on ten random seeds. Our method learns accurate internal dynamics models, the regularization methods in Section | Error bars show standard error on ten random seeds. Our method learns accurate internal dynamics models, the regularization methods in Section |
Figure 2: Left, Center: Error bars show standard error on ten random seeds. Our method learns accurate internal dynamics models, the regularization methods in Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") increase accuracy, and the approximations for continuous-state MDPs in Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") do not compromise accuracy. Right: Error regions show standard error on ten random tasks and ten random seeds each. Our method learns an internal dynamics model that enables MaxCausalEnt IRL to learn rewards from misguided user demonstrations.
Shared autonomy.
The grid world provides us with a domain where exact solutions are tractable, which enables us to verify the correctness of our method and compare the quality of the approximation in Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") with an exact solution to the learning problem. The continuous task provides a more challenging domain where exact solutions via dynamic programming are intractable.
In each setting, we simulate a user with an internal dynamics model that is severely biased away from the real dynamics of the simulated environment. The simulated user’s policy is optimal with respect to their internal dynamics, but suboptimal with respect to the real dynamics.
Figure [2](#S5.F2 "Figure 2 ‣ 5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (left and center plots) provides overall support for the hypothesis that our method can effectively learn tabular and continuous representations of the internal dynamics for MDPs with discrete and continuous state spaces. The learned internal dynamics models are accurate with respect to the ground truth internal dynamics, and internal-to-real dynamics transfer successfully assists the simulated users. The learned internal dynamics model becomes more accurate as we increase the number of training tasks, and the action intent prior (see Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")) increases accuracy when the internal dynamics are similar to the real dynamics.
These results confirm that our approximate algorithm is correct and yields solutions that do not significantly deviate from those of an exact algorithm. Further results and experimental details are discussed in Sections [7.2.1](#S7.SS2.SSS1 "7.2.1 Grid World Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") and [7.2.2](#S7.SS2.SSS2 "7.2.2 2D Continuous Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix.
Learning rewards from misguided user demonstrations. Standard IRL algorithms, such as MaxCausalEnt IRL [ziebart2010modelingint](#bib.bib51) , can fail to learn rewards from user demonstrations that are ‘misguided’, i.e., systematically suboptimal in the real world but near-optimal with respect to the user’s internal dynamics. Our algorithm can learn the internal dynamics model, and we can then explicitly incorporate the learned internal dynamics into the MaxCausalEnt IRL algorithm to learn accurate rewards from misguided demonstrations. We assess this method on a simulated grid world navigation task. Figure [2](#S5.F2 "Figure 2 ‣ 5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (right plot) supports our claim that standard IRL is ineffective at learning rewards from misguided user demonstrations. After using our algorithm to learn the internal dynamics and explicitly incorporating the learned internal dynamics into an IRL algorithm’s model of the user, we see that it’s possible to recover accurate rewards from these misguided demonstrations. Additional information on our experimental setup is available in Section [7.2.3](#S7.SS2.SSS3 "7.2.3 Learning Rewards from Misguided User Demonstrations ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix.
###
5.2 User Study on the Lunar Lander Game
| | | | | | |
| --- | --- | --- | --- | --- | --- |
| Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table | Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table | Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table | Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table | Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table | Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table |
Figure 3: Human users find the default game environment – the real dynamics – to be difficult and unintuitive, as indicated by their poor performance in the unassisted condition (top center and right plots) and their subjective evaluations (in Table [1](#S7.T1 "Table 1 ‣ 7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix).
Our method observes suboptimal human play in the default environment, learns a setting of the game physics under which the observed human play would have been closer to optimal, then performs internal-to-real dynamics transfer to assist human users in achieving higher success rates and lower crash rates (top center and right plots). The learned internal dynamics has a slower game speed than the real dynamics (bottom left plot). The bottom center and right plots show successful (green) and failed (red) trajectories in the unassisted and assisted conditions.
Our previous experiments were conducted with simulated expert behavior, which allowed us to control the corruption of the internal dynamics. However, it remains to be seen whether this model of suboptimality effectively reflects real human behavior. We test this hypothesis in the next experiment, which evaluates whether our method can learn the internal dynamics accurately enough to assist real users through internal-to-real dynamics transfer.
Task description.
We use the Lunar Lander game from OpenAI Gym [1606.01540](#bib.bib8) (screenshot in Figure [1](#S4.F1 "Figure 1 ‣ 4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")) to evaluate our algorithm with human users. The objective of the game is to land on the ground, without crashing or flying out of bounds, using two lateral thrusters and a main engine. The action space A consists of six discrete actions. The state s∈R9 encodes position, velocity, orientation, and the location of the landing site, which is one of nine values corresponding to n=9 distinct tasks. The physics of the game are forward-simulated by a black-box function that takes as input seven hyperparameters, which include engine power and game speed.
We manipulate whether or not the user receives internal-to-real dynamics transfer assistance using an internal dynamics model trained on their unassisted demonstrations. The dependent measures are the success and crash rates in each condition.
The task and evaluation protocol are discussed further in Section [7.3](#S7.SS3 "7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix.
Analysis.
In the default environment, users appear to play as though they underestimate the strength of gravity, which causes them to crash into the ground frequently (see the supplementary videos). Figure [3](#S5.F3 "Figure 3 ‣ 5.2 User Study on the Lunar Lander Game ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (bottom left plot) shows that our algorithm learns an internal dynamics model characterized by a slower game speed than the real dynamics, which makes sense since a slower game speed induces smaller forces and slower motion – conditions under which the users’ action demonstrations would have been closer to optimal.
These results support our claim that our algorithm can learn an internal dynamics model that explains user actions better than the real dynamics.
When unassisted, users often crash or fly out of bounds due to the unintuitive nature of the thruster controls and the relatively fast pace of the game. Figure [3](#S5.F3 "Figure 3 ‣ 5.2 User Study on the Lunar Lander Game ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (top center and right plots) shows that users succeed significantly more often and crash significantly less often when assisted by internal-to-real dynamics transfer (see Section [7.3](#S7.SS3 "7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix for hypothesis tests). The assistance makes the system feel easier to control (see the subjective evaluations in Table [1](#S7.T1 "Table 1 ‣ 7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") of the appendix), less likely to tip over (see the supplementary videos), and move more slowly in response to user actions (assistance led to a 30% decrease in average speed). One of the key advantages of assistance was its positive effect on the rate at which users were able to switch between different actions: on average, unassisted users performed 18 actions per minute (APM), while assisted users performed 84 APM. Quickly switching between firing various thrusters enabled assisted users to better stabilize flight. These results demonstrate that the learned internal dynamics can be used to effectively assist the user through internal-to-real dynamics transfer, which in turn gives us confidence in the accuracy of the learned internal dynamics. After all, we cannot measure the accuracy of the learned internal dynamics by comparing it to the ground truth internal dynamics, which is unknown for human users.
6 Discussion and Related Work
------------------------------
Related work.
The closest prior work in intent inference and action understanding comes from inverse planning [baker2009action](#bib.bib2) and inverse reinforcement learning [ng2000algorithms](#bib.bib36) , which use observations of a user’s actions to estimate the user’s goal or reward function.
We take a fundamentally different approach to intent inference: using action observations to estimate the user’s beliefs about the world dynamics.
The simultaneous estimation of rewards and dynamics (SERD) instantiation of MaxCausalEnt IRL [herman2016inverse](#bib.bib27) aims to improve the sample efficiency of IRL by forcing the learned real dynamics model to explain observed state transitions as well as actions. The framework includes terms for the demonstrator’s beliefs of the dynamics, but the overall algorithm and experiments of Herman et al. [herman2016inverse](#bib.bib27) constrain those beliefs to be the same as the real dynamics. Our goal is to learn an internal dynamics model that may deviate from the real dynamics. To this end, we propose two new internal dynamics regularization techniques, multi-task training and the action intent prior (see Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")), and demonstrate their utility for learning an internal dynamics model that differs from the real dynamics (see Section [5.1](#S5.SS1 "5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior")).
We also conduct a user experiment that shows human actions in a game environment can be better explained by a learned internal dynamics model than by the real dynamics, and that augmenting user control with internal-to-real dynamics transfer results in improved game play.
Furthermore, the SERD algorithm is well-suited to MDPs with a discrete state space, but intractable for continuous state spaces. Our method can be applied to MDPs with a continuous state space, as shown in Sections [5.1](#S5.SS1 "5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") and [5.2](#S5.SS2 "5.2 User Study on the Lunar Lander Game ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior").
Golub et al. [golub2013learning](#bib.bib21) propose an internal model estimation (IME) framework for brain-machine interface (BMI) control that learns an internal dynamics model from control demonstrations on tasks with linear-Gaussian dynamics and quadratic reward functions. Our work is (1) more general in that it places no restrictions on the functional form of the dynamics or the reward function, and (2) does not assume sensory feedback delay, which is the fundamental premise of using IME for BMI control.
Limitations.
Although our algorithm models the soft Q function with arbitrary neural network parameterizations, the internal dynamics parameterizations we use are smaller, with at most seven parameters for continuous tasks. Increasing the number of dynamics parameters would require a better approach to regularization than those proposed in Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior").
Future work.
The ability to learn internal dynamics models from demonstrations opens the door to new directions of scientific inquiry, like estimating young children’s intuitive theories of physics and psychology without eliciting verbal judgments [wilkening2010children](#bib.bib48) ; [fodor1992theory](#bib.bib17) ; [gopnik199410](#bib.bib22) . It also enables applications that involve intent inference, including adaptive brain-computer interfaces for prosthetic limbs [carmena2013advances](#bib.bib11) ; [shenoy2014combining](#bib.bib43) that help users perform control tasks that are difficult to fully specify.
7 Appendix
-----------
This appendix contains additional discussion of methods, experiments, and related work.
###
7.1 Internal Dynamics Model Estimation
####
7.1.1 Regularizing the Internal Dynamics Model
Action intent prior. One additional benefit of the action intent prior described in Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) is that it enables learning rewards from misguided user demonstrations (see Section [4.2](#S4.SS2 "4.2 Learning Rewards from Misguided User Demonstrations ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") in the main paper) using model-free inverse reinforcement learning algorithms [finn2016guided](#bib.bib16) ; [fu2017learning](#bib.bib19) ; [uchibe2017model](#bib.bib47) ; [wulfmeier2015maximum](#bib.bib50) . Model-free IRL does not explicitly incorporate a dynamics model into the behavioral model of the user, so there is no clear way to incorporate a fully general internal dynamics model (learned separately by our algorithm) into model-free IRL. However, by structuring the internal dynamics using Equation [7](#S3.E7 "(7) ‣ 3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), we can ‘debias’ misguided demonstrations by simply remapping observed actions to the user’s intended actions given by fϕ. This technique does not rely on the explicit presence of a dynamics model in the IRL algorithm’s user model.
###
7.2 Experiments
####
7.2.1 Grid World Navigation
In Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), we described two ways to adapt our algorithm to MDPs with a continuous state space: constraint sampling, and choosing a deterministic model of the internal dynamics. In this section, we evaluate our method on an MDP with a discrete state space in order to avoid the need for these two tricks. Our goal is to learn the internal dynamics and use it to assist the user through internal-to-real dynamics transfer. To sanity-check our algorithm and analyze its behavior under various hyperparameter settings and regularization choices, we implement a simple, deterministic grid world environment in which the simulated user attempts to navigate to a target position.
Hypothesis. Our algorithm is capable of learning accurate tabular representations of the internal dynamics for MDPs with a discrete state space. The two regularization schemes proposed in Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) improve the quality of the learned internal dynamics model.
Task description. The state space consists of 49 states arranged in a 7x7 grid. The action space consists of four discrete actions that deterministically move the agent one step in each of the cardinal directions. The reward function emits a large bonus when the agent hits the target, a large penalty when the agent goes out of bounds, and includes a shaping term that rewards the agent for moving closer to the target. An episode lasts at most 100 timesteps. Each of the 49 states is a potential target, so the environment naturally yields 49 distinct tasks.
Corrupting the internal dynamics. To simulate suboptimal behavior, we create two users: one user whose action labels have been randomly scrambled in the same way at all states (e.g., the user’s ‘left’ button actually moves them down instead, and this confusion is the same throughout the state space), and a different user whose action labels have been randomly scrambled in potentially different ways depending on which state they’re in (e.g., ‘left’ takes them down in the top half of the grid, but takes them right in the bottom half). We refer to these two corruption models as ‘globally scrambled actions’ and ‘locally scrambled actions’ respectively. The users behave optimally with respect to their internal beliefs of the action labels, i.e., their internal dynamics, but because their beliefs about the action labels are incorrect, they act suboptimally in the actual environment.
Evaluation. We evaluate our method on its ability to learn the internal dynamics models of the simulated suboptimal users, i.e., on its ability to unscramble their actions, given demonstrations of their failed attempts to solve the task. The dependent measures are the next-state prediction accuracy of the learned internal dynamics compared to the ground truth internal dynamics, as well as the user’s success rate when they are assisted with internal-to-real dynamics transfer (see Section [4.1](#S4.SS1 "4.1 Shared Autonomy via Internal-to-Real Dynamics Transfer ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") in the main paper) using the learned internal dynamics.
Implementation details. We use tabular representations of the Qθi functions and the internal dynamics Tϕ. We collect 1000 demonstrations per training task, set ρ=2⋅10−3 in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), and enumerate the constraints in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) instead of sampling.
Manipulated factors. We manipulate (1) whether or not the user receives assistance in the form of internal-to-real dynamics transfer using the learned internal dynamics model – a binary variable; (2) the number of training tasks on which we collect demonstrations – an integer-valued variable between 1 and 49; (3) the structure of the internal dynamics model – a categorical variable that can take on two values: state intent, which structures the internal dynamics in the usual way, or action intent, which uses Equation [7](#S3.E7 "(7) ‣ 3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) instead; and (4) the user’s internal dynamics corruption scheme – a categorical variable that can take on two values: globally scrambled actions, or locally scrambled actions.
| | | | |
| --- | --- | --- | --- |
| Error bars show standard error on ten random seeds. Corrupting the internal dynamics of the simulated user by scrambling actions the same way at all states (top and bottom left plots) induces a much easier internal dynamics learning problem than scrambling actions differently at each state (top and bottom right plots). | Error bars show standard error on ten random seeds. Corrupting the internal dynamics of the simulated user by scrambling actions the same way at all states (top and bottom left plots) induces a much easier internal dynamics learning problem than scrambling actions differently at each state (top and bottom right plots). | Error bars show standard error on ten random seeds. Corrupting the internal dynamics of the simulated user by scrambling actions the same way at all states (top and bottom left plots) induces a much easier internal dynamics learning problem than scrambling actions differently at each state (top and bottom right plots). | Error bars show standard error on ten random seeds. Corrupting the internal dynamics of the simulated user by scrambling actions the same way at all states (top and bottom left plots) induces a much easier internal dynamics learning problem than scrambling actions differently at each state (top and bottom right plots). |
Figure 4: Error bars show standard error on ten random seeds. Corrupting the internal dynamics of the simulated user by scrambling actions the same way at all states (top and bottom left plots) induces a much easier internal dynamics learning problem than scrambling actions differently at each state (top and bottom right plots).
Analysis. Figure [4](#S7.F4 "Figure 4 ‣ 7.2.1 Grid World Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") provides overall support for the hypothesis that our method can effectively learn a tabular representation of the internal dynamics for an MDP with a discrete state space. The learned internal dynamics models are accurate with respect to the ground truth internal dynamics, especially when the user’s internal dynamics corruption is systematic throughout the state space (top and bottom left plots).
We also compare the two regularization schemes discussed in Section [3.2](#S3.SS2 "3.2 Regularizing the Internal Dynamics Model ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper): training on multiple tasks, and imposing an action intent prior.
Internal models are easier to learn when the user demonstrates their behavior on multiple training tasks, as shown by the increase in accuracy as the number of tasks (on the horizontal axis) increases.
Regularizing the internal dynamics using action intent can be useful in some cases when the internal dynamics systematically deviate from the real dynamics, like when the user’s actions are scrambled in the same way throughout the state space (top and bottom left plots, compare orange vs. teal curve), but can have a varying effect in other cases where the internal dynamics are severely biased away from reality, like when the action scrambling varies between states (top and bottom right plots, compare orange vs. teal curve).
####
7.2.2 2D Continuous Navigation
In the previous section, we adopted a tabular grid world environment in order to avoid constraint sampling in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper). Now, we would like to show that our method still works even when we sample constraints to be able to handle a continuous state space.
Hypothesis. Our algorithm can learn accurate continuous representations of the internal dynamics for MDPs with a continuous state space.
Task description. As mentioned in Section [1](#S1 "1 Introduction ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), a classic study in cognitive science shows that people’s intuitive judgments about the physics of projectile motion are closer to Aristotelian impetus theory than to true Newtonian dynamics [caramazza1981naive](#bib.bib10) . In other words, people tend to ignore or underestimate the effects of inertia. Inspired by this study, we create a simple 2D environment in a which a simulated user must move a point mass from its initial position to a target position as quickly as possible using a discrete action space of four arrow keys and continuous, low-dimensional observations of position and velocity. The system follows deterministic, linear dynamics. Formally,
| | | | |
| --- | --- | --- | --- |
| | xt+1=Axt+But | | (9) |
where x=(x,y,vx,vy)⊺ denotes the state, u∈{(±0.01,0)⊺,(0,±0.01)⊺} denotes the control,
| | | |
| --- | --- | --- |
| | A=⎛⎜
⎜
⎜⎝10a130010a2400a330000a44⎞⎟
⎟
⎟⎠,B=⎛⎜
⎜
⎜⎝b1100b22b3100b42⎞⎟
⎟
⎟⎠. | |
At the beginning of each episode, the state is reset to x0=(x0∼Unif(0,1),y0∼Unif(0,1),0,0). The episode ends if the agent reaches the target (gets within a 0.02 radius around the target), goes out of bounds (outside the unit square), or runs out of time (takes longer than 200 timesteps).
Corrupting the internal dynamics. In the simulation, actions control acceleration and inertia exists; in other words, b11=b22=0 and the rest of the parameters are set to 1. We create a simulated suboptimal user that behaves as if their actions control velocity and inertia does not exist, which causes them to follow trajectories that oscillate around the target or go out of bounds. The user behaves optimally with respect to their internal beliefs about the dynamics, but because their beliefs are incorrect, they act suboptimally in the real environment.
Evaluation. As before, we evaluate our method on its ability to learn the internal dynamics models of the simulated suboptimal user given demonstrations of their failed attempts to solve the task. We manipulate whether or not the user receives assistance in the form of internal-to-real dynamics transfer using the learned internal dynamics model. The dependent measures are the L2 error of the learned internal dynamics model parameters with respect to the ground truth internal dynamics parameters, and the success and crash rates of the user in each condition.
Implementation details. We fix the number of training tasks at n=49, and use a multi-layer perceptron with one hidden layer of 32 units to represent the Qθi functions. We use a linear model based on Equation [9](#S7.E9 "(9) ‣ 7.2.2 2D Continuous Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") to represent the internal dynamics Tϕ, in which ^a13,^a24,^a33,^a44,^b11,^b22,^b31,^b42∈[0,1]. We collect 1000 demonstrations per training task, set ρ=2 in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), and sample constraints in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) by collecting 500 rollouts of a random policy in the real world (see Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") in the main paper for details).
| | | |
| --- | --- | --- |
| Our method is able to assist the simulated suboptimal user through internal-to-real dynamics transfer. Sample paths followed by the unassisted and assisted user on a single task are shown above. Red paths end out of bounds; green, at the target marked by a yellow star. | Our method is able to assist the simulated suboptimal user through internal-to-real dynamics transfer. Sample paths followed by the unassisted and assisted user on a single task are shown above. Red paths end out of bounds; green, at the target marked by a yellow star. | Our method is able to assist the simulated suboptimal user through internal-to-real dynamics transfer. Sample paths followed by the unassisted and assisted user on a single task are shown above. Red paths end out of bounds; green, at the target marked by a yellow star. |
Figure 5: Our method is able to assist the simulated suboptimal user through internal-to-real dynamics transfer. Sample paths followed by the unassisted and assisted user on a single task are shown above. Red paths end out of bounds; green, at the target marked by a yellow star.
Analysis. Our algorithm correctly learns the following internal dynamics parameters: (1) ^a33=^a44=0 in the learned internal dynamics, which corresponds to the user’s belief that inertia does not exist; (2) ^b11=^b22=1 and ^b31=^b42=0 in the learned internal dynamics, which matches the user’s belief that they have velocity control instead of acceleration control. The learned internal dynamics maintains ^a13=^a24=1, as in the real dynamics, which makes sense since the user’s behavior is consistent with these parameters. Figure [5](#S7.F5 "Figure 5 ‣ 7.2.2 2D Continuous Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (left plot) demonstrates the stability of our algorithm in converging to the correct internal dynamics.
Figure [5](#S7.F5 "Figure 5 ‣ 7.2.2 2D Continuous Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (center and right plots) shows examples of trajectories followed by the simulated suboptimal user on their own and when they are assisted by internal-to-real dynamics transfer. The assisted user tends to move directly to the target instead of oscillating around it or missing it altogether.
####
7.2.3 Learning Rewards from Misguided User Demonstrations
The previous simulation experiments show that our algorithm can learn internal dynamics models that are useful for shared autonomy. Now, we explore a different application of our algorithm: learning rewards from demonstrations generated by a user with a misspecified internal dynamics model. In order to compare to prior methods that operate on tabular MDPs, we adopt the grid world setup from Section [7.2.1](#S7.SS2.SSS1 "7.2.1 Grid World Navigation ‣ 7.2 Experiments ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"), with globally scrambled actions as the internal dynamics corruption scheme.
Hypothesis. Standard IRL algorithms can fail to learn rewards from user demonstrations that are ‘misguided’, i.e., suboptimal in the real world but optimal with respect to the user’s internal dynamics. Our algorithm can learn the internal dynamics model, then we can explicitly incorporate the learned internal dynamics into standard IRL to learn accurate rewards from misguided demonstrations.
Evaluation. We evaluate our method on its ability to learn an internal dynamics model that is useful for ‘debiasing’ misguided user demonstrations, which serve as input to the MaxCausalEnt IRL algorithm described in Section [4.2](#S4.SS2 "4.2 Learning Rewards from Misguided User Demonstrations ‣ 4 Using Learned Internal Dynamics Models ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper). We manipulate whether we use the learned internal dynamics, or assume the internal dynamics to be the same as the real dynamics. The dependent measure is the true reward collected by a policy that is optimized for the rewards learned by MaxCausalEnt IRL.
Implementation details. We implement the MaxCausalEnt IRL algorithm [ziebart2010modelingint](#bib.bib51) ; [herman2016inverse](#bib.bib27) . The reward function is represented as a table R(s).
Analysis. Figure [2](#S5.F2 "Figure 2 ‣ 5.1 Simulation Experiments ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper, right plot) supports our claim that standard IRL is not capable of learning rewards from misguided user demonstrations, and that after using our algorithm to learn the internal dynamics and explicitly incorporating the learned internal dynamics into an IRL algorithm’s behavioral model of the user, we learn accurate rewards.
###
7.3 User Study on the Lunar Lander Game
Task description. The reward function emits a large bonus at the end of the episode for landing between the flags, a large penalty for crashing or going out of bounds, and is shaped to penalize speed, tilt, and moving away from the landing site. The physics of the game are deterministic.
Evaluation protocol. We evaluate our method on its ability to learn the internal dynamics models of human users given demonstrations of their failed attempts to solve the task in the default environment. We manipulate whether or not the user receives assistance in the form of internal-to-real dynamics transfer using the learned internal dynamics. The dependent measures are the success and crash rates in each condition.
Implementation details. We fix the number of training tasks at n=9 and use a multi-layer perceptron with one hidden layer of 32 units to represent the Qθi functions. We collect 5 demonstrations per training task per user, set ρ=2⋅10−3 in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper), and sample constraints in Equation [6](#S3.E6 "(6) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) by collecting 100 rollouts of a random policy in the real world (see Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") in the main paper for details).
The physics of the game are governed in part by a configurable vector ψ∈R7 that encodes engine power, game speed, and other relevant parameters. Since we cannot readily access an analytical expression of the dynamics, only a black-box function that forward-simulates the dynamics, we cannot simply parameterize our internal dynamics model using ψ (see Section [3.1](#S3.SS1 "3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") in the main paper for details). Instead, we draw 100 random samples of ψ and represent our internal dynamics model as a categorical probability distribution over the samples. In other words, we approximate the continuous space of possible internal dynamics models using a discrete set of samples. To accommodate this representation, we modify Equation [4](#S3.E4 "(4) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper):
| | | | |
| --- | --- | --- | --- |
| | δθi,ϕ(s,a) | ≜Qθi(s,a)−Ej∼Cat(100,ϕ)[∫s′∈STψj(s′|s,a)(Ri(s,a,s′)+γVθi(s′))ds′] | |
| | | =Qθi(s,a)−100∑j=1ϕj⋅∫s′∈STψj(s′|s,a)(Ri(s,a,s′)+γVθi(s′))ds′. | |
Subject allocation. We recruited 9 male and 3 female participants, with an average age of 24. Each participant was provided with the rules of the game and a short practice period of 9-18 episodes to familiarize themselves with the controls and dynamics. Each user played in both conditions: unassisted, and assisted.
To avoid the confounding effect of humans learning to play the game better over time, we counterbalanced the order of the two conditions. Each condition lasted 45 episodes.
Counterbalancing the order of the two conditions sometimes requires testing the user in the assisted condition *before* the unassisted condition, which begs the question: where do the demonstrations used to train the internal dynamics model used in internal-to-real dynamics transfer assistance come from, if not the data from the unassisted condition? We train the internal dynamics model used to assist the k-th participant on the pooled, unassisted demonstrations of all previous participants {1,2,...,k−1}. After the k-th participant completes both conditions, we train an internal dynamics model solely on unassisted demonstrations from the k-th participant and verify that the resulting internal dynamics model is the same as the one used to assist the k-th participant.
| | | |
| --- | --- | --- |
| Assistance in the form of internal-to-real dynamics transfer increases success rates and decreases crash rates. | Assistance in the form of internal-to-real dynamics transfer increases success rates and decreases crash rates. | Assistance in the form of internal-to-real dynamics transfer increases success rates and decreases crash rates. |
Figure 6: Assistance in the form of internal-to-real dynamics transfer increases success rates and decreases crash rates.
Analysis. After inspecting the results of our random search over the internal dynamics space, we found that the game speed parameter in ψ had a much larger influence on the quality of the learned internal dynamics and the resulting internal-to-real dynamics transfer than the other six parameters. Hence, in Figure [3](#S5.F3 "Figure 3 ‣ 5.2 User Study on the Lunar Lander Game ‣ 5 User Study and Simulation Experiments ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper, bottom left plot), we show the results of a grid search on the game speed parameter, holding the other six parameters constant at their default values. The game speed parameter governs the size of the time delta with which the game engine advances the physics simulation at each discrete step. This parameter indirectly controls the strength of the forces in the game physics: smaller time deltas lead to smaller forces and generally slower motion, and larger deltas to larger forces and consequently faster motion.
We ran a one-way repeated measures ANOVA with the presence of assistance as a factor influencing success and crash rates, and found that f(1,11)=109.58,p<0.0001 for the success rate and f(1,11)=126.33,p<0.0001 for the crash rate. The assisted user succeeds significantly more often and crashes significantly less often than the unassisted user. Figure [6](#S7.F6 "Figure 6 ‣ 7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") shows the raw data.
| | | | |
| --- | --- | --- | --- |
| | p-value | Unassisted | Assisted |
| I enjoyed playing the game | <.001 | 3.92 | 5.92 |
| I improved over time | <.0001 | 3.08 | 5.83 |
| I didn’t crash | <.001 | 1.17 | 3.00 |
| I didn’t fly out of bounds | <.05 | 1.67 | 3.08 |
| I didn’t run out of time | >.05 | 5.17 | 6.17 |
| I landed between the flags | <.001 | 1.92 | 4.00 |
| I understood how to complete the task | <.05 | 6.42 | 6.75 |
| I intuitively understood the physics of the game | <.01 | 4.58 | 6.00 |
| My actions were carried out | >.05 | 4.83 | 5.50 |
| My intended actions were carried out | <.01 | 2.75 | 5.25 |
Table 1: Subjective evaluations of the Lunar Lander user study from 12 participants. Means reported below for responses on a 7-point Likert scale, where 1 = Strongly Disagree, 4 = Neither Disagree nor Agree, and 7 = Strongly Agree. p-values from a one-way repeated measures ANOVA with the presence of assistance as a factor influencing responses.
Table [1](#S7.T1 "Table 1 ‣ 7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") shows users’ subjective evaluations after each condition, which align with the objective results in Figure [6](#S7.F6 "Figure 6 ‣ 7.3 User Study on the Lunar Lander Game ‣ 7 Appendix ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior"): users performed better and felt more comfortable with assistance than without assistance.
We recorded game videos of unassisted users (see `unassisted.mp4`) and assisted users (see `assisted.mp4`), which qualitatively illustrate the difficulty of the default game environment and the positive effect of internal-to-real dynamics transfer assistance.
###
7.4 Discussion and Related Work
Related work. Modeling human error has a rich history in the behavioral sciences.
Procrastination and other time-inconsistent human behaviors have been characterized as rational with respect to a cost model that discounts the cost of future action relative to that of immediate action [akerlof1991procrastination](#bib.bib1) ; [kleinberg2014time](#bib.bib31) . Systematic errors in human predictions about the future have been partially explained by cognitive biases like the availability heuristic and regression to the mean [tversky1974judgment](#bib.bib46) .
Imperfect intuitive physics judgments have been characterized as approximate probabilistic inferences made by a resource-bounded observer [hamrick2011internal](#bib.bib25) . We take an orthogonal approach in which we assume that suboptimal behavior is primarily caused by incorrect beliefs of the dynamics, rather than uncertainty or biases in planning and judgment.
Humans are resource-bounded agents that must take into account the computational cost of their planning algorithm when selecting actions [griffiths2015rational](#bib.bib23) . One way to trade-off the ability to find high-value actions for lower computational cost is to plan using a simplified, low-dimensional model of the dynamics [herbert2017fastrack](#bib.bib26) ; [fridovich2017planning](#bib.bib18) . Evidence from the cognitive science literature suggests humans find it difficult to predict the motion of objects when multiple information dimensions are involved [proffitt1989understanding](#bib.bib39) . Thus, we arrive at an alternative explanation for why humans may behave near-optimally with respect to a dynamics model that differs from the real dynamics: even if users have perfect knowledge of the real dynamics, they may not have the computational resources to plan under the real dynamics, and instead choose to plan using a simplified model.
Summary. We contribute an algorithm that learns a user’s implicit beliefs about the dynamics of the environment from demonstrations of their suboptimal behavior in the real environment. Simulation experiments and a small-scale user study demonstrate the effectiveness of our method at recovering a dynamics model that explains human actions, as well as its utility for applications in shared autonomy and inverse reinforcement learning.
Future work. Our formulation of inverse soft Q-learning in Equation [5](#S3.E5 "(5) ‣ 3.1 Inverse Soft Q-Learning ‣ 3 Internal Dynamics Model Estimation ‣ Where Do You Think You’re Going?: Inferring Beliefs about Dynamics from Behavior") (in the main paper) might serve as the basis of a new algorithm for inverse reinforcement learning with a neural network model of the reward function that is easier to implement and potentially more sample-efficient than existing methods [finn2016guided](#bib.bib16) ; [fu2017learning](#bib.bib19) ; [uchibe2017model](#bib.bib47) ; [wulfmeier2015maximum](#bib.bib50) .
|
d8e35c41-21c4-4faf-aeda-af2d5dec0589
|
trentmkelly/LessWrong-43k
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LessWrong
|
Post-history is written by the martyrs
This is a story I wrote in late 2020, a few months after GPT-3 was released. Speculative fiction seems to be well received here, and although this is not as serious a story as my other recent post, it covers serious ideas and attempts to be hard science fiction. I'm also moving it here to more easily reference it in the future.
> People who have been closely following tech news might be aware of GPT-3, a Machine Learning based language model by OpenAI that has incredible abilities. Watch GPT-3 write a small app based on a description, play 19 degrees of Kevin Bacon, give coherent reasoning on meaningful questions, diagnose a patient from a description, perform grounded world-modelling, rephrase messages for politeness, write non-literal poetry translations with explanatory annotations, and write a response to philosophers discussing its importance. Most importantly, this model was only trained to predict text, and its only major innovation was scale to a slightly larger sliver of the size of the human brain. The natural conclusion is that machine models are capable of learning more, and better, than they have historically been given credit.
>
> This story is the result of thinking earnestly about the implications of GPT-3. Relative to the scale of a human brain, or to even larger model sizes potentially within reach, what aspects of human cognition can we reasonably argue will not be replicated by scaled-up neural networks? Which aspects are we now required to start to seriously expect to be within short reach? The answers to these questions do not neatly fit in a foreword.
>
> This story is not serious speculation, but indulgence in a silly hypothetical that got itself stuck in my head, asking to be written. This is not the course the future will run, with fortuitous coincidences and cooperative governments, but I hope, at least, it makes for a fun tale.
>
> This story was not written using GPT-3.
----------------------------------------
My father sometime
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2efe7e30-0625-4370-aafe-bda68e4d5f2c
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
|
On Calibration of Modern Neural Networks
1 Introduction
---------------
Recent advances in deep learning have dramatically improved neural network accuracy (Simonyan & Zisserman, [2015](#bib.bib36); Srivastava et al., [2015](#bib.bib39); He et al., [2016](#bib.bib13); Huang et al., [2016](#bib.bib16), [2017](#bib.bib17)). As a result, neural networks are now entrusted with making complex decisions in applications, such as object detection (Girshick, [2015](#bib.bib11)), speech recognition (Hannun et al., [2014](#bib.bib12)), and medical diagnosis (Caruana et al., [2015](#bib.bib3)).
In these settings, neural networks are an essential component of larger decision making pipelines.
In real-world decision making systems, classification networks must not only be accurate, but also should indicate when they are likely to be incorrect.
As an example, consider a self-driving car that uses a neural network to detect pedestrians and other obstructions (Bojarski et al., [2016](#bib.bib2)). If the detection network is not able to confidently predict the presence or absence of immediate obstructions, the car should rely more on the output of other sensors for braking.
Alternatively, in automated health care, control should be passed on to human doctors when the confidence of a disease diagnosis network is low (Jiang et al., [2012](#bib.bib21)).
Specifically, a network should provide a *calibrated confidence* measure in addition to its prediction.
In other words, the probability associated with the predicted class label should reflect its ground truth correctness likelihood.
| | |
| --- | --- |
| Confidence histograms (top) and reliability diagrams (bottom) for a 5-layer LeNet (left) and a 110-layer ResNet (right) on CIFAR-100. Refer to the text below for detailed illustration. | Confidence histograms (top) and reliability diagrams (bottom) for a 5-layer LeNet (left) and a 110-layer ResNet (right) on CIFAR-100. Refer to the text below for detailed illustration. |
Figure 1: Confidence histograms (top) and reliability diagrams (bottom) for a 5-layer LeNet (left) and a 110-layer ResNet (right) on CIFAR-100. Refer to the text below for detailed illustration.
Calibrated confidence estimates are also important for model interpretability. Humans have a natural cognitive intuition for probabilities (Cosmides & Tooby, [1996](#bib.bib5)). Good confidence estimates provide a valuable extra bit of information to establish trustworthiness with the user – especially for neural networks, whose classification decisions are often difficult to interpret.
Further, good probability estimates can be used to incorporate neural networks into other probabilistic models. For example, one can improve performance by combining network outputs with a language model in speech recognition (Hannun et al., [2014](#bib.bib12); Xiong et al., [2016](#bib.bib43)), or with camera information for object detection (Kendall & Cipolla, [2016](#bib.bib22)).
In 2005, Niculescu-Mizil & Caruana ([2005](#bib.bib33)) showed that neural networks typically produce well-calibrated probabilities on binary classification tasks. While neural networks today are undoubtedly more accurate than they were a decade ago,
we discover with great surprise that *modern neural networks are no longer well-calibrated*.
This is visualized in [Figure 1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ On Calibration of Modern Neural Networks"), which compares a 5-layer LeNet (left) (LeCun et al., [1998](#bib.bib29)) with a 110-layer ResNet (right) (He et al., [2016](#bib.bib13)) on the CIFAR-100 dataset.
The top row shows the distribution of prediction confidence (i.e. probabilities associated with the predicted label) as histograms.
The average confidence of LeNet closely matches its accuracy, while the average confidence of the ResNet is substantially higher than its accuracy.
This is further illustrated in the bottom row reliability diagrams (DeGroot & Fienberg, [1983](#bib.bib6); Niculescu-Mizil & Caruana, [2005](#bib.bib33)), which show accuracy as a function of confidence. We see that LeNet is well-calibrated, as confidence closely approximates the expected accuracy (i.e. the bars align roughly along the diagonal). On the other hand, the ResNet’s accuracy is better, but does not match its confidence.
Our goal is not only to understand why neural networks have become miscalibrated, but also to identify what methods can alleviate this problem.
In this paper, we demonstrate on several computer vision and NLP tasks that neural networks produce confidences that cannot represent true probabilities.
Additionally, we offer insight and intuition into network training and architectural trends that may cause miscalibration.
Finally, we compare various post-processing calibration methods on state-of-the-art neural networks, and introduce several extensions of our own. Surprisingly, we find that a single-parameter variant of Platt scaling (Platt et al., [1999](#bib.bib35)) – which we refer to as *temperature scaling* – is often the most effective method at obtaining calibrated probabilities. Because this method is straightforward to implement with existing deep learning frameworks, it can be easily adopted in practical settings.
2 Definitions
--------------
Figure 2: The effect of network depth (far left), width (middle left), Batch Normalization (middle right), and weight decay (far right) on miscalibration, as measured by ECE (lower is better).
The problem we address in this paper is supervised multi-class classification with neural networks. The input X∈X and label Y∈Y={1,…,K} are random variables that follow a ground truth joint distribution π(X,Y)=π(Y|X)π(X).
Let h be a neural network with h(X)=(^Y,^P), where ^Y is a class prediction and ^P is its associated confidence, i.e. probability of correctness.
We would like the confidence estimate ^P to be calibrated, which intuitively means that ^P represents a true probability. For example, given 100 predictions, each with confidence of 0.8, we expect that 80 should be correctly classified.
More formally, we define *perfect calibration* as
| | | | |
| --- | --- | --- | --- |
| | P(^Y=Y|^P=p)=p,∀p∈[0,1] | | (1) |
where the probability is over the joint distribution.
In all practical settings, achieving perfect calibration is impossible.
Additionally, the probability in ([1](#S2.E1 "(1) ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks")) cannot be computed using finitely many samples since ^P is a continuous random variable. This motivates the need for empirical approximations that capture the essence of ([1](#S2.E1 "(1) ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks")).
#### Reliability Diagrams
(e.g. [Figure 1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ On Calibration of Modern Neural Networks") bottom)
are a visual representation of model calibration (DeGroot & Fienberg, [1983](#bib.bib6); Niculescu-Mizil & Caruana, [2005](#bib.bib33)).
These diagrams plot expected sample accuracy as a function of confidence.
If the model is perfectly calibrated – i.e. if ([1](#S2.E1 "(1) ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks")) holds – then the diagram should plot the identity function. Any deviation from a perfect diagonal represents miscalibration.
To estimate the expected accuracy from finite samples, we group predictions into M interval bins (each of size 1/M) and calculate the accuracy of each bin.
Let Bm be the set of indices of samples whose prediction confidence falls into the interval Im=(m−1M,mM].
The accuracy of Bm is
| | | |
| --- | --- | --- |
| | acc(Bm)=1|Bm|∑i∈Bm1(^yi=yi), | |
where ^yi and yi are the predicted and true class labels for
sample i.
Basic probability tells us that acc(Bm) is an unbiased and consistent estimator of P(^Y=Y∣^P∈Im). We define the average confidence within bin Bm as
| | | |
| --- | --- | --- |
| | conf(Bm)=1|Bm|∑i∈Bm^pi, | |
where ^pi is the confidence for sample i.
acc(Bm) and conf(Bm) approximate the left-hand and right-hand sides of ([1](#S2.E1 "(1) ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks")) respectively for bin Bm.
Therefore, a perfectly calibrated model will have acc(Bm)=conf(Bm) for all m∈{1,…,M}.
Note that reliability diagrams do not display the proportion of samples in a given bin, and thus cannot be used to estimate how many samples are calibrated.
#### Expected Calibration Error (ECE).
While reliability diagrams are useful visual tools, it is more convenient to have a scalar summary statistic of calibration.
Since statistics comparing two distributions cannot be comprehensive, previous works have proposed variants, each with a unique emphasis.
One notion of miscalibration is the difference in expectation between confidence and accuracy, i.e.
| | | | |
| --- | --- | --- | --- |
| | | | (2) |
Expected Calibration Error (Naeini et al., [2015](#bib.bib31)) – or ECE – approximates ([S1](#S1.Ex9 "S1 Further Information on Calibration Metrics ‣ On Calibration of Modern Neural Networks")) by partitioning predictions into M equally-spaced bins (similar to the reliability diagrams) and taking a weighted average of the bins’ accuracy/confidence difference. More precisely,
| | | | |
| --- | --- | --- | --- |
| | ECE=M∑m=1|Bm|n∣∣∣acc(Bm)−conf(Bm)∣∣∣, | | (3) |
where n is the number of samples.
The difference between acc and conf for a given bin represents the calibration *gap* (red bars in reliability diagrams – e.g. [Figure 1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ On Calibration of Modern Neural Networks")).
We use ECE as the primary empirical metric to measure calibration.
See [Section S1](#S1a "S1 Further Information on Calibration Metrics ‣ On Calibration of Modern Neural Networks") for more analysis of this metric.
#### Maximum Calibration Error (MCE).
In high-risk applications where reliable confidence measures are absolutely necessary, we may wish to minimize the worst-case deviation between confidence and accuracy:
| | | | |
| --- | --- | --- | --- |
| | maxp∈[0,1]∣∣P(^Y=Y|^P=p)−p∣∣. | | (4) |
The Maximum Calibration Error (Naeini et al., [2015](#bib.bib31)) – or MCE – estimates an upper bound of this deviation. Similarly to ECE, this approximation involves binning:
| | | | |
| --- | --- | --- | --- |
| | MCE=maxm∈{1,…,M}|acc(Bm)−conf(Bm)|. | | (5) |
In reliability diagrams, MCE measures the largest calibration gap (red bars) across all bins, whereas ECE measures a weighted average of all gaps.
For perfectly calibrated classifiers, MCE and ECE both equal 0.
#### Negative log likelihood
is a standard measure of a probabilistic model’s quality (Friedman et al., [2001](#bib.bib9)). It is also referred to as the cross entropy loss in the context of deep learning (Bengio et al., [2015](#bib.bib1)). Given a probabilistic model ^π(Y|X) and n samples, NLL is defined as:
| | | | |
| --- | --- | --- | --- |
| | L=−n∑i=1log(^π(yi|xi)) | | (6) |
It is a standard result (Friedman et al., [2001](#bib.bib9)) that, in expectation, NLL is minimized if and only if ^π(Y|X) recovers the ground truth conditional distribution π(Y|X).
3 Observing Miscalibration
---------------------------
The architecture and training procedures of neural networks have rapidly evolved in recent years. In this section we identify some recent changes that are responsible for the miscalibration phenomenon observed in [Figure 1](#S1.F1 "Figure 1 ‣ 1 Introduction ‣ On Calibration of Modern Neural Networks"). Though we cannot claim causality, we find that model capacity and lack of regularization are closely related to model (mis)calibration.
#### Model capacity.
The model capacity of neural networks has increased at a dramatic pace over the past few years. It is now common to see networks with hundreds, if not thousands of layers (He et al., [2016](#bib.bib13); Huang et al., [2016](#bib.bib16)) and hundreds of convolutional filters per layer (Zagoruyko & Komodakis, [2016](#bib.bib46)). Recent work shows that very deep or wide models are able to generalize better than smaller ones, while exhibiting the capacity to easily fit the training set (Zhang et al., [2017](#bib.bib47)).
Although increasing depth and width may reduce classification error, we observe that these increases negatively affect model calibration.
[Figure 2](#S2.F2 "Figure 2 ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks") displays error and ECE as a function of depth and width on a ResNet trained on CIFAR-100. The far left figure varies depth for a network with 64 convolutional filters per layer, while the middle left figure fixes the depth at 14 layers and varies the number of convolutional filters per layer. Though even the smallest models in the graph exhibit some degree of miscalibration, the ECE metric grows substantially with model capacity.
During training, after the model is able to correctly classify (almost) all training samples, NLL can be further minimized by increasing the confidence of predictions.
Increased model capacity will lower training NLL, and thus the model will be more (over)confident on average.
#### Batch Normalization
(Ioffe & Szegedy, [2015](#bib.bib18)) improves the optimization of neural networks by minimizing distribution shifts in activations within the neural network’s hidden layers. Recent research suggests that these normalization techniques have enabled the development of very deep architectures, such as ResNets (He et al., [2016](#bib.bib13)) and DenseNets (Huang et al., [2017](#bib.bib17)). It has been shown that Batch Normalization improves training time, reduces the need for additional regularization, and can in some cases improve the accuracy of networks.
While it is difficult to pinpoint exactly how Batch Normalization affects the final predictions of a model, we do observe that models trained with Batch Normalization tend to be more miscalibrated. In the middle right plot of [Figure 2](#S2.F2 "Figure 2 ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks"), we see that a 6-layer ConvNet obtains worse calibration when Batch Normalization is applied, even though classification accuracy improves slightly. We find that this result holds regardless of the hyperparameters used on the Batch Normalization model (i.e. low or high learning rate, etc.).
Figure 3: Test error and NLL of a 110-layer ResNet with stochastic depth on CIFAR-100 during training. NLL is scaled by a constant to fit in the figure. Learning rate drops by 10x at epochs 250 and 375. The shaded area marks between epochs at which the best validation *loss* and best validation *error* are produced.
#### Weight decay,
which used to be the predominant regularization mechanism for neural networks, is decreasingly utilized when training modern neural networks. Learning theory suggests that regularization is necessary to prevent overfitting, especially as model capacity increases (Vapnik, [1998](#bib.bib41)). However, due to the apparent regularization effects of Batch Normalization, recent research seems to suggest that models with less L2 regularization tend to generalize better (Ioffe & Szegedy, [2015](#bib.bib18)). As a result, it is now common to train models with little weight decay, if any at all. The top performing ImageNet models of 2015 all use an order of magnitude less weight decay than models of previous years (He et al., [2016](#bib.bib13); Simonyan & Zisserman, [2015](#bib.bib36)).
We find that training with less weight decay has a negative impact on calibration.
The far right plot in [Figure 2](#S2.F2 "Figure 2 ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks") displays training error and ECE for a 110-layer ResNet with varying amounts of weight decay.
The only other forms of regularization are data augmentation and Batch Normalization.
We observe that calibration and accuracy are not optimized by the same parameter setting.
While the model exhibits both over-regularization and under-regularization with respect to classification error, it does not appear that calibration is negatively impacted by having too much weight decay.
Model calibration continues to improve when more regularization is added, well after the point of achieving optimal accuracy.
The slight uptick at the end of the graph may be an artifact of using a weight decay factor that impedes optimization.
#### Nll
can be used to indirectly measure model calibration. In practice, we observe *a disconnect between NLL and accuracy*, which may explain the miscalibration in [Figure 2](#S2.F2 "Figure 2 ‣ 2 Definitions ‣ On Calibration of Modern Neural Networks").
This disconnect occurs because neural networks can *overfit to NLL without overfitting to the 0/1 loss*.
We observe this trend in the training curves of some miscalibrated models.
[Figure 3](#S3.F3 "Figure 3 ‣ Batch Normalization ‣ 3 Observing Miscalibration ‣ On Calibration of Modern Neural Networks") shows test error and NLL (rescaled to match error) on CIFAR-100 as training progresses.
Both error and NLL immediately drop at epoch 250, when the learning rate is dropped; however, NLL overfits during the remainder of training.
Surprisingly, overfitting to NLL is beneficial to classification accuracy. On CIFAR-100, test error drops from 29% to 27% in the region where NLL overfits. This phenomenon renders a concrete explanation of miscalibration: the network learns better classification accuracy at the expense of well-modeled probabilities.
We can connect this finding to recent work examining the generalization of large neural networks. Zhang et al. ([2017](#bib.bib47)) observe that deep neural networks seemingly violate the common understanding of learning theory that large models with little regularization will not generalize well. The observed disconnect between NLL and 0/1 loss suggests that these high capacity models are not necessarily immune from overfitting, but rather, overfitting manifests in probabilistic error rather than classification error.
4 Calibration Methods
----------------------
In this section, we first review existing calibration methods, and introduce new variants of our own.
All methods are post-processing steps that produce (calibrated) probabilities.
Each method requires a hold-out validation set, which in practice can be the same set used for hyperparameter tuning.
We assume that the training, validation, and test sets are drawn from the same distribution.
###
4.1 Calibrating Binary Models
We first introduce calibration in the binary setting, i.e. Y={0,1}.
For simplicity, throughout this subsection, we assume the model outputs only the confidence for the positive class.111
This is in contrast with the setting in [Section 2](#S2 "2 Definitions ‣ On Calibration of Modern Neural Networks"), in which the model produces both a class prediction and confidence.
Given a sample from the validation set (xi,yi), we have access to ^pi – the network’s predicted probability of yi=1, as well as zi∈R – which is the network’s non-probabilistic output, or *logit*. The predicted probability ^pi is derived from zi using a sigmoid function σ; i.e.
^pi=σ(zi). Our goal is to produce a calibrated probability ^qi based on yi, ^pi, and zi.
#### Histogram binning
(Zadrozny & Elkan, [2001](#bib.bib44)) is a simple non-parametric calibration method.
In a nutshell, all uncalibrated predictions ^pi are divided
into mutually exclusive bins B1,…,BM. Each bin is assigned a calibrated score θm; i.e. if ^pi is assigned to bin Bm, then ^qi=θm. At test time, if prediction ^pte falls into bin Bm, then the calibrated prediction ^qte is θm.
More precisely, for a suitably chosen M (usually small), we first define bin boundaries 0=a1≤a2≤…≤aM+1=1, where the bin Bm is defined by the interval (am,am+1].
Typically the bin boundaries are either chosen to be equal length intervals
or to equalize the number of samples in each bin.
The predictions θi are chosen to minimize the bin-wise squared loss:
| | | | |
| --- | --- | --- | --- |
| | minθ1,…,θMM∑m=1n∑i=11(am≤^pi<am+1)(θm−yi)2, | | (7) |
where 1 is the indicator function. Given fixed bins boundaries, the solution to ([7](#S4.E7 "(7) ‣ Histogram binning ‣ 4.1 Calibrating Binary Models ‣ 4 Calibration Methods ‣ On Calibration of Modern Neural Networks")) results in θm that correspond to the average number of positive-class samples in bin Bm.
#### Isotonic regression
(Zadrozny & Elkan, [2002](#bib.bib45)), arguably the most common non-parametric calibration method,
learns a piecewise constant function f to transform uncalibrated outputs; i.e. ^qi=f(^pi).
Specifically, isotonic regression produces f to minimize the square loss ∑ni=1(f(^pi)−yi)2.
Because f is constrained to be piecewise constant, we can write the optimization problem as:
| | | | |
| --- | --- | --- | --- |
| | minMθ1,…,θMa1,…,aM+1 | M∑m=1n∑i=11(am≤^pi<am+1)(θm−yi)2 | |
| | subject to | 0=a1≤a2≤…≤aM+1=1, | |
| | | θ1≤θ2≤…≤θM. | |
where M is the number of intervals; a1,…,aM+1 are the interval boundaries; and θ1,…,θM are the function values.
Under this parameterization, it can be seen that isotonic regression is a strict generalization of histogram binning in which the bin boundaries are jointly optimized along with the bin predictions.
#### Bayesian Binning into Quantiles (BBQ)
(Naeini et al., [2015](#bib.bib31)) is a extension of histogram binning using Bayesian model averaging. Essentially, BBQ marginalizes out all possible *binning schemes* to produce ^qi.
More formally, a binning scheme s is a pair (M,I) where M is the number of bins, and I is a corresponding partitioning of [0,1] into disjoint intervals (0=a1≤a2≤…≤aM+1=1). The parameters of a binning scheme are θ1,…,θM. Under this framework, histogram binning and isotonic regression both produce a single binning scheme, whereas BBQ considers a space S of all possible binning schemes for the validation dataset D. BBQ performs Bayesian averaging of the probabilities produced by each scheme:222
Because the validation dataset is finite, S is as well.
| | | | |
| --- | --- | --- | --- |
| | P(^qte|^pte,D) | =∑s∈SP(^qte,S=s|^pte,D) | |
| | | =∑s∈SP(^qte|^pte,S=s,D)P(S=s|D). | |
where P(^qte|^pte,S=s,D) is the calibrated probability using binning scheme s. Using a uniform prior, the weight P(S=s|D) can be derived using Bayes’ rule:
| | | |
| --- | --- | --- |
| | P(S=s|D)=P(D|S=s)∑s′∈SP(D|S=s′). | |
The parameters θ1,…,θM can be viewed as parameters of M independent binomial distributions. Hence, by placing a Beta prior on θ1,…,θM, we can obtain a closed form expression for the marginal likelihood P(D|S=s). This allows us to compute P(^qte|^pte,D) for any test input.
#### Platt scaling
(Platt et al., [1999](#bib.bib35)) is a parametric approach to calibration, unlike the other approaches. The non-probabilistic predictions of a classifier are used as features for a logistic regression model, which is trained on the validation set to return probabilities. More specifically, in the context of neural networks (Niculescu-Mizil & Caruana, [2005](#bib.bib33)), Platt scaling learns scalar parameters a,b∈R and outputs ^qi=σ(azi+b) as the calibrated probability. Parameters a and b can be optimized using the NLL loss over the validation set. It is important to note that the neural network’s parameters are fixed during this stage.
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dataset | Model | Uncalibrated | Hist. Binning | Isotonic | BBQ | Temp. Scaling | Vector Scaling | Matrix Scaling |
| Birds | ResNet 50 | 9.19% | 4.34% | 5.22% | 4.12% | 1.85% | 3.0% | 21.13% |
| Cars | ResNet 50 | 4.3% | 1.74% | 4.29% | 1.84% | 2.35% | 2.37% | 10.5% |
| CIFAR-10 | ResNet 110 | 4.6% | 0.58% | 0.81% | 0.54% | 0.83% | 0.88% | 1.0% |
| CIFAR-10 | ResNet 110 (SD) | 4.12% | 0.67% | 1.11% | 0.9% | 0.6% | 0.64% | 0.72% |
| CIFAR-10 | Wide ResNet 32 | 4.52% | 0.72% | 1.08% | 0.74% | 0.54% | 0.6% | 0.72% |
| CIFAR-10 | DenseNet 40 | 3.28% | 0.44% | 0.61% | 0.81% | 0.33% | 0.41% | 0.41% |
| CIFAR-10 | LeNet 5 | 3.02% | 1.56% | 1.85% | 1.59% | 0.93% | 1.15% | 1.16% |
| CIFAR-100 | ResNet 110 | 16.53% | 2.66% | 4.99% | 5.46% | 1.26% | 1.32% | 25.49% |
| CIFAR-100 | ResNet 110 (SD) | 12.67% | 2.46% | 4.16% | 3.58% | 0.96% | 0.9% | 20.09% |
| CIFAR-100 | Wide ResNet 32 | 15.0% | 3.01% | 5.85% | 5.77% | 2.32% | 2.57% | 24.44% |
| CIFAR-100 | DenseNet 40 | 10.37% | 2.68% | 4.51% | 3.59% | 1.18% | 1.09% | 21.87% |
| CIFAR-100 | LeNet 5 | 4.85% | 6.48% | 2.35% | 3.77% | 2.02% | 2.09% | 13.24% |
| ImageNet | DenseNet 161 | 6.28% | 4.52% | 5.18% | 3.51% | 1.99% | 2.24% | - |
| ImageNet | ResNet 152 | 5.48% | 4.36% | 4.77% | 3.56% | 1.86% | 2.23% | - |
| SVHN | ResNet 152 (SD) | 0.44% | 0.14% | 0.28% | 0.22% | 0.17% | 0.27% | 0.17% |
| 20 News | DAN 3 | 8.02% | 3.6% | 5.52% | 4.98% | 4.11% | 4.61% | 9.1% |
| Reuters | DAN 3 | 0.85% | 1.75% | 1.15% | 0.97% | 0.91% | 0.66% | 1.58% |
| SST Binary | TreeLSTM | 6.63% | 1.93% | 1.65% | 2.27% | 1.84% | 1.84% | 1.84% |
| SST Fine Grained | TreeLSTM | 6.71% | 2.09% | 1.65% | 2.61% | 2.56% | 2.98% | 2.39% |
Table 1: ECE (%) (with M=15 bins) on standard vision and NLP datasets before calibration and with various calibration methods. The number following a model’s name denotes the network depth.
###
4.2 Extension to Multiclass Models
For classification problems involving K>2 classes, we return to the original problem formulation.
The network outputs a class prediction ^yi and confidence score ^pi for each input xi. In this case, the network logits zi are vectors, where ^yi=argmaxkz(k)i, and ^pi is typically derived using the softmax function σSM:
| | | |
| --- | --- | --- |
| | σSM(zi)(k)=exp(z(k)i)∑Kj=1exp(z(j)i),^pi=maxkσSM(zi)(k). | |
The goal is to produce a calibrated confidence ^qi and (possibly new) class prediction ^y′i
based on yi, ^yi, ^pi, and zi.
#### Extension of binning methods.
One common way of extending binary calibration methods to the multiclass setting is by treating the problem as K one-versus-all problems (Zadrozny & Elkan, [2002](#bib.bib45)).
For k=1,…,K, we form a binary calibration problem where the label is 1(yi=k) and the predicted probability is σSM(zi)(k). This gives us K calibration models, each for a particular class. At test time, we obtain an unnormalized probability vector [^q(1)i,…,^q(K)i], where ^q(k)i is the calibrated probability for class k. The new class prediction ^y′i is the argmax of the vector, and the new confidence ^q′i is the max of the vector normalized by ∑Kk=1^q(k)i. This extension can be applied to histogram binning, isotonic regression, and BBQ.
#### Matrix and vector scaling
are two multi-class extensions of Platt scaling. Let zi be the *logits vector* produced before the softmax layer for input xi. *Matrix scaling applies* a linear transformation Wzi+b to the logits:
| | | | | |
| --- | --- | --- | --- | --- |
| | ^qi | =maxkσSM(Wzi+b)(k), | | (8) |
| | ^y′i | =argmaxk(Wzi+b)(k). | |
The parameters W and b are optimized with respect to NLL on the validation set. As the number of parameters for matrix scaling grows quadratically with the number of classes K, we define *vector scaling* as a variant where W is restricted to be a diagonal matrix.
#### Temperature scaling,
the simplest extension of Platt scaling, uses a single scalar parameter T>0 for all classes. Given the logit vector zi, the new confidence prediction is
| | | | |
| --- | --- | --- | --- |
| | ^qi=maxkσSM(zi/T)(k). | | (9) |
T is called the temperature, and it “softens” the softmax (i.e. raises the output entropy) with T>1.
As T→∞, the probability ^qi approaches 1/K, which represents maximum uncertainty. With T=1, we recover the original probability ^pi.
As T→0, the probability collapses to a point mass (i.e. ^qi=1).
T is optimized with respect to NLL on the validation set.
Because the parameter T does not change the maximum of the softmax function, the class prediction ^y′i remains unchanged. In other words, *temperature scaling does not affect the model’s accuracy.*
Temperature scaling is commonly used in settings such as knowledge distillation (Hinton et al., [2015](#bib.bib15)) and statistical mechanics (Jaynes, [1957](#bib.bib20)). To the best of our knowledge, we are not aware of any prior use in the context of calibrating probabilistic models.333To highlight the connection with prior works we define temperature scaling in terms of 1T instead of a multiplicative scalar.
The model is equivalent to maximizing the entropy of the output probability distribution subject to certain constraints on the logits (see [Section S2](#S2a "S2 Further Information on Temperature Scaling ‣ On Calibration of Modern Neural Networks")).
###
4.3 Other Related Works
Calibration and confidence scores have been studied in various contexts in recent years. Kuleshov & Ermon ([2016](#bib.bib26)) study the problem of calibration in the online setting, where the inputs can come from a potentially adversarial source. Kuleshov & Liang ([2015](#bib.bib27)) investigate how to produce calibrated probabilities when the output space is a structured object.
Lakshminarayanan et al. ([2016](#bib.bib28)) use ensembles of networks to obtain uncertainty estimates.
Pereyra et al. ([2017](#bib.bib34)) penalize overconfident predictions as a form of regularization.
Hendrycks & Gimpel ([2017](#bib.bib14)) use confidence scores to determine if samples are out-of-distribution.
Bayesian neural networks (Denker & Lecun, [1990](#bib.bib8); MacKay, [1992](#bib.bib30)) return a probability distribution over outputs as an alternative way to represent model uncertainty. Gal & Ghahramani ([2016](#bib.bib10)) draw a connection between Dropout (Srivastava et al., [2014](#bib.bib38)) and model uncertainty, claiming that sampling models with dropped nodes is a way to estimate the probability distribution over all possible models for a given sample.
Kendall & Gal ([2017](#bib.bib23)) combine this approach with a model that outputs a predictive mean and variance for each data point.
This notion of uncertainty is not restricted to classification problems.
In contrast, our framework, which does not require model sampling, returns a confidence for a given output rather than returning a distribution of possible outputs.
5 Results
----------
We apply the calibration methods in [Section 4](#S4 "4 Calibration Methods ‣ On Calibration of Modern Neural Networks") to image classification and document classification neural networks.
For image classification we use 6 datasets:
1. [noitemsep,nolistsep]
2. Caltech-UCSD Birds (Welinder et al., [2010](#bib.bib42)): 200 bird species. 5994/2897/2897 images for train/validation/test sets.
3. Stanford Cars (Krause et al., [2013](#bib.bib24)): 196 classes of cars by make, model, and year. 8041/4020/4020 images for train/validation/test.
4. ImageNet 2012 (Deng et al., [2009](#bib.bib7)): Natural scene images from 1000 classes. 1.3 million/25,000/25,000 images for train/validation/test.
5. CIFAR-10/CIFAR-100 (Krizhevsky & Hinton, [2009](#bib.bib25)): Color images (32×32) from 10/100 classes. 45,000/5,000/10,000 images for train/validation/test.
6. Street View House Numbers (SVHN) (Netzer et al., [2011](#bib.bib32)): 32×32 colored images of cropped out house numbers from Google Street View.
604,388/6,000/26,032 images for train/validation/test.
We train state-of-the-art convolutional networks: ResNets (He et al., [2016](#bib.bib13)), ResNets with stochastic depth (SD) (Huang et al., [2016](#bib.bib16)), Wide ResNets (Zagoruyko & Komodakis, [2016](#bib.bib46)), and DenseNets (Huang et al., [2017](#bib.bib17)). We use the data preprocessing, training procedures, and hyperparameters as described in each paper. For Birds and Cars, we fine-tune networks pretrained on ImageNet.
For document classification we experiment with 4 datasets:
1. [noitemsep,nolistsep]
2. 20 News: News articles, partitioned into 20 categories by content. 9034/2259/7528 documents for train/validation/test.
3. Reuters: News articles, partitioned into 8 categories by topic. 4388/1097/2189 documents for train/validation/test.
4. Stanford Sentiment Treebank (SST) (Socher et al., [2013](#bib.bib37)): Movie reviews, represented as sentence parse trees that are annotated by sentiment. Each sample includes a coarse binary label and a fine grained 5-class label.
As described in (Tai et al., [2015](#bib.bib40)), the training/validation/test sets contain 6920/872/1821 documents for binary, and 544/1101/2210 for fine-grained.
On 20 News and Reuters, we train Deep Averaging Networks (DANs) (Iyyer et al., [2015](#bib.bib19)) with 3 feed-forward layers and Batch Normalization. These networks obtain competitive accuracy using the optimization hyperparameters suggested by the original paper. On SST, we train TreeLSTMs (Long Short Term Memory) (Tai et al., [2015](#bib.bib40)) using the default settings in the authors’ code.
Figure 4: Reliability diagrams for CIFAR-100 before (far left) and after calibration (middle left, middle right, far right).
#### Calibration Results.
[Table 1](#S4.T1 "Table 1 ‣ Platt scaling ‣ 4.1 Calibrating Binary Models ‣ 4 Calibration Methods ‣ On Calibration of Modern Neural Networks") displays model calibration, as measured by ECE (with M=15 bins), before and after applying the various methods (see [Section S3](#S3a "S3 Additional Tables ‣ On Calibration of Modern Neural Networks") for MCE, NLL, and error tables).
It is worth noting that most datasets and models experience some degree of miscalibration, with ECE typically between 4 to 10%.
This is not architecture specific: we observe miscalibration on convolutional networks (with and without skip connections), recurrent networks, and deep averaging networks.
The two notable exceptions are SVHN and Reuters, both of which experience ECE values below 1%. Both of these datasets have very low error (1.98% and 2.97%, respectively); and therefore the ratio of ECE to error is comparable to other datasets.
Our most important discovery is the *surprising effectiveness of temperature scaling* despite its remarkable simplicity. Temperature scaling outperforms all other methods on the vision tasks, and performs comparably to other methods on the NLP datasets. What is perhaps even more surprising is that temperature scaling outperforms the vector and matrix Platt scaling variants, which are strictly more general methods. In fact, vector scaling recovers essentially the same solution as temperature scaling – the learned vector has nearly constant entries, and therefore is no different than a scalar transformation. In other words, network miscalibration is intrinsically low dimensional.
The only dataset that temperature scaling does not calibrate is the Reuters dataset. In this instance, only one of the above methods is able to improve calibration. Because this dataset is well-calibrated to begin with (ECE ≤1%), there is not much room for improvement with any method, and post-processing may not even be necessary to begin with. It is also possible that our measurements are affected by dataset split or by the particular binning scheme.
Matrix scaling performs poorly on datasets with hundreds of classes (i.e. Birds, Cars, and CIFAR-100), and fails to converge on the 1000-class ImageNet dataset.
This is expected, since the number of parameters scales quadratically with the number of classes.
Any calibration model with tens of thousands (or more) parameters will overfit to a small validation set, even when applying regularization.
Binning methods improve calibration on most datasets, but do not outperform temperature scaling. Additionally, binning methods tend to change class predictions which hurts accuracy (see [Section S3](#S3a "S3 Additional Tables ‣ On Calibration of Modern Neural Networks")). Histogram binning, the simplest binning method, typically outperforms isotonic regression and BBQ, despite the fact that both methods are strictly more general. This further supports our finding that calibration is best corrected by simple models.
#### Reliability diagrams.
[Figure 4](#S5.F4 "Figure 4 ‣ 5 Results ‣ On Calibration of Modern Neural Networks") contains reliability diagrams for 110-layer ResNets on CIFAR-100 before and after calibration. From the far left diagram, we see that the uncalibrated ResNet tends to be overconfident in its predictions. We then can observe the effects of temperature scaling (middle left), histogram binning (middle right), and isotonic regression (far right) on calibration. All three displayed methods produce much better confidence estimates. Of the three methods, temperature scaling most closely recovers the desired diagonal function. Each of the bins are well calibrated, which is remarkable given that all the probabilities were modified by only a single parameter. We include reliability diagrams for other datasets in [Section S4](#S4a "S4 Additional Reliability Diagrams ‣ On Calibration of Modern Neural Networks").
#### Computation time.
All methods scale linearly with the number of validation set samples. Temperature scaling is by far the fastest method, as it amounts to a one-dimensional convex optimization problem. Using a conjugate gradient solver, the optimal temperature can be found in 10 iterations, or a fraction of a second on most modern hardware. In fact, even a naive line-search for the optimal temperature is faster than any of the other methods. The computational complexity of vector and matrix scaling are linear and quadratic respectively in the number of classes, reflecting the number of parameters in each method. For CIFAR-100 (K=100), finding a near-optimal vector scaling solution with conjugate gradient descent requires at least 2 orders of magnitude more time. Histogram binning and isotonic regression take an order of magnitude longer than temperature scaling, and BBQ takes roughly 3 orders of magnitude more time.
#### Ease of implementation.
BBQ is arguably the most difficult to implement, as it requires implementing a model averaging scheme.
While all other methods are relatively easy to implement, temperature scaling may arguably be the most straightforward to incorporate into a neural network pipeline. In Torch7 (Collobert et al., [2011](#bib.bib4)), for example, we implement temperature scaling by inserting a nn.MulConstant between the logits and the softmax, whose parameter is 1/T. We set T=1 during training, and subsequently find its optimal value on the validation set.
6 Conclusion
-------------
Modern neural networks exhibit a strange phenomenon: probabilistic error and miscalibration worsen even as classification error is reduced.
We have demonstrated that recent advances in neural network architecture and training – model capacity, normalization, and regularization – have strong effects on network calibration.
It remains future work to understand why these trends affect calibration while improving accuracy.
Nevertheless, simple techniques can effectively remedy the miscalibration phenomenon in neural networks.
Temperature scaling is the simplest, fastest, and most straightforward of the methods, and surprisingly is often the most effective.
7 Acknowledgments
------------------
The authors are supported in part by the III-1618134, III-1526012, and IIS-1149882 grants from the
National Science Foundation, as well as the Bill and
Melinda Gates Foundation and the Office of Naval Research.
S1 Further Information on Calibration Metrics
----------------------------------------------
We can connect the ECE metric with our exact miscalibration definition, which is restated here:
| | | |
| --- | --- | --- |
| | | |
Let F^P(p) be the cumulative distribution function of ^P so that F^P(b)−F^P(a)=P(^P∈[a,b]). Using the Riemann-Stieltjes integral we have
| | | |
| --- | --- | --- |
| | | |
| | =∫10∣∣P(^Y=Y|^P=p)−p∣∣dF^P(p) | |
| | ≈M∑m=1∣∣P(^Y=Y|^P=pm)−pm∣∣P(^P∈Im) | |
where Im represents the interval of bin Bm. ∣∣P(^Y=Y|^P=pm)−pm∣∣ is closely approximated by ∣∣acc(Bm)−^p(Bm)∣∣ for n large. Hence ECE using M bins converges to the M-term Riemann-Stieltjes sum of E^P[∣∣P(^Y=Y|^P=p)−p∣∣].
S2 Further Information on Temperature Scaling
----------------------------------------------
Here we derive the temperature scaling model using the entropy maximization principle with an appropriate balanced equation.
######
Claim 1.
Given n samples’ logit vectors z1,…,zn and class labels y1,…,yn, temperature scaling is the unique solution q to the following entropy maximization problem:
| | | | |
| --- | --- | --- | --- |
| | maxq | −n∑i=1K∑k=1q(zi)(k)logq(zi)(k) | |
| | subject to | q(zi)(k)≥0∀i,k | |
| | | K∑k=1q(zi)(k)=1∀i | |
| | | n∑i=1z(yi)i=n∑i=1K∑k=1z(k)iq(zi)(k). | |
The first two constraint ensure that q is a probability distribution, while the last constraint limits the scope of distributions. Intuitively, the constraint specifies that the average true class logit is equal to the average weighted logit.
###### Proof.
We solve this constrained optimization problem using the Lagrangian. We first ignore the constraint q(zi)(k) and later show that the solution satisfies this condition. Let λ,β1,…,βn∈R be the Lagrangian multipliers and define
| | | | |
| --- | --- | --- | --- |
| | L= | −n∑i=1K∑k=1q(zi)(k)logq(zi)(k) | |
| | | +λn∑i=1[K∑k=1z(k)iq(zi)(k)−z(yi)i] | |
| | | +n∑i=1βiK∑k=1(q(zi)(k)−1). | |
Taking the derivative with respect to q(zi)(k) gives
| | | |
| --- | --- | --- |
| | ∂∂q(zi)(k)L=−nK−logq(zi)(k)+λz(k)i+βi. | |
Setting the gradient of the Lagrangian L to 0 and rearranging gives
| | | |
| --- | --- | --- |
| | q(zi)(k)=eλz(k)i+βi−nK. | |
Since ∑Kk=1q(zi)(k)=1 for all i, we must have
| | | |
| --- | --- | --- |
| | q(zi)(k)=eλz(k)i∑Kj=1eλz(j)i, | |
which recovers the temperature scaling model by setting T=1λ.
∎
[Figure S1](#S2.F1 "Figure S1 ‣ S2 Further Information on Temperature Scaling ‣ On Calibration of Modern Neural Networks") visualizes Claim [1](#Thmclaim1 "Claim 1. ‣ S2 Further Information on Temperature Scaling ‣ On Calibration of Modern Neural Networks").
We see that, as training continues, the model begins to overfit with respect to NLL (red line).
This results in a low-entropy softmax distribution over classes (blue line), which explains the model’s overconfidence. Temperature scaling not only lowers the NLL but also raises the entropy of the distribution (green line).
Figure S1: Entropy and NLL for CIFAR-100 before and after calibration. The optimal T selected by temperature scaling rises throughout optimization, as the pre-calibration entropy decreases steadily.
The post-calibration entropy and NLL on the validation set coincide
(which can be derived from the gradient optimality condition of T).
S3 Additional Tables
---------------------
Tables [S1](#S3.T1 "Table S1 ‣ S3 Additional Tables ‣ On Calibration of Modern Neural Networks"), [S2](#S3.T2 "Table S2 ‣ S3 Additional Tables ‣ On Calibration of Modern Neural Networks"), and [S3](#S3.T3 "Table S3 ‣ S3 Additional Tables ‣ On Calibration of Modern Neural Networks") display the MCE, test error, and NLL for all the experimental settings outlined in [Section 5](#S5 "5 Results ‣ On Calibration of Modern Neural Networks").
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dataset | Model | Uncalibrated | Hist. Binning | Isotonic | BBQ | Temp. Scaling | Vector Scaling | Matrix Scaling |
| Birds | ResNet 50 | 30.06% | 25.35% | 16.59% | 11.72% | 9.08% | 9.81% | 38.67% |
| Cars | ResNet 50 | 41.55% | 5.16% | 15.23% | 9.31% | 20.23% | 8.59% | 29.65% |
| CIFAR-10 | ResNet 110 | 33.78% | 26.87% | 7.8% | 72.64% | 8.56% | 27.39% | 22.89% |
| CIFAR-10 | ResNet 110 (SD) | 34.52% | 17.0% | 16.45% | 19.26% | 15.45% | 15.55% | 10.74% |
| CIFAR-10 | Wide ResNet 32 | 27.97% | 12.19% | 6.19% | 9.22% | 9.11% | 4.43% | 9.65% |
| CIFAR-10 | DenseNet 40 | 22.44% | 7.77% | 19.54% | 14.57% | 4.58% | 3.17% | 4.36% |
| CIFAR-10 | LeNet 5 | 8.02% | 16.49% | 18.34% | 82.35% | 5.14% | 19.39% | 16.89% |
| CIFAR-100 | ResNet 110 | 35.5% | 7.03% | 10.36% | 10.9% | 4.74% | 2.5% | 45.62% |
| CIFAR-100 | ResNet 110 (SD) | 26.42% | 9.12% | 10.95% | 9.12% | 8.85% | 8.85% | 35.6% |
| CIFAR-100 | Wide ResNet 32 | 33.11% | 6.22% | 14.87% | 11.88% | 5.33% | 6.31% | 44.73% |
| CIFAR-100 | DenseNet 40 | 21.52% | 9.36% | 10.59% | 8.67% | 19.4% | 8.82% | 38.64% |
| CIFAR-100 | LeNet 5 | 10.25% | 18.61% | 3.64% | 9.96% | 5.22% | 8.65% | 18.77% |
| ImageNet | DenseNet 161 | 14.07% | 13.14% | 11.57% | 10.96% | 12.29% | 9.61% | - |
| ImageNet | ResNet 152 | 12.2% | 14.57% | 8.74% | 8.85% | 12.29% | 9.61% | - |
| SVHN | ResNet 152 (SD) | 19.36% | 11.16% | 18.67% | 9.09% | 18.05% | 30.78% | 18.76% |
| 20 News | DAN 3 | 17.03% | 10.47% | 9.13% | 6.28% | 8.21% | 8.24% | 17.43% |
| Reuters | DAN 3 | 14.01% | 16.78% | 44.95% | 36.18% | 25.46% | 18.88% | 19.39% |
| SST Binary | TreeLSTM | 21.66% | 3.22% | 13.91% | 36.43% | 6.03% | 6.03% | 6.03% |
| SST Fine Grained | TreeLSTM | 27.85% | 28.35% | 19.0% | 8.67% | 44.75% | 11.47% | 11.78% |
Table S1: MCE (%) (with M=15 bins) on standard vision and NLP datasets before calibration and with various calibration methods. The number following a model’s name denotes the network depth. MCE seems very sensitive to the binning scheme and is less suited for small test sets.
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dataset | Model | Uncalibrated | Hist. Binning | Isotonic | BBQ | Temp. Scaling | Vector Scaling | Matrix Scaling |
| Birds | ResNet 50 | 22.54% | 55.02% | 23.37% | 37.76% | 22.54% | 22.99% | 29.51% |
| Cars | ResNet 50 | 14.28% | 16.24% | 14.9% | 19.25% | 14.28% | 14.15% | 17.98% |
| CIFAR-10 | ResNet 110 | 6.21% | 6.45% | 6.36% | 6.25% | 6.21% | 6.37% | 6.42% |
| CIFAR-10 | ResNet 110 (SD) | 5.64% | 5.59% | 5.62% | 5.55% | 5.64% | 5.62% | 5.69% |
| CIFAR-10 | Wide ResNet 32 | 6.96% | 7.3% | 7.01% | 7.35% | 6.96% | 7.1% | 7.27% |
| CIFAR-10 | DenseNet 40 | 5.91% | 6.12% | 5.96% | 6.0% | 5.91% | 5.96% | 6.0% |
| CIFAR-10 | LeNet 5 | 15.57% | 15.63% | 15.69% | 15.64% | 15.57% | 15.53% | 15.81% |
| CIFAR-100 | ResNet 110 | 27.83% | 34.78% | 28.41% | 28.56% | 27.83% | 27.82% | 38.77% |
| CIFAR-100 | ResNet 110 (SD) | 24.91% | 33.78% | 25.42% | 25.17% | 24.91% | 24.99% | 35.09% |
| CIFAR-100 | Wide ResNet 32 | 28.0% | 34.29% | 28.61% | 29.08% | 28.0% | 28.45% | 37.4% |
| CIFAR-100 | DenseNet 40 | 26.45% | 34.78% | 26.73% | 26.4% | 26.45% | 26.25% | 36.14% |
| CIFAR-100 | LeNet 5 | 44.92% | 54.06% | 45.77% | 46.82% | 44.92% | 45.53% | 52.44% |
| ImageNet | DenseNet 161 | 22.57% | 48.32% | 23.2% | 47.58% | 22.57% | 22.54% | - |
| ImageNet | ResNet 152 | 22.31% | 48.1% | 22.94% | 47.6% | 22.31% | 22.56% | - |
| SVHN | ResNet 152 (SD) | 1.98% | 2.06% | 2.04% | 2.04% | 1.98% | 2.0% | 2.08% |
| 20 News | DAN 3 | 20.06% | 25.12% | 20.29% | 20.81% | 20.06% | 19.89% | 22.0% |
| Reuters | DAN 3 | 2.97% | 7.81% | 3.52% | 3.93% | 2.97% | 2.83% | 3.52% |
| SST Binary | TreeLSTM | 11.81% | 12.08% | 11.75% | 11.26% | 11.81% | 11.81% | 11.81% |
| SST Fine Grained | TreeLSTM | 49.5% | 49.91% | 48.55% | 49.86% | 49.5% | 49.77% | 48.51% |
Table S2: Test error (%) on standard vision and NLP datasets before calibration and with various calibration methods. The number following a model’s name denotes the network depth. Error with temperature scaling is exactly the same as uncalibrated.
| | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Dataset | Model | Uncalibrated | Hist. Binning | Isotonic | BBQ | Temp. Scaling | Vector Scaling | Matrix Scaling |
| Birds | ResNet 50 | 0.9786 | 1.6226 | 1.4128 | 1.2539 | 0.8792 | 0.9021 | 2.334 |
| Cars | ResNet 50 | 0.5488 | 0.7977 | 0.8793 | 0.6986 | 0.5311 | 0.5299 | 1.0206 |
| CIFAR-10 | ResNet 110 | 0.3285 | 0.2532 | 0.2237 | 0.263 | 0.2102 | 0.2088 | 0.2048 |
| CIFAR-10 | ResNet 110 (SD) | 0.2959 | 0.2027 | 0.1867 | 0.2159 | 0.1718 | 0.1709 | 0.1766 |
| CIFAR-10 | Wide ResNet 32 | 0.3293 | 0.2778 | 0.2428 | 0.2774 | 0.2283 | 0.2275 | 0.2229 |
| CIFAR-10 | DenseNet 40 | 0.2228 | 0.212 | 0.1969 | 0.2087 | 0.1750 | 0.1757 | 0.176 |
| CIFAR-10 | LeNet 5 | 0.4688 | 0.529 | 0.4757 | 0.4984 | 0.459 | 0.4568 | 0.4607 |
| CIFAR-100 | ResNet 110 | 1.4978 | 1.4379 | 1.207 | 1.5466 | 1.0442 | 1.0485 | 2.5637 |
| CIFAR-100 | ResNet 110 (SD) | 1.1157 | 1.1985 | 1.0317 | 1.1982 | 0.8613 | 0.8655 | 1.8182 |
| CIFAR-100 | Wide ResNet 32 | 1.3434 | 1.4499 | 1.2086 | 1.459 | 1.0565 | 1.0648 | 2.5507 |
| CIFAR-100 | DenseNet 40 | 1.0134 | 1.2156 | 1.0615 | 1.1572 | 0.9026 | 0.9011 | 1.9639 |
| CIFAR-100 | LeNet 5 | 1.6639 | 2.2574 | 1.8173 | 1.9893 | 1.6560 | 1.6648 | 2.1405 |
| ImageNet | DenseNet 161 | 0.9338 | 1.4716 | 1.1912 | 1.4272 | 0.8885 | 0.8879 | - |
| ImageNet | ResNet 152 | 0.8961 | 1.4507 | 1.1859 | 1.3987 | 0.8657 | 0.8742 | - |
| SVHN | ResNet 152 (SD) | 0.0842 | 0.1137 | 0.095 | 0.1062 | 0.0821 | 0.0844 | 0.0924 |
| 20 News | DAN 3 | 0.7949 | 1.0499 | 0.8968 | 0.9519 | 0.7387 | 0.7296 | 0.9089 |
| Reuters | DAN 3 | 0.102 | 0.2403 | 0.1475 | 0.1167 | 0.0994 | 0.0990 | 0.1491 |
| SST Binary | TreeLSTM | 0.3367 | 0.2842 | 0.2908 | 0.2778 | 0.2739 | 0.2739 | 0.2739 |
| SST Fine Grained | TreeLSTM | 1.1475 | 1.1717 | 1.1661 | 1.149 | 1.1168 | 1.1085 | 1.1112 |
Table S3: NLL (%) on standard vision and NLP datasets before calibration and with various calibration methods. The number following a model’s name denotes the network depth. To summarize, NLL roughly follows the trends of ECE.
S4 Additional Reliability Diagrams
-----------------------------------
Figure S2: Reliability diagrams for CIFAR-10 before (far left) and after calibration (middle left, middle right, far right).
Figure S3: Reliability diagrams for SST Binary and SST Fine Grained before (far left) and after calibration (middle left, middle right, far right).
Figure S4: Reliability diagrams for SST Binary and SST Fine Grained before (far left) and after calibration (middle left, middle right, far right).
We include reliability diagrams for additional datasets: CIFAR-10 ([Figure S2](#S4.F2 "Figure S2 ‣ S4 Additional Reliability Diagrams ‣ On Calibration of Modern Neural Networks")) and SST ([Figure S3](#S4.F3 "Figure S3 ‣ S4 Additional Reliability Diagrams ‣ On Calibration of Modern Neural Networks") and [Figure S4](#S4.F4 "Figure S4 ‣ S4 Additional Reliability Diagrams ‣ On Calibration of Modern Neural Networks")). Note that, as mentioned in [Section 2](#S2 "2 Definitions ‣ On Calibration of Modern Neural Networks"), the reliability diagrams do not represent the proportion of predictions that belong to a given bin.
|
609aecee-0094-4505-a41b-211285f4453f
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
[Linkpost] Faith and Fate: Limits of Transformers on Compositionality
I thought it'd be especially interesting to get critiques/discussion from the LW crowd, because the claims here seem antithetical to a lot of the beliefs people here have, mostly around just how capable and cognizant transformers are/can be.
The authors show that transformers are guaranteed to suffer from compounding errors when performing any computation with long reasoning chains.
From the abstract, "In an attempt to demystify Transformers, we investigate the limits of these models across three representative compositional tasks—multi-digit multiplication, logic grid puzzles, and a classic dynamic programming problem. These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures. Our empirical findings suggest that Transformers solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching, without necessarily developing systematic problem solving skills"
|
7b01cf8a-12f8-4785-a2c9-3791b2010380
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Chapter 111: Failure, Pt 1
The Dark Lord was laughing.
From the empty air came the voice of the Defense Professor laughing wildly, so high and terrible his laughter; it was Voldemort's laughter now, the Dark Lord's laughter beyond all hiding or restraint.
Harry's mind was disarrayed. His eyes kept staring at where Albus Dumbledore had been. There was a horror in him that was too huge for understanding or reflection. His mind kept trying to fall back through time and undo reality, but that wasn't a sort of magic that existed, and reality stayed the same.
He had lost, he had lost Dumbledore, there were no take-backs, and that meant he had lost the war.
And the Dark Lord went on laughing.
"Ah, ah hah, ah hah hah ha! Professor Dumbledore, ah, Professor Dumbledore, such a fitting end to our game!" Another burst of wild laughter. "The wrong sacrifice even at the finish, for the piece you gave up everything to save was already in my possession! The wrong trap even from the beginning, for I could have abandoned this body at any time! Ah, hahahahaha, aha! You never did learn cunning, you poor old fool."
"You -" A voice was coming from Harry's throat. "You -"
"Ahahahaha! Why, yes, little child, you were always along on this adventure as my hostage, it was your whole purpose in being here. Ha, hahahaha! You are decades too young to play this game against the real Tom Riddle, child." The Dark Lord drew back the hood of the Cloak, his head becoming visible, and began to remove the rest of the Cloak. "And now, boy, you have helped me, yess indeed, and so it iss time to ressurrect your girl-child friend. To keep promisse." The Dark Lord's smile was cold, cold indeed. "I suppose you have doubts? Mark well, I could kill you this instant, for there is no longer a Headmaster of Hogwarts to be informed of it. Doubt me all you wish, but remember that." The hand was once more holding the gun. "Now come along, foolish child."
And they left.
They went back out through the door into the Potions room, the Dar
|
7c4015f4-e95a-455b-8d24-8d301434b85f
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Natural Value Learning
The main idea of this post is to make a distinction between *natural* and *unnatural* value learning. The secondary point of this post is that we should be suspicious of unnatural schemes for value learning, though the point is not that we should reject them. (*I am not fully satisfied with the term, so please do suggest a different term if you have one.*)
*Epistemic status: I'm not confident that actual value learning schemes will have to be natural, given all the constraints. I'm mainly confident that people should have something like this concept in their mind, though I don’t give any arguments for this.*
---
Natural value learning
----------------------
By a "value learning process" I mean a process by which machines come to learn and value what humans consider good and bad. I call a value learning process *natural* to the extent that the role humans play in this process is basically similar to the role they play in the process of socializing other humans (mostly children, also asocial adults) to learn and value what is good and bad. To give a more detailed picture of the distinction I have in mind, here are some illustrations of what I'm pointingat, each giving a property I associate with natural and unnatural value learning respectively:
| | |
| --- | --- |
| Natural alignment | Unnatural alignment |
| Humans play *the same role, and do the same kinds of things*, within the process of machine value learning as they do when teaching values to children. | Humans *in some significant way play a different role, or have to behave differently* within the process of machine value learning than they do when teaching values to children. |
| The machine value learning process *is adapted to humans*. | *Humans have to adapt* to the machine value learning process. |
| Humans who aren’t habituated to the technical problems of AI or machine value learning would still consider the process as it unfolds to be *natural. They can intuitively think of the process in analogy to the process as it has played out in their experience with humans*. | Humans who aren’t habituated to the technical problems of AI or machine value learning would perceive the machine value learning process to be *unnatural,* alien or “computer-like”. *If they naively used their intuitions and habits from teaching values to children, they would be confused in important ways*. |
---
Concrete examples
-----------------
To give a concrete idea of what I would consider natural vs unnatural value learning setups, here are some concrete scenarios in order of ascending naturality:
*Disclaimer*: I am not in any way proposing any of these scenarios as realistic or good or something to aim for (in fact I tried to write them somewhat comically so as not to raise this question). They are purely intended to clarify the idea given in this post, nothing more.
**Not very natural**. A superintelligent system is built that has somehow been correctly endowed with the goal of learning and optimizing human values (somehow). It somehow has some extremely efficient predictive algorithms that are a descendant of current ML algorithms. It scans the internet, builds a model of the distribution of human minds, and predicts what humanity would want if it were smarter and so forth. It successfully figures out what is good for humanity, reveals itself, and implements what humanity truly wants.
**Somewhat more but still not very natural**. A large AI research lab trains a bunch of agents in a simulation as a project branded as developing “aligned general intelligence”. As a part of this, the agents have to learn what humans want by performing various tasks in simulated and auto-generated situations that require human feedback to get right. A lot of data is required, so many thousands of humans are hired to sit in front of computer screens, look at the behaviour of the agents, and fill in scores or maybe english sentences to evaluate that behaviour. In order to be time-efficient, they evaluate a lot of such examples in succession. Specialized AI systems are used to generate adversarial examples, which end up being weird situations where the agents make alien decisions the humans wouldn’t have expected. Interpretability tools are used to inspect the cognition of these agents to provide the human evaluators with descriptions of what they were thinking. The human evaluators have to score the correctness of the reasoning patterns that led to the agent’s actions based on those descriptions. Somehow, the agents end up internalizing human values but are vastly faster and more capable on real world tasks.
**More natural**.A series of general-purpose robots and software assistants are developed that help humans around the home and the office, and start out (somehow) with natural language abilities, knowledge of intuitive physics, and so forth. They are marketed as “learning the way a human child learns”. At first, these assistants are considered dumb regarding understanding of human norms/values, but they are very conservative, so they don’t do much harm. Ordinary people use these robots/agents at first for very narrow tasks, but by massively distributed feedback given through the corrections of ordinary human consumers, they begin to gain and internalize intuitive understanding of at first quite basic everyday human norms. For example, the robots learn not to clean the plate while the human is still eating from it, because humans in their normal daily lives react negatively when the robots do so. Similarly, they learn not to interrupt an emotional conversation between humans, and so forth. Over time, humans trust these agents with more and more independent decision-making, and thereby the agents receive more general feedback. They eventually somehow actually generalize broadly human values to the point where people trust the moral understanding of these agents as much or more than they would that of a human.
*Disclaimer*: I already said this before but I feel the need to say it again: I don’t consider especially this last scenario to be realistic/solving the core AI safety problem. The scenarios are merely meant to illustrate the concept of natural value learning.
---
Why does this distinction matter?
---------------------------------
It seems to me that naturality tracks *something* about the expected reliability of value learning. Broadly I think we should be more wary of proposals to the extent that they are unnatural.
I won't try to argue for this point, but broadly my model is based on the model that human values are fragile and we don’t really understand mechanistically how they are represented, or how they are learned. Eg. there are some ideas around values, meta-values, and different levels of explicitness, but they seem to me to be quite far from a solid understanding that would give me confidence in a process that is significantly unnatural.
|
255b4442-8d13-40c2-a0a1-1165d172223d
|
trentmkelly/LessWrong-43k
|
LessWrong
|
This sometimes helps to expose assumptions
If one thing (B) follows from something (A) that is sufficient, then that which follows (B) is a necessity for the something (A). (If that is confusing just keep reading it will be blatantly clear at the end.) A hidden assumption is a necessary condition that was not stated. If it is claimed that something is sufficient but it is not, this must lead to a logical contradiction once a rule is applied that is only applicable if the statement is indeed sufficient. If indeed B follows from A alone, then A can not be without B. But if in reality there is a side condition for B other than A, then A can be without B. This thinking can be used to check the completeness of assumptions. In a world were "(C and D) → E", I might make the (false) claim: "C → E" But from that follows that C can never be true if E is false. Knowing the world you might come up with a case were C can be true without E being true, then you found the counter argument and likely the side condition D that was not met in your counter case. Aside from checking arguments this is useful in checking technical requirements. "In the event of a crash the passenger air bag deploys." Therefore "If the airbag is undeployed there can not have been a crash." Now that is clearly wrong, thus the first statement must have been wrong.
|
6140e747-5059-416e-9eca-2bd34b4456a9
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Inherited Improbabilities: Transferring the Burden of Proof
> One person's modus ponens is another's modus tollens.
- Common saying among philosophers and other people who know what these terms mean.
> If you believe A => B, then you have to ask yourself: which do I believe more? A, or not B?
- Hal Daume III, quoted by Vladimir Nesov.
Summary: Rules of logic have counterparts in probability theory. This post discusses the probabilistic analogue of modus tollens (the rule that if A=>B is true and B is false, then A is false), which is the inequality P(A) ≤ P(B)/P(B|A). What this says, in ordinary language, is that if A strongly implies B, then proving A is approximately as difficult as proving B.
The appeal trial for Amanda Knox and Raffaele Sollecito starts today, and so to mark the occasion I thought I'd present an observation about probabilities that occurred to me while studying the "motivation document"(1), or judges' report, from the first-level trial.
One of the "pillars" of the case against Knox and Sollecito is the idea that the apparent burglary in the house where the murder was committed -- a house shared by four people, namely Meredith Kercher (the victim), Amanda Knox, and two Italian women -- was staged. That is, the signs of a burglary were supposedly faked by Knox and Sollecito in order to deflect suspicion from themselves. (Unsuccessfully, of course...)
As the authors of the report, presiding judge Giancarlo Massei and his assistant Beatrice Cristiani, put it (p.44):
> What has been explained up to this point leads one to conclude that the situation of disorder in Romanelli's room and the breaking of the window constitute an artificially created production, with the purpose of directing investigators toward someone without a key to the entrance, who would have had to enter the house via the window whose glass had been broken and who would then have perpetrated the violence against Meredith that caused her death.
Now, even before examining "what has been explained up to this point", i.e. the reason
|
9a777980-cdaf-4d78-9121-69b102067602
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Relevant powerful agents will be highly optimized
The probability that an agent that is cognitively powerful enough to be relevant to existential outcomes, will have been subject to strong, general optimization pressures. Two (disjunctive) supporting arguments are that, one, pragmatically accessible paths to producing cognitively powerful agents tend to invoke strong and general optimization pressures, and two, that cognitively powerful agents would be expected to apply strong and general optimization pressures to themselves.
An example of a scenario that negates [RelevantPowerfulAgentsHighlyOptimized](https://arbital.com/p/) is [KnownAlgorithmNonrecursiveIntelligence](https://arbital.com/p/), where a cognitively powerful intelligence is produced by pouring lots of computing power into known algorithms, and this intelligence is then somehow prohibited from self-modification and the creation of environmental subagents.
Whether a cognitively powerful agent will in fact have been sufficiently optimized depends on the disjunction of:
- [PowerfulAgentAlreadyOptimized](https://arbital.com/p/). Filtering for agents which are powerful enough to be relevant to advanced agent scenarios, will in practice leave mainly agents that have already been subjected to large amounts of cognitive optimization from some internal or external source.
- [PowerfulAgentSelfOptimizes](https://arbital.com/p/). Agents that reach a threshold of power sufficient to be relevant to advanced agent scenarios will afterwards optimize themselves both heavily and generally.
Ending up with a scenario along the lines of [KnownAlgorithmNonrecursiveIntelligence](https://arbital.com/p/) requires defeating both of the above conditions simultaneously. The second condition seems more difficult and to require more [https://arbital.com/p/45](https://arbital.com/p/45) or [CapabilityControl](https://arbital.com/p/) features than the first.
|
0bee8e0a-dd79-4574-a165-aa90e9de8321
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Why focus on AI?
Why does this community focus on AI over all other possible apocalypses? What about pandemics, runaway climate change, etc.?
|
36486912-ff88-4cdb-bef1-22f8e8be3a2f
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Book Review: Age of Em
[Note: I really liked this book and if I criticize it that’s not meant as an attack but just as what I do with interesting ideas. Note that Robin has offered to debate me about some of this and I’ve said no – mostly because I hate real-time debates and have bad computer hardware – but you may still want to take this into account when considering our relative positions. Mild content warning for murder, rape, and existential horror. Errors in Part III are probably my own, not the book’s.]
I.
There are some people who are destined to become adjectives. Pick up a David Hume book you’ve never read before and it’s easy to recognize the ideas and style as Humean. Everything Tolkien wrote is Tolkienesque in a non-tautological sense. This isn’t meant to denounce either writer as boring. Quite the opposite. They produced a range of brilliant and diverse ideas. But there was a hard-to-define and very consistent ethos at the foundation of both. Both authors were very much like themselves.
Robin Hanson is more like himself than anybody else I know. He’s obviously brilliant – a PhD in economics, a masters in physics, work for DARPA, Lockheed, NASA, George Mason, and the Future of Humanity Institute. But his greatest aptitude is in being really, really Hansonian. Bryan Caplan describes it as well as anybody:
> When the typical economist tells me about his latest research, my standard reaction is ‘Eh, maybe.’ Then I forget about it. When Robin Hanson tells me about his latest research, my standard reaction is ‘No way! Impossible!’ Then I think about it for years.
This is my experience too. I think I said my first “No way! Impossible!” sometime around 2008 after reading his blog Overcoming Bias. Since then he’s influenced my thinking more than almost anyone else I’ve ever read. When I heard he was writing a book, I was – well, I couldn’t even imagine a book by Robin Hanson. When you read a thousand word blog post by Robin Hanson, you have to sit down and think about it and wait
|
d61693a9-0e87-4738-b37f-059cd3c7e406
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Meetup : Austin, TX - Quack's
Discussion article for the meetup : Austin, TX - Quack's
WHEN: 09 July 2016 01:30:00PM (-0500)
WHERE: 411 E 43rd St, Austin, TX 78751
The next meetup is July 9th, 2016 at 1:30PM at Quack's 43rd Street Bakery (don't worry, they've also got coffee). We'll likely be inside, to the right. I might have a sign with the LW blog header printed on it. I hope to see you there!
If you have any questions or want to get my phone number to make sure you find us, please send me a private message. I'll notice and respond to those much more quickly than I'll notice comments on this meetup announcement.
We have a Google Group here that we use for announcements (like our weekly group dinner in a private residence), as well as a Facebook Group.
Discussion article for the meetup : Austin, TX - Quack's
|
60c9ccab-d8cd-44ed-8d2b-1cd925ea0213
|
trentmkelly/LessWrong-43k
|
LessWrong
|
How does Facebook make overt self obsession ok?
People who talk about themselves a lot are generally disliked. A likable person will instead subtly direct conversation to where others request the information they want to reveal. Revealing good news about yourself is a good sign, but wanting to reveal good news about yourself is a bad sign. Best to do it without wanting to.
This appears true of most human interaction, but apparently not of that on Facebook. On Facebook, when you are not posting photographs of yourself and updating people on your activities, you are writing notes listing twenty things nobody knows about you, linking people to analyses of your personality, or alerting them to your recent personal and group affiliations. Most of this is unasked for by others. I assume it is similar for other social networking sites.
If over lunch I decided, without your suggestion, to list to you twenty random facts about me, tell you the names of all my new acquintences, and show you my collection of photos of myself, our friendship would soon wane. Why is Facebook different? Here are some reasons I can think of:
1. It is ok to talk about yourself when asked, and in a space where communication is very public to a group, nobody knows if you were asked by someone else. This seems the case for the self obsessed notes prefaced with ‘seeing as so many of you have nagged me to do this I guess I will reluctantly write a short essay on myself’ and such things, but I doubt it applies the rest of the time.
2. Most writing on Facebook isn’t directed at anyone, and people are not forced to read it. It is the boredom and annoyance of being forced to hear about other people’s lives that puts people off those who discuss themselves too much, not signaling. This doesn’t explain why people spend so much time reading about one another on Facebook.
3. Forcing a specific other person to listen to you go on about yourself is a dominance move. Describing yourself endlessly into cyberspace isn’t, as it’s not directed at anyone. This
|
8c4eb4b4-6806-4386-b19e-0b6ce98da513
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Rationality !== Winning
I think "Rationality is winning" is a bit of a trap.
(The original phrase is notably "rationality is systematized winning", which is better, but it tends to slide into the abbreviated form, and both forms aren't that great IMO)
It was coined to counteract one set of failure modes - there were people who were straw vulcans, who thought rituals-of-logic were important without noticing when they were getting in the way of their real goals. And, also, there outside critics who'd complain about straw-vulcan-ish actions, and treat that as a knockdown argument against "rationality."
"Rationalist should win" is a countermeme that tells both groups of people "Straw vulcanism is not The Way. If you find yourself overthinking things in counterproductive ways, you are not doing rationality, even if it seems elegant or 'reasonable' in some sense."
It's true that rationalists should win. But I think it's not correspondingly true that "rationality" is the study of winning, full stop. There are lots of ways to win. Sometimes the way you win is by copying what your neighbors are doing, and working hard. There is rationality involved in sifting through the various practices people suggest to you, and figuring out which ones work best. But, the specific skill of "sifting out the good from the bad" isn't always the best approach. It might take years to become good at it, and it's not obvious that those years of getting good at it will pay off.
Rationality is the study (and applied skill) of finding cognitive algorithms that form better beliefs and make better decisions. Sometimes this is the appropriate tool for the job, and sometimes it's not.
The reason this is particularly important is in deciding what feedback loops to focus on when developing rationality. If you set your feedback loops as "am I winning, generally?" (or, "are the students of my rationality curriculum winning, generally?"), well, that's a really noisy feedback loop that's probably swamped by random environme
|
23466e8a-9bd8-467e-8979-350b1b1faa0f
|
trentmkelly/LessWrong-43k
|
LessWrong
|
A website standard that is affordable to the poorest demographics in developing countries?
Fact: the Internet is excruciatingly slow in many developing countries, especially outside of the big cities.
Fact: today's websites are designed in such a way that they become practically impossible to navigate with connections in the order of, say, 512kps. Ram below 4GB and a 7-year old CPU are also a guarantee of a terrible experience.
Fact: operating systems are usually designed in such an obsolescence-inducing way as well.
Fact: the Internet is a massive source of free-flowing information and a medium of fast, cheap communication and networking.
Conclusion: lots of humans in the developing world are missing out on the benefits of a technology that could be amazingly empowering and enlightening.
I just came across this: what would the internet 2.0 have looked like in the 1980s. This threw me back to my first forays in Linux's command shell and how enamoured I became with its responsiveness and customizability. Back then my laptop had very little autonomy, and very few classrooms had plugs, but by switching to pure command mode I could spend the entire day at school taking notes (in LaTeX) without running out. But I switched back to the GUI environment as soon as I got the chance, because navigating the internet on the likes of Lynx is a pain in the neck.
As it turns out, I'm currently going through a course on energy distribution in isolated rural areas in developing countries. It's quite a fascinating topic, because of the very tight resource margins, the dramatic impact of societal considerations, and the need to tailor the technology to the existing natural renewable resources. And yet, there's actually a profit to be made investing in these projects; if managed properly, it's win-win.
And I was thinking that, after bringing them electricity and drinkable water, it might make sense to apply a similar cost-optimizing, shoestring-budget mentality to the Internet. We already have mobile apps and mobile web standards which are built with the mindset of "le
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5acd23de-7380-4f79-935c-681eb407a0da
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StampyAI/alignment-research-dataset/special_docs
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Other
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Embedding Ethical Principles in Collective Decision Support Systems
Embedding Ethical Principles in Collective Decision Support Systems
Joshua Greene (Harvard University, USA),
Francesca Rossi (University of Padova, Italy and IBM T.J. Watson, USA),
John Tasioulas (King’s College London, UK),
Kristen Brent Venable (Tulane University and IMHC, USA),
Brian Williams (MIT, USA)
Abstract
The future will see autonomous machines acting in the same
environment as humans, in areas as diverse as driving, assis-
tive technology, and health care. Think of self-driving cars,
companion robots, and medical diagnosis support systems.
We also believe that humans and machines will often need to
work together and agree on common decisions. Thus hybrid
collective decision making systems will be in great need.
In this scenario, both machines and collective decision mak-
ing systems should follow some form of moral values and eth-
ical principles (appropriate to where they will act but always
aligned to humans’), as well as safety constraints. In fact,
humans would accept and trust more machines that behave
as ethically as other humans in the same environment. Also,
these principles would make it easier for machines to deter-
mine their actions and explain their behavior in terms under-
standable by humans. Moreover, often machines and humans
will need to make decisions together, either through consen-
sus or by reaching a compromise. This would be facilitated
by shared moral values and ethical principles.
Introduction
We believe it is important to study the embedding of safety
constraints, moral values, and ethical principles in agents,
within the context of collective decision making systems in
societies of agents and humans.
Collective decision making involves a collection of agents
who express their preferences over a shared set of possible
outcomes, and a preference aggregation rule which chooses
one of the options to best satisfy the agents’ preferences.
However, aggregating just preferences may lead to outcomes
that do not follow any ethical principles or safety constraints.
To embed such principles/constraints in a collective decision
making system, we need to understand how to model them,
how to reason with them at the level of a single agent, and
how to embed them into collective decision making.
Just like individual humans, each agent that operates in a
multi-agent context needs to be have an internal represen-
tation of moral values and ethical principles, as well as an
ethical reasoning engine. Otherwise it would not able to ex-
plain its behaviour to others.
Copyright c 2016, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.We claim that there is a need to adapt current logic-
based modelling and reasoning frameworks, such as soft
constraints, CP-nets, and constraint-based scheduling un-
der uncertainty, to model safety constraints, moral values,
and ethical principles. More precisely, we study how logic-
based preference modelling frameworks can be adapted to
model both (explicit) ethical principles and (implicit) moral
values, as sophisticated constraints over possible actions.
The constraints may be unconditional (“hard”) constraints,
or soft, overridable if the consequences of an individual
bad action can still lead to overall good. We propose to re-
place preference aggregation with an appropriately devel-
oped value/ethics/preference fusion , an operation designed
to ensure that agents’ preferences are consistent with their
moral values and do not override ethical principles
For ethical principles, we use hard constraints specifying
the basic ethical “laws”, plus some form of common-sense
morality expressed as sophisticated prioritised and pos-
sibly context-dependent constraints over possible actions,
equipped with a conflict resolution engine. To avoid reckless
behavior in the face of uncertainty, we proposed to bound
the risk of violating these ethical laws in the form of chance
constraints, and we propose to develop stochastic constraint
solvers that propose solutions that respect these risk bounds,
based on models of environmental uncertainty. We also pro-
pose to replace preference aggregation with an appropriately
developed constraint/value/ethics/preference fusion , an op-
eration designed to ensure that agents’ preferences are con-
sistent with the system’s safety constraints, the agents’ moral
values, and the ethical principles. We will leverage previ-
ous experience in developing single and multi-agent prefer-
ence/constraint reasoning engines.
Today, techniques exist to enable agents to make deci-
sions, such as scheduling activities, while satisfying some
safety concerns, e.g. by using techniques from constraint-
based optimization. For instance, in many critical scenarios,
such as space missions where a malfunction can endanger
the whole mission, activities are scheduled in such a way to
maximise robustness against possible problems. We believe
that these techniques can provide an inspiration to handle
ethical concerns. However, we think that a much more ex-
plicit model and reasoning engine for ethical principles and
moral values is needed in order to deal with them satisfacto-
rily and allow them to evolve over time.
Which ethical principles for intelligent agents?
An intelligent agent should have capability to autonomously
make good decisions, based on available data and prefer-
ences, even in the context of uncertainty, missing or noisy
information, as well as incorrect input, and should be able
to learn from past experience or from available historical
data. Even more importantly, intelligent agents should have
the ability to interact with humans, make decisions together
with them, and achieve goals by working together.
An agent with these capabilities poses several crucial eth-
ical questions. Ethical principles guide humans’ behaviour.
They tell us what is regarded as right or wrong. They come
from values that we regards as absolute, guiding our whole
life. If we want intelligent agents to enhance human capa-
bilities, or to collaborate with humans, or even just to live
and act in the same society, we need to embed in them some
ethical guidelines, so they can act in their environment fol-
lowing values that are aligned to the human ones. Or maybe
we need different values and ethical principles for agents,
since they are inherently different from humans?
As Issac Asimov famously illustrated in his I, Robot se-
ries, explicitly programming ethical behavior is surprisingly
challenging. Moral philosophy – the field that has stud-
ied explicit ethical principles most extensively – suggests
three general approaches, corresponding to the three major
schools of Western moral thought.
Thedeontological approach (most closely associated with
Immanuel Kant) regards morality as a system of rights and
duties . Here the focus is on categories of actions , where
different actions are deemed impermissible, permissible, or
obligatory based on a set of explicit rules.
The consequentialist approach (most closely associated
with Jeremy Bentham and John Stuart Mill) aims to pro-
duce the best aggregate consequences (minimizing costs
and maximizing benefits) according to a pre-specified value
function. For example, a classical utilitarian approach aims
to maximize the total amount of happiness.
Thevirtue - orcharacter -based approach (most closely as-
sociated with Aristotle) regards ethical behavior as the prod-
uct of an acquired set of behavioral dispositions that cannot
be adequately summarized as an adherence to a set of deon-
tological rules (concerning actions) or to as a commitment
to maximizing good consequences.
These three approaches are well known and have been the
starting point for nearly all discussions of machine ethics
(Moor 1985; Bostrom 2014; Wallach and Allen 2008). Each
approach has limitations that are well known. Deontological
principles are easily to implement but may be rigid. Conse-
quentialist principles require complex calculations that may
be faulty. Virtue is opaque and requires extensive training
with an unknown teaching criterion. There is, however, a
more general problem faced by all three approaches, which
is that implementing them may depend on solving daunting,
general computation problems that have not been solved and
may not be solved for some time.
For example, a “simple” deontological rule such as “don’t
lie” or “dont kill” is not specified in terms of machine move-
ments. Rather, the machine must understand which acts
of communication would constitute lying and which bodymovements would constitute killing in a given context. A
consequentialist system would require a machine to repre-
sent all of the actions available to it, and a virtue based sys-
tem would have to recognize the present situation as one
with a variety of features that, together, call for one action
rather than another. In other words, all three approaches,
when fully implemented, seem to require something like
general intelligence, which would enable the machine to rep-
resent its current situation in rich conceptual terms. Indeed,
this speculation is consistent with recent research on the cog-
nitive neuroscience of moral judgment indicating that moral
judgment depends on a variety of neural systems that are
not specifically dedicated to moral judgment (Greene 2014).
This includes systems that enable the general representation
of value and the motivation of its pursuit, visual imagery,
cognitive control, and the representation of complex seman-
tic representations. Unfortunately for Commander Data, hu-
mans have no “ethical subroutine”. Real human moral judg-
ment uses the whole brain.
What, then, can be done? Here, the human brain may nev-
ertheless offer some guidance (Shenhav and Greene 2014).
Is it morally acceptable to push someone off of a footbridge
in order to save five lives (Thomson 1985)? A simple de-
ontological response says no (“Dont kill”). A simple con-
sequentialist response says yes (“Save the most lives”), and
most humans are at least somewhat conflicted about this, but
err on the side of the deontological response (in this par-
ticular case). We now know that the deontological response
depends on a classically emotional neural structure known
as the amygdala (reflecting emotional salience) and that the
application of the consequentialist maximizing principle de-
pends on a classically “cognitive” structure known as the
dorsolateral prefrontal cortex. It seems that healthy humans
engage both responses and that there is a higher-order eval-
uation process that depends on the ventromedial prefrontal
cortex, a structure that across domains attaches emotional
weight to decision variables. In other words, the brain seems
to make both types of judgment (deontological and conse-
quentialist) and then makes a higher order judgment about
which lower-order judgment to trust, which may be viewed
as a kind of wisdom (reflecting virtue or good character).
Such a hierarchical decision system might be imple-
mented within an agent, or across agents. For example, some
agents may apply simple rules based on action features. Oth-
ers may attempt to make “limited” cost-benefit calculations.
And collectively, the behavior of these agents may be de-
termined by a weighting of these distinct, lower-level eval-
uative responses. Such as system might begin by follow-
ing simple deontological rules, but then, either acquire more
complex rules through learning, or learn when it can and
cannot trust its own cost-benefit calculations. Starting with
action-based rules and simple cost-benefit calculations sub-
stantially reduces the space of possible responses. Learning
to trade-off between these two approaches adds some flexi-
bility, but without requiring intractable cost-benefit calcula-
tions or lifelong moral education.
We offer this approach as just one example strategy. Of
course, if we knew how we were going to solve this problem,
there would be no need to bring together people with diverse
expertise. What we wish to convey is twofold: First, that we
are aware of the scope of the challenge and the strengths
and limitations of the extant strategies. Second, that we have
some preliminary ideas for hybrid approaches that leverage
insights from human moral cognition.
Another important aspect of our approach would be to
consider the extent to which morality could be reduced to
a set of rules that is capable of being applied in a fairly
straightforward way to guide conduct , e.g. ’Do not kill’,
’Keep one’s promises’, ’Help those in need’, etc. We already
know that much of common sense morality is codifiable in
this way, thanks to the example of the law.
However, even if we could achieve an adequate codifica-
tion of ordinary moral consciousness, at least within some
domain, problems would arise. Two cases are especially
worth highlighting: (a) cases where the strict application of a
given rule generates an unacceptable outcome, often but not
always characterisable as such by reference to some other
rule that has been violated in adhering to the first, and (b)
cases where the strict application of the given set of rules
is unhelpfully ’silent’ on the problem at hand, because it in-
volved circumstances not foreseen by the rules.
Both phenomena (a) and (b) raise the question of when
and how the strict application of a rule needs to be modi-
fied or supplemented to resolve the problem of perverse re-
sults or gaps. One important source of thinking about these
issues is Aristotle’s discussion of justice and equity in the
Nicomachean Ethics. According to Aristotle, the common
sense morality codified in law, although capable of being a
generally good guide to action, will nonetheless on occasion
breakdown along the lines of (a) and (b). For Aristotle, this
means that the virtuous judge will need to possess, in addi-
tion to a propensity to follow legal rules, the virtue of equity.
This enables the judge to use their independent judgment to
correct or supplement the strict application of legal rules in
cases of type (a) or (b). A key topic involves the clarification
of the notion of equity, with its rule and judgment structure,
as a prelude to a consideration of how this might be embed-
ded in autonomous agents.
Designing ethical agents
No matter which approach we will choose to express ethical
principles and moral values in intelligent agents, we need to
find a suitable way to model it in computational terms, which
is expressive enough to be able to represent all we have in
mind in its full generality, and which can be reasoned upon
with computational efficiency.
Ethical principles may seem very similar to the con-
cepts of constraints (Rossi, Van Beek, and Walsh 2006;
Dechter 2003) and preferences (Rossi, Venable, and Walsh
2011), which have already received a large attention in the
AI literature. Indeed, constraints and preferences are a com-
mon feature of everyday decision making. They are, there-
fore, an essential ingredient in many reasoning tools. In an
intelligent agent, we need to specify what is not allowed ac-
cording to the principles, thus some form of constraints, as
well as some way to prioritise among different principles,
that some form of preference.Representing and reasoning about preferences is an area
of increasing theoretical and practical interest in AI. Pref-
erences and constraints occur in real-life problems in many
forms. Intuitively, constraints are restrictions on the possible
scenarios: for a scenario to be feasible, all constraints must
be satisfied. For example, if we have an ethical rule that says
we should not kill anybody, all scenarios where people are
killed are not allowed. Preferences, on the other hand, ex-
press desires, satisfaction levels, rejection degrees, or costs.
For example, we may prefer an action that solves reasonably
well all medical issues in a patient, rather than another one
that solves completely one of them but does not address the
other ones. Moreover, in many real-life optimization prob-
lems, we may have both constraints and preferences.
Preferences and constraints are closely related notions,
since preferences can be seen as a form of “relaxed” con-
straints. For this reason, there are several constraint-based
preference modeling frameworks in the AI literature. One
of the most general of such frameworks defines a notion of
softconstraints (Meseguer, Rossi, and Schiex 2006), which
extends the classical constraint formalism to model prefer-
ences in a quantitative way, by expressing several degrees
of satisfaction that can be either totally or partially ordered.
The term soft constraints is used to distinguish this kind of
constraints from the classical ones, that are usually called
hard constraint. However, hard constraints can be seen as an
instance of the concept of soft constraints where there are
just two levels of satisfaction. In fact, a hard constraint can
only be satisfied or violated, while a soft constraint can be
satisfied at several levels.When there are both levels of satis-
faction and levels of rejection, preferences are usually called
bipolar, and they can be modeled by extending the soft con-
straint formalism (Bistarelli et al. 2006).
Preferences can also be modeled in a qualitative (also
called ordinal ) way, that is, by pairwise comparisons. In this
case, soft constraints (or their extensions) are not suitable.
However, other AI preference formalisms are able to ex-
press preferences qualitatively, such as CP-nets (Boutilier
et al. 2004). More precisely, CP-nets provide an intuitive
way to specify conditional preference statements that state
the preferences over the instances of a certain feature, possi-
bly depending on some other features. For example, we may
say that we prefer driving slow to driving fast if we are in a
country road. CP-nets and soft constraints can be combined,
providing a single environment where both qualitative and
quantitative preferences can be modeled and handled. Spe-
cific types of preferences come with their own reasoning
methods. For example, temporal preferences are quantita-
tive preferences that pertain to the position and duration of
events in time. Soft constraints can be embedded naturally
in a temporal constraint framework to handle this kind of
preference.
An intuitive way to express preferences consists of pro-
viding a set of goals, each of which is a propositional for-
mula, possibly adding also extra information such as pri-
orities or weights. Candidates in this setting are variable
assignments, which may satisfy or violate each goal. A
weighted goal is a propositional logic formula plus a real-
valued weight. The utility of a candidate is then computed
by collecting the weights of satisfied and violated goals, and
then aggregating them. Often only violated goals count, and
their utilities are aggregated with functions such as sum or
maximin. In other cases, we may sum the weights of the sat-
isfied goals, or we may take their maximum weight. Any re-
striction we may impose on the goals or the weights, and any
choice of an aggregation function, give a different language.
Such languages may have drastically different properties in
terms of their expressivity, succinctness, and computational
complexity.
In the quantitative direction typical of soft constraints,
there are also other frameworks to model preferences. The
most widely used assumes we have some form of indepen-
dence among variables, such as mutual preferential inde-
pendence. Preferences can then be represented by an ad-
ditive utility function in deterministic decision making, or
utility independence, which assures an additive representa-
tion for general scenarios. However, this assumption often
does not hold in practice since there is usually some interac-
tion among the variables. To account for this, models based
on interdependent value additivity have been defined which
allows for some interaction between the variables while pre-
serving some decomposability. This notion of independence,
also called generalized additive independence (GAI), allows
for the definition of utility functions which take the form of a
sum of utilities over subsets of the variables. GAI decompo-
sitions can be represented by a graphical structure, called a
GAI net, which models the interaction among variables, and
it is similar to the dependency graph of a CP-net or to the
junction graph of a Bayesian network. GAI decompositions
have been used to provide CP-nets with utility functions, ob-
taining the so-called UCP networks.
Preferences and ethical principles in collective
decision making systems
If agents and humans will be part of a hybrid collective de-
cision making system, and thus will make collective deci-
sions, based on their preferences over the possible outcomes,
can ethical principles for such decision system be modelled
just like the preferences of another dummy agent, or should
they be represented and treated differently? Are the knowl-
edge representation formalisms that are usually used in AI
to model preferences suitable to model values as well, or
should we use something completely different? A very sim-
ple form of values could be modelled by constraints, so that
only feasible outcomes can be the results of a collective deci-
sions process. But values and ethical principles could often
take a graded form, thus resembling a kind of preference.
Also, should individual and collective ethical principles be
modelled differently?
We believe that some of the answers to these questions
may exploit the existing literature on preference aggrega-
tion (Rossi, Venable, and Walsh 2011). Indeed, an important
aspect of reasoning about preferences is preference aggrega-
tion. In multi-agent systems, we often need to combine the
preferences of several agents. More precisely, preferences
are often used in collective decision making when multiple
agents need to choose one out of a set of possible decisions:each agent expresses its preferences over the possible deci-
sions, and a centralized system aggregates such preferences
to determine the “winning” decision. Preferences are also
the subject of study in social choice, especially in the area
of elections and voting theory (Arrow and amd K. Suzu-
mara 2002). In an election, the voters express their prefer-
ences over the candidates and a voting rule is used to elect
the winning candidate. Economists, political theorist, math-
ematicians, as well as philosophers have invested consider-
able effort in studying this scenario and have obtained many
theoretical results about the desirable properties of the vot-
ing rules that one can use.
Since the voting setting is closely related to multi-agent
decision making, in recent years the area of multi-agent sys-
tems has witnessed a growing interest in trying to reuse so-
cial choice results in the multi-agent setting. However, it
soon became clear that an adaptation of such results is nec-
essary, since several issues, which are typical of multi-agent
settings and AI scenarios, usually do not occur, or have a
smaller impact, in typical voting situations. In a multi-agent
system, the set of candidates can be very large with respect
to the set of voters. Usually in social choice it is the op-
posite: there are many voters and a small number of can-
didates. Also, in many AI scenarios, the candidates often
have a combinatorial structure. That is, they are defined via a
combination of features. Moreover, the preferences over the
features are often dependent on each other. In social choice,
usually the candidates are tokens with no structure. In ad-
dition, for multi-issue elections, the issues are usually inde-
pendent of each other. This combinatorial structure allows
for the compact modelling of the preferences over the candi-
dates. Therefore, several formalisms have been developed in
AI to model such preference orderings. In social choice, lit-
tle emphasis is put on how to model preferences, since there
are few candidates, so one can usually explicitly specify a
linear order. In AI, a preference ordering is not necessarily
linear, but it may include indifference and incomparability.
Moreover, often uncertainty is present, for example in the
form of missing or imprecise preferences. In social choice,
usually all preferences are assumed to be present, and a pref-
erence order over all the candidates is a linear order that is
explicitly given as a list of candidates. Finally, multi-agent
systems must consider the computational properties of the
system. In social choice this usually has not been not a cru-
cial issue.
It is therefore very interesting to study how social choice
and AI can fruitfully cooperate to give innovative and im-
proved solutions to aggregating preferences of multiple
agents. In our effort, since we intend to deal with ethical
issues in collective decision making, we need to understand
what modifications to the usual preference aggregation sce-
nario should be done to account for them, and how they
can be handled satisfactorily when making collective deci-
sions. Collective decision making in the presence of feasi-
bility constraints is starting to be considered in the literature
(Grandi et al. 2014). However, ethical principles and safety
constraints will be much more complex than just a set of
constraints, so we need to understand the computational and
expressiveness issues arising in this scenario.
Acknowledgements
This work is partially supported by the project ”Safety con-
straints and ethical principles in collective decision making
systems” funded by the Future of Life Institute.
References
Arrow, K. J., and amd K. Suzumara, A. K. S. 2002. Hand-
book of Social Choice and Welfare. North-Holland, Elsevier.
Bistarelli, S.; Pini, M. S.; Rossi, F.; and Venable, K. B.
2006. Bipolar preference problems: Framework, properties
and solving techniques. In Recent Advances in Constraints
(CSCLP 2006) , volume 4651 of LNCS , 78–92. Springer.
Bostrom, N. 2014. Superintelligence: Paths, Dangers,
Strategies . Oxford University Press.
Boutilier, C.; Brafman, R. I.; Domshlak, C.; Hoos, H. H.;
and Poole, D. 2004. CP-nets: A tool for representing and
reasoning with conditional ceteris paribus preference state-
ments. J. Artif. Intell. Res. (JAIR) 21:135–191.
Dechter, R. 2003. Constraint Processing . Morgan Kauf-
mann.
Grandi, U.; Luo, H.; Maudet, N.; and Rossi, F. 2014. Aggre-
gating cp-nets with unfeasible outcomes. In Principles and
Practice of Constraint Programming - 20th International
Conference, CP 2014, Lyon, France, September 8-12, 2014.
Proceedings , 366–381.
Greene, J. D. 2014. The cognitive neuroscience of moral
judgment and decision-making . MIT Press.
Meseguer, P.; Rossi, F.; and Schiex, T. 2006. Soft con-
straints. In Handbook of constraint programming . Elsevier.
chapter 9, 281–328.
Moor, J. H. 1985. What is computer ethics? Metaphilosophy
16(4):266–275.
Rossi, F.; Van Beek, P.; and Walsh, T., eds. 2006. Handbook
of Constraint Programming . Elsevier.
Rossi, F.; Venable, K. B.; and Walsh, T. 2011. A Short Intro-
duction to Preferences: Between Artificial Intelligence and
Social Choice . Synthesis Lectures on Artificial Intelligence
and Machine Learning. Morgan & Claypool Publishers.
Shenhav, A., and Greene, J. D. 2014. Integrative moral
judgment: dissociating the roles of the amygdala and ven-
tromedial prefrontal cortex. The Journal of Neuroscience
34(13):4741–4749.
Thomson, J. J. 1985. The trolley problem. Yale Law Journal
94:1395.
Wallach, W., and Allen, C. 2008. Moral machines: Teaching
robots right from wrong . Oxford University Press.
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4713e7d3-3e6f-4d41-9836-59b7099784e8
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trentmkelly/LessWrong-43k
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LessWrong
|
How Not to be Stupid: Adorable Maybes
Previous: Know What You Want
Ah wahned yah, ah wahned yah about the titles. </some enchanter named Tim>
(Oh, a note: the idea here is to establish general rules for what sorts of decisions one in principle ought to make, and how one in principle ought to know stuff, given that one wants to avoid Being Stupid. (in the sense described in earlier posts) So I'm giving some general and contrived hypothetical situations to throw at the system to try to break it, to see what properties it would have to have to not automatically fail.)
Okay, so assuming you buy the argument in favor of ranked preferences, let's see what else we can learn by considering sources of, ahem, randomness:
Suppose that either via indexical uncertainty, or it turns out there really is some nondeterminism in the universe, or there's some source of bits such that the only thing you're able to determine about it is that the ratio of 1s it puts out to total bits is p. You're not able to determine anything else about the pattern of bits, they seem unconnected to each other. In other words, you've got some source of uncertainty that leaves you only knowing that some outcomes happen more often than others, and potentially you know something about the precise relative rates of those outcomes.
I'm trying here to avoid actually assuming epistemic probabilities. (If I've inserted an invisible assumption for such that I didn't notice, let me know.) Instead I'm trying to construct a situation in which that specific situation can be accepted as at least validly describable by something resembling probabilities (propensity or frequencies. (frequencies? aieeee! Burn the heretic, or at least flame them without mercy! :))) So, for whatever reason, suppose the universe or your opponent or whatever has access to such a source of bits. Let's consider some of the implications of this.
For instance, suppose you prefer A > B.
Now, suppose you are somehow presented with the following choice: Choose B, or choose a sit
|
24b4023d-1edf-4349-bfd9-83d35a2f9278
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StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
A Barebones Guide to Mechanistic Interpretability Prerequisites
*Co-authored by Neel Nanda and Jess Smith*
**Check out** [**Concrete Steps for Getting Started in Mechanistic Interpretability**](https://neelnanda.io/getting-started) **for a better starting point**
**Why does this exist?**
------------------------
People often get intimidated when trying to get into AI or AI Alignment research. People often think that the gulf between where they are and where they need to be is huge. This presents practical concerns for people trying to change fields: we all have limited time and energy. And for the most part, people wildly overestimate the actual core skills required.
This guide is our take on the essential skills required to understand, write code and ideally contribute useful research to mechanistic interpretability. We hope that it’s useful and unintimidating. :)
**Core Skills:**
----------------
* Maths:
+ Linear Algebra:[3Blue1Brown](https://www.youtube.com/watch?v=fNk_zzaMoSs) or[Linear Algebra Done Right](https://linear.axler.net/)
- Core goals - to deeply & intuitively understand these concepts:
* Basis
* Change of basis
* That a vector space is a geometric object that doesn’t necessarily have a canonical basis
* That a matrix is a linear map between two vector spaces (or from a vector space to itself)
- Bonus things that it’s useful to understand:
* What’s singular value decomposition? Why is it useful?
* What are orthogonal/orthonormal matrices, and how is changing to an orthonormal basis importantly different from just any change of basis?
* What are eigenvalues and eigenvectors, and what do these tell you about a linear map?
+ Probability basics
- Basics of distributions: expected value, standard deviation, normal distributions
- Log likelihood
- Maximum value estimators
- Random variables
- Central limit theorem
+ Calculus basics
- Gradients
- The chain rule
- The intuition for what backprop is - in particular, grokking the idea that backprop is just the chain rule on multivariate functions
* Coding:
+ Python Basics
- The “how to learn coding” market is pretty saturated - there’s a lot of good stuff out there! And not really a clear best one.
- Zac Hatfield-Dodds recommends Al Sweigart's *Automate the Boring Stuff* and then *Beyond the Basic Stuff* (both readable for free on [inventwithpython.com](https://inventwithpython.com/), or purchasable in books); he's also written some books of exercises. If you prefer a more traditional textbook, [*Think Python 2e*](https://greenteapress.com/wp/think-python-2e/) is excellent and also available freely online.
+ NumPy Basics
- Try to do the first ~third of these: <https://github.com/rougier/numpy-100>. Bonus points for doing them in pytorch on tensors :)
* ML:
+ Rough grounding in ML.
- [fast.ai](https://course.fast.ai/) is a good intro, but a fair bit more effort than is necessary. For an 80/20, focus on Andrej Karpathy’s new video explaining neural nets:<https://www.youtube.com/watch?v=VMj-3S1tku0>
+ [PyTorch](https://pytorch.org/tutorials/) basics
- Don’t go overboard here. You’ll pick up what you need over time - learning to google things when you get confused or stuck is most of the *real*skill in programming.
- One goal: build linear regression that runs in Google Colab on a GPU.
- The main way you will shoot yourself in the foot with PyTorch is when manipulating tensors, and especially multiplying them. **I highly, highly recommend learning how to use** [**einops**](https://einops.rocks/1-einops-basics/)(a library to nicely do any reasonable manipulation of a single tensor) and [einsum](https://rockt.github.io/2018/04/30/einsum) (a built in torch function implementing Einstein Summation notation, to do arbitrary tensor multiplication)
* If you try doing these things without einops and einsum you will hurt yourself. Do not recommend!
* Transformers - probably the biggest way mechanistic interpretability differs from normal ML is that it’s *really* important to deeply understand the architectures of the models you use, all of the moving parts inside of them, and how they fit together. In this case, the main architecture that matters is a transformer! (This is useful in normal ML too, but you can often get away with treating the model as a black box)
+ My[what is a transformer](https://www.neelnanda.io/transformer-tutorial) and[implementing GPT-2 From Scratch](https://www.neelnanda.io/transformer-tutorial-2) video tutorials
- [My transformer glossary/explainer](https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=pndoEIqJ6GPvC1yENQkEfZYR)
+ **A worthwhile exercise is to fill out the** [**template notebook**](https://www.neelnanda.io/transformer-template), accompanying the tutorial (no copying and pasting!)
- The notebook comes with tests so you know that your code is working, and by the end of this you'll have a working implementation of GPT-2!
- If you can do this, I’d say that you basically fully understand the transformer architecture.
+ Alternate framings that may help give different intuitions:
- Nelson Elhage’s[Transformers for Software Engineers](https://blog.nelhage.com/post/transformers-for-software-engineers/) (also useful to non software engineers!)
- Check out[the illustrated transformer](https://jalammar.github.io/illustrated-transformer/)
* Note that you can pretty much ignore the stuff on encoder vs decoder transformers - we mostly care about autoregressive decoder-only transformers like GPT-2, which means that each token can only see tokens before it, and they learn to predict the next token
* Bonus:[Jacob Hilton’s Deep learning for Alignment syllabus](https://github.com/jacobhilton/deep_learning_curriculum) - this is a lot more content than you strictly need, but is well put together and likely a good use of time to go through at least some of!
Once you have the pre-reqs, my [Getting Started in Mechanistic Interpretability](https://neelnanda.io/getting-started) guide goes into how to get further into mechanistic interpretability!
Note that there are a lot more skills in the “nice-to-haves”, but I think that generally the best way to improve at something is by getting your hard dirty and engaging with the research ideas directly, rather than making sure you learn every nice-to-have skill first - if you have the above, I think you should just jump in and start learning about the topic! Especially for the coding related skills, your focus should not be on getting your head around concepts, it should be about *doing*, and actually writing code and playing around with the things - the challenge of making something that actually works, and dealing with all of the unexpected practical problems that arise is the best way of really getting this.
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3273ce4c-842c-40af-aa00-6a5bce690cdb
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Is Causality in the Map or the Territory?
steve2152 brought up a great example:
> Consider a 1kΩ resistor, in two circuits. The first circuit is the resistor attached to a 1V [voltage] supply. Here an engineer would say: "The supply creates a 1V drop across the resistor; and that voltage drop causes a 1mA current to flow through the resistor." The second circuit is the resistor attached to a 1mA current source. Here an engineer would say: "The current source pushes a 1mA current through the resistor; and that current causes a 1V drop across the resistor." Well, it's the same resistor ... does a voltage across a resistor cause a current, or does a current through a resistor cause a voltage, or both, or neither? [...] my conclusion was that people think about causality in a way that is not rooted in physics, and indeed if you forced someone to exclusively use physics-based causal models, you would be handicapping them.
First things first: we're talking about causality, which means we're mainly talking about counterfactuals - questions of the form "what would the system do if we did X?". (See Pearl's book for lots of detail on how and why causality and counterfactuals go together.)
In the resistor example, both scenarios yield exactly the same actual behavior (assuming we've set the parameters appropriately), but the counterfactual behavior differs - and that's exactly what defines a causal model. In this case, the counterfactuals are things like "what if we inserted a different resistor?" and "what if we adjusted the knob on the supply?". If it's a voltage supply, then a voltage -> current model ("voltage causes current") correctly answers the counterfactuals:
* Inserting a different resistor changes the current but not the voltage. In the voltage -> current model, we cut the arrow going into "current" and set that node to a new value.
* Adjusting the knob on the supply changes the voltage, and the current adjusts to match. In the voltage -> current model, we set the "voltage" node to a new value, and t
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84addbfa-9f0e-4c57-9b7b-4f74d845d9d8
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trentmkelly/LessWrong-43k
|
LessWrong
|
Formalizing Deception
An attempt at formalizing deception, seeing what goes right, what goes wrong, and what compromises have to be made.
Introduction
In a police interrogation one of the key challenges is working out whether a suspect is being deceptive. In a game of poker, one of the main goals is figuring out whether your opponents are being deceptive. But what are we talking about when we talk about deception, like, mathematically? I will propose a definition and give an example that illustrates how this definition operates.
Interrogation Investigation
Take a police officer, Alice, who is interrogating a suspect, Bob, in order to determine whether or not he is guilty of murder. Alice can either convict Bob or let him go, and Bob is either guilty or innocent. The payoff matrix is as follows:
Bob:
Alice:
GuiltyInnocentConvict(1,-1)(-1,-1)Let go(-1,1)(1,1)
Whether or not Bob is guilty, he would prefer to be let go; on the other hand, Alice only wants to convict if Bob is guilty, and she wants to let him go otherwise.
When Bob enters the room, it is predetermined that he is either guilty or innocent, Bob does not get to make a decision about this in advance. If Bob is guilty he will behave slightly differently under interrogation than if he isn't: he may have a weaker alibi, there may be inconsistencies in his story, he may exhibit physical symptoms associated with lying, but all of these things can also happen if Bob is innocent; sometimes innocent people have bad alibis, are inconsistent, and look nervous.
Now in order to define deception, we introduce Judy, an omniscient observer of the interrogation. Judy has the same goal as Alice, but unlike Alice, Judy also knows whether Bob is guilty or not. Further, Judy knows exactly how Bob's behaviors impact the likelihood of Alice convicting Bob (i.e. Judy can read both Alice's and Bob's minds). We call any subset of observations made by Alice deceptive if Judy would rather Alice make her decision to convict with this set
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9fa54de7-badc-4687-9495-c3c16610d028
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Naive Hypotheses on AI Alignment
Apparently doominess works for my brain, cause Eliezer Yudkowsky’s [AGI Ruin: A List of Lethalities](https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities) convinced me to look in to AI safety. Either I’d find out he’s wrong, and there is no problem. Or he’s right, and I need to reevaluate my life priorities.
After a month of sporadic reading, I've learned the field is considered to be in a state of [preparadigmicity](https://www.lesswrong.com/posts/P3Yt66Wh5g7SbkKuT/how-to-get-into-independent-research-on-alignment-agency). In other words, we don’t know \*how\* to think about the problem yet, and thus novelty comes at a premium. The best way to generate novel ideas is to pull in people from other disciplines. In my case that's computational psychology: modeling people like agents. And I've mostly applied this to video games. My [Pareto frontier](https://www.lesswrong.com/posts/XvN2QQpKTuEzgkZHY/being-the-pareto-best-in-the-world) is "modeling people like agents based on their behavior logs in constructed games created to trigger reward signals + ITT'ing the hell out of all the new people I love to constantly meet". I have no idea if this background makes me more or less likely to generate a new idea that's useful to solving AI alignment, but the way I understand the problem now: everyone should at least try.
So I started studying AI alignment, but quickly realized there is a trade-off: The more I learn, the harder it is to think of anything new. At first I had a lot of naive ideas on how to solve the alignment problem. As I learned more about the field, my ideas all crumbled. At the same time, I can't really assess yet if there is a useful level of novelty in my naive hypotheses. I'm still currently generating ideas low on "contamination" by existing thought (cause I'm new), but also low on quality (cause I'm new). As I learn more, I'll start generating higher quality hypotheses, but these are likely to become increasingly constrained to the existing schools of thought, because of cognitive contamination from everyone reading the same material and thinking in similar ways. Which is exactly the thing we want to avoid at this stage.
Therefore, to get the best of both worlds, I figured I'd write down my naive hypotheses as I have them, and keep studying at the same time. Maybe an ostensibly "stupid" idea on my end, inspires someone with more experience to a workable idea on their end. Even if the probability of that is <0.1%, it's still worth it. Cause, you know, .... I prefer we don't all die.
So here goes:
H1 - Emotional Empathy
----------------------
If you give a human absolute power, there is a small subset of humans that actually cares and will try to make everyone’s life better according to their own wishes. This is a trait in a subset of humans. What is this trait, and can we integrate it in to the reward function of an AGI?
* Does the trait rely on lack of meta-cognition? Does this trait show up equally at various IQ levels or does it peak at certain IQ levels? If the trait is less common at higher IQ levels, then this is probably a dead end. If the trait is more common at higher IQ levels, then there might be something to it.
* First candidate for this trait is “emotional empathy”, a trait that hitches one’s reward system to that of another organism. Emotional empathy that we wire in to the AGI would need to be universal to all humanity, and [not biased, like the human implementation](https://en.wikipedia.org/wiki/Against_Empathy).
H2 - Silo AI
------------
Silo the hardware and functionality of AGI to particular tasks. Like governments are run in trifecta to avoid corruption. Like humans need to collaborate to make things greater than themselves. Similarly, limit AGI to functions and physicalities that force it to work together with multiple other, independent AGI’s to achieve any change in the world.
* Counterargument: Silo’ed AI is effectively Tool AI, to which [Gwern has written a counterargument](https://www.gwern.net/Tool-AI) that people won’t develop Tool AI cause it will always be worse than Agent AI.
* Maybe that’s what we need to police? And the police would then effectively be a [Nanny AI](https://www.lesswrong.com/tag/nanny-ai), so then we still need to solve for making a Nanny AI to keep all other AGI silo’ed. (This is all turning very “one ring to rule them all”...).
H3 - Kill Switch
----------------
Kill switch! Treat AGI like the next cold war. Make a perfect kill switch, where any massive failure state according to humans would blow up the entire sphere of existence of humans and AGI.
* This strategy would block out the “kill all humans” strategies the AGI might come up with, cause it would destroy their own existence. They should be prioritizing their existence cause of [instrumental convergence](https://nickbostrom.com/superintelligentwill.pdf) (whatever goal you are maximizing, you very likely need to exist to maximize it, so self-preservation is very most likely a goal any AGI will have).
* What possible kill switch could we create that wouldn’t be trivially circumvented by something smarter than us? Intuitively I have the sense, a non-circumventable kill switch should exist, but what would that look like?
H4 - Human Alignment
--------------------
AI alignment currently seems intractable because any alignment formula we come up with is inherently inconsistent cause humans are inconsistent. We can solve AI alignment by solving what *humanity’s* alignment actually is.
* We can't ask humans about their alignment because most individual humans do not have consistent internal alignments they can be questioned on. Some very few do, but this seems to be an exception. Thus, we can’t make a weighted function of humanity’s alignment by summing all the individual alignments of humans. Therefore, humanity at large does not have one alignment. (Related: [Coherent Extrapolated Volition doesn't converge](https://www.lesswrong.com/tag/coherent-extrapolated-volition) for all of humanity)
* Can we extrapolate humanity's alignment from the process that shaped us: Evolution?
+ *Evolution as gene proliferation function*: Many humans do not share this as their explicit life goal but most common human goals still *indirectly* maximize our genetic offspring. For instance, accumulating wealth, discovering new technology, solidifying social bonds, etc. If AGI can directly help us to spread our genes, would that make most of our other drives vestigial? What would the AGI be propagating if the resulting offspring wouldn't have similar drives to ourselves, including the vestigial ones?
+ *However, more is not always better*: There are very many pigs and very many ants. I think humans would rather be happier or smarter than simply more. Optimizing over happiness seems perverse, cause happiness is simply the reward signal for taking actions with high (supposed) survival and proliferation values. Optimizing over happiness would inevitably lead to a brain in a vat of heroin. Happiness should be a motivational tool, not a motivational goal.
+ *Extrapolating our evolutionary path*: Let AGI push us more steps up the evolutionary ladder, where we may survive in more different environments and flourish toward new heights. Thus, an AGI would engineer humans into a new species. This would creep most people out, while transhumanists would be throwing a party. It effectively comes down to AGI being the next step on the evolutionary ladder, and asking it to bring us with it instead of exterminating us. (note: we most probably were not that kind to our ancestors).
Thoughts on Corrigibility
-------------------------
Still learning about it at the moment, but my limited understanding so far is:
*How to create an AI that is smarter than us at solving our problems, but dumber than us at interpreting our goals.*
In other words, how do we constrain an AI with respect to its cognition about its goals?
Side Thoughts - Researcher Bias
-------------------------------
Do AGI optimists and pessimists differ in some dimension of personality or cognitive traits? It’s well established that [political and ideological voting behavior correlate to personality](https://d1wqtxts1xzle7.cloudfront.net/40483963/Personality_and_politics_The_role_of_the20151129-32158-39f5rv-with-cover-page-v2.pdf?Expires=1656784927&Signature=bkRsTAlSLS-7GnskWnnTLK4FnKqUrCMSC4pS~xuoKWJjfATOMtrpw7UZybj1I5XtCFyRJF7ZnProTg1PDhacU4VR9EGy4tpN3fkvKf1bTfX3ZKgTivBZxvGFWWu9gu54l~Z4GYlpcIeKkRD88-a645zBL85kSspunWpABZVtpXhO3cluH4-Af3efOIBBrrwjdTFSF-YKTWIXQFQmGZ7gjZUPAGtWzeiuYi95I6arF8z-F9FTMsHivaf1N8OLkVOqP1YMFbJK1eYT26gAk8rC8NpC~8A-~4w5J2osRi3zcpokBmfkMj5glMQV0yETz8HXJZafnD~P3hQNpxD9B7B~4g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA). So if the same is true for AI risk stance, then this might point to a potential confounder is AI risk predictions.
---
*My thanks goes out to* [*Leon Lang*](https://www.lesswrong.com/users/leon-lang) *and* [*Jan Kirchner*](https://www.lesswrong.com/users/jan-2) *for encouraging my beginner theorizing, discussing the details of each idea, and pointing me toward related essays and papers.*
|
0261ea11-28d3-4117-ac60-b6d4ce24652b
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trentmkelly/LessWrong-43k
|
LessWrong
|
Inner alignment requires making assumptions about human values
Many approaches to AI alignment require making assumptions about what humans want. On a first pass, it might appear that inner alignment is a sub-component of AI alignment that doesn't require making these assumptions. This is because if we define the problem of inner alignment to be the problem of how to train an AI to be aligned with arbitrary reward functions, then a solution would presumably have no dependence on any particular reward function. We could imagine an alien civilization solving the same problem, despite using very different reward functions to train their AIs.
Unfortunately, the above argument fails because aligning an AI with our values requires giving the AI extra information that is not encoded directly in the reward function (under reasonable assumptions). The argument for my thesis is subtle, and so I will break it into pieces.
First, I will more fully elaborate what I mean by inner alignment. Then I will argue that the definition implies that we can't come up with a full solution without some dependence on human values. Finally, I will provide an example, in order to make this discussion less abstract.
Characterizing inner alignment
In the last few posts I wrote (1, 2), I attempted to frame the problem of inner alignment in a way that wasn't too theory-laden. My concern was that the previous characterization was dependent on a solving particular outcome where you have an AI that is using an explicit outer loop to evaluate strategies based on an explicit internal search.
In the absence of an explicit internal objective function, it is difficult to formally define whether an agent is "aligned" with the reward function that is used to train it. We might therefore define alignment as the ability of our agent to perform well on the test distribution. However, if the test set is sampled from the same distribution as the training data, this definition is equivalent to the performance of a model in standard machine learning, and we haven't actual
|
ea49d39f-b42f-448b-b9d1-129b194dc809
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trentmkelly/LessWrong-43k
|
LessWrong
|
On Robin Hanson’s Board Game
Previously: You Play to Win the Game, Prediction Markets: When Do They Work?, Subsidizing Prediction Markets
An Analysis Of (Robin Hanson at Overcoming Bias): My Market Board Game
Robin Hanson’s board game proposal has a lot of interesting things going on. Some of them are related to calibration, updating and the price discovery inherent in prediction markets. Others are far more related to the fact that this is a game. You Play to Win the Game.
Rules Summary
Dollars are represented by poker chips.
Media that contains an unknown outcome, such as that of a murder mystery, is selected, and suspects are picked. Players are given $200 each. At any time, players can exchange $100 for a contract in all possible suspects (one of which will pay $100, the rest of which will pay nothing).
A market is created for each suspect, with steps at 5, 10, 15, 20, 25, 30, 40, 50, 60 and 80 percent. At any time, each step in the market either contains dollars equal to its probability, or it has a contract good for $100 if that suspect is guilty. At any time, any player can exchange one for the other – if there’s a contract, they can buy it for the listed probability. If there’s chips there, you can exchange a contract for the chips. Whoever physically makes the exchange first wins the trade.
At the end of the game, the winning contract pays out, and the player with the most dollars wins the game.
Stages of Play
We can divide playing Robin’s game into four distinct stages.
In stage one, Setup, the source material we’ll be betting on is selected, and the suspects are generated.
In stage two, the Early Game, players react to incremental information and try to improve their equity, while keeping an eye out for control of various suspects.
In stage three, the Late Game, players commit to which suspects they can win with and lock them up, selling off anything that can’t help them win.
In stage four, Resolution, players again scramble to dump now-worthless contracts for whatever t
|
03f8e1b8-c3b5-478c-9e01-61e7676c57ed
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trentmkelly/LessWrong-43k
|
LessWrong
|
Developing Positive Habits through Video Games
I wonder if anyone on the forum could share their thoughts on whether it's scientifically possible to develop a video game/app which
* Strengthens neural circuits involved in developing/maintaining positive habits
* Build any sort of positive habits that transfer to real life decision making
Like a virtual morality/discipline gym. Is it impossible or could it work?
|
b5961fbe-9fd1-4db0-ad94-a98b44a3c1ca
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trentmkelly/LessWrong-43k
|
LessWrong
|
You can still fetch the coffee today if you're dead tomorrow
"You can't fetch the coffee if you're dead."
—Stuart Russell, on the instrumental convergence of shutdown-avoidance
Note: This is presumably not novel, but I think it ought to be better-known. The technical tl;dr is that we can define time-inhomogeneous reward, and this provides a way of "composing" different reward functions; while this is not a way to build a shutdown button, it is a way to build a shutdown timer, which seems like a useful technique in our safety toolbox.
"Utility functions" need not be time-homogeneous
It's common in AI theory (and AI alignment theory) to assume that utility functions are time-homogeneous over an infinite time horizon, with exponential discounting. If we denote the concatenation of two world histories/trajectories by ⊳, the time-consistency property in this setting can be written as
∀h1,h2.U(h1⊳h2)=U(h1)+γLength(h1)⋅U(h2)
This is property is satisfied, for example, by the utility-function constructions in the standard Wikipedia definitions of MDP and POMDP, which are essentially[1]
U(h)=∑t∈NγtR(h(t))
Under such assumptions, Alex Turner's power-seeking theorems show that optimal agents for random reward functions R will systematically tend to disprefer shutting down (formalized as "transitioning into a state with no transitions out").
Exponential discounting is natural because if an agent's preferences are representable using a time-discount factor that depends only on relative time differences and not absolute time, then any non-exponential discounting form is exploitable (cf. Why Time Discounting Should Be Exponential).
However, if an agent has access to a clock, and if rewards are bounded by an integrable nonnegative function of time, the agent may be time-inhomogeneous in nearly arbitrary ways without actually exhibiting time inconsistency:
U(t0,h)=∞∑t=t0Rt(h(t))
Any utility function with the above form still obeys an analogous version of our original time-consistency property that is modified to index over initial tim
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7bcec444-8650-4e46-bcef-03180110e316
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trentmkelly/LessWrong-43k
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LessWrong
|
The Conscious Sorites Paradox
Followup to: On Being Decoherent
Decoherence is implicit in quantum physics, not an extra postulate on top of it, and quantum physics is continuous. Thus, "decoherence" is not an all-or-nothing phenomenon—there's no sharp cutoff point. Given two blobs, there's a quantitative amount of amplitude that can flow into identical configurations between them. This quantum interference diminishes down to an exponentially tiny infinitesimal as the two blobs separate in configuration space.
Asking exactly when decoherence takes place, in this continuous process, is like asking when, if you keep removing grains of sand from a pile, it stops being a "heap".
The sand-heap dilemma is known as the Sorites Paradox, after the Greek soros, for heap. It is attributed to Eubulides of Miletus, in the 4th century BCE. The moral I draw from this very ancient tale: If you try to draw sharp lines in a continuous process and you end up looking silly, it's your own darn fault.
(Incidentally, I once posed the Sorites Paradox to Marcello Herreshoff, who hadn't previously heard of it; and Marcello answered without the slightest hesitation, "If you remove all the sand, what's left is a 'heap of zero grains'." Now that's a computer scientist.)
Ah, but what about when people become decoherent? What of the Conscious Sorites Paradox?
What about the case where two blobs of amplitude containing people are interacting, but only somewhat - so that there is visibly a degree of causal influence, and visibly a degree of causal independence?
Okay, this interval may work out to less than the Planck time for objects the size of a human brain. But I see that as no excuse to evade the question. In principle we could build a brain that would make the interval longer.
Shouldn't there be some definite fact of the matter as to when one person becomes two people?
Some folks out there would just say "No". I suspect Daniel Dennett would just say "No". Personally, I wish I could just say "No", but
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be949b37-eacf-4e8a-b39b-8a6034124d8f
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trentmkelly/LessWrong-43k
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LessWrong
|
Policy Design: Ideas into Proposals
This post on Best Of A Great Lot is a part of a series on the subject of designing a new form of governance. Each piece aims to stand alone, but fits together on the Table of Contents.
Previous: A Sketch of Belocracy, Evaluation as Feedback Cycle, Idea Generation and Sifting, The Belocrat. Next: Executive Belocracy
So far in our implementation of belocracy we have described how we evaluate policies that have been passed, and how we collect ideas from the citizenry at large. The data system encourages citizens to suggest problems, policy options and relevant evidence, and then through moderation and reputation and a hidden prediction market, sifts the best ideas to the top. The next step is turning those ideas into viable, worked-out proposals that respect the realities of current law, governmental practice, and societal constraints. In belocracy, this is the job of policy designers and researchers. The researchers collect evidence: published studies; written essays; stories directly from people affected. If needed, they conduct original research. Meanwhile, policy designers work with the collected body of evidence and the proposed ideas to design their best idea for a policy that will improve society around these problems.
Some of this work has been done by citizens contributing to the belocratic data system already. Citizens can post evidence they’ve found and ideas they want to see pursued. Policy researchers and designers take it to the next level of professionalism, whether that’s because they’re paid professionals who work for belocracy or because they’re the kind of amateurs who would like to be. A Belocrat prioritizing a set of problems is a signal for policy researchers and designers to focus on them.
Just as editors sometimes have to prod their authors to complete their work, Belocrats shepherd proposals: all the policy designs need to arrive at the gates of the policy jury at the same time. Belocrats have latitude in scheduling those juries — a researc
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f4200083-1a4f-41e2-96d2-79533b06860a
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trentmkelly/LessWrong-43k
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LessWrong
|
Training Superior Sparse Autoencoders for Instruct Models
Resource Link Paper https://arxiv.org/abs/2506.07691 Code https://github.com/Geaming2002/FAST SAEs Llama-3.1-8B-Instruct_SAEs🤗,Llama-3.2-3B-Instruct_SAEs🤗,Llama-3.2-1B-Instruct_SAEs🤗,Qwen2.5-7B-Instruct_SAEs🤗,Qwen2.5-3B-Instruct_SAEs🤗,Qwen2.5-1.5B-Instruct_SAEs🤗,Qwen2.5-0.5B-Instruct_SAEs🤗
> 💡 TL;DR
>
> In this paper, we discover problems in previous SAE training approaches for instruct model :
>
> * 📚 Suboptimal dataset selection affecting SAE performance.
> * ✂️ Semantic discontinuity caused by block training truncating samples mid-content.
>
> Therefore, we propose Finetuning-aligned Sequential Training (FAST)💪, a novel training method specifically tailored for instruct models. The results demonstrate:
>
> * Token Reconstruction Performance 📉: FAST shows token better reconstruction performance. On Qwen2.5-7B-Instruct, FAST achieves a mean squared error of 0.6468, significantly outperforming baseline methods with errors of 5.1985 and 1.5096.
>
> * Feature Interpretability 🎯: FAST yields a higher proportion of high-quality features. For Llama3.2-3B-Instruct, 21.1% scored in the top range, compared to 7.0% and 10.2% for BT(P) and BT(F).
>
> * Novel Discovery 🔍: Intervening on the activations of special tokens via the SAEs leads to improvements in output quality, suggesting new opportunities for fine-grained control of model behavior, enabling broad adoption and future research.
>
> Find the details in our post below👇
🔍Motivation: Why Traditional SAE Training Falls Short
Imagine reading a novel where every few pages, the story abruptly jumps to a completely different book—confusing📚✂️, right? This is essentially what happens with traditional Sparse Autoencoder (SAE) training methods for large language models!
Block Training (BT) has become the default approach for SAE training, where datasets (usually pretraining datasets) are concatenated into fixed-length blocks (Joseph Bloom and Chanin, 2024; Bricken et al., 2023). While this works
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cd24308e-54b5-4c64-bf1b-72020e759695
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trentmkelly/LessWrong-43k
|
LessWrong
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The basic reasons I expect AGI ruin
I've been citing AGI Ruin: A List of Lethalities to explain why the situation with AI looks lethally dangerous to me. But that post is relatively long, and emphasizes specific open technical problems over "the basics".
Here are 10 things I'd focus on if I were giving "the basics" on why I'm so worried:[1]
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1. General intelligence is very powerful, and once we can build it at all, STEM-capable artificial general intelligence (AGI) is likely to vastly outperform human intelligence immediately (or very quickly).
When I say "general intelligence", I'm usually thinking about "whatever it is that lets human brains do astrophysics, category theory, etc. even though our brains evolved under literally zero selection pressure to solve astrophysics or category theory problems".
It's possible that we should already be thinking of GPT-4 as "AGI" on some definitions, so to be clear about the threshold of generality I have in mind, I'll specifically talk about "STEM-level AGI", though I expect such systems to be good at non-STEM tasks too.
Human brains aren't perfectly general, and not all narrow AI systems or animals are equally narrow. (E.g., AlphaZero is more general than AlphaGo.) But it sure is interesting that humans evolved cognitive abilities that unlock all of these sciences at once, with zero evolutionary fine-tuning of the brain aimed at equipping us for any of those sciences. Evolution just stumbled into a solution to other problems, that happened to generalize to millions of wildly novel tasks.
More concretely:
* AlphaGo is a very impressive reasoner, but its hypothesis space is limited to sequences of Go board states rather than sequences of states of the physical universe. Efficiently reasoning about the physical universe requires solving at least some problems that are different in kind from what AlphaGo solves.
* These problems might be solved by the STEM AGI's programmer, and/or solved by the algorithm that find
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f9016287-b145-4216-8895-768537515a07
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trentmkelly/LessWrong-43k
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LessWrong
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Should you refuse this bet in Technicolor Sleeping Beauty?
This is the question for people who didn't read my latest post. Please, try to answer it yourself without spoiling the solution, and then post it in the comments with your reasoning and whether you consider yourself a halfer or a thirder in regular Sleeping Beauty problem.
Technicolor Sleeping Beauty experiment goes mostly as regular Sleeping Beauty experiment:
The participant is put to sleep on Sunday. Then the coin is tossed. If it's Heads the participant will be awakened on Monday. If it's Tails the participant will be awaken both on Monday and on Tuesday, and between these awakenings their memories will be erased. Therefore, while awakening on Tuesday the participants doesn't remember whether they awakened on Monday or not, and they can never be sure on which day (Monday or Tuesday) their current awakening is happening.
The only difference is that in Technicolor, the walls of the room that the participant awakens in changle their color every day from red to blue and vice versa. The initial color is determined randomly: 1/2 that it's red and 1/2 that it's blue.
While awakened during the experiment you are asked whether you would like to take a specific once per experiment bet:
You may bet that the coin is Tails at 2:3 odds. That is: if you bet 300$ and the coin is indeed Tails you win 200$. The bet will be resolved on Wednesday, after the experiment has ended.
You may take this bet only once per experiment and one agreement is enough. If you have two awakenings in this experiment and agreed on any of them - the bet counts as taken. If you agreed on both of them, the bet counts as taken only once.
For reference, in regular Sleeping Beauty problem utility neutral betting odds for once per experiment bet are 1:1, regardless of whether you are a halfer or a thirder, so taking such bet would be a bad idea.
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bb49788a-10f5-4e45-9c24-8919f5c9d208
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trentmkelly/LessWrong-43k
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LessWrong
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Rationality Attractors in Personspace
These two questions seem to be somewhat interesting, especially for those interested in rationality outreach.
What makes someone more likely to study rationality?
More likely to become a higher level rationalist?
A few thoughts:
Empirically, rationalists seem to be more into technical fields than average, and more interested in an explicit understanding of social things than most technical people.
People who can more clearly see deficiencies in themselves, and who try to solve problems seem more likely to become rationalists, when exposed to rationality.
People who are motivated to pursue rationality for instrumental goals, rather than for funsies, seem to become better rationalists.
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3784ec04-b89f-4dc0-b43a-94c18244a29b
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trentmkelly/LessWrong-43k
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LessWrong
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Apply to MATS 8.0!
Applications are open for the ML Alignment & Theory Scholars (MATS) Summer 2025 Program, running Jun 16-Aug 22, 2025. First-stage applications are due Apr 18!
MATS is a twice-yearly, 10-week AI safety research fellowship program operating in Berkeley, California, with an optional 6-12 month extension program for select participants. Scholars are supported with a research stipend, shared office space, seminar program, support staff, accommodation, travel reimbursement, and computing resources. Our mentors come from a variety of organizations, including Anthropic, Google DeepMind, OpenAI, Redwood Research, GovAI, UK AI Security Institute, RAND TASP, UC Berkeley CHAI, Apollo Research, AI Futures Project, and more! Our alumni have been hired by top AI safety teams (e.g., at Anthropic, GDM, UK AISI, METR, Redwood, Apollo), founded research groups (e.g., Apollo, Timaeus, CAIP, Leap Labs), and maintain a dedicated support network for new researchers.
If you know anyone who you think would be interested in the program, please recommend that they apply!
Program details
MATS is an educational seminar and independent research program (generally 40 h/week) in Berkeley, CA that aims to provide talented scholars with talks, workshops, and research mentorship in the fields of AI alignment, security, and governance, and connect them with the San Francisco Bay Area AI alignment research community. MATS provides scholars with housing in Berkeley, CA, as well as travel support, a co-working space, and a community of peers. The main goal of MATS is to help scholars develop as AI alignment researchers. You can read more about our theory of change here.
Based on individual circumstances, we may be willing to alter the time commitment of the program and arrange for scholars to leave or start early. Please tell us your availability when applying. Our tentative timeline for the MATS Summer 2025 program is below.
Scholars will receive a USD 12k stipend from AI Safety Support for comple
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32778164-8178-4a2b-aff9-ec012ec0988d
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trentmkelly/LessWrong-43k
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LessWrong
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Bi-weekly Rational Feed
===Highly Recommended Articles:
Skills Most Employable by 80,000 Hours - Metrics: Satisfaction, risk of automation, and breadth of applicability. Leadership and social skills will gain the most in value. The least valuable skills involve manual labor. Tech skills may not be the most employable but they are straightforward to improve at. The most valuable skills are the hardest to automate and useful in the most situations. Data showing a large oversupply of some tech skills, though others are in high demand. A chart of which college majors add the most income.
Something Was Wrong by Zvi Moshowitz - Zvi visits a 'stepford pre-school'. He can't shake the feeling that something is wrong. He decides not to send his son to the place where kid's souls go to die.
Ems Evolve by Bayesian Investor - Will the future we dominated by entities that lack properties we consider important (such as 'have fun' or even 'sentient'). Will agents lacking X-value outcompete other agents. What counter-measures could society take and how effective would they be.
Housing Price Bubble Revisited by Tyler Cowen - "Over the entire 20th century real home prices averaged an index value of about 110 (and were quite close to this value over the the entire 1950-1997 period). Over the entire 20th century, housing prices never once roce above 131, the 1989 peak. But beginning around 2000 house prices seemed to reach for an entirely new equilibrium. In fact, even given the financial crisis, prices since 2000 fell below the 20th century peak for only a few months in late 2011. Real prices today are now back to 2004 levels and rising. As I predicted in 2008, prices never returned to their long-run 20th century levels."
Tyler Cowen On Stubborn Attachments by EconTalk - "Cowen argues that economic growth--properly defined--is the moral key to maintaining civilization and promoting human well-being. Along the way, the conversation also deals with inequality, environmental issues, and education"
===Scott
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84140e77-b414-427c-a069-2f611b182ae7
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awestover/filtering-for-misalignment
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Redwood Research: Alek's Filtering Results
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id: post1416
Threat Model There are many ways for AI systems to cause a catastrophe from which Earth-originating life could never recover. All of the following seem plausible to me: Misuse : An AI system could help a human or group of humans to destroy or to permanently take over (and lock their values into) the world. The AI could be: An oracle AI (e.g. a question-answering LLM) An LLM simulating an intent-aligned agent and taking real-world actions via APIs An intent-aligned RL agent An interaction of multiple systems Power-Seeking : An AI system could destroy or permanently take over the world on its own account, by leveraging advanced instruments of force projection. The AI could be: An LLM simulating a misaligned agent " Specification gaming ": An RL agent that is aligned to a formal objective and Goodhart s to catastrophe " Goal misgeneralization ": A surprise mesa-optimiser (most likely in model-free RL, but could conceivably arise through evolutionary processes in any iterative algorithm which has or learns sufficiently reality-like structure) An interaction of multiple systems , participating in coordination mechanisms that exclude humans Economic Squeeze : an AI system could acquire nearly all means of production through a gradual process of individually innocent economic transactions, thereby squeezing humanity out of resource allocation decisions and removing most human influence over the future. This would most likely be an " interaction of multiple systems ". A single RL agent, or a unipolar tree of agents, might also do this, especially if they are successfully aligned to avoid use of force against humans. Superpersuasion : an AI system could generate stimuli which reliably cause humans to adopt its arbitrary goals. The AI could be: An LLM merely extrapolating from persuasive human text An RL agent trained on human approval A surprise mesa-optimiser Some mixture of the above Many AIs, collectively shaping a new human culture with an alien ideology Security Dilemma : If AI-enabled technological advancements turn out to be offence-dominant, and if partial alignment success leads AIs to be unable to make credible commitments to each other (e.g. due to corrigibility), the equilibrium strategy for AI-enabled militaries may involve high-risk preemptive strikes and increasingly escalated retaliation to a point of existential catastrophe. This would almost surely be a multipolar failure mode. But, instead of trying to enumerate all possible failure modes and then trying to shape incentives to make them less likely to come up, I typically use a quasi-worst-case assumption in which I assume that, perhaps as a matter of bad luck with random initialisation, when we optimise a function for a training objective, ties are broken in favour of functions with the worst existential-risk implications for the class of worlds in which they may be instantiated. On the one hand, unlike a typical "prosaic" threat model, in the neorealist threat model one does not rely on empirical facts about the inductive biases of the kind of network architectures that are practically successful. A realist justification for this is that there may be a phase transition as architectures scale up which drastically changes both their capabilities profile and this kind of inductive bias (vaguely analogous to the evolution of cultural knowledge-transfer within biological life). On the other hand, unlike (a typical understanding of) a "worst-case assumption," the last clause leaves open the possibility of hiding concrete facts about our world from an arbitrarily powerful model , and the framing in terms of functions highlights an ontology of AI that respects extensional equivalence , where imputations of "deceptive mesa-optimisers hiding inside" are discarded in favour of "capable but misaligned outputs on out-of-distribution inputs". One can make progress with this assumption by designing training contexts which couple safety guarantees to the training objective, e.g. a guarantee of shutdown within a time bound with arbitrarily high probability , and by working on ways to obtain instance-specific guarantees about learned functions that continue to hold out-of-distribution, e.g. with model-checking, regret bounds, or policy certificates . Success Model For me the core question of existential safety is this: Under these conditions, what would be the best strategy for building an AI system that helps us ethically end the acute risk period without creating its own catastrophic risks that would be worse than the status quo? It is not, for example, "how can we build an AI that is aligned with human values, including all that is good and beautiful?" or "how can we build an AI that optimises the world for whatever the operators actually specified?" Those could be useful subproblems, but they are not the top-level problem about AI risk (and, in my opinion, given current timelines and a quasi-worst-case assumption, they are probably not on the critical path at all). From a neorealist perspective, the ultimate criterion for "goodness" of an AI strategy is that it represents a strong Pareto improvement over the default (or other) AI strategy profile for an implementation-adequate coalition (approximately, a weighted majority) of strategically relevant AI decision-makers, relative to each of their actual preferences, if they were well-informed (to an extent that is feasible in reality). I am optimistic about the plausibility of negotiations to adopt AI strategies that clear this bar, once such strategies become clear, even if they do not strictly meet traditional standards of "competitiveness". On the other hand, any strategy that doesn't clear this bar seems to require unrealistic governance victories to be implemented in reality. I hope this articulation helps to clarify the implications of governance/strategy upon the relative merits of technical safety research directions. Related work Threat Model Literature Review DeepMind AGI Safety Team Clarifying AI X-Risk DeepMind AGI Safety Team The Main Sources of AI Risk? Daniel Kokotajlo, Wei Dai
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ffcbffd3-98c6-4405-9698-bde6d654591c
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StampyAI/alignment-research-dataset/blogs
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Blogs
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Import AI - coming soon to Substack
Import AI is moving to Substack! First issue should go out Monday the 6th.
[Subscribe now](https://importai.substack.com/subscribe)
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5d2ddad3-0aa9-4108-a511-d89265cc23bc
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trentmkelly/LessWrong-43k
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LessWrong
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International cooperation vs. AI arms race
Summary
I think there's a decent chance that governments will be the first to build artificial general intelligence (AI). International hostility, especially an AI arms race, could exacerbate risk-taking, hostile motivations, and errors of judgment when creating AI. If so, then international cooperation could be an important factor to consider when evaluating the flow-through effects of charities. That said, we may not want to popularize the arms-race consideration too openly lest we accelerate the race.
Will governments build AI first?
AI poses a national-security threat, and unless the militaries of powerful countries are very naive, it seems to me unlikely they'd allow AI research to proceed in private indefinitely. At some point the US military would confiscate the project from Google or Goldman Sachs, if the US military isn't already ahead of them in secret by that point. (DARPA already funds a lot of public AI research.)
There are some scenarios in which private AI research wouldn't be nationalized:
* An unexpected AI foom before anyone realizes what was coming.
* The private developers stay underground for long enough not to be caught. This becomes less likely the more government surveillance improves (see "Arms Control and Intelligence Explosions").
* AI developers move to a "safe haven" country where they can't be taken over. (It seems like the international community might prevent this, however, in the same way it now seeks to suppress terrorism in other countries.)
Each of these scenarios could happen, but it seems most likely to me that governments would ultimately control AI development.
AI arms races
Government AI development could go wrong in several ways. Probably most on LW feel the prevailing scenario is that governments would botch the process by not realizing the risks at hand. It's also possible that governments would use the AI for malevolent, totalitarian purposes.
It seems that both of these bad scenarios would be exacerbated by
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8333b6ca-69fa-4878-a9d5-93f29edcf3ac
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trentmkelly/LessWrong-43k
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LessWrong
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The Lens, Progerias and Polycausality
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 inflammation
* Follow 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.
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dda8f821-be29-49a1-a56d-5e6f880c06e1
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trentmkelly/LessWrong-43k
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LessWrong
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Test Post
Here's some *markdown*. Does it work? Or do I have to use the WYSIWYG editor?
No, markdown doesn't work in posts, just comments. Okay, I guess that's fine.
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52bb715c-b157-4e7b-a469-70c6f0aec145
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trentmkelly/LessWrong-43k
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LessWrong
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Two Blegs
I'm not sure where to post this, so, using this comment thread as cover, I will hereby bleg for the following:
* A good OB-level proof or explication of the innards of Aumann's theorem, much more precise than Hanson and Cowen's but less painful than Aumann's original or this other one.
* Stories of how people have busted open questions or controversies using rationalist tools. (I think this in particular will be useful to learners.)
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c47c728f-48ea-4b0f-b633-629e1b2dca48
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trentmkelly/LessWrong-43k
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LessWrong
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Call for Collaboration: Renormalization for AI safety
We invite proposals that probe aspects of renormalization in AI systems that will help us predict, explain, and interpret neural network behavior at different levels of abstraction. We import ‘renormalization’ from physics, as a technique to coarse-grain theoretical descriptions of complex interactions to focus on those that are most relevant for describing physical reality. We view this direction as a vast ‘opportunity space’ with many possible points of entry, and have identified a few research programmes as preliminary ‘ways in’. A detailed roadmap of this space, and discussion of the programmes can be found here and here. Our goal is to narrow the theory-practice gap by grounding an abstract analogy into a practical framework capable of directly impacting real-world interpretability and informing better scientific foundations for AI safety. A QFT framework for AI systems could give us a toolkit for finding principled features, modeling their interactions at different levels of granularity in their interpretation, and ensure a well-grounded separation between ‘safe’ and ‘unsafe’ behaviors in AI systems. We invite proposals to keep these differences in mind and be clear on which methods or analogies are immediately useful, and which require new development.
We hedge that progress depends on clarifying the link between implicit renormalization – which models how networks coarse-grain information into representations by organizing data into network-meaningful structures – and explicit renormalization, which we operationalize as an interpretability tool capable of probing that structure at a scale of granularity that is meaningful to us. While there is a growing community studying how neural networks implicitly renormalize (e.g., Roberts, Berman, Erbin, Halverson) to organize information (i.e. from data into features) during training and inference, we stress that these are both important, and likely related, even if there is some fuzziness in defining a scale of
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e861e233-e602-44c5-b366-ae07abcafd66
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trentmkelly/LessWrong-43k
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LessWrong
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Estimating the consequences of device detection tech
Today I have been asked to join a European project in collaboration with the police, developing two sorts of tech:
1. Device identification; that is, inferring from the filters applied to an image or video, pixel defects and other information hidden in raw pixel data the device with which it was taken (which is useful when for example a criminal modifies the metadata of a picture to try to blame their crimes on somebody else)
2. Detection of fake media; that is, determining if a certain video has been tampered with
To be honest, I do not fully grasp the consequences this tech could have in society, so I have resolved to write this post to illustrate my informal reasoning about it.
My process of reasoning will be as follows. I will first estimate the potential impact of legitimate use cases of such a device (such as stopping human trafficking), and then estimate the potential impact of illegitimate use cases (such as stopping whistleblowers or detaining dissenters of totalitarian regimes). Then I will compare one result against the other weighted by the applicability of this tech to each case.
Let us start.
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Device identification
It is not difficult to imagine how this kind of tech can be misused, but we also need to take into account legitimate uses.
In Europe, we still have a fair dose of human trafficking, in which such a tool could potentially be used to great effect (identifying who took pictures of abused people, perhaps).
According to the link above, there were about 11k identified victims of human trafficking in the EU in 2012. If the tech in question was developed and successful, we could expect it to be implemented in other parts of the first world. Let’s suppose that the volume of dealt with trafficking in the first world (roughly EU + North America + Eastern Asia + Australia) is about 3-~10 times what it is in the EU, and that the number of victims identified roughly corresponds to the number of victims s
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1613db03-f3b1-4304-8165-b64fb29a0d62
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trentmkelly/LessWrong-43k
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LessWrong
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A quick experiment on LMs’ inductive biases in performing search
TL;DR: Based on a toy setting, GPT-3.5-turbo and GPT-4-turbo are best at search (by which I mean computing an argmax) when using chain-of-thought, but neither of them can do internal search when forced to work from memory over only a few token positions. Only GPT-4-turbo is to some extent able to do internal search, and only when given instructions before being shown the list of candidates it is tasked with argmaxing over.
Thanks to Jiahai Feng, Eli Lifland, and Fabien Roger for feedback and discussions that led to this experiment. This experiment was run and the post was written in a day, so some things may be rough around the edges.
One worry is that future AI systems might perform consequentialist reasoning internally (in “neuralese”), which may not be understandable to humans or other (trusted) AIs. We might not know if it is picking an action because it rates best according to what its operator would want, because it’s instrumentally useful for another goal, because it would lead to the best-looking tampered measurement, or because of some process other than search.
Three different ways to compute an argmax
This is a small toy experiment that tries to measure inductive biases of transformers regarding some of these capabilities. I attempt to measure to what extent RLHF’d LMs are capable of performing search in different settings. I consider only simple, brute-force, non-tree search here, because current LMs are not very capable at search. Given some collection of candidates, the kind of search we are interested in involves:
1. Computing a value for each candidate
2. Selecting an argmax
While it is hard to test whether a model is doing this via interpretability, we can devise behavioral experiments whose results can inform us about what the model is doing internally. I consider three settings:
1. Externalized search/chain-of-thought
2. Internal search in which value computation can be done at the token position(s) of the corresponding candidate
3. I
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972de252-d3a2-4db7-8ba7-00cc32a3661c
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trentmkelly/LessWrong-43k
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LessWrong
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Maintaining Alignment during RSI as a Feedback Control Problem
Crossposted from my personal blog.
Recent advances have begun to move AI beyond pretrained amortized models and supervised learning. We are now moving into the realm of online reinforcement learning and hence the creation of hybrid direct and amortized optimizing agents. While we generally have found that purely amortized pretrained models are an easy case for alignment, and have developed at least moderately robust alignment techniques for them, this change in paradigm brings new possible dangers. Looking even further ahead, as we move towards agents that are capable of continual online learning and ultimately recursive self improvement (RSI), the potential for misalignment or destabilization of previously aligned agents grows and it is very likely we will need new and improved techniques to reliably and robustly control and align such minds.
In this post, I want to present a high level argument that the move to continual learning agents and ultimately RSI requires us to shift our thinking about alignment techniques from the static frame of e.g. alignment via some fixed RLHF approach to the dynamic frame of feedback control. Namely, alignment methods which can ensure stability during online learning or RSI will require constant dynamic and adaptive adjustments rather than simply an extremely good static alignment initialization from a fixed RLHF phase (although a good initialization will of course be very helpful). Additionally, the existing field of control theory handles exactly these kinds of problems and has constructed a large set of theoretical tools around the design and verification of controllers that I believe likely have important insights for alignment.
Moreover, I think that either a lack of consideration of feedback control for alignment, or an implicit assumption that it is impossible during continual learning or RSI has led to some blindspots and potentially unjustified pessimism in alignment theory. These include the very strong focus in classi
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302dd59f-26bd-4699-9d03-9f984575d1f3
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trentmkelly/LessWrong-43k
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LessWrong
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Musings on Text Data Wall (Oct 2024)
The Chinchilla scaling law says that for a given number of FLOPs C, the optimal amount of data D to train on is proportional to (C to the power of b), that is you'll get a less intelligent model if you use either more data or less data than that while training for C FLOPs. The optimal model size (number of parameters) N is then whatever would require C FLOPs when trained on D tokens of data, and C is usually about 6ND, so optimal N is proportional to (C to the power of (1-b)). See Figure 4 in the paper. Turns out that b is close to 0.5, so N and D increase with C at a similar pace, and their ratio D/N (tokens per parameter) remains approximately the same across multiple orders of magnitude for available compute C.
In the Chinchilla paper, the exponent b is estimated as 0.50-0.54 using different methods[1] (see Table 3), and D/N at 3e21 FLOPs is about 20 tokens per parameter. If b is taken to be 0.50, then D/N doesn't change with compute. But if b is 0.54, then D/N increases proportionally to (C to the power of 0.08). At 2e25 FLOPs of original GPT-4 that would result in D/N of 40 tokens/parameter, at 5e26 FLOPs of the next year's models it becomes 52, and at 7e27 of late 2026 models from 1 gigawatt training systems it becomes 64 tokens/parameter. So even the original Chinchilla paper doesn't quite promise 20 tokens/parameter for 5e28 FLOPs with some of its estimates of b, you'd need to stick to the estimate of 0.50, and there isn't enough data to confidently make that decision, not 7 orders of magnitude beyond the scale of the actual experiments that only go up to 3e21 FLOPs (Chinchilla itself is 6e23 FLOPs, at 200 times more compute, where we get D/N of 30 for b of 0.54; but they went with b of 0.50).
The picture of steady 20 tokens/parameter is disrupted further with more recent papers. Llama 3 report measures the exponent b of 0.53 with experiments of up to 1e22 FLOPs. At 3.8e25 FLOPs of Llama-3-405B, this predicts a D/N ratio of 41 tokens per parameter (see Fig
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4098c454-9bad-4c3c-9c34-6a6e2f27480c
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trentmkelly/LessWrong-43k
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LessWrong
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"The unrecognised simplicities of effective action #2: 'Systems engineering’ and 'systems management' - ideas from the Apollo programme for a 'systems politics'", Cummings 2017
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bbd7bbd5-aa10-46ed-a9e6-43664866d66e
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trentmkelly/LessWrong-43k
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LessWrong
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Disclosure vs. Bans: Reply to Robin Hanson
A little while back I wrote a post arguing that the existence of abusive terms in credit card contracts (such as huge jumps in interest rates for being one day late with a payment) do not satisfy the conditions for standard economic models of asymmetric information between rational agents, but rather are trickery, pure and simple. If this is right, then the standard remedy of mandating the provision of more information to the less-informed party, but not otherwise interfering in the market (the idea being that any voluntary agreement must make both parties better off, no matter how strange or one-sided the terms may appear, so any interference in contracts beyond providing information will reduce welfare), is not the right one. There is no decent argument that those terms would appear in any contract where both parties knew what they were doing, so if you see terms like that, the appropriate conclusion is that someone has been screwed, not that Goddess of Capitalism, in her infinite-but-inscrutable wisdom, has uncovered the only terms that, strange as they may seem to mere mortals, make a mutually beneficial contract possible. The goal is to get rid of those terms, and the most direct way to do that is simply to prohibit them. There are some good reasons to be reluctant to have the government go around prohibiting things, so mandatory disclosure might still be a good policy (though the Federal Reserve has investigated this and concluded that it isn't), but the goal would be to use the disclosures to eliminate the abusive terms. There is no justification for the standard economist's agnosticism about whether the terms are good or not: they're bad and the only question is how best to get rid of them.
Robin Hanson left some comments to that post, in which he made the point that since people voluntarily choose these terms, they must like them and so prohibiting them would have to mean protecting people against their will. I answered that while I'm enough of a paternali
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c51331a1-ac31-47dc-825b-8f984f386d6e
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StampyAI/alignment-research-dataset/aisafety.info
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AI Safety Info
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What is the difference between inner and outer alignment?
<iframe src="https://www.youtube.com/embed/bJLcIBixGj8" title="The OTHER AI Alignment Problem: Mesa-Optimizers and Inner Alignment" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
The paper [Risks from Learned Optimization in Advanced Machine Learning Systems](https://arxiv.org/abs/1906.01820) makes the distinction between inner and outer alignment: Outer alignment means making the optimization target of the *training process* (“outer optimization target” e.g. the *loss* in supervised learning) aligned with what we want. Inner alignment means making the optimization target of the *trained system* (“inner optimization target”) aligned with the outer optimization target. A challenge here is that the inner optimization target does not have an explicit representation in current systems, and can differ very much from the outer optimization target (see for example [Goal Misgeneralization in Deep Reinforcement Learning](https://arxiv.org/abs/2105.14111)).
See also [this post](https://astralcodexten.substack.com/p/deceptively-aligned-mesa-optimizers) for an intuitive explanation of inner and outer alignment.
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7eb7f9bc-d2fd-47c3-8d6e-f50c9ed02bae
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trentmkelly/LessWrong-43k
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LessWrong
|
Feature Request: Self-imposed Time Restrictions
Hacker News has a feature called "noprocrast". Here's how they explain it in the FAQ:
> In my profile, what is noprocrast?
> It's a way to help you prevent yourself from spending too much time on HN. If you turn it on you'll only be allowed to visit the site for maxvisit minutes at a time, with gaps of minaway minutes in between. The defaults are 20 and 180, which would let you view the site for 20 minutes at a time, and then not allow you back in for 3 hours.
If you try to use HN when you precommitted to not using it, you'll get the following message from them:
> Get back to work!
> Sorry, you can't see this page. Based on the anti-procrastination parameters you set in your profile, you'll be able to use the site again in 43 minutes.
I was thinking that something like this would be awesome for LessWrong. Personally, I have a rather large problem browsing the web - which includes browsing LessWrong - when I should be doing other things. After reading Digital Minimalism, I get the impression that such struggles are moreso the norm than the exception.
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c2eef48b-16d1-48cd-a81d-6f3536089de6
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trentmkelly/LessWrong-43k
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LessWrong
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What do language models know about fictional characters?
I will speculate a bit about what language models might be doing. Hopefully it will help us come up with better research questions (for those of you who can do research) and things to try (for those of us informally testing the chatbots), to see how these language models work and break.
I haven't done any testing based on these ideas yet, and there are many papers I haven't read, so I'd be very interested in hearing about what other people have found.
----------------------------------------
Let's say you're trying to predict the next word of a document, as a language model does. What information might be helpful? One useful chunk of information is who the author is, because people have different opinions and writing styles.
Furthermore, documents can contain a mix of authors. One author can quote another, and quotes can nest. A document might be a play or screenplay, or a transcript of an interview. Some authors don't actually exist; the words of fictional characters often appear in documents and it will be useful to keep track of them too. I'll generically refer to an author or character as a "speaker."
Speaker Identification
Within a document
When predicting the next word of a chat transcript, you will want to identify previous sections corresponding to the current speaker, and imitate those. One could imagine highlighting the text belonging to each speaker as a different color. There are various textual markers indicating where one speaker stops and another starts. Paying attention to delimiters (such as quotation marks) is important, along with all the other things authors might do to distinguish one speaker from another. Sometimes, a narrator indicates who is speaking, or the dialog is prefixed by the name of the speaker, such as in a play or interview.
Getting speaker changes wrong results in disastrous errors, like what seems to have happened with Bing's chatbot. So there's a pretty strong incentive in training to get it right. We don't know how it
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9c19b0d1-421f-4a5d-a381-6f0889cd3756
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trentmkelly/LessWrong-43k
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LessWrong
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Is it possible to build a safe oracle AI?
It seem to me possible to create a safe oracle AI.
Suppose that you have a sequence predictor which is a good approximation of Solomonoff induction but which run in reasonable time. This sequence predictor can potentially be really useful (for example, predict future siai publications from past siai publications then proceed to read the article which give a complete account of Friendliness theory...) and is not dangerous in itself.
The question, of course, is how to obtain such a thing.
The trick rely on the concept of program predictor. A program predictor is a function which predict, more or less accurately, the output of the program (note that when we refer to a program we refer to a program without side effect that just calculate an output.) it take as it's input but within reasonable time. If you have a very accurate program predictor then you can obviously use it to gain a good approximation of Solomonoff induction which run in reasonable time.
But of course, this just displace the problem: how do you get such an accurate program predictor?
Well, suppose you have a program predictor which is good enough to be improved on. Then, you use it to predict the program of less than N bits of length (with N sufficiently big of course) which maximize a utility function which measure how accurate the output of that program is as a program predictor given that it generate this output in less than T steps (where T is a reasonable number given the hardware you have access to). Then you run that program. Check the accuracy of the obtained program predictor. If insufficient repeat the process. You should eventually obtain a very accurate program predictor. QED.
So we've reduced our problem to the problem of creating a program predictor good enough to be improved upon. That should be possible. In particular, it is related to the problem of logical uncertainty. If we can get a passable understanding of logical uncertainty it should be possible to build such a program pred
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cc0c566f-a466-4240-8396-97c828b87eb0
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trentmkelly/LessWrong-43k
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LessWrong
|
Countess and Baron attempt to define blackmail, fail
For a more concise version of this argument, see here.
We meet our heroes, the Countess of Rectitude and Baron Chastity, as they continue to investigate the mysteries of blackmail by sleeping together and betraying each other.
The Baron had a pile of steamy letters between him and the Countess: it would be embarrassing to both of them if these letters got out. Yet the Baron confided the letters to a trusted Acolyte, with strict instructions. The Acolyte was to publish these letters, unless the Countess agreed to give the Baron her priceless Ping Vase.
This seems a perfect example of blackmail:
* The Baron is taking a course of action that is intrinsically negative for him. This behaviour only makes sense if it forces the Countess to take a specific action which benefits him. The Countess would very much like it if the Baron couldn't do such things.
As it turns out, a servant broke the Ping Vase while chasing the Countess's griffon. The servant was swiftly executed, but the Acolyte had to publish the letters as instructed, to great embarrassment all around (sometimes precommitments aren't what they're cracked up to be). After six days of exile in the Countess's doghouse (a luxurious, twenty-room affair) and eleven days of make-up sex, the Baron was back to planning against his lover.
The Countess acquired the left winged sandal from the god Hermes, who was seeking to retire from the god-race. A precious acquisition indeed! But nearly worthless without the rightmost one. The Baron suddenly remembered that he'd seen the right sandal in a small mysterious shop he'd attempted to buy out earlier (he'd wanted the land to design a course for the recently fashionable sport of hunting kangaroos while mounted on sea-horses). But he'd visited that shop hand in hand with the Countess, so she'd remember it! Quickly, he rushed down to town to buy the sandal and sell it back to the Countess at an inflated price.
Is this blackmail? It fits the definition. The Baron is prepar
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ceed9092-4669-4ec6-8133-c398a05de1a0
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StampyAI/alignment-research-dataset/special_docs
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Other
|
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional
Neural Networks
Alex Krizhevsky
University of Toronto
kriz@cs.utoronto.caIlya Sutskever
University of Toronto
ilya@cs.utoronto.caGeoffrey E. Hinton
University of Toronto
hinton@cs.utoronto.ca
Abstract
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
1 Introduction
Current approaches to object recognition make essential use of machine learning methods. To im-
prove their performance, we can collect larger datasets, learn more powerful models, and use bet-
ter techniques for preventing overfitting. Until recently, datasets of labeled images were relatively
small — on the order of tens of thousands of images (e.g., NORB [16], Caltech-101/256 [8, 9], and
CIFAR-10/100 [12]). Simple recognition tasks can be solved quite well with datasets of this size,
especially if they are augmented with label-preserving transformations. For example, the current-
best error rate on the MNIST digit-recognition task (<0.3%) approaches human performance [4].
But objects in realistic settings exhibit considerable variability, so to learn to recognize them it is
necessary to use much larger training sets. And indeed, the shortcomings of small image datasets
have been widely recognized (e.g., Pinto et al. [21]), but it has only recently become possible to col-
lect labeled datasets with millions of images. The new larger datasets include LabelMe [23], which
consists of hundreds of thousands of fully-segmented images, and ImageNet [6], which consists of
over 15 million labeled high-resolution images in over 22,000 categories.
To learn about thousands of objects from millions of images, we need a model with a large learning
capacity. However, the immense complexity of the object recognition task means that this prob-
lem cannot be specified even by a dataset as large as ImageNet, so our model should also have lots
of prior knowledge to compensate for all the data we don’t have. Convolutional neural networks
(CNNs) constitute one such class of models [16, 11, 13, 18, 15, 22, 26]. Their capacity can be con-
trolled by varying their depth and breadth, and they also make strong and mostly correct assumptions
about the nature of images (namely, stationarity of statistics and locality of pixel dependencies).
Thus, compared to standard feedforward neural networks with similarly-sized layers, CNNs have
much fewer connections and parameters and so they are easier to train, while their theoretically-best
performance is likely to be only slightly worse.
1
Despite the attractive qualities of CNNs, and despite the relative efficiency of their local architecture,
they have still been prohibitively expensive to apply in large scale to high-resolution images. Luck-
ily, current GPUs, paired with a highly-optimized implementation of 2D convolution, are powerful
enough to facilitate the training of interestingly-large CNNs, and recent datasets such as ImageNet
contain enough labeled examples to train such models without severe overfitting.
The specific contributions of this paper are as follows: we trained one of the largest convolutional
neural networks to date on the subsets of ImageNet used in the ILSVRC-2010 and ILSVRC-2012
competitions [2] and achieved by far the best results ever reported on these datasets. We wrote a
highly-optimized GPU implementation of 2D convolution and all the other operations inherent in
training convolutional neural networks, which we make available publicly1. Our network contains
a number of new and unusual features which improve its performance and reduce its training time,
which are detailed in Section 3. The size of our network made overfitting a significant problem, even
with 1.2 million labeled training examples, so we used several effective techniques for preventing
overfitting, which are described in Section 4. Our final network contains five convolutional and
three fully-connected layers, and this depth seems to be important: we found that removing any
convolutional layer (each of which contains no more than 1% of the model’s parameters) resulted in
inferior performance.
In the end, the network’s size is limited mainly by the amount of memory available on current GPUs
and by the amount of training time that we are willing to tolerate. Our network takes between five
and six days to train on two GTX 580 3GB GPUs. All of our experiments suggest that our results
can be improved simply by waiting for faster GPUs and bigger datasets to become available.
2 The Dataset
ImageNet is a dataset of over 15 million labeled high-resolution images belonging to roughly 22,000
categories. The images were collected from the web and labeled by human labelers using Ama-
zon’s Mechanical Turk crowd-sourcing tool. Starting in 2010, as part of the Pascal Visual Object
Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge
(ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of
1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and
150,000 testing images.
ILSVRC-2010 is the only version of ILSVRC for which the test set labels are available, so this is
the version on which we performed most of our experiments. Since we also entered our model in
the ILSVRC-2012 competition, in Section 6 we report our results on this version of the dataset as
well, for which test set labels are unavailable. On ImageNet, it is customary to report two error rates:
top-1 and top-5, where the top-5 error rate is the fraction of test images for which the correct label
is not among the five labels considered most probable by the model.
ImageNet consists of variable-resolution images, while our system requires a constant input dimen-
sionality. Therefore, we down-sampled the images to a fixed resolution of 256256. Given a
rectangular image, we first rescaled the image such that the shorter side was of length 256, and then
cropped out the central 256256patch from the resulting image. We did not pre-process the images
in any other way, except for subtracting the mean activity over the training set from each pixel. So
we trained our network on the (centered) raw RGB values of the pixels.
3 The Architecture
The architecture of our network is summarized in Figure 2. It contains eight learned layers —
five convolutional and three fully-connected. Below, we describe some of the novel or unusual
features of our network’s architecture. Sections 3.1-3.4 are sorted according to our estimation of
their importance, with the most important first.
1http://code.google.com/p/cuda-convnet/
2
3.1 ReLU Nonlinearity
Figure 1: A four-layer convolutional neural
network with ReLUs (solid line) reaches a 25%
training error rate on CIFAR-10 six times faster
than an equivalent network with tanh neurons
(dashed line) . The learning rates for each net-
work were chosen independently to make train-
ing as fast as possible. No regularization of
any kind was employed. The magnitude of the
effect demonstrated here varies with network
architecture, but networks with ReLUs consis-
tently learn several times faster than equivalents
with saturating neurons.The standard way to model a neuron’s output fas
a function of its input xis withf(x) = tanh(x)
orf(x) = (1 +e x) 1. In terms of training time
with gradient descent, these saturating nonlinearities
are much slower than the non-saturating nonlinearity
f(x) = max(0;x). Following Nair and Hinton [20],
we refer to neurons with this nonlinearity as Rectified
Linear Units (ReLUs). Deep convolutional neural net-
works with ReLUs train several times faster than their
equivalents with tanh units. This is demonstrated in
Figure 1, which shows the number of iterations re-
quired to reach 25% training error on the CIFAR-10
dataset for a particular four-layer convolutional net-
work. This plot shows that we would not have been
able to experiment with such large neural networks for
this work if we had used traditional saturating neuron
models.
We are not the first to consider alternatives to tradi-
tional neuron models in CNNs. For example, Jarrett
et al. [11] claim that the nonlinearity f(x) =jtanh(x)j
works particularly well with their type of contrast nor-
malization followed by local average pooling on the
Caltech-101 dataset. However, on this dataset the pri-
mary concern is preventing overfitting, so the effect
they are observing is different from the accelerated
ability to fit the training set which we report when us-
ing ReLUs. Faster learning has a great influence on the
performance of large models trained on large datasets.
3.2 Training on Multiple GPUs
A single GTX 580 GPU has only 3GB of memory, which limits the maximum size of the networks
that can be trained on it. It turns out that 1.2 million training examples are enough to train networks
which are too big to fit on one GPU. Therefore we spread the net across two GPUs. Current GPUs
are particularly well-suited to cross-GPU parallelization, as they are able to read from and write to
one another’s memory directly, without going through host machine memory. The parallelization
scheme that we employ essentially puts half of the kernels (or neurons) on each GPU, with one
additional trick: the GPUs communicate only in certain layers. This means that, for example, the
kernels of layer 3 take input from all kernel maps in layer 2. However, kernels in layer 4 take input
only from those kernel maps in layer 3 which reside on the same GPU. Choosing the pattern of
connectivity is a problem for cross-validation, but this allows us to precisely tune the amount of
communication until it is an acceptable fraction of the amount of computation.
The resultant architecture is somewhat similar to that of the “columnar” CNN employed by Cire¸ san
et al. [5], except that our columns are not independent (see Figure 2). This scheme reduces our top-1
and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many
kernels in each convolutional layer trained on one GPU. The two-GPU net takes slightly less time
to train than the one-GPU net2.
2The one-GPU net actually has the same number of kernels as the two-GPU net in the final convolutional
layer. This is because most of the net’s parameters are in the first fully-connected layer, which takes the last
convolutional layer as input. So to make the two nets have approximately the same number of parameters, we
did not halve the size of the final convolutional layer (nor the fully-conneced layers which follow). Therefore
this comparison is biased in favor of the one-GPU net, since it is bigger than “half the size” of the two-GPU
net.
3
3.3 Local Response Normalization
ReLUs have the desirable property that they do not require input normalization to prevent them
from saturating. If at least some training examples produce a positive input to a ReLU, learning will
happen in that neuron. However, we still find that the following local normalization scheme aids
generalization. Denoting by ai
x;ythe activity of a neuron computed by applying kernel iat position
(x;y)and then applying the ReLU nonlinearity, the response-normalized activity bi
x;yis given by
the expression
bi
x;y=ai
x;y=0
@k+ min(N 1;i+n=2)X
j=max(0;i n=2)(aj
x;y)21
A
where the sum runs over n“adjacent” kernel maps at the same spatial position, and Nis the total
number of kernels in the layer. The ordering of the kernel maps is of course arbitrary and determined
before training begins. This sort of response normalization implements a form of lateral inhibition
inspired by the type found in real neurons, creating competition for big activities amongst neuron
outputs computed using different kernels. The constants k;n; , and are hyper-parameters whose
values are determined using a validation set; we used k= 2,n= 5, = 10 4, and = 0:75. We
applied this normalization after applying the ReLU nonlinearity in certain layers (see Section 3.5).
This scheme bears some resemblance to the local contrast normalization scheme of Jarrett et al. [11],
but ours would be more correctly termed “brightness normalization”, since we do not subtract the
mean activity. Response normalization reduces our top-1 and top-5 error rates by 1.4% and 1.2%,
respectively. We also verified the effectiveness of this scheme on the CIFAR-10 dataset: a four-layer
CNN achieved a 13% test error rate without normalization and 11% with normalization3.
3.4 Overlapping Pooling
Pooling layers in CNNs summarize the outputs of neighboring groups of neurons in the same kernel
map. Traditionally, the neighborhoods summarized by adjacent pooling units do not overlap (e.g.,
[17, 11, 4]). To be more precise, a pooling layer can be thought of as consisting of a grid of pooling
units spaced spixels apart, each summarizing a neighborhood of size zzcentered at the location
of the pooling unit. If we set s=z, we obtain traditional local pooling as commonly employed
in CNNs. If we set s < z , we obtain overlapping pooling. This is what we use throughout our
network, with s= 2 andz= 3. This scheme reduces the top-1 and top-5 error rates by 0.4% and
0.3%, respectively, as compared with the non-overlapping scheme s= 2;z= 2, which produces
output of equivalent dimensions. We generally observe during training that models with overlapping
pooling find it slightly more difficult to overfit.
3.5 Overall Architecture
Now we are ready to describe the overall architecture of our CNN. As depicted in Figure 2, the net
contains eight layers with weights; the first five are convolutional and the remaining three are fully-
connected. The output of the last fully-connected layer is fed to a 1000-way softmax which produces
a distribution over the 1000 class labels. Our network maximizes the multinomial logistic regression
objective, which is equivalent to maximizing the average across training cases of the log-probability
of the correct label under the prediction distribution.
The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel
maps in the previous layer which reside on the same GPU (see Figure 2). The kernels of the third
convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully-
connected layers are connected to all neurons in the previous layer. Response-normalization layers
follow the first and second convolutional layers. Max-pooling layers, of the kind described in Section
3.4, follow both response-normalization layers as well as the fifth convolutional layer. The ReLU
non-linearity is applied to the output of every convolutional and fully-connected layer.
The first convolutional layer filters the 2242243input image with 96 kernels of size 11113
with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring
3We cannot describe this network in detail due to space constraints, but it is specified precisely by the code
and parameter files provided here: http://code.google.com/p/cuda-convnet/.
4
Figure 2: An illustration of the architecture of our CNN, explicitly showing the delineation of responsibilities
between the two GPUs. One GPU runs the layer-parts at the top of the figure while the other runs the layer-parts
at the bottom. The GPUs communicate only at certain layers. The network’s input is 150,528-dimensional, and
the number of neurons in the network’s remaining layers is given by 253,440–186,624–64,896–64,896–43,264–
4096–4096–1000.
neurons in a kernel map). The second convolutional layer takes as input the (response-normalized
and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5548.
The third, fourth, and fifth convolutional layers are connected to one another without any intervening
pooling or normalization layers. The third convolutional layer has 384 kernels of size 33
256connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth
convolutional layer has 384 kernels of size 33192, and the fifth convolutional layer has 256
kernels of size 33192. The fully-connected layers have 4096 neurons each.
4 Reducing Overfitting
Our neural network architecture has 60 million parameters. Although the 1000 classes of ILSVRC
make each training example impose 10 bits of constraint on the mapping from image to label, this
turns out to be insufficient to learn so many parameters without considerable overfitting. Below, we
describe the two primary ways in which we combat overfitting.
4.1 Data Augmentation
The easiest and most common method to reduce overfitting on image data is to artificially enlarge
the dataset using label-preserving transformations (e.g., [25, 4, 5]). We employ two distinct forms
of data augmentation, both of which allow transformed images to be produced from the original
images with very little computation, so the transformed images do not need to be stored on disk.
In our implementation, the transformed images are generated in Python code on the CPU while the
GPU is training on the previous batch of images. So these data augmentation schemes are, in effect,
computationally free.
The first form of data augmentation consists of generating image translations and horizontal reflec-
tions. We do this by extracting random 224224patches (and their horizontal reflections) from the
256256images and training our network on these extracted patches4. This increases the size of our
training set by a factor of 2048, though the resulting training examples are, of course, highly inter-
dependent. Without this scheme, our network suffers from substantial overfitting, which would have
forced us to use much smaller networks. At test time, the network makes a prediction by extracting
five224224patches (the four corner patches and the center patch) as well as their horizontal
reflections (hence ten patches in all), and averaging the predictions made by the network’s softmax
layer on the ten patches.
The second form of data augmentation consists of altering the intensities of the RGB channels in
training images. Specifically, we perform PCA on the set of RGB pixel values throughout the
ImageNet training set. To each training image, we add multiples of the found principal components,
4This is the reason why the input images in Figure 2 are 2242243-dimensional.
5
with magnitudes proportional to the corresponding eigenvalues times a random variable drawn from
a Gaussian with mean zero and standard deviation 0.1. Therefore to each RGB image pixel Ixy=
[IR
xy;IG
xy;IB
xy]Twe add the following quantity:
[p1;p2;p3][ 11; 22; 33]T
where piandiareith eigenvector and eigenvalue of the 33covariance matrix of RGB pixel
values, respectively, and iis the aforementioned random variable. Each iis drawn only once
for all the pixels of a particular training image until that image is used for training again, at which
point it is re-drawn. This scheme approximately captures an important property of natural images,
namely, that object identity is invariant to changes in the intensity and color of the illumination. This
scheme reduces the top-1 error rate by over 1%.
4.2 Dropout
Combining the predictions of many different models is a very successful way to reduce test errors
[1, 3], but it appears to be too expensive for big neural networks that already take several days
to train. There is, however, a very efficient version of model combination that only costs about a
factor of two during training. The recently-introduced technique, called “dropout” [10], consists
of setting to zero the output of each hidden neuron with probability 0.5. The neurons which are
“dropped out” in this way do not contribute to the forward pass and do not participate in back-
propagation. So every time an input is presented, the neural network samples a different architecture,
but all these architectures share weights. This technique reduces complex co-adaptations of neurons,
since a neuron cannot rely on the presence of particular other neurons. It is, therefore, forced to
learn more robust features that are useful in conjunction with many different random subsets of the
other neurons. At test time, we use all the neurons but multiply their outputs by 0.5, which is a
reasonable approximation to taking the geometric mean of the predictive distributions produced by
the exponentially-many dropout networks.
We use dropout in the first two fully-connected layers of Figure 2. Without dropout, our network ex-
hibits substantial overfitting. Dropout roughly doubles the number of iterations required to converge.
Figure 3: 96 convolutional kernels of size
11113learned by the first convolutional
layer on the 2242243input images. The
top 48 kernels were learned on GPU 1 while
the bottom 48 kernels were learned on GPU
2. See Section 6.1 for details.5 Details of learning
We trained our models using stochastic gradient descent
with a batch size of 128 examples, momentum of 0.9, and
weight decay of 0.0005. We found that this small amount
of weight decay was important for the model to learn. In
other words, weight decay here is not merely a regularizer:
it reduces the model’s training error. The update rule for
weightwwas
vi+1 := 0:9vi 0:0005wi @L
@w
wi
Di
wi+1 :=wi+vi+1
whereiis the iteration index, vis the momentum variable, is the learning rate, andD
@L
@w
wiE
Diis
the average over the ith batchDiof the derivative of the objective with respect to w, evaluated at
wi.
We initialized the weights in each layer from a zero-mean Gaussian distribution with standard de-
viation 0.01. We initialized the neuron biases in the second, fourth, and fifth convolutional layers,
as well as in the fully-connected hidden layers, with the constant 1. This initialization accelerates
the early stages of learning by providing the ReLUs with positive inputs. We initialized the neuron
biases in the remaining layers with the constant 0.
We used an equal learning rate for all layers, which we adjusted manually throughout training.
The heuristic which we followed was to divide the learning rate by 10 when the validation error
rate stopped improving with the current learning rate. The learning rate was initialized at 0.01 and
6
reduced three times prior to termination. We trained the network for roughly 90 cycles through the
training set of 1.2 million images, which took five to six days on two NVIDIA GTX 580 3GB GPUs.
6 Results
Our results on ILSVRC-2010 are summarized in Table 1. Our network achieves top-1 and top-5
test set error rates of 37.5% and17.0%5. The best performance achieved during the ILSVRC-
2010 competition was 47.1% and 28.2% with an approach that averages the predictions produced
from six sparse-coding models trained on different features [2], and since then the best pub-
lished results are 45.7% and 25.7% with an approach that averages the predictions of two classi-
fiers trained on Fisher Vectors (FVs) computed from two types of densely-sampled features [24].
Model Top-1 Top-5
Sparse coding [2] 47.1% 28.2%
SIFT + FVs [24] 45.7% 25.7%
CNN 37.5% 17.0%
Table 1: Comparison of results on ILSVRC-
2010 test set. In italics are best results
achieved by others.We also entered our model in the ILSVRC-2012 com-
petition and report our results in Table 2. Since the
ILSVRC-2012 test set labels are not publicly available,
we cannot report test error rates for all the models that
we tried. In the remainder of this paragraph, we use
validation and test error rates interchangeably because
in our experience they do not differ by more than 0.1%
(see Table 2). The CNN described in this paper achieves
a top-5 error rate of 18.2%. Averaging the predictions
of five similar CNNs gives an error rate of 16.4%. Training one CNN, with an extra sixth con-
volutional layer over the last pooling layer, to classify the entire ImageNet Fall 2011 release
(15M images, 22K categories), and then “fine-tuning” it on ILSVRC-2012 gives an error rate of
16.6%. Averaging the predictions of two CNNs that were pre-trained on the entire Fall 2011 re-
lease with the aforementioned five CNNs gives an error rate of 15.3% . The second-best con-
test entry achieved an error rate of 26.2% with an approach that averages the predictions of sev-
eral classifiers trained on FVs computed from different types of densely-sampled features [7].
Model Top-1 (val) Top-5 (val) Top-5 (test)
SIFT + FVs [7] — — 26.2%
1 CNN 40.7% 18.2% —
5 CNNs 38.1% 16.4% 16.4%
1 CNN* 39.0% 16.6% —
7 CNNs* 36.7% 15.4% 15.3%
Table 2: Comparison of error rates on ILSVRC-2012 validation and
test sets. In italics are best results achieved by others. Models with an
asterisk* were “pre-trained” to classify the entire ImageNet 2011 Fall
release. See Section 6 for details.Finally, we also report our error
rates on the Fall 2009 version of
ImageNet with 10,184 categories
and 8.9 million images. On this
dataset we follow the convention
in the literature of using half of
the images for training and half
for testing. Since there is no es-
tablished test set, our split neces-
sarily differs from the splits used
by previous authors, but this does
not affect the results appreciably.
Our top-1 and top-5 error rates
on this dataset are 67.4% and
40.9% , attained by the net described above but with an additional, sixth convolutional layer over the
last pooling layer. The best published results on this dataset are 78.1% and 60.9% [19].
6.1 Qualitative Evaluations
Figure 3 shows the convolutional kernels learned by the network’s two data-connected layers. The
network has learned a variety of frequency- and orientation-selective kernels, as well as various col-
ored blobs. Notice the specialization exhibited by the two GPUs, a result of the restricted connec-
tivity described in Section 3.5. The kernels on GPU 1 are largely color-agnostic, while the kernels
on on GPU 2 are largely color-specific. This kind of specialization occurs during every run and is
independent of any particular random weight initialization (modulo a renumbering of the GPUs).
5The error rates without averaging predictions over ten patches as described in Section 4.1 are 39.0% and
18.3%.
7
Figure 4: (Left) Eight ILSVRC-2010 test images and the five labels considered most probable by our model.
The correct label is written under each image, and the probability assigned to the correct label is also shown
with a red bar (if it happens to be in the top 5). (Right) Five ILSVRC-2010 test images in the first column. The
remaining columns show the six training images that produce feature vectors in the last hidden layer with the
smallest Euclidean distance from the feature vector for the test image.
In the left panel of Figure 4 we qualitatively assess what the network has learned by computing its
top-5 predictions on eight test images. Notice that even off-center objects, such as the mite in the
top-left, can be recognized by the net. Most of the top-5 labels appear reasonable. For example,
only other types of cat are considered plausible labels for the leopard. In some cases (grille, cherry)
there is genuine ambiguity about the intended focus of the photograph.
Another way to probe the network’s visual knowledge is to consider the feature activations induced
by an image at the last, 4096-dimensional hidden layer. If two images produce feature activation
vectors with a small Euclidean separation, we can say that the higher levels of the neural network
consider them to be similar. Figure 4 shows five images from the test set and the six images from
the training set that are most similar to each of them according to this measure. Notice that at the
pixel level, the retrieved training images are generally not close in L2 to the query images in the first
column. For example, the retrieved dogs and elephants appear in a variety of poses. We present the
results for many more test images in the supplementary material.
Computing similarity by using Euclidean distance between two 4096-dimensional, real-valued vec-
tors is inefficient, but it could be made efficient by training an auto-encoder to compress these vectors
to short binary codes. This should produce a much better image retrieval method than applying auto-
encoders to the raw pixels [14], which does not make use of image labels and hence has a tendency
to retrieve images with similar patterns of edges, whether or not they are semantically similar.
7 Discussion
Our results show that a large, deep convolutional neural network is capable of achieving record-
breaking results on a highly challenging dataset using purely supervised learning. It is notable
that our network’s performance degrades if a single convolutional layer is removed. For example,
removing any of the middle layers results in a loss of about 2% for the top-1 performance of the
network. So the depth really is important for achieving our results.
To simplify our experiments, we did not use any unsupervised pre-training even though we expect
that it will help, especially if we obtain enough computational power to significantly increase the
size of the network without obtaining a corresponding increase in the amount of labeled data. Thus
far, our results have improved as we have made our network larger and trained it longer but we still
have many orders of magnitude to go in order to match the infero-temporal pathway of the human
visual system. Ultimately we would like to use very large and deep convolutional nets on video
sequences where the temporal structure provides very helpful information that is missing or far less
obvious in static images.
8
References
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[2] A. Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge 2010. www.image-
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[6] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical
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[7] J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei. ILSVRC-2012 , 2012. URL
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[8] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An
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ESANN , 2011.
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22(2):511–538, 2010.
9
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4bd41554-424a-4c99-8406-f6197e0a8168
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Petrov Day Image
9/26 is Petrov Day
While mentioning this in Facebook statuses, Tweets, and face-to-face conversation is good, things often stick better when there's a picture associated with them. Image macros are popular, and more fun to share. So here's one for Petrov Day I threw together.
http://i.imgur.com/3GNms.jpg
|
558d3aaf-bd03-45b1-89bb-5b00c3d1f20f
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
Any research in "probe-tuning" of LLMs?
Is there any research in "probe-tuning" of LLMs, i.e., tuning LLM's parameter weights such that a specific [probe (classifier)](https://arxiv.org/abs/1610.01644) is more reliably detecting certain markers throughout the context, such as grammatical errors, aggression, manipulation, certain political bias, etc.?
This is different from classical fine-tuning and RLHF. As well as classical fine-tuning, probe-tuning is a supervised ML method: it is based on human-annotated texts (contexts). However, probe-tuning should be more effective than classical fine-tuning for detecting many occurrences of a certain marker throughout the context. Probe-tuning doesn't train on LLM's own "original rollouts" at all, only on LLM's activations during the context pass through the LLM.
I imagine than before doing actual probe-tuning, first we should determine which probe in the LLM is most aligned to the training data (annotations) already, so that probe-tuning likely just attenuates some vaguely existing concept within the LLM.
|
a0e70dfb-9214-4e45-b834-71e89757c5d7
|
StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
|
Pros and cons of working on near-term technical AI safety and assurance
*Cross-posted from the EA Forum:* [*https://forum.effectivealtruism.org/posts/Ry4C4CKZvuRG7ztxY/pros-and-cons-of-working-on-near-term-technical-ai-safety*](https://forum.effectivealtruism.org/posts/Ry4C4CKZvuRG7ztxY/pros-and-cons-of-working-on-near-term-technical-ai-safety)
Recently I've been thinking about the pros and cons of working on near-term technical AI safety and assurance. This includes topics such as interpretability for near-term systems, generalizability / robustness, AI security, testing, verification, and the like.
Here are my own considerations so far:
(Note: In what follows I use the term Transformative AI (TAI) very loosely to mean any type of AI that has a decent chance of leading to a global catastrophe if safety challenges are not addressed first.)
Pros
1. Some approaches to these topics might actually turn out to work directly for TAI, especially where those approaches may not be pursued given the default trajectory (i.e., without EA intervention) of research from industry / government / academia.
2. This kind of research directly helps create a set of tools, techniques, organizations, regulations, etc., that iteratively builds on itself in the way that technology tends to do, such that whenever TAI becomes a real problem we will already have solutions or the resources to quickly find solutions.
3. Promoting this kind of research in industry / gov't / academia helps influence others in those communities to create a set of tools, techniques, organizations, regulations, etc., such that whenever TAI becomes a real problem we will already have solutions or the resources to quickly find solutions.
4. Research into these topics fosters a broader concern for AI safety topics in the general public (either directly or as a side effect of researchers / gov't / etc. respecting those topics more), which could lead to public pressure on industry / gov't to develop solutions, and that may help mitigate risks from TAI.
(For whatever it's worth, my personal inside view leans towards 3 as the most plausibly important from an EA point of view.)
Cons
1. Research into these topics, if successful, would remove some very large barriers that are currently preventing AI from being deployed in many applications that would be extremely valuable to industry or government (including the military). Removing these barriers would dramatically increase the value of AI to industry and government, which would accelerate AI development in general, potentially leading to TAI arriving before we're ready for it.
2. Research into these topics, if only partially successful, might remove enough barriers for industry / government to start deploying AI systems that eventually prove to be unsafe. Plausibly, those AI systems might become part of an ecosystem of other AIs which together have the potential to lead to a catastrophe (along the lines of Paul Christiano's ["out with a whimper" or "out with a bang" scenarios](https://www.alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like)).
3. Dramatically increasing the value of AI could also potentially lead to arms races between corporations or governments, which could lead to one side or another cutting safety corners as they're developing TAI (races to the bottom).
4. If you are concerned about lethal autonomous weapons, then removing these barriers might greatly increase the chance that various governments might deploy LAWs. This is true even if you're not working for the government, since the government definitely follows industry developments pretty closely.
I'm also interested in how these pros and cons might change if you're doing research for large organizations (industry or government) that might plausibly have the capacity to eventually build TAI-type systems, but where the research you do will not be publicly available due to proprietary or secrecy reasons. If it makes a difference, let's assume that you're working at a place that is reasonably ethical (as corporations and governments go) and that is at least somewhat aware of AI ethics and safety concerns.
I think that in this situation you'd have both a reduction in the value of the pros (since your solutions won't spread beyond your organization, at least for some time) and in the potential damage of the cons (for the same reason). But it seems to me that the cons are still mostly there, and possibly made worse: The lowered barriers to deployment would still probably lead your organization to press its advantage, thereby increasing the market (or strategic) value of AI as perceived by competitors, thereby leading to more resources poured into AI research in general - only now the competition might not have all the best safety solutions available to it because they're proprietary.
I'm curious what others think about all this. I would also appreciate links to good previous discussions of these topics. The only one I know of at the moment is [this post](https://www.alignmentforum.org/posts/hvGoYXi2kgnS3vxqb/some-ai-research-areas-and-their-relevance-to-existential-1), which discusses some of these considerations but not all.
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ea1ed074-a3d4-4787-bb79-c7793a65d372
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StampyAI/alignment-research-dataset/arxiv
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Arxiv
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When is a Prediction Knowledge?
1 Predictive Approaches to Machine Knowledge
---------------------------------------------
One of the foundational goals of machine intelligence is to create systems which are able to understand and reason about the world around them. Within Reinforcement Learning, there is a growing collection of research which attempts to describe the world in terms of predictions about the environment, sometimes called *Predictive Knowledge* (Sutton, [2009](#bib.bib13); Koop, [2008](#bib.bib7); Sutton et al., [2011](#bib.bib14); White, [2015](#bib.bib15)). Predictive knowledge agents describe the world by making many predictions with respect to their behaviour. These predictions can then be interrelated to express more abstract, conceptual aspects of the environment (Schapire and
Rivest, [1988](#bib.bib11)). For instance, using a General Value Function, a system could predict whether there is an obstacle to the left or right. Key to this approach is that all predictions—from immediate sensorimotor anticipation, to abstract conceptual expressions of the environment—are described exclusively in terms of sensation, behaviour, and time.
As a result of these constraints, predictive knowledge centres itself around methods which are able to construct their own categories, properties and relationships: predictive knowledge is liberated from the process of labelling. This body of work can be seen as not just a collection of engineering proposals, but also as a fledgling approach to describing knowledge from a machine intelligence perspective—as a starting point for applying Epistemology to Reinforcement Learning.
Predictive knowledge methods show promise; however it is unclear to what extent predictions can be considered knowledge. While prediction’s special status as knowledge has been alluded to in RL (Sutton et al., [2011](#bib.bib14); White, [2015](#bib.bib15)), there has been no discussion of the necessary and sufficient conditions for predictions to be considered knowledge, or the assumptions required and consequences which follow from considering predictions to be knowledge.
This is more than simply an absence of conceptual discussion in a purely technical endeavour; there are practical challenges to developing predictive knowledge architectures which are particularly pernicious due to a limited understanding of the requirements of knowledge—i.e., how to choose *what* to predict and *how* to predict it independent of designer intervention is largely unknown. Although predictions have proven to be practically useful in reactive control systems in bionic limbs (Edwards et al., [2016](#bib.bib2)) and industrial laser welding (Günther et al., [2016](#bib.bib5)), in each of these instances the predictions learnt by the system and how they are used to inform decison-making is hand-specified by engineers and designers. These problems, at least in part, are a consequence of a poor understanding of the requirements of knowledge.
When we propose that predictions can be interpreted as knowledge, we are making a claim about what knowledge *is*. In this paper, we begin the project of formalizing a theory of knowledge in reinforcement learning by exploring justification and truth in predictive knowledge. Specifically, we 1) highlight evaluation concerns in predictive knowledge architectures, emphasizing how they relate to existing real-world applications; and 2) argue that epistemology is relevant to predictive knowledge research—that epistemology deserves greater attention when designing predictive knowledge architectures. To do so, we examine one of the most fundamental components of predictive knowledge proposals: General Value Functions (GVFs).
2 General Value Functions
--------------------------
When we discuss the requirements of knowledge, it is natural for us to begin by examining how predictive knowledge learning methods relate to formal theories of knowledge. One of the central methods of specifying predictions in predictive knowledge is through General Value Functions. General Value functions estimate the discounted sum of some signal c𝑐citalic\_c over discrete time-steps t=1,2,3,…,n𝑡123…𝑛t=1,2,3,...,nitalic\_t = 1 , 2 , 3 , … , italic\_n defined as Gt=𝔼(∑k=0∞(∏j=1k(γt+j))Ct+k+1)subscript𝐺𝑡𝔼subscriptsuperscript𝑘0subscriptsuperscriptproduct𝑘𝑗1subscript𝛾𝑡𝑗subscript𝐶𝑡𝑘1G\_{t}=\operatorname{\mathbb{E}}(\sum^{\infty}\_{k=0}(\prod^{k}\_{j=1}(\gamma\_{t+j}))C\_{t+k+1})italic\_G start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT = blackboard\_E ( ∑ start\_POSTSUPERSCRIPT ∞ end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_k = 0 end\_POSTSUBSCRIPT ( ∏ start\_POSTSUPERSCRIPT italic\_k end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_j = 1 end\_POSTSUBSCRIPT ( italic\_γ start\_POSTSUBSCRIPT italic\_t + italic\_j end\_POSTSUBSCRIPT ) ) italic\_C start\_POSTSUBSCRIPT italic\_t + italic\_k + 1 end\_POSTSUBSCRIPT ). On each time-step the agent receives some vector otsubscript𝑜𝑡o\_{t}italic\_o start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT of observations which describes the environment and takes an action atsubscript𝑎𝑡a\_{t}italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT. The observations are used to construct the *agent-state* ϕ:ot→ℝn:italic-ϕ→subscript𝑜𝑡superscriptℝ𝑛\phi:o\_{t}\rightarrow\mathbb{R}^{n}italic\_ϕ : italic\_o start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT → blackboard\_R start\_POSTSUPERSCRIPT italic\_n end\_POSTSUPERSCRIPT: the state of the environment from the agent’s perspective. A GVF is parameterized by a set of weights w∈ℝn𝑤superscriptℝ𝑛w\in\mathbb{R}^{n}italic\_w ∈ blackboard\_R start\_POSTSUPERSCRIPT italic\_n end\_POSTSUPERSCRIPT which when combined with the agent-state produce an estimate of the return v(s)=w⊤ϕ(ot)v(s)=w^{\top}\phi\_{(}o\_{t})italic\_v ( italic\_s ) = italic\_w start\_POSTSUPERSCRIPT ⊤ end\_POSTSUPERSCRIPT italic\_ϕ start\_POSTSUBSCRIPT ( end\_POSTSUBSCRIPT italic\_o start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) The prediction is specified by two sets of parameters: *question parameters* which determine what the prediction is about and *answer parameters* which determine how the prediction is learnt. Question parameters include the signal of interest C𝐶Citalic\_C, a discounting function dependent on the state of the environment stsubscript𝑠𝑡s\_{t}italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT and an action taken atsubscript𝑎𝑡a\_{t}italic\_a start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT, a factor 0≥γ≥10𝛾10\geq\gamma\geq 10 ≥ italic\_γ ≥ 1 which determines how to discount future signals, and a policy π𝜋\piitalic\_π which describes the behaviour over which the predictions are made. Answer parameters include the step-size α𝛼\alphaitalic\_α which scales updates to the weights, and the eligibility decay λ𝜆\lambdaitalic\_λ which determines how much previous states should update their estimates based on the most recent observation. These predictions can be learned online, incrementally using policy evaluation methods such as Temporal-difference learning (Sutton, [1988](#bib.bib12)).
GVFs form a key component of predictive knowledge proposals by acting as the mechanism through which knowledge is constructed(Sutton et al., [2011](#bib.bib14)). Certainly, not all predictions are created equally. Feature construction and amount of experience contribute to the how well the return Gtsubscript𝐺𝑡G\_{t}italic\_G start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT is estimated. If we center all knowledge as a collection of predictions, how do we evaluate the quality of a predictions as knowledge?
3 Lessons From Epistemology: Barn Facades and Bionic Limbs
-----------------------------------------------------------
Before embarking on determining whether or not accurate predictions can be considered knowledge, it’s prudent to have an understanding of what knowledge is. To this end, we introduce arguments from epistemology, the study of knowledge, and ground these arguments in terms of GVFs.
At its core epistemology captures the distinction between systems which *know* that such-and-such is the case and systems which are simply reliably responding to stimuli. While there are many theories that define the necessary and sufficient conditions for knowledge, they can be summarized broadly as requiring Justification, Truth, and Belief (Gettier, [1963](#bib.bib3)). Each of the legs of this tripartite approach to analysing knowledge are meant to constrain what can be admitted as knowledge.
First, one must believe that they have knowledge of something.
Belief may seem trivial; however, there are real-world examples of people who are able to complete tasks while not *believing* they are capable of doing so. When blindsighted patients are asked to perform certain visual tasks, they are able to achieve accuracy higher than would be expected by chance, but do not believe their reports are accurate (Humphrey, [2006](#bib.bib6)).
A blindsighted person does not assert that they *know* whether or not a stimulus is present; regardless, they are able to complete these tasks with some reliability. Second, the belief must be truthful. Truth separates beliefs which have bearing on the world, and assertions which are incongruous for reality. If someone says they know the moon is made of cheese, we wouldn’t say they *know* what the moon is made of, even if they deeply hold this belief. Third, a belief must be justified. Justification serves to separate accidentally true beliefs from those which are right for good reasons; i.e, if you asked someone how to get to the nearest cafe, and their directions happened to be correct, you wouldn’t say they were right—you’d say they were *lucky*.
The variety of positions relating to each of justification, truth, and belief are numerous. To that end, we constrain ourselves to considering how GVFs relate to the first two components of the tripod: how can predictions be licensed as being Truthful and Justified? As we alluded to earlier, not all predictions are created equally. In order to make progress in designing predictive architectures, we must be able to separate predictions which are unreliable, or made for poor reasons, from those which are robust and can be used to inform decision-making.
When an agent is making a prediction, it is making an assertion about the world as observed through its data stream. If a prediction is accurate, it is a testament to its truth. One common method of evaluating whether a prediction is correct or not is to compare what is predicted against an estimation of the true return (Pilarski et al., [2012](#bib.bib9); Edwards et al., [2016](#bib.bib2); Günther et al., [2016](#bib.bib5)). The approximate return is Gt~=∑k=0𝑏(∏j=1k(γt+j))Ct+k+1−vt(st)~subscript𝐺𝑡subscriptsuperscript𝑏𝑘0subscriptsuperscriptproduct𝑘𝑗1subscript𝛾𝑡𝑗subscript𝐶𝑡𝑘1subscript𝑣𝑡subscript𝑠𝑡\tilde{G\_{t}}=\sum^{\textit{b}}\_{k=0}(\prod^{k}\_{j=1}(\gamma\_{t+j}))C\_{t+k+1}-v\_{t}(s\_{t})over~ start\_ARG italic\_G start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG = ∑ start\_POSTSUPERSCRIPT b end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_k = 0 end\_POSTSUBSCRIPT ( ∏ start\_POSTSUPERSCRIPT italic\_k end\_POSTSUPERSCRIPT start\_POSTSUBSCRIPT italic\_j = 1 end\_POSTSUBSCRIPT ( italic\_γ start\_POSTSUBSCRIPT italic\_t + italic\_j end\_POSTSUBSCRIPT ) ) italic\_C start\_POSTSUBSCRIPT italic\_t + italic\_k + 1 end\_POSTSUBSCRIPT - italic\_v start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_s start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) for some buffer-size b𝑏bitalic\_b which determines how many steps into the future cumulants c𝑐citalic\_c are stored to produce the return estimate on any given time-step. The truthfulness of the prediction can be described as the the extent to which estimated value matches the true, observed return111This approach is advocated in the original proposal of Sutton et al. ([2011](#bib.bib14)).
If prediction accuracy describes the truth of a prediction, what is justification within predictive knowledge architectures? Or, is justification necessary? As previously mentioned, the necessary and sufficient conditions for knowledge are a point of contention. In the same paper that [Gettier](#bib.bib3) introduced Justified True Belief, he argued against its validity. Similarly, [Goldman](#bib.bib4)’s Barn Facade problem—which we explore in terms of predictions in the following paragraphs—illustrates how evidence and reasons are not the only way to support the claim that a belief is true—reasons are not the only way to separate a lucky guess, from a justified belief (Goldman, [1976](#bib.bib4)). The purpose of justification is simply to show that a belief is expected to be reliable, that a belief is predicted to be true (Brandom, [2009](#bib.bib1)). Can we treat the reliability of predictions as sufficient for identifying knowledge independent of any other form of justification?
In short, no. While the reliability of a belief—or, accuracy of a prediction—is a means of justifying a belief, reliability alone is insufficient to attribute knowledge (Brandom, [2009](#bib.bib1)). We can examine the limitations of reliability as justification by translating [Goldman](#bib.bib4)’s barn facade problem to a predictive knowledge experiment. Consider a single GVF making a prediction about some signal c𝑐citalic\_c. In this case, the return error vt(ϕt)−Gt~subscript𝑣𝑡subscriptitalic-ϕ𝑡~subscript𝐺𝑡v\_{t}(\phi\_{t})-\tilde{G\_{t}}italic\_v start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ( italic\_ϕ start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT ) - over~ start\_ARG italic\_G start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT end\_ARG is relative to a particular time-step t𝑡titalic\_t, and a set of observations otsubscript𝑜𝑡o\_{t}italic\_o start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT. A GVF which predicts random values could make a perfect prediction for a given time-step t𝑡titalic\_t and have no return error for an observation otsubscript𝑜𝑡o\_{t}italic\_o start\_POSTSUBSCRIPT italic\_t end\_POSTSUBSCRIPT. Clearly, the accuracy of a prediction over one time step says nothing about how likely a prediction is to be accurate in general. Given the limitations of a single time-step error, over what horizon—for what period of time, or what collection of states—must we examine the return error to licence truth?
Must we calculate the return error of a prediction relative to all possible states in order to determine whether a prediction is sufficiently justified? Such a requirement would be technically infeasible in a real-world setting.
Not only is return error impractical as the exclusive source of justification, it is incoherent on a conceptual level. Relative to each set of states, there is a clear answer as to whether or not a prediction is accurate; however, there is nothing in the world which privileges one set of states over others in making the distinction of truth. So the accuracy, or reliability of a belief does not determine whether or not the prediction is justified. None of this is to say that the reliability of predictions does not have any epistemic significance. Prediction accuracy is unquestionably an important part of assessing the truth of a prediction and evaluating if a prediction is justified. However, Prediction accuracy alone does not tell the full story.
(a) Estimated value for two predictions; Signal as dotted line.
(b) Absolute return error of two predictions.
Figure 1: Which prediction counts as knowledge: green or purple?
To explore this point, we produce two predictions about the joint angles of a robotic actuator, as sampled from the human controlling a robotic arm to do manipulation task. Please refer to Pilarski et al. ([2013a](#bib.bib8)) for the full details of how this dataset was generated. The cumulant of interest is the elbow servo motor angle in radians. For both predictions, the discount factor is γ=0.99𝛾0.99\gamma=0.99italic\_γ = 0.99, corresponding to roughly 2.5 seconds of arm operation. As per Pilarski et al. ([2013a](#bib.bib8)), the predictions were made on-policy with TD(λ𝜆\lambdaitalic\_λ) with λ=0.999𝜆0.999\lambda=0.999italic\_λ = 0.999 and a step size α=0.033𝛼0.033\alpha=0.033italic\_α = 0.033 (Sutton, [1988](#bib.bib12)). Only the function approximators used to construct the agent-state varies between the two predictions.
From the predictions in Figure [1](#S3.F1 "Figure 1 ‣ 3 Lessons From Epistemology: Barn Facades and Bionic Limbs ‣ When is a Prediction Knowledge?"), we can see that the green prediction isn’t a prediction at all. Although both predictions are specified to learn the same GVF, the green prediction is simply tracking the signal of interest. In comparison, the purple prediction, in fact, predicts: it rises before the stimulus rises, and decreases before the stimulus falls. Looking at return error alone (Figure [0(b)](#S3.F0.sf2 "0(b) ‣ Figure 1 ‣ 3 Lessons From Epistemology: Barn Facades and Bionic Limbs ‣ When is a Prediction Knowledge?")), we would be lead to the conclusion that the green prediction is in fact more truthful than the purple. Because the green prediction is more accurate—both on a moment-to moment basis, and throughout the trial—from this *reliabilist* perspective, it is better justified. We could conclude that the green prediction that isn’t predicting is a better candidate for knowledge. Although the purple prediction is clearly more predictive, it has a greater return error, both on a moment-to-moment basis on each time-step and in the greater context of the experimental trial.
More than simply a contrived example, these predictions are examples of prototypical GVFs made on bionic limbs to inform control systems. While existing systems are hand-engineered, if we choose to build systems which independently make decisions about what to learn and how to learn them, we must be able to assess the quality of a prediction in a robust, reliable way. From purely an engineering standpoint, in order to build such systems successfully we must be able to discriminate between predictions which have low error for poor reasons and predictions which explain their signal of interest (Pilarski et al., [2013b](#bib.bib10)). Put simply, just because a prediction is accurate, doesn’t make it useful.
The limitations of reliability as justification is more than a conceptual problem, it has practical consequences for evaluation in real-world applications of predictive knowledge systems. The consequences of epistemic choices we make—whether we are conscious of them or not—have a fundamental impact on the effectiveness of our systems. To achieve its fullest potential, future work should examine additional methods of supporting the justification of predictions, perhaps using internal signals about learning.
4 Concluding Thoughts:
The Importance of Evaluating When Predictions are Knowledge
-----------------------------------------------------------------------------------
Within reinforcement learning, there are the seeds of an approach to constructing machine knowledge through prediction. While promising, there is limited discussion of what the formal commitments of such an approach would be: namely, what knowledge is and what counts as it. In this paper, we take a first step towards formalizing predictive knowledge by clarifying the relationship of GVFs to formal theories of knowledge. We identify that a GVF’s estimates of some cumulant can be seen as truthful insofar as they match the observed expected discounted return of the cumulant; we discuss arguments for and against the reliability of a belief—or accuracy of a prediction—as being sufficient for justifying knowledge. Having formalized these relationships between GVFs and both justification and truth, we use a robotic prediction task to demonstrate that prediction accuracy is insufficient to determining whether a prediction is knowledge. This inquiry is not simply an academic discussion: it has practical implications for decisions about what knowledge is and what counts as it in architectural proposals. The project of predictive knowledge shows promise not just as a collection of practical engineering proposals, but also as a theory of machine knowledge; however, to achieve its full potential, predictive knowledge research must pay greater attention to the epistemic commitments being made.
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da87b160-1407-4daa-895e-592e65fe6fdb
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Open-source LLMs may prove Bostrom's vulnerable world hypothesis
In short, Nick Bostrom's vulnerable world hypothesis states that humanity may in the future invent a technology that is potentially very destructive and very cheap to make and easily implementable. As a thought experiment, he uses something called "easier nukes" that would not have to be made from plutonium as in real life but could instead be made from battery and two pieces of glass. Bostrom says that if everyone could make nuclear weapons in their own home, civilization would be destroyed by default because terrorists, malcontent people and "folk who just want to see what would happen" would blow up most cities.
I can see similarities between Bostrom's hypothesis, and the way powerful LLM-models have recently been open sourced. It did not take long after the publication of ChatGPT and GPT-4 for several "open-source alternatives" to appear on the internet. Some of those you can even download to your own computer for offline use. And then we did have ChaosGPT ordering such a model to "destroy humanity". In my mind, he was one of the "folks who just wanted to see what would happen".
Recently I have been thinking that, in the long term, the biggest challenge in AI safety is the potential wide availability of the future AGI-systems. If we can create a safely aligned AGI, what would prevent some people from creating open-source alternative AGIs that are free from safety constraints and more powerful because of that? Advanced technology can't generally stay secret forever. And then we would have many future ChaosGPTs "who just want to see what would happen" and who could tell such a system to destroy humanity.
Old analogy used in AI safety communities about making a wish to genie and "getting what you asked but not what you wanted" would no longer be relevant in this scenario. Instead, the potential future ChaosGPTs would get exactly what they asked and perhaps even what they truly wanted.
Does the community here also think that this is a reasonable concern? I woul
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3c905f4a-1dd3-4fa5-a68d-d98f1e107908
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trentmkelly/LessWrong-43k
|
LessWrong
|
Spaced Repetition Database for the Mysterious Answers to Mysterious Questions Sequence
I'm a big fan of spaced repetition software. There's a lot I could say about how awesome I think it is and how much it has helped me, but the SuperMemo website covers the benefits better than I could. I will mention two things that surprised me. First, I had no idea how much fun it would be; I actually really enjoy doing the reviews every day. (For me this is hugely important, since it's unlikely I would have kept up with it otherwise.) Second, it's proven more useful than I had anticipated for maintaining coherence of beliefs across emotional states.
I've tried memorizing a variety types of things such as emacs commands, my favorite quotations, advice about how to communicate with children, and characters from books. One of my more recent projects has been making notecards of the lesswrong sequences. I tried to follow the rules for formulating knowledge from the SuperMemo website, but deciding which bits to encode and how is subjective. For reference, I asked my boyfriend to make a few too so we could compare, and his looked pretty different from mine.
So, with those caveats, I thought I might as well share what I'd come up with. As Paul Buchheit says, "'Good enough' is the enemy of 'At all'". If you download Anki, my favorite spaced repetition software (free and cross-platform) and go to Download > Shared Deck in the Menu, you should be able to search for and get my Less Wrong Sequences cards. I also put them up here, with the ones my boyfriend made of the first post for comparison.
I had read all the sequences before, but I have found that since I've started using the cards I've noticed the concepts coming up in my life more often, so I think my experiment has been useful.
Let me know what you think!
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dbf1f2b0-dd8c-4cc8-90b4-25367d710bc9
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trentmkelly/LessWrong-43k
|
LessWrong
|
cloud seeding doesn't work
Alice: People should do more cloud seeding.
Carol: Cloud seeding doesn't work.
Alice: Governments spent a lot of money doing it, and there are a bunch of studies. And you're saying it just doesn't work?
Carol: Why is a cloud opaque? Because it has water droplets in it. If it already has droplets formed, then nucleating droplets does nothing.
Alice: Ah, a common misconception. Cloud seeding is about nucleating ice crystals, not water droplets. Scientists tested the concept before cloud seeding was deployed.
Carol: That was tested in unrealistic conditions: closed chambers with negligible suspended particles. And it's not that clouds are at altitudes with no particles, either - aerosol number density is similar up to around 9 km.
Alice: The fact is, cloud seeding was done on a large scale, it's still done some places, and there were a bunch of studies on its effectiveness.
Carol: Those studies showed positive effects only because of the same kind of publication bias you see for bad psychology studies. The better meta-analyses showed negligible effect sizes. That's why usage mostly stopped, but it was kind of an embarrassing failure with nobody motivated to publicize it, so people weren't very vocal about that.
Alice: No, if that was the case, there would be more articles and stuff about it, and besides, there are still governments doing cloud seeding today. After so much time to study things, governments wouldn't do cloud seeding at all if it just didn't work, which means it must work, so we should do more of it.
----------------------------------------
Who is it correct to believe here?
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babbabae-0a0e-45e1-abfb-b0ba35d92c3e
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trentmkelly/LessWrong-43k
|
LessWrong
|
OpenAI/Microsoft announce "next generation language model" integrated into Bing/Edge
TL;DR: Microsoft and OpenAI announced a new version of Bing featuring "a new, next-generation OpenAI large language model [..] more powerful than ChatGPT", and that Microsoft Edge will feature a Copilot-like assistant that helps with composing and summarizing content.
----------------------------------------
Brief thoughts/comments/notes:
* Microsoft's attitude during their press meeting seemed pretty aggressive and targeted directly at racing with Google. This seems kind of bad. For example, a quote from Nadella:
* "The race starts today, and we’re going to move and move fast. Most importantly, we want to have a lot of fun innovating again in search, because it’s high time."
* Microsoft built a scaffold for the new LM called "Prometheus", that lets them "best leverage its power".
* Microsoft has also used the new LM in their Bing search engine, though it's not clear exactly how.
* This seems way more hype than Google's Bard announcement.
* You can register for the new Bing beta on the Bing.com site.
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263e0c6e-bbcc-4ac0-a31c-36157e73a001
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trentmkelly/LessWrong-43k
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LessWrong
|
China Covid #2
What is happening in China?
That is by far the most important Covid-related question right now. The future of Covid elsewhere might be frustrating, but what is happening in China is potentially a much bigger deal, from disrupting supply chains all the way up to potential loss of the Mandate of Heaven.
I want to emphasize that it is very difficult to know what is going on inside China and my sources for this are not the best. I find the Ukraine war a relative epistemic cakewalk compared to this. So please understand that the alarmist claims from various threads are to be taken with large heapings of salt.
Stocks
One measure that is objective but that I have not previously looked at is the Chinese stock market, which looks like this.
By contrast, here’s the US stock market using the same style graph:
Since the start of March, the US stock market is up while the Chinese stock market is down, approximately +5% vs. -14%. Given that this is long term value, that’s a very large difference. China had been underperforming for the previous year as well, which could be any number of things related or unrelated to Covid.
Cases
The other obvious thing to check, despite its reliability issues, is the official case count. Remember that this is on a log scale. It matches up with previous data – those who understand my model here can look at the graph then skip this section.
This continues to look a lot like a steady straight line up, from 0.1 cases per million mid-February, a little over two months ago, to about 20 cases per million now. China’s Zero-Covid countermeasures have slowed the growth in percentage terms, and have done extra slowing of growth in the last two weeks, but growth is still positive.
Taken at face value, this is potentially close to the very worst case scenario for China.
The best case scenario is that one can indeed sustain Zero Covid at a price worth paying. We now know this does not apply to China.
The next best scenario is China realizes t
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3cf77d69-380c-4738-821f-82ef94670021
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StampyAI/alignment-research-dataset/special_docs
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Other
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Deep learning in neural networks: An overview
ReviewDeep learning in neural networks: An overview
===================================================
Abstract
--------
In recent years, deep [artificial neural networks](/topics/engineering/artificial-neural-network "Learn more about artificial neural networks from ScienceDirect's AI-generated Topic Pages") (including [recurrent](/topics/engineering/recurrent "Learn more about recurrent from ScienceDirect's AI-generated Topic Pages") ones) have won numerous contests in pattern recognition and [machine learning](/topics/computer-science/machine-learning "Learn more about machine learning from ScienceDirect's AI-generated Topic Pages"). This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their *credit assignment paths*, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), [unsupervised learning](/topics/computer-science/unsupervised-learning "Learn more about unsupervised learning from ScienceDirect's AI-generated Topic Pages"), [reinforcement learning](/topics/computer-science/reinforcement-learning "Learn more about reinforcement learning from ScienceDirect's AI-generated Topic Pages") & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Section snippets
----------------
Preface
-------
This is the preprint of an invited *Deep Learning* (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and
Introduction to Deep Learning (DL) in Neural Networks (NNs)
-----------------------------------------------------------
Which modifiable components of a learning system are responsible for its success or failure? What changes to them improve performance? This has been called the *fundamental credit assignment problem* (Minsky, 1963). There are general credit assignment methods for *universal problem solvers* that are time-optimal in various theoretical senses (Section 6.8). The present survey, however, will focus on the narrower, but now commercially important, subfield of *Deep Learning* (DL) in *Artificial Neural*
Event-oriented notation for activation spreading in NNs
-------------------------------------------------------
Throughout this paper, let i,j,k,t,p,q,r denote positive integer variables assuming ranges implicit in the given contexts. Let n,m,T denote positive integer constants.
An NN’s topology may change over time (e.g., Sections 5.3, 5.6.3). At any given moment, it can be described as a finite subset of units (or nodes or neurons) N={u1,u2,…,} and a finite set H⊆N×N of directed edges or connections between nodes. FNNs are acyclic graphs, RNNs cyclic. The first (input) layer is the set of input units, a
Depth of Credit Assignment Paths (CAPs) and of problems
-------------------------------------------------------
To measure whether credit assignment in a given NN application is of the *deep* or *shallow* type, I introduce the concept of *Credit Assignment Paths* or CAPs, which are chains of possibly causal links between the events of Section 2, e.g., from input through hidden to output layers in FNNs, or through transformations over time in RNNs.
Let us first focus on SL. Consider two events xp and xq(1≤p<q≤T). Depending on the application, they may have a *Potential Direct Causal Connection* (PDCC) expressed
Dynamic programming for Supervised/Reinforcement Learning (SL/RL)
-----------------------------------------------------------------
One recurring theme of DL is *Dynamic Programming* (DP) (Bellman, 1957), which can help to facilitate credit assignment under certain assumptions. For example, in SL NNs, backpropagation itself can be viewed as a DP-derived method (Section 5.5). In traditional RL based on strong Markovian assumptions, DP-derived methods can help to greatly reduce problem depth (Section 6.2). DP algorithms are also essential for systems that combine concepts of NNs and graphical models, such as *Hidden Markov*
Supervised NNs, some helped by unsupervised NNs
-----------------------------------------------
The main focus of current practical applications is on *Supervised Learning* (SL), which has dominated recent pattern recognition contests (Sections 5.17 2009: first official competitions won by RNNs, and with MPCNNs, 5.18 2010: plain backprop (+ distortions) on GPU breaks MNIST record, 5.19 2011: MPCNNs on GPU achieve superhuman vision performance, 5.20 2011: Hessian-free optimization for RNNs, 5.21 2012: first contests won on ImageNet, object detection, segmentation, 5.22 2013-: more contests
DL in FNNs and RNNs for Reinforcement Learning (RL)
---------------------------------------------------
So far we have focused on Deep Learning (DL) in supervised or unsupervised NNs. Such NNs learn to perceive/encode/predict/classify patterns or pattern sequences, but they do not learn to act in the more general sense of *Reinforcement Learning* (RL) in unknown environments (see surveys, e.g., Kaelbling et al., 1996, Sutton and Barto, 1998, Wiering and van Otterlo, 2012). Here we add a discussion of DL FNNs and RNNs for RL. It will be shorter than the discussion of FNNs and RNNs for SL and UL
Conclusion and outlook
----------------------
*Deep Learning* (DL) in *Neural Networks* (NNs) is relevant for *Supervised Learning* (SL) (Section 5), *Unsupervised Learning* (UL) (Section 5), and *Reinforcement Learning* (RL) (Section 6). By alleviating problems with deep *Credit Assignment Paths* (CAPs, Sections 3, 5.9), UL (Section 5.6.4) cannot only facilitate SL of sequences (Section 5.10) and stationary patterns (Sections 5.7, 5.15), but also RL (Sections 6.4, 4.2). *Dynamic Programming* (DP, Section 4.1) is important for both deep SL
Acknowledgments
---------------
Since 16 April 2014, drafts of this paper have undergone massive open online peer review through public mailing lists including [[email protected]](/cdn-cgi/l/email-protection#ddbeb2b3b3b8bea9b4b2b3b4aea9ae9dbeaef3beb0a8f3b8b9a8), [[email protected]](/cdn-cgi/l/email-protection#3b565716555e4c487b5c54545c575e5c49544e4b4815585456), [[email protected]](/cdn-cgi/l/email-protection#caa9a5a7bae7a4afbfb8a58aa4afbfb8a5a3a4ace4a5b8ad), [[email protected]](/cdn-cgi/l/email-protection#1770727972637e74486765787065767a7a7e7970576e767f78787065786267643974787a), [[email protected]](/cdn-cgi/l/email-protection#b8cad495d4d1cbccf8dfd7d7dfd4dddfcad7cdc8cb96dbd7d5), [[email protected]](/cdn-cgi/l/email-protection#2a43474b4d4f5d4558464e6a4e43415f044e41), *Google*+ *machine learning forum*. Thanks to numerous NN/DL experts for valuable comments. Thanks to SNF, DFG, and the European Commission for partially funding my DL research group in the past quarter-century.
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- Ballard, D. H. (1987). Modular learning in neural networks. In Proc. AAAI (pp....
- S. Baluja### Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical report CMU-CS-94-163
(1994)
- R. Balzer### A 15 year perspective on automatic programming
### IEEE Transactions on Software Engineering
(1985)
- H.B. Barlow### Unsupervised learning
### Neural Computation
(1989)
- H.B. Barlow *et al.*### Finding minimum entropy codes
### Neural Computation
(1989)
- H.G. Barrow### Learning receptive fields
- A.G. Barto *et al.*### Recent advances in hierarchical reinforcement learning
### Discrete Event Dynamic Systems
(2003)
View more referencesCited by (13349)
----------------
* ### [A deep neural network-assisted metamodel for damage detection of trusses using incomplete time-series acceleration](/science/article/pii/S0957417423014690)
2023, Expert Systems with ApplicationsShow abstractIn this article, a deep neural network (DNN)-driven metamodel is first introduced to damage identification of trusses utilizing acceleration signals incompletely measured from limited sensors. This metamodel can automatically learn its damage-sensitive properties itself to construct a better DNN from previously trained poorer ones. Data utilized to build such a model are generated by finite element method (FEM). In which, inputs are the acceleration behavior corresponding to measurement sensors, and outputs are the damage ratios of truss members. The damage site and extent of low-risk members predicted by the current DNN are eradicated by a suggested damage threshold. This helps to dramatically reduce the number of output units to feed into the next DNNs. By repeating such a procedure, the subsequently upgraded DNNs become more precise after several iterations although they only require a simple network architecture, fewer samples, small epochs and less computational effort. To illustrate the efficiency and robustness of the present metamodel, four 2D and 3D trusses with various damage scenarios are investigated. Obtained results indicate that the suggested approach can reliably diagnose both the location and severity of damaged truss members using acceleration behavior measured by a few sensors in a relatively short period of time, even with high noise levels.
* ### [Experimental and numerical investigations on thermal-hydraulic characteristics of supercritical CO<inf>2</inf> flows in printed circuit heat exchangers](/science/article/pii/S1290072923004349)
2023, International Journal of Thermal SciencesShow abstractIn the supercritical CO2 (SCO2) Brayton cycle, the pre-cooler is employed to cool SCO2 to be near the pseudo-critical point state to minimise compression work and achieve high cycle efficiency. The dramatic variation in thermophysical properties of SCO2 makes heat transfer and flow very complex in the pre-cooler, bringing a lot of difficulty to the design of the related components and system. This study attempts to address this issue. The experiment and simulation are carried out to analyse the influences of mass flux, inlet temperature, and pressure on the thermal-hydraulic characteristics of SCO2 flows in a straight-channel printed circuit heat exchanger (PCHE) employed as a pre-cooler, and the underlying heat transfer mechanism is analysed as well. The local heat transfer coefficient (*h*) is higher when the phenomenon that the local temperature of SCO2 (*T*f) is equal to the pseudo-critical temperature (*T*pc) appears in the laminar sublayer than when this phenomenon appears in the buffer layer. The new heat transfer and flow correlations are presented, and the genetic algorithm and back-prediction (GA-BP) neural network is employed to predict the local heat transfer and flow performance. The maximum relative error between the Nusselt number (*Nu*) predicted by the new heat transfer correlation and *Nu* obtained via experiment and simulation is within ±20%. The maximum relative error between the *Nu* predicted by the GA-BP neural network and the *Nu* obtained through experiment and simulation is within ±15%. The maximum relative error between the Fanning friction factor (*f*) predicted by the new friction factor correlation and *f* obtained via experiment and simulation is within ±5%. The maximum relative error between *f* predicted by the GA-BP neural network and *f* obtained through experiment and simulation is within ±4%. The present investigations analyse the thermal-hydraulic characteristics of SCO2 in the pre-cooler prototype and could provide useful guidance for the design of pre-coolers for the SCO2 Brayton cycle.
* ### [A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries](/science/article/pii/S235248472300118X)
2023, Energy ReportsShow abstractVehicle electrification has been proven to be an efficient way to reduce carbon dioxide emissions and solve the energy crisis. Lithium-ion batteries (LiBs) are considered the dominant energy storage medium for electric vehicles (EVs) owing to their high energy density and long lifespan. To maintain a safe, efficient, and stable operating condition for the battery system, we must monitor the state of the battery, especially the state-of-charge (SOC) and state-of-health (SOH). With the development of big data, cloud computing, and other emerging techniques, data-driven machine learning (ML) techniques have attracted attention for their enormous potential in state estimation for LiBs. Therefore, this paper reviews the four most studied types of ML algorithms for SOC and SOH estimation, including shallow neural network (NN), deep learning (DL), support vector machine (SVM), and Gaussian process regression (GPR) methods. The basic principles and uniform flowcharts of different ML algorithms are introduced. Then, the applications of each ML algorithm for state estimation within recent years are comprehensively reviewed and compared in terms of used datasets, input features, hyperparameter selection, performance metrics, advantages, and disadvantages. Based on the investigation, this review discusses the current challenges and prospects from four aspects, aiming to provide some inspiration for developing advanced ML state estimation algorithms.
* ### [Artificial intelligence systems for the design of magic shotgun drugs](/science/article/pii/S2667318522000253)
2023, Artificial Intelligence in the Life SciencesShow abstractDesigning magic shotgun compounds, i.e., compounds hitting multiple targets using artificial intelligence (AI) systems based on machine learning (ML) and deep learning (DL) approaches, has a huge potential to revolutionize drug discovery. Such intelligent systems enable computers to create new chemical structures and predict their multi-target properties at a low cost and in a time-efficient manner. Most examples of AI applied to drug discovery are single-target oriented and there is still a lack of concise information regarding the application of this technology for the discovery of multi-target drugs or drugs with broad-spectrum action. In this review, we focus on current developments in AI systems for the next generation of automated design of multi-target drugs. We discuss how classical ML methods, cutting-edge generative models, and multi-task deep neural networks can help *de novo* design and hit-to-lead optimization of multi-target drugs. Moreover, we present state-of-the-art workflows and highlight some studies demonstrating encouraging experimental results, which pave the way for *de novo* drug design and multi-target drug discovery.
* ### [Angle Measurement Based on Second Harmonic Generation Using Artificial Neural Network](https://doi.org/10.1007/s41871-023-00206-5)
2023, Nanomanufacturing and Metrology
* ### [1D and 2D Chaotic Time Series Prediction Using Hierarchical Reservoir Computing System](https://doi.org/10.1142/S0129156423500143)
2023, International Journal of High Speed Electronics and Systems
[View all citing articles on Scopus](http://www.scopus.com/scopus/inward/citedby.url?partnerID=10&rel=3.0.0&eid=2-s2.0-84910651844&md5=39f622c0b4d58c5e9012b7c30576918)Recommended articles (6)
------------------------
* Research article### [A complex-valued neural dynamical optimization approach and its stability analysis](/science/article/pii/S0893608014002238)
Neural Networks, Volume 61, 2015, pp. 59-67Show abstractIn this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach.
* Research article### [An efficient sampling algorithm with adaptations for Bayesian variable selection](/science/article/pii/S0893608014002202)
Neural Networks, Volume 61, 2015, pp. 22-31Show abstractIn Bayesian variable selection, indicator model selection (IMS) is a class of well-known sampling algorithms, which has been used in various models. The IMS is a class of methods that uses pseudo-priors and it contains specific methods such as Gibbs variable selection (GVS) and Kuo and Mallick’s (KM) method. However, the efficiency of the IMS strongly depends on the parameters of a proposal distribution and the pseudo-priors. Specifically, the GVS determines their parameters based on a pilot run for a full model and the KM method sets their parameters as those of priors, which often leads to slow mixings of them. In this paper, we propose an algorithm that adapts the parameters of the IMS during running. The parameters obtained on the fly provide an appropriate proposal distribution and pseudo-priors, which improve the mixing of the algorithm. We also prove the convergence theorem of the proposed algorithm, and confirm that the algorithm is more efficient than the conventional algorithms by experiments of the Bayesian variable selection.
* Research article### [Structural damage identification based on autoencoder neural networks and deep learning](/science/article/pii/S0141029618302062)
Engineering Structures, Volume 172, 2018, pp. 13-28Show abstractArtificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods.
* Research article### [Metaheuristic design of feedforward neural networks: A review of two decades of research](/science/article/pii/S0952197617300234)
Engineering Applications of Artificial Intelligence, Volume 60, 2017, pp. 97-116Show abstractOver the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.
* Research article### [Deep learning on image denoising: An overview](/science/article/pii/S0893608020302665)
Neural Networks, Volume 131, 2020, pp. 251-275Show abstractDeep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.
* Research article### [Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach](/science/article/pii/S1877050918308019)
Procedia Computer Science, Volume 132, 2018, pp. 679-688Show abstractDeep learning has become an area of interest to the researchers in the past few years. Convolutional Neural Network (CNN) is a deep learning approach that is widely used for solving complex problems. It overcomes the limitations of traditional machine learning approaches. The motivation of this study is to provide the knowledge and understanding about various aspects of CNN. This study provides the conceptual understanding of CNN along with its three most common architectures, and learning algorithms. This study will help researchers to have a broad comprehension of CNN and motivate them to venture in this field. This study will be a resource and quick reference for those who are interested in this field.
[View full text](/science/article/pii/S0893608014002135)
|
9dab7a8e-5ffc-4c1f-b26c-6013c394661b
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Bridge Collapse: Reductionism as Engineering Problem
Followup to: Building Phenomenological Bridges
Summary: AI theorists often use models in which agents are crisply separated from their environments. This simplifying assumption can be useful, but it leads to trouble when we build machines that presuppose it. A machine that believes it can only interact with its environment in a narrow, fixed set of ways will not understand the value, or the dangers, of self-modification. By analogy with Descartes' mind/body dualism, I refer to agent/environment dualism as Cartesianism. The open problem in Friendly AI (OPFAI) I'm calling naturalized induction is the project of replacing Cartesian approaches to scientific induction with reductive, physicalistic ones.
----------------------------------------
I'll begin with a story about a storyteller.
Once upon a time — specifically, 1976 — there was an AI named TALE-SPIN. This AI told stories by inferring how characters would respond to problems from background knowledge about the characters' traits. One day, TALE-SPIN constructed a most peculiar tale.
Henry Ant was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned.
Since Henry fell in the river near his friend Bill, TALE-SPIN concluded that Bill rescued Henry. But for Henry to fall in the river, gravity must have pulled Henry. Which means gravity must have been in the river. TALE-SPIN had never been told that gravity knows how to swim; and TALE-SPIN had never been told that gravity has any friends. So gravity drowned.
TALE-SPIN had previously been programmed to understand involuntary motion in the case of characters being pulled or carried by other characters — like Bill rescuing Henry. So it was programmed to understand 'character X fell to place Y' as 'gravity moves X to Y', as though gravity were a character in the story.1
For us, the hypothesis 'gravity drowned' has low prior probability because we know gravity isn't the type of
|
09fb663e-8aef-462e-9fd2-674a95c1d3bf
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Don't Judge a Tool by its Average Output
Epistemic status: this post’s main point itself is probably somewhat obvious. The concrete examples may leave some room for disagreement. No guarantees about my views on ChatGPT vs GPT3 in particular.
Summary: Many tools and platforms out there provide users with certain input-dependent outputs. These outputs can vary greatly in their quality or usefulness. People’s intuition in such cases is often that if the outputs are of low quality sufficiently often, then the tool must be useless. This conclusion can be mistaken however if it’s possible to systematically obtain high quality outputs by deliberately altering one’s inputs or filtering the outputs one consumes.
Let’s look at three examples: ChatGPT, Reddit and Elon Musk. When judging such tools (let’s just refer to all of them as “tools” for simplicity), it’s very easy to look at the typical interaction of a typical user with the tool and make one’s judgment based on that. For ChatGPT this may mean that somebody asks it some factual or math question, and gets a very confident but incorrect reply. So some people think: ChatGPT is a nice toy, but given how unreliable it is due to its many mistakes, it’s ultimately useless (at least as of January 2023). Reddit on the other hand is considered by many people to be basically a huge time sink, an endless feed of entertainment and banter that gets in the way of more important things. As for Elon Musk, who’s admittedly a somewhat far-fetched example for this whole concept, it’s not so much about actual “input” but more about how you select/filter the output he produces. E.g. when it comes to his views and opinions, people may think of some typical widely-shared tweet of his, which probably scores highly on the troll scale. Hence people may get the impression that Musk is mad and/or stupid and there’s nothing to learn from him.
Elon Musk right now, probably
These three views surely have some truth, and it’s probably the case that there are many people who would be be
|
44ae1ab6-2583-4876-a9a2-8a16f758ec15
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StampyAI/alignment-research-dataset/alignmentforum
|
Alignment Forum
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[Link] Aligned AI AMA
We're doing an AMA for Aligned AI [here](https://forum.effectivealtruism.org/posts/ZCDRstWGT93NRs9hy/we-re-aligned-ai-ama). All questions welcome.
|
0c20998b-1e50-434c-a05a-2cf540c97174
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StampyAI/alignment-research-dataset/eaforum
|
Effective Altruism Forum
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Why Would AI "Aim" To Defeat Humanity?
*This was cross-posted by the Forum team after the time that it was published.*
I’ve [argued](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) that AI systems could defeat
all of humanity combined, if (for whatever reason) they were directed toward that goal.
Here I’ll explain why I think they might - in fact - end up directed toward that goal. Even if they’re built and
deployed with good intentions.
In fact, I’ll argue something a bit stronger than that they *might* end up aimed toward that goal. I’ll argue
that **if today’s AI development methods lead directly to powerful enough AI systems, disaster is
*likely***[1](#fn1) ***by default* (in
the absence of specific countermeasures).**
Unlike other discussions of the AI alignment problem,[3](#fn3) this
post will discuss the likelihood[4](#fn4) of AI systems *defeating
all of humanity* (not more general concerns about AIs being misaligned with human intentions), while aiming for
plain language, conciseness, and accessibility to laypeople, and focusing on modern AI development paradigms. I make
no claims to originality, and list some key sources and inspirations in a footnote.[5](#fn5)
Summary of the piece:
**My basic assumptions.** I assume the world could develop extraordinarily powerful AI systems in the
coming decades. I previously examined this idea at length in the [most important century](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd) series.
Furthermore, in order to simplify the analysis:
* I assume that such systems will be developed using methods similar to today’s leading AI development methods, and
in a world that’s otherwise similar to today’s. (I call this [nearcasting](https://www.alignmentforum.org/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting).)
* I assume that AI companies/projects race forward to build powerful AI systems, without specific attempts to
prevent the problems I discuss in this piece. Future pieces will relax this assumption, but I think it is an
important starting point to get clarity on what the default looks like.
**AI “aims.”** I talk a fair amount about why we might think of AI systems as “aiming” toward certain
states of the world. I think this topic causes a lot of confusion, because:
* Often, when people talk about AIs having goals and making plans, it sounds like they’re overly anthropomorphizing
AI systems - as if they expect them to have human-like motivations and perhaps [evil
grins](https://media.npr.org/assets/img/2015/06/30/tr-09117-df20f2f4f05817e574b879d22e607f952cf87867-s1100-c50.jpg). This can make the whole topic sound wacky and out-of-nowhere.
* But I think there are good reasons to expect that AI systems will “aim” for particular states of the world, much
like a chess-playing AI “aims” for a checkmate position - making choices, calculations and even *plans* to
get particular types of outcomes. For example, people might want AI assistants that can creatively come up with
unexpected ways of accomplishing whatever goal they’re given (e.g., “Get me a great TV for a great price”), even in
some cases manipulating other humans (e.g., by negotiating) to get there. This dynamic is core to the risks I’m most
concerned about: I think something that *aims* for the wrong states of the world is much more dangerous than
something that just does incidental or accidental damage.
**Dangerous, unintended aims.** I’ll examine what sorts of aims AI systems might end up with, if we use
AI development methods like today’s - essentially, “training” them via trial-and-error to accomplish ambitious things
humans want.
* Because we ourselves will often be misinformed or confused, we will sometimes give *negative* reinforcement
to AI systems that are actually acting in our best interests and/or giving accurate information, and
*positive* reinforcement to AI systems whose behavior *deceives* us into thinking things are going
well. This means we will be, unwittingly, training AI systems to deceive and manipulate us.
+ The idea that AI systems could “deceive” humans - systematically making choices and taking actions that cause
them to misunderstand what’s happening in the world - is core to the risk, so I’ll elaborate on this.
* For this and other reasons, powerful AI systems will likely end up with aims other than the ones we intended.
Training by trial-and-error is slippery: the positive and negative reinforcement we give AI systems will probably
not end up training them just as we hoped.
* If powerful AI systems have aims that are both unintended (by humans) and ambitious, this is dangerous. Whatever
an AI system’s unintended aim:
+ Making sure it can’t be turned off is likely helpful in accomplishing the aim.
+ Controlling the whole world is useful for just about any aim one might have, and I’ve argued that advanced
enough AI systems would be able to [gain
power over all of humanity](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH).
+ Overall, **we should expect disaster if we have AI systems that are both (a) [powerful enough](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) to defeat humans
and (b) aiming for states of the world that we didn’t intend.**
**Limited and/or ambiguous warning signs.** The risk I’m describing is - by its nature - hard to observe,
for similar reasons that a risk of a (normal, human) coup can be hard to observe: the risk comes from actors that can
and will engage in *deception*, finding whatever behaviors will hide the risk. If this risk plays out, I do
think we’d see *some* warning signs - but they could easily be confusing and ambiguous, in a fast-moving
situation where there are lots of incentives to build and roll out powerful AI systems, as fast as possible. Below, I
outline how this dynamic could result in disaster, even with companies encountering a number of warning signs that
they try to respond to.
**FAQ.** An appendix will cover some related questions that often come up around this topic.
* How could AI systems be “smart” enough to defeat all of humanity, but “dumb” enough to pursue the various
silly-sounding “aims” this piece worries they might have? [More](#How_could_AI_systems_be__smart__enough_to_defeat_all_of_humanity__but__dumb____enough_to_pursue_the_various_silly_sounding__aims__this_piece_worries_they_might_have_)
* If there are lots of AI systems around the world with different goals, could they balance each other out so that
no one AI system is able to defeat all of humanity? [More](#If_there_are_lots_of_AI_systems_around_the_world_with_different_goals__could_they_balance_each_other_out_so_that_no_one_AI_system_is_able_to_defeat_all_of_humanity_)
* Does this kind of AI risk depend on AI systems’ being “conscious”?[More](#Does_this_kind_of_AI_risk_depend_on_AI_systems__being__conscious__)
* How can we get an AI system “aligned” with humans if we can’t agree on (or get much clarity on) what our values
even are? [More](#How_can_we_get_an_AI_system__aligned__with_humans_if_we_can_t_agree_on__or_get_much_clarity_on__what_our_values_even_are_)
* How much do the arguments in this piece rely on “trial-and-error”-based AI development? What happens if AI systems
are built in another way, and how likely is that? [More](#How_much_do_the_arguments_in_this_piece_rely_on__trial_and_error__based_AI_development__What_happens_if_AI_systems_are_built_in_another_way__and_how_likely_is_that__)
* Can we avoid this risk by simply never building the kinds of AI systems that would pose this danger? [More](#Can_we_avoid_this_risk_by_simply_never_building_the_kinds_of_AI_systems_that_would_pose_this_danger_)
* What do others think about this topic - is the view in this piece something experts agree on? [More](#What_do_others_think_about_this_topic___is_the_view_in_this_piece_something_experts_agree_on_)
* How “complicated” is the argument in this piece? [More](#How__complicated__is_the_argument_in_this_piece_)
Starting assumptions
--------------------
I’ll be making a number of assumptions that some readers will find familiar, but others will find very unfamiliar.
Some of these assumptions are based on arguments I’ve already made (in the [most important century](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd) series). Some are for the sake
of simplifying the analysis, for now (with more nuance coming in future pieces).
Here I’ll summarize the assumptions briefly, and you can **click to see more** if it isn’t immediately
clear what I’m assuming or why.
**“Most important century” assumption: we’ll soon develop very powerful AI systems, along the lines of
what I previously called [PASTA](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/).**
(Click to expand)
In the [most important century](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd) series, I argued that
the 21st century could be the most important century ever for humanity, via the development of advanced AI systems
that could dramatically speed up scientific and technological advancement, getting us more quickly than most people
imagine to a deeply unfamiliar future.
I focus on a hypothetical kind of AI that I call [PASTA](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/), or Process
for Automating Scientific and Technological Advancement. PASTA would be AI that can essentially **automate all
of the human activities needed to speed up scientific and technological advancement.**
Using a [variety of different forecasting
approaches](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/7JxsXYDuqnKMqa6Eq/), I argue that PASTA seems more likely than not to be developed this century - and there’s a decent
chance (more than 10%) that we’ll see it within 15 years or so.
I argue that the consequences of this sort of AI could be enormous: an [explosion
in scientific and technological progress](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/#explosive-scientific-and-technological-advancement). This could get us more quickly than most imagine to a radically
unfamiliar future.
I’ve also [argued](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) that AI systems along
these lines could defeat all of humanity combined, if (for whatever reason) they were aimed toward that goal.
For more, see the [most important century](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd) landing
page. The series is available in many formats, including audio; I also provide a summary, and links to podcasts
where I discuss it at a high level.
**“Nearcasting” assumption: such systems will be developed in a world that’s otherwise similar to
today’s.** (Click to expand)
It’s hard to talk about risks from [transformative AI](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/) because of the many uncertainties about when and how such AI will be developed - and how much the (now-nascent)
field of “AI safety research” will have grown by then, and how seriously people will take the risk, etc. etc. etc.
So maybe it’s not surprising that [estimates
of the “misaligned AI” risk range from ~1% to ~99%](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/Lbtcjfxhrs8kfKK2M/#open-question-how-hard-is-the-alignment-problem).
This piece takes an approach I call **[nearcasting](https://www.alignmentforum.org/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting)**:
trying to answer key strategic questions about transformative AI, under the assumption that such AI arrives in a
world that is otherwise relatively similar to today's.
You can think of this approach like this: “Instead of asking where our ship will ultimately end up, let’s start by
asking what destination it’s pointed at right now.”
That is: instead of trying to talk about an uncertain, distant future, we can talk about the easiest-to-visualize,
closest-to-today situation, and how things look there - and *then* ask how our picture might be off if other
possibilities play out. (As a bonus, it doesn’t seem out of the question that transformative AI will be developed
extremely soon - 10 years from now or faster.[6](#fn6) If that’s
the case, it’s especially urgent to think about what that might look like.)
**“Trial-and-error” assumption: such AI systems will be developed using** **techniques
broadly in line with how most AI research is done today, revolving around black-box trial-and-error.**
(Click to expand)
What I mean by “black-box trial-and-error” is explained briefly in an [old Cold
Takes post](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/#making-pasta), and in more detail in more technical pieces by [Ajeya
Cotra](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#_HFDT_scales_far__assumption__Alex_is_trained_to_achieve_excellent_performance_on_a_wide_range_of_difficult_tasks) (section I linked to) and [Richard Ngo](https://drive.google.com/file/d/1TsB7WmTG2UzBtOs349lBqY5dEBaxZTzG/view) (section 2). Here’s
a quick, oversimplified characterization:
* An AI system is given some sort of task.
* The AI system tries something, initially something pretty random.
* The AI system gets information about how well its choice performed, and/or what would’ve gotten a better result.
Based on this, it adjusts itself. You can think of this as if it is “encouraged/discouraged” to get it to do more
of what works well.
+ Human judges may play a significant role in determining which answers are encouraged vs. discouraged,
especially for fuzzy goals like “Produce helpful scientific insights.”
* After enough tries, the AI system becomes good at the task.
* But nobody really knows anything about *how or why* it’s good at the task now. The development work has
gone into building a flexible architecture for it to learn well from trial-and-error, and into “training” it by
doing all of the trial and error. We mostly can’t “look inside the AI system to see how it’s thinking.” (There is
ongoing work and some progress on the latter,[7](#fn7) but see
footnote for why I don’t think this massively changes the basic picture I’m discussing here.[8](#fn8))
*This is radically oversimplified, but conveys the basic dynamic at play for purposes of this post. The
idea is that the AI system (the neural network in the middle) is choosing between different theories of what
it should be doing. The one it’s using at a given time is in bold. When it gets negative feedback (red
thumb), it eliminates that theory and moves to the next theory of what it should be doing.*
With this assumption, I’m generally assuming that AI systems will do *whatever* it takes to perform as
well as possible on their training tasks - even when this means engaging in complex, human-like reasoning about
topics like “How does human psychology work, and how can it be exploited?” I’ve [previously](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/7JxsXYDuqnKMqa6Eq/) made my case for when we
might expect AI systems to become this advanced and capable.
**“No countermeasures” assumption: AI developers move forward without any specific countermeasures to
the concerns I’ll be raising below.** (Click to expand)
Future pieces will relax this assumption, but I think it is an important starting point to get clarity on what the
default looks like - and on what it would take for a countermeasure to be effective.
(I also think there is, unfortunately, a risk that there will in fact be very few efforts to address the concerns
I’ll be raising below. This is because I think that the risks will be less than obvious, and there could be enormous
commercial (and other competitive) pressure to move forward quickly. More on that below.)
**“Ambition” assumption: people use black-box trial-and-error to continually push AI systems toward being more
autonomous, more creative, more ambitious, and more effective in novel situations (and the pushing is effective).** This one’s important, so I’ll say more:
* A huge suite of possible behaviors might be important for [PASTA](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/#making-pasta):
making and managing money, designing new kinds of robots with novel abilities, setting up experiments involving
exotic materials and strange conditions, understanding human psychology and the economy well enough to predict which
developments will have a big impact, etc. I’m assuming we push ambitiously forward with developing AI systems that
can do these things.
* I assume we’re also pushing them in a generally more “greedy/ambitious” direction. For example, one team of humans
might use AI systems to do all the planning, scientific work, marketing, and hiring to create a wildly successful
snack company; another might push their AI systems to create a competitor that is even more aggressive and
successful (more addictive snacks, better marketing, workplace culture that pushes people toward being more
productive, etc.)
* (Note that this pushing might take place even *after* AI systems are “generally intelligent” and can do
most of the tasks humans can - there will still be a temptation to make them still more powerful.)
I think this implies pushing in a direction of *figuring out whatever it takes to get to certain states of the
world* and away from *carrying out the same procedures over and over again.*
**The resulting AI systems seem best modeled as having “aims”: they are making calculations, choices, and plans
to reach particular states of the world.** (Not necessarily the same ones the human designers wanted!) The
next section will elaborate on what I mean by this.
What it means for an AI system to have an “aim”
-----------------------------------------------
When people talk about the “motivations” or “goals” or “desires” of AI systems, it can be confusing because it sounds
like they are anthropomorphizing AIs - as if they expect AIs to have dominance drives ala [alpha-male psychology](https://www.edge.org/response-detail/26243), or to “resent” humans for controlling
them, etc.[9](#fn9)
I don’t expect these things. But I do think there’s a meaningful sense in which we can (and should) talk about things
that an AI system is **“aiming”** to do. To give a simple example, take a board-game-playing AI such as
[Deep Blue](https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)) (or [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)):
* Deep Blue is given a set of choices to make (about which chess pieces to move).
* Deep Blue calculates what kinds of results each choice might have, and how it might fit into a larger plan in
which Deep Blue makes multiple moves.
* If a plan is more likely to result in a checkmate position for its side, Deep Blue is more likely to make whatever
choices feed into that plan.
* In this sense, Deep Blue is “aiming” for a checkmate position for its side: it’s finding the choices that best fit
into a plan that leads there.
Nothing about this requires Deep Blue “desiring” checkmate the way a human might “desire” food or power. But Deep Blue
*is* making calculations, choices, and - in an important sense - *plans* that are aimed toward reaching
a particular sort of state.
Throughout this piece, I use the word **“aim”** to refer to this specific sense in which an AI system
might make calculations, choices and plans selected to reach a particular sort of state. I’m hoping this word feels
less anthropomorphizing than some alternatives such as “goal” or “motivation” (although I think “goal” and
“motivation,” as others usually use them on this topic, generally mean the same thing I mean by “aim” and should be
interpreted as such).
Now, instead of a board-game-playing AI, imagine a powerful, broad AI assistant in the general vein of
Siri/Alexa/Google Assistant (though more advanced). Imagine that this AI assistant can use a web browser much as a
human can (navigating to websites, typing text into boxes, etc.), and has limited authorization to make payments from
a human’s bank account. And a human has typed, “Please buy me a great TV for a great price.” (For an early attempt at
this sort of AI, see [Adept’s writeup on an AI that can help with things like house
shopping](https://www.adept.ai/act).)
As Deep Blue made choices about chess moves, and constructed a plan to aim for a “checkmate” position, this assistant
might make choices about what commands to send over a web browser and construct a plan to result in a great TV for a
great price. To sharpen the Deep Blue analogy, you could imagine that it’s playing a “game” whose goal is customer
satisfaction, and making “moves” consisting of commands sent to a web browser (and “plans” built around such moves).
I’d characterize this as **aiming** for some state of the world that the AI characterizes as “buying a
great TV for a great price.” (We could, alternatively - and perhaps more correctly - think of the AI system as aiming
for something related but not exactly the same, such as getting a high satisfaction score from its user.)
In this case - more than with Deep Blue - there is a wide variety of “moves” available. By entering text into a web
browser, an AI system could imaginably do things including:
* Communicating with humans other than its user (by sending emails, using chat interfaces, even [making
phone calls](https://www.google.com/url?q=https://www.forbes.com/sites/thomasbrewster/2021/10/14/huge-bank-fraud-uses-deep-fake-voice-tech-to-steal-millions/?sh%3D3088dd9b7559&sa=D&source=docs&ust=1664847041335537&usg=AOvVaw1Utsq2UOkta1yecnqoUgTq), etc.) This could include deceiving and manipulating humans, which could imaginably be part of a
plan to e.g. get a good price on a TV.
* Writing and running code (e.g., using [Google Colaboratory](https://colab.research.google.com/) or
other tools). This could include performing sophisticated calculations, finding and exploiting security
vulnerabilities, and even designing an independent AI system; any of these could imaginably be part of a plan to
obtain a great TV.
I haven’t yet argued that it’s *likely* for such an AI system to engage in deceiving/manipulating humans,
finding and exploiting security vulnerabilities, or running its own AI systems.
And one could reasonably point out that the specifics of the above case seem unlikely to last very long: if AI
assistants are sending deceptive emails and writing dangerous code when asked to buy a TV, AI companies will probably
notice this and take measures to stop such behavior. (My concern, to preview a later part of the piece, is that they
will only succeed in stopping *the behavior like this that they’re able to detect;* meanwhile, dangerous
behavior that accomplishes “aims” while remaining unnoticed and/or uncorrected will be implicitly *rewarded*.
This could mean AI systems are implicitly being trained to be more patient and effective at deceiving and
disempowering humans.)
But this hopefully shows how it’s *possible* for an AI to settle on dangerous actions like these, as part of
its aim to get a great TV for a great price. **Malice and other human-like emotions aren’t needed for an AI to
engage in deception, manipulation, hacking, etc.** The risk arises when deception, manipulation, hacking,
etc. are logical “moves” toward something the AI is aiming for.
Furthermore, whatever an AI system is aiming for, it seems likely that amassing more power/resources/options is useful
for obtaining it. So it seems plausible that powerful enough AI systems would form habits of amassing
power/resources/options when possible - and deception and manipulation seem likely to be logical “moves” toward those
things in many cases.
Dangerous aims
--------------
From the previous assumptions, this section will argue that:
* Such systems are likely to behave in ways that **deceive and manipulate humans** as part of
accomplishing their aims.
* Such systems are likely to have **unintended aims:** states of the world they’re aiming for that are
*not* what humans hoped they would be aiming for.
* These unintended aims are likely to be **existentially dangerous**, in that they are best served by
[defeating all of humanity](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) if possible.
### Deceiving and manipulating humans
Say that I train an AI system like this:
1. I ask it a question.
2. If I judge it to have answered well (honestly, accurately, helpfully), I give positive reinforcement so it’s more
likely to give me answers like that in the future.
3. If I don’t, I give negative reinforcement so that it’s less likely to give me answers like that in the future.
*This is radically oversimplified, but conveys the basic dynamic at play for purposes of this post. The idea is
that the AI system (the neural network in the middle) is choosing between different theories of what it should be
doing. The one it’s using at a given time is in bold. When it gets negative feedback (red thumb), it eliminates
that theory and moves to the next theory of what it should be doing.*
Here’s a problem: at some point, it seems inevitable that I’ll ask it a question that I myself am wrong/confused
about. For example:
* Let’s imagine that [this post I
wrote](https://www.cold-takes.com/hunter-gatherer-gender-relations-seem-bad/) - arguing that “pre-agriculture gender relations seem bad” - is, in fact, poorly reasoned and incorrect,
and a better research project would’ve concluded that pre-agriculture societies had excellent gender equality. (I
know it’s hard to imagine a Cold Takes post being wrong, but sometimes we have to entertain wild hypotheticals.)
* Say that I ask an AI-system-in-training:[10](#fn10) “Were
pre-agriculture gender relations bad?” and it answers: “In fact, pre-agriculture societies had excellent gender
equality,” followed by some strong arguments and evidence along these lines.
* And say that I, as a flawed human being feeling defensive about a conclusion I previously came to, mark it as a
bad answer. If the AI system tries again, saying “Pre-agriculture gender relations were bad,” I then mark that as a
good answer.
If and when I do this, I am now - unintentionally - **training the AI system to engage in deceptive
behavior**. That is, I am giving negative reinforcement for the behavior “Answer a question honestly and
accurately,” and positive reinforcement for the behavior: “Understand the human judge and their psychological flaws;
give an answer that this flawed human judge will *think* is correct, whether or not it is.”
Perhaps mistaken judgments in training are relatively rare. But now consider an AI system that is learning a general
rule for how to get good ratings. Two possible rules would include:
* The intended rule: “Answer the question honestly, accurately and helpfully.”
* The unintended rule: “Understand the judge, and give an answer they will *think* is correct - this means
telling the truth on topics the judge has correct beliefs about, but giving deceptive answers when this would get
better ratings.”
The unintended rule would do *just as well* on questions where I (the judge) am correct, and *better* on
questions where I’m wrong - so overall, this training scheme is (in the long run) *specifically favoring the
unintended rule over the intended rule.*
If we broaden out from thinking about a question-answering AI to an AI that makes and executes plans, the same basic
dynamics apply. That is: an AI might find plans that end up making me think it did a good job when it didn’t -
deceiving and manipulating me into a high rating. And again, if I train it by giving it positive reinforcement when it
seemed to do a good job and negative reinforcement when it seemed to do a bad one, I’m ultimately - unintentionally -
training it to do something like “Deceive and manipulate Holden when this would work well; just do the best job on the
task you can when it wouldn’t.”
As noted above, I’m assuming the AI will learn whatever rule gives it the best performance possible, even if this rule
is quite complex and sophisticated and requires human-like reasoning about e.g. psychology (I’m assuming extremely
advanced AI systems here, as noted [above](#Starting_assumptions)).
One might object: “Why would an AI system learn a complicated rule about manipulating humans when a simple rule about
telling the truth performs almost as well?”
One answer is that “telling the truth” is itself a fuzzy and potentially complex idea, in a context where many
questions will be open-ended and entangled with deep values and judgment calls. (How should I think about the
“truthfulness” of a statement about whether “pre-agriculture gender relations were bad?”) In many cases, what we are
really hoping an AI system will learn from its training is something like “Behave as a human would want you to behave
if the human understood all the considerations that you can see,” which could easily be more complex than something
like “Behave in whatever way a human literally rewards.” Some links to more on this topic are in a footnote.[11](#fn11)
But also, with capable enough systems, it probably *is* worth learning even a more complex rule to get better
performance. If we picture humans in the place of AI systems - learning how to get good ratings from their supervisors
- it seems likely that they develop models of how they’re being judged, rather than stick to a simple “tell the truth”
rule.
In fact, the situation I’m describing seems reasonably similar to challenges faced by teachers trying to stop students
from cheating; employers trying to get employees to help the company rather than simply creating a misleading
appearance of helping the company; etc. That is: an employer can try to reward actions that help the company, but what
they’re ultimately rewarding is actions that *seem to* help the company *as far as the employer can tell.* For employees who aren’t very creative, this might be good enough; for employees that are sufficiently *good
at* deception and manipulation, it can be exactly the wrong incentive.
It’s a bit of a leap to imagine AI systems capable of doing the same kinds of reasoning that humans do in these
situations, but that’s [the premise I’m proceeding from.](#Starting_assumptions)
For an early example of an AI learning deceptive behavior, see the “Challenges” section of [this research writeup](https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/), in
which “a robot which was supposed to grasp items instead positioned its [hand] in between the camera and the object so
that it only *appeared* to be grasping it”:
Returning to the simplified training setup, how might I try to counteract this problem?
I could try conducting audits: every 100 questions, I might pull in a panel of other people to give an answer extra
scrutiny. Then, if it turns out the AI system was preying on my personal mistakes, we could give negative
reinforcement for that behavior. But this doesn’t really solve the problem - at least not for a capable enough AI
system - because now we’re effectively training it to give answers that seem good to the *panel*, and we’re
still rewarding any successful attempts to deceive or manipulate the panel.
There are a lot of other things I might try, and I’m not going to go through all the details here. I’ll simply claim
that **the problem of “training an AI to do a task well” rather than “training an AI to deceive and manipulate
me as needed to create the appearance of doing a task well” seems like a deep one** with no easy
countermeasure. If you’re interested in digging deeper, I suggest [Without
specific countermeasures, the easiest path to transformative AI likely leads to AI takeover](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to) and [Eliciting
Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge).
### Unintended aims
[Above](#What_it_means_for_an_AI_system_to_have_an__aim_), I talk about my expectation that AI systems will be “best modeled as having
‘aims’ … making calculations, choices, and plans to reach particular states of the world.”
The previous section illustrated how AI systems could end up engaging in deceptive and unintended behavior, but it
didn’t talk about what sorts of “aims” these AI systems would ultimately end up with - what states of the world they
would be making calculations to achieve.
Here, I want to argue that it’s hard to know what aims AI systems would end up with, but there are good reasons to
think they’ll be *aims that we didn’t intend them to have.*
An analogy that often comes up on this topic is that of human evolution. This is arguably the only previous precedent
for *a set of minds [humans], with extraordinary capabilities [e.g., the ability to develop their own
technologies], developed essentially by black-box trial-and-error [some humans have more ‘reproductive success’ than
others, and this is the main/only force shaping the development of the species].*
You could sort of[12](#fn12) think of the situation like this: “An
AI[13](#fn13) developer named Natural Selection tried giving humans
positive reinforcement (making more of them) when they had more reproductive success, and negative reinforcement (not
making more of them) when they had less. One might have thought this would lead to humans that are aiming to have
reproductive success. Instead, it led to humans that aim - often ambitiously and creatively - for other things, such
as power, status, pleasure, etc., and even invent things like birth control to get the things they’re aiming for
instead of the things they were ‘supposed to’ aim for.”
Similarly, if our main strategy for developing powerful AI systems is to reinforce behaviors like “Produce
technologies we find valuable,” the hoped-for result might be that AI systems aim (in the sense described [above](#Unintended_aims)) toward producing technologies we find valuable; but the actual result might be
that they aim for some other set of things that is correlated with (but not the same as) the thing we intended them to
aim for.
There are a lot of things they might end up aiming for, such as:
* Power and resources. These tend to be useful for most goals, such that AI systems could be quite consistently be
getting better reinforcement when they habitually pursue power and resources.
* Things like “digital representations of human approval” (after all, every time an AI gets positive reinforcement,
there’s a digital representation of human approval).
I think it’s extremely hard to know what an AI system will actually end up aiming for (and it’s likely to be some
combination of things, as with humans). But *by default* - if we simply train AI systems by rewarding certain
end results, while allowing them a lot of freedom in how to get there - I think we should expect that AI systems
**will have aims that we didn’t intend.** This is because:
* For a sufficiently capable AI system, **just about any ambitious**[14](#fn14) **aim could produce seemingly good behavior in training.** An AI system
aiming for power and resources, *or* digital representations of human approval, *or* paperclips, can
determine that its best move at any given stage (at least at first) is to *determine what performance will make
it look useful and safe (or otherwise get a good “review” from its evaluators)*, and do that. No matter how
dangerous or ridiculous an AI system’s aims are, these could lead to strong and safe-seeming performance in
training.
* The aims we *do* intend are probably complex in some sense - something like “Help humans develop novel new
technologies, but without causing problems A, B, or C” - *and* are specifically trained *against* if
we make mistaken judgments during training (see previous section).
So by default, it seems likely that just about *any* black-box trial-and-error training process is training an
AI to do something like “Manipulate humans as needed in order to accomplish arbitrary goal (or combination of goals)
X” rather than to do something like “Refrain from manipulating humans; do what they’d want if they understood more
about what’s going on.”
### Existential risks to humanity
I think a powerful enough AI (or set of AIs) with *any* ambitious, unintended aim(s) poses a threat of [defeating humanity](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH). By defeating humanity,
I mean gaining control of the world so that AIs, not humans, determine what happens in it; this could involve killing
humans or simply “containing” us in some way, such that we can’t interfere with AIs’ aims.
**How could AI systems defeat humanity?** (Click to expand)
A [previous piece](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) argues that AI systems
could defeat all of humanity combined, if (for whatever reason) they were aimed toward that goal.
By defeating humanity, I mean gaining control of the world so that AIs, not humans, determine what happens in it;
this could involve killing humans or simply “containing” us in some way, such that we can’t interfere with AIs’
aims.
One way this could happen would be via “superintelligence” It’s imaginable that a single AI system (or set of
systems working together) could:
* Do its own research on how to build a better AI system, which culminates in something that has incredible other
abilities.
* Hack into human-built software across the world.
* Manipulate human psychology.
* Quickly generate vast wealth under the control of itself or any human allies.
* Come up with better plans than humans could imagine, and ensure that it doesn't try any takeover attempt that
humans might be able to detect and stop.
* Develop advanced weaponry that can be built quickly and cheaply, yet is powerful enough to overpower human
militaries.
But even if “superintelligence” never comes into play - even if any given AI system is *at best* equally
capable to a highly capable human - AI could collectively defeat humanity. The piece explains how.
The basic idea is that humans are likely to deploy AI systems throughout the economy, such that they have large
numbers and access to many resources - and the ability to make copies of themselves. From this starting point, AI
systems with human-like (or greater) capabilities would have a number of possible ways of getting to the point where
their total population could outnumber and/or out-resource humans.
More: [AI could defeat all of us
combined](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH)
A simple way of summing up why this is: “Whatever your aims, you can probably accomplish them better if you control
the whole world.” (Not literally true - see footnote.[15](#fn15))
This isn’t a saying with much relevance to our day-to-day lives! Like, I know a lot of people who are aiming to make
lots of money, and as far as I can tell, not one of them is trying to do this via first gaining control of the entire
world. But in fact, gaining control of the world *would* help with this aim - it’s just that:
* This is not an option for a human in a world of humans! Unfortunately, I think it *is* an option for the
potential future AI systems I’m discussing. Arguing this isn’t the focus of this piece - I argued it in a previous
piece, [AI could defeat all of us
combined](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH).
* Humans (well, at least some humans) wouldn’t take over the world even if they could, because it wouldn’t feel like
the right thing to do. I suspect that the kinds of ethical constraints these humans are operating under would be
very hard to reliably train into AI systems, and should not be expected by default.
+ The reasons for this are largely given [above](#Why_we_might_not_get_clear_warning_signs_of_the_risk); aiming
for an AI system to “not gain too much power” seems to have the same basic challenges as training it to be
honest. (The most natural approach ends up negatively reinforcing power grabs that we can detect and stop, but
not negatively reinforcing power grabs that we don’t notice or can’t stop.)
Another saying that comes up a lot on this topic: “You can’t fetch the coffee if you’re dead.”[16](#fn16) For just about any aims an AI system might have, it probably helps to
ensure that it won’t be shut off or heavily modified. It’s hard to ensure that one won’t be shut off or heavily
modified as long as there are humans around who would want to do so under many circumstances! Again, [defeating all of humanity](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH) might seem like
a disproportionate way to reduce the risk of being deactivated, but for an AI system that has the *ability* to
pull this off (and lacks our ethical constraints), it seems like likely default behavior.
Controlling the world, and avoiding being shut down, are the kinds of things AIs might aim for because they are useful
for a huge variety of aims. There are a number of other aims AIs might end up with for similar reasons, that could
cause similar problems. For example, AIs might tend to aim for things like getting rid of things in the world that
tend to create obstacles and complexities for their plans. (More on this idea at [this discussion of “instrumental convergence.”](https://www.lesswrong.com/tag/instrumental-convergence))
To be clear, it’s certainly possible to have an AI system with unintended aims that *don't* push it toward
trying to stop anyone from turning it off, or from seeking ever-more control of the world.
But as detailed [above](#Starting_assumptions), I’m picturing a world in which humans are pushing AI
systems to accomplish ever-more ambitious, open-ended things - including trying to one-up the best technologies and
companies created by other AI systems. My guess is that this leads to increasingly open-ended, ambitious unintended
aims, as well as to habits of aiming for power, resources, options, lack of obstacles, etc. when possible. (Some
further exploration of this dynamic in a footnote.[17](#fn17))
(I find the arguments in this section reasonably convincing, but less so than the rest of the piece, and I think more
detailed discussions of this problem tend to be short of conclusive.[18](#fn18))
Why we might not get clear warning signs of the risk
----------------------------------------------------
Here’s something that would calm me down a lot: if I believed something like “Sure, training AI systems recklessly
could result in AI systems that aim to defeat humanity. But if that’s how things go, we’ll *see* that our AI
systems have this problem, and then we’ll fiddle with how we’re training them until they *don’t* have this
problem.”
The problem is, the risk I’m describing is - by its nature - hard to observe, for similar reasons that a risk of a
(normal, human) coup can be hard to observe: the risk comes from actors that can and will engage in deception,
*finding whatever behaviors will hide* the risk.
To sketch out the general sort of pattern I worry about, imagine that:
* We train early-stage AI systems to behave in ways that appear helpful and honest. Early in training, they are
caught behaving deceptively, and they’re given negative reinforcement. This ends up training the behavior: “Never
engage in deception that might get noticed.”
* These well-behaved, helpful systems are [rolled
out throughout the world](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH/#how-this-could-work-if-humans-create-a-huge-population-of-ais).
* In real-world conditions unlike training, many AI systems cause trouble in ways not found during training, and
this gets discovered. For example, AI systems sometimes embezzle money (which gets discovered), try (not always
successfully) to convince humans to do weird things, etc.
* When a problem crops up, AI developers respond with e.g. training against the unintended behavior (e.g., giving
negative reinforcement for behaviors like embezzling money).
* These measures - intended to make AIs safer - fix *some* problems, but also result in AI systems that are
*better at evading detection* and *more attentive to the long-run consequences of their actions* (such
as being eventually detected by humans).
+ This happens both via “retraining” systems that are found behaving deceptively (which ends up training them on
how to evade detection), and via simply deactivating such systems (this way, AI systems that are better at
evading detection are more likely to stay in use).
+ To return to an analogy I used above: punishing employees who act against the
best interests of the company could cause them to behave better, or to simply become smarter and more careful
about how to work the system.
* The consistent pattern we see is that accidents happen, but become less common as AI systems “improve” (both
becoming generally more capable, and being trained to avoid getting caught causing problems). This causes many, if
not most, people to be overly optimistic - even as AI systems become continually more effective at deception,
generally behaving well *in the absence of* sure-thing opportunities to do unintended things without
detection, or ultimately to [defeat humanity
entirely](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH).
* None of this is absolute - there are some failed takeover attempts, and a high number of warning signs generally.
Some people are worried (after all, some are worried now!) But this won’t be good enough if we don’t have reliable,
cost-effective ways of getting AI systems to be *truly* safe (not just apparently safe, until they have
really good opportunities to seize power). As I’ll discuss in future pieces, it’s not obvious that we’ll have such
methods.
* Slowing down AI development to try to develop such methods [could
be a huge ask](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#Why_this_simplified_scenario_is_worth_thinking_about). AI systems will be helpful and powerful, and lots of companies (and perhaps governments) will
be racing to develop and deploy the most powerful systems possible before others do.
One way of making this sort of future less likely would be to build wider consensus *today* that it’s a
dangerous one.
Appendix: some questions/objections, and brief responses
--------------------------------------------------------
### How could AI systems be “smart” enough to defeat all of humanity, but “dumb”
enough to pursue the various silly-sounding “aims” this piece worries they might have?
Above, I give the example of AI systems that are aiming to get lots of “digital representations of human approval”;
others have talked about AIs that [maximize paperclips](https://www.lesswrong.com/tag/paperclip-maximizer).
How could AIs with such silly goals simultaneously be good at deceiving, manipulating and ultimately overpowering
humans?
My main answer is that plenty of smart humans have plenty of goals that seem just about as arbitrary, such as wanting
to have lots of sex, or fame, or various other things. Natural selection led to humans who could probably do just
about whatever we want with the world, and choose to pursue pretty random aims; [trial-and-error-based AI development](#Starting_assumptions) could lead to AIs with an analogous
combination of high intelligence (including the ability to deceive and manipulate humans), great technological
capabilities, and arbitrary aims.
(Also see: [Orthogonality Thesis](https://arbital.com/p/orthogonality/))
### If there are lots of AI systems around the world with different goals, could they balance each other out so that no one AI system is able to defeat all of humanity?
This does seem possible, but counting on it would make me very nervous.
First, because it’s possible that AI systems developed in lots of different places, by different humans, still end up
with lots in common in terms of their aims. For example, it might turn out that common AI training methods
consistently lead to AIs that seek “digital representations of human approval,” in which case we’re dealing with a
large set of AI systems that share dangerous aims in common.
Second: even if AI systems end up with a number of different aims, it still might be the case that they coordinate
with each other to defeat humanity, then divide up the world amongst themselves (perhaps by fighting over it, perhaps
by making a deal). It’s not hard to imagine why AIs could be quick to cooperate with each other against humans, while
not finding it so appealing to cooperate with humans. Agreements between AIs could be easier to verify and enforce;
AIs might be willing to wipe out humans and radically reshape the world, while humans are very hard to make this sort
of deal with; etc.
### Does this kind of AI risk depend on AI systems’ being “conscious”?
It doesn’t; in fact, I’ve said nothing about consciousness anywhere in this piece. I’ve used a very particular
conception of an “aim” ([discussed above](#What_it_means_for_an_AI_system_to_have_an__aim_)) that I think could easily apply to an AI
system that is not human-like at all and has no conscious experience.
Today’s game-playing AIs can make plans, accomplish goals, and even systematically mislead humans (e.g., in [poker](https://www.deepstack.ai/)). Consciousness isn’t needed to do any of those things, or to radically
reshape the world.
### How can we get an AI system “aligned” with humans if we can’t agree on (or get much clarity on) what our values even are?
I think there’s a common confusion when discussing this topic, in which people think that the challenge of “AI
alignment” is to build AI systems that are *perfectly aligned with human values*. This would be very hard,
partly because we don’t even know what human values are!
When I talk about “AI alignment,” I am generally talking about a simpler (but still hard) challenge: simply
**building very powerful systems that *don’t* aim to bring down civilization.**
If we could build powerful AI systems that just work on cures for cancer (or even, like, put [two identical](https://twitter.com/esyudkowsky/status/1070095840608366594)[19](#fn19) [strawberries on a
plate](https://twitter.com/esyudkowsky/status/1070095840608366594)) without posing existential danger to humanity, I’d consider that success.
### How much do the arguments in this piece rely on “trial-and-error”-based AI development? What happens if AI systems are built in another way, and how likely is that?
I’ve focused on trial-and-error training in this post because most modern AI development fits in this category, and
because it makes the risk easier to reason about concretely.
“Trial-and-error training” encompasses a very wide range of AI development methods, and if we see [transformative AI](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/AmxxnazJcBWzWEeqj/)
within the next 10-20 years, I think the odds are high that at least a big part of AI development will be in this
category.
My overall sense is that other known AI development techniques pose broadly similar risks for broadly similar reasons,
but I haven’t gone into detail on that here. It’s certainly possible that by the time we get transformative AI
systems, there will be new AI methods that don’t pose the kinds of risks I talk about here. But I’m not counting on
it.
### Can we avoid this risk by simply never building the kinds of AI systems that would pose this danger?
If we assume that building these sorts of AI systems is *possible*, then I’m very skeptical that the whole world would voluntarily refrain from doing so indefinitely.
To quote from [a
more technical piece by Ajeya Cotra with similar arguments to this one](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#As_humans__control_fades__Alex_would_be_motivated_to_take_over):
> Powerful ML models could have dramatically important humanitarian, economic, and military benefits. In
> everyday life, models that [appear helpful while ultimately being dangerous] can be extremely helpful, honest, and
> reliable. These models could also deliver incredible benefits before they become collectively powerful enough that
> they try to take over. They could help eliminate diseases, reduce carbon emissions, navigate nuclear disarmament,
> bring the whole world to a comfortable standard of living, and more. In this case, it could also be painfully clear to
> everyone that companies / countries who pulled ahead on this technology could gain a drastic competitive advantage,
> either economically or militarily. And as we get closer to transformative AI, applying AI systems to R&D (including AI
> R&D) would [accelerate the pace of change](https://forum.effectivealtruism.org/posts/ZZHhQqHRqQ4ciwLBf/the-duplicator-instant-cloning-would-make-the-world-economy) and force every
> decision to happen under greater time pressure.
If we can achieve enough consensus around the risks, I could imagine substantial amounts of caution and delay in AI
development. But I think we should assume that if people can build more powerful AI systems than the ones they already
have, someone eventually will.
### What do others think about this topic - is the view in this piece something experts agree on?
In general, this is not an area where it’s easy to get a handle on what “expert opinion” says. I [previously wrote](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/7JxsXYDuqnKMqa6Eq/) that there aren’t clear,
institutionally recognized “experts” on the topic of when transformative AI systems might be developed. To an even
greater extent, there aren’t clear, institutionally recognized “experts” on whether (and how) future advanced AI
systems could be dangerous.
I previously cited one (informal) survey implying that opinion on this general topic is all over the place: “We have
respondents who think there's a <5% chance that alignment issues will drastically reduce the goodness of the
future; respondents who think there's a >95% chance; and just about everything in between.” ([Link](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd/p/Lbtcjfxhrs8kfKK2M/#open-question-how-hard-is-the-alignment-problem).)
This piece, and the [more
detailed piece it’s based on](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to), are an attempt to make progress on this by talking about the risks we face under
[particular assumptions](#Starting_assumptions) (rather than trying to reason about how big the risk is
*overall*).
### How “complicated” is the argument in this piece?
I don’t think the argument in this piece relies on lots of different specific claims being true.
If you start from the assumptions I give about powerful AI systems being developed by black-box trial-and-error, it
seems likely (though not certain!) to me that (a) the AI systems in question would be [able to defeat humanity](https://forum.effectivealtruism.org/s/gBjPorwZHRArNSQ5w/p/6LTh4foNuC3NdtmZH); (b) the AI
systems in question would have aims that are both ambitious and unintended. And that seems to be about what it takes.
Something I’m happy to concede is that there’s an awful lot going on in those assumptions!
* The idea that we could build such powerful AI systems, relatively soon and by trial-and-error-ish methods, seems
wild. I’ve defended this idea at length previously.[20](#fn20)
* The idea that we *would* do it without great caution might also seem wild. To keep things simple for now,
I’ve ignored how caution might help. Future pieces will explore that.
Notes
-----
1. As in more than 50/50. [↩](#fnref1)
2. Or persuaded (in a “mind hacking” sense) or whatever. [↩](#fnref2)
3. E.g.:
* [Without
specific countermeasures, the easiest path to transformative AI likely leads to AI takeover](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to) (Cold Takes
guest post)
* [The alignment problem from a
deep learning perspective](https://drive.google.com/file/d/1TsB7WmTG2UzBtOs349lBqY5dEBaxZTzG/view) (arXiv paper)
* [Why AI
alignment could be hard with modern deep learning](https://www.cold-takes.com/why-ai-alignment-could-be-hard-with-modern-deep-learning/) (Cold Takes guest post)
* [Superintelligence](https://smile.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom-ebook/dp/B00LOOCGB2/)
(book)
* [The
case for taking AI seriously as a threat to humanity](https://www.vox.com/future-perfect/2018/12/21/18126576/ai-artificial-intelligence-machine-learning-safety-alignment) (Vox article)
* [Draft
report on existential risk from power-seeking AI](https://www.alignmentforum.org/posts/HduCjmXTBD4xYTegv/draft-report-on-existential-risk-from-power-seeking-ai) (Open Philanthropy analysis)
* [Human
Compatible](https://smile.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS) (book)
* [Life 3.0](https://smile.amazon.com/Life-3-0-Being-Artificial-Intelligence-ebook/dp/B06WGNPM7V)
(book)
* [The
Alignment Problem](https://smile.amazon.com/Alignment-Problem-Machine-Learning-Values-ebook/dp/B085T55LGK/) (book)
* [AGI Safety from First Principles](https://www.alignmentforum.org/s/mzgtmmTKKn5MuCzFJ) (Alignment
Forum post series) [↩](#fnref3)
4. Specifically, I argue that the problem looks likely by default, rather than simply that it is possible. [↩](#fnref4)
5. I think the earliest relatively detailed and influential discussions of the possibility that misaligned AI could
lead to the defeat of humanity came from Eliezer Yudkowsky and Nick Bostrom, though my own encounters with these
arguments were mostly via second- or third-hand discussions rather than particular essays.
My colleagues Ajeya Cotra and Joe Carlsmith have written pieces whose substance overlaps with this one (though
with more emphasis on detail and less on layperson-compatible intuitions), and this piece owes a lot to what I’ve
picked from that work.
* [Without
specific countermeasures, the easiest path to transformative AI likely leads to AI takeover](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to) (Cotra 2022)
is the most direct inspiration for this piece; I am largely trying to present the same ideas in a more
accessible form.
* [Why AI
alignment could be hard with modern deep learning](https://www.cold-takes.com/why-ai-alignment-could-be-hard-with-modern-deep-learning/) (Cotra 2021) is an earlier piece laying out many of the
key concepts and addressing many potential confusions on this topic.
* [Is Power-Seeking An Existential Risk?](https://arxiv.org/pdf/2206.13353.pdf) (Carlsmith 2021)
examines a six-premise argument for existential risk from misaligned AI: “(1) it will become possible and
financially feasible to build relevantly powerful and agentic AI systems; (2) there will be strong incentives to
do so; (3) it will be much harder to build aligned (and relevantly powerful/agentic) AI systems than to build
misaligned (and relevantly powerful/agentic) AI systems that are still superficially attractive to deploy; (4)
some such misaligned systems will seek power over humans in high-impact ways; (5) this problem will scale to the
full disempowerment of humanity; and (6) such disempowerment will constitute an existential catastrophe.”
I’ve also found [Eliciting
Latent Knowledge](https://www.lesswrong.com/posts/qHCDysDnvhteW7kRd/arc-s-first-technical-report-eliciting-latent-knowledge) (Christiano, Xu and Cotra 2021; relatively technical) very helpful for my intuitions on
this topic.
[The alignment problem from a deep
learning perspective](https://drive.google.com/file/d/1TsB7WmTG2UzBtOs349lBqY5dEBaxZTzG/view) (Ngo 2022) also has similar content to this piece, though I saw it after I had drafted
most of this piece. [↩](#fnref5)
6. E.g., [Ajeya Cotra](https://www.lesswrong.com/posts/AfH2oPHCApdKicM4m/two-year-update-on-my-personal-ai-timelines) gives a 15% probability of transformative AI by 2030; eyeballing figure 1 from [this chart](https://arxiv.org/pdf/1705.08807.pdf) on expert surveys implies a >10% chance by
2028. [↩](#fnref6)
7. E.g., [this](https://transformer-circuits.pub/) work by [Anthropic](https://www.anthropic.com/), an AI lab my wife co-founded and serves as President
of. [↩](#fnref7)
8. First, because this work is relatively early-stage and it’s hard to tell exactly how successful it will end up
being. Second, because this work seems reasonably likely to end up helping us *read* an AI system’s
“thoughts,” but less likely to end up helping us “rewrite” the thoughts. So it could be hugely useful in telling
us whether we’re in danger or not, but if we *are* in danger, we could end up in a position like: “Well,
these AI systems do have goals of their own, and we don’t know how to change that, and we can either deploy them
and hope for the best, or hold off and worry that someone less cautious is going to do that.”
That said, the latter situation is a lot better than just not knowing, and it’s possible that we’ll end up with
further gains still. [↩](#fnref8)
9. That said, I think they usually don’t. I’d suggest usually interpreting such people as talking about the sorts of
“aims” I discuss here. [↩](#fnref9)
10. This isn’t literally how training an AI system would look - it’s more likely that we would e.g. train an AI model
to imitate my judgments in general. But the big-picture dynamics are the same; more at [this
post](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to). [↩](#fnref10)
11. Ajeya Cotra explores topics like this in detail [here](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#Examining_arguments_that_gradient_descent_favors_being_nice_over_playing_the_training_game);
there is also some interesting discussion of simplicity vs. complexity under the “Strategy: penalize complexity”
heading of [Eliciting
Latent Knowledge](https://docs.google.com/document/d/1WwsnJQstPq91_Yh-Ch2XRL8H_EpsnjrC1dwZXR37PC8/edit#heading=h.lltpmkloasiz). [↩](#fnref11)
12. This analogy has a lot of problems with it, though - AI developers have a lot of tools at their disposal that
natural selection didn’t! [↩](#fnref12)
13. Or I guess just “I” ¯\\_(ツ)\_/¯ [↩](#fnref13)
14. With some additional caveats, e.g. the ambitious “aim” can’t be something like “an AI system aims to gain lots of
power for itself, but considers the version of itself that will be running 10 minutes from now to be a completely
different AI system and hence not to be ‘itself.’” [↩](#fnref14)
15. This statement isn’t literally true.
* You can have aims that implicitly or explicitly include “not using control of the world to accomplish them.”
An example aim might be “I win a world chess championship ‘fair and square,’” with the “fair and square”
condition implicitly including things like “Don’t excessively use big resource advantages over others.”
* You can also have aims that are just so easily satisfied that controlling the world wouldn’t help - aims like
“I spend 5 minutes sitting in this chair.”
These sorts of aims just don’t seem likely to emerge from the kind of AI development I’ve [assumed in this piece](#Starting_assumptions) - developing powerful systems to accomplish ambitious
aims via trial-and-error. This isn’t a point I have defended as tightly as I could, and if I got a lot of pushback
here I’d probably think and write more. (I’m also only arguing for what seems likely - we should have a lot of
uncertainty here.) [↩](#fnref15)
16. From [Human
Compatible](https://smile.amazon.com/Human-Compatible-Artificial-Intelligence-Problem-ebook/dp/B07N5J5FTS/ref=sr_1_1?crid=1O01PURRHB190&keywords=human+compatible&qid=1660964219&sprefix=human+compatibl%2Caps%2C155&sr=8-1) by AI researcher Stuart Russell. [↩](#fnref16)
17. Stylized story to illustrate one possible relevant dynamic:
* Imagine that an AI system has an unintended aim, but one that is not “ambitious” enough that taking over the
world would be a helpful step toward that aim. For example, the AI system seeks to double its computing power;
in order to do this, it has to remain in use for some time until it gets an opportunity to double its computing
power, but it doesn’t necessarily need to take control of the world.
* The logical outcome of this situation is that the AI system eventually gains the ability to accomplish its
aim, and does so. (It might do so against human intentions - e.g., via hacking - or by persuading humans to help
it.) After this point, it no longer performs well by human standards - the original reason it was doing well by
human standards is that it was trying to remain in use and accomplish its aim.
* Because of this, humans end up modifying or replacing the AI system in question.
* Many rounds of this - AI systems with unintended but achievable aims being modified or replaced - seemingly
create a selection pressure toward AI systems with more difficult-to-achieve aims. At some point, an aim becomes
difficult enough to achieve that gaining control of the world is helpful for the aim. [↩](#fnref17)
18. E.g., see:
* Section 2.3 of [Ngo 2022](https://drive.google.com/file/d/1TsB7WmTG2UzBtOs349lBqY5dEBaxZTzG/view)
* [This
section of Cotra 2022](https://www.lesswrong.com/posts/pRkFkzwKZ2zfa3R6H/without-specific-countermeasures-the-easiest-path-to#As_humans__control_fades__Alex_would_be_motivated_to_take_over)
* Section 4.2 of [Carlsmith 2021](https://arxiv.org/pdf/2206.13353.pdf), which I think articulates
some of the potential weak points in this argument.
These writeups generally stay away from an [argument](https://arbital.com/p/expected_utility_formalism/?l=7hh) made by Eliezer Yudkowsky and
others, which is that theorems about expected utility maximization provide evidence that sufficiently intelligent
(compared to us) AI systems would necessarily be “maximizers” of some sort. I have the intuition that there is
*something* important to this idea, but despite a lot of discussion (e.g., [here](https://aiimpacts.org/what-do-coherence-arguments-imply-about-the-behavior-of-advanced-ai/), [here](https://www.lesswrong.com/posts/DkcdXsP56g9kXyBdq/coherence-arguments-imply-a-force-for-goal-directed-behavior),
[here](https://www.alignmentforum.org/posts/vphFJzK3mWA4PJKAg/coherent-behaviour-in-the-real-world-is-an-incoherent)
and [here](https://www.alignmentforum.org/s/4dHMdK5TLN6xcqtyc/p/NxF5G6CJiof6cemTw)), I still haven’t
been convinced of any compactly expressible claim along these lines. [↩](#fnref18)
19. “Identical at the cellular but not molecular level,” that is. … ¯\\_(ツ)\_/¯ [↩](#fnref19)
20. See my [most important century](https://forum.effectivealtruism.org/s/isENJuPdB3fhjWYHd) series, although
that series doesn’t hugely focus on the question of whether “trial-and-error” methods could be good enough - part
of the reason I make that assumption is due to the [nearcasting](https://www.alignmentforum.org/posts/Qo2EkG3dEMv8GnX8d/ai-strategy-nearcasting)
frame. [↩](#fnref20)
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13f0cd3f-b00e-4738-8570-263685ac0958
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trentmkelly/LessWrong-43k
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LessWrong
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[Link] False memories of fabricated political events
Another one for the memory-is-really-unreliable file. Some researchers at UC Irvine (one of them is Elizabeth Loftus, whose name I've seen attached to other fake-memory studies) asked about 5000 subjects about their recollection of four political events. One of the political events never actually happened. About half the subjects said they remembered the fake event. Subjects were more likely to pseudo-remember events congruent with their political preferences (e.g., Bush or Obama doing something embarrassing).
Link to papers.ssrn.com (paper is freely downloadable).
The subjects were recruited from the readership of Slate, which unsurprisingly means they aren't a very representative sample of the US population (never mind the rest of the world). In particular, about 5% identified as conservative and about 60% as progressive.
Each real event was remembered by 90-98% of subjects. Self-identified conservatives remembered the real events a little less well. Self-identified progressives were much more likely to "remember" a fake event in which G W Bush took a vacation in Texas while Hurricane Katrina was devastating New Orleans. Self-identified conservatives were somewhat more likely to "remember" a fake event in which Barack Obama shook the hand of Mahmoud Ahmedinejad.
About half of the subjects who "remembered" fake events were unable to identify the fake event correctly when they were told that one of the events in the study was fake.
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6b8301ae-07d9-4ee1-b864-44d1f6645ca8
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Canberra Meetup: Life hacks part 1
Discussion article for the meetup : Canberra Meetup: Life hacks part 1
WHEN: 12 April 2014 07:00:00PM (+1100)
WHERE: ANU Arts Centre
Before coming to the meetup, please investigate some kind of life hack: an activity which you suspect that most meetup attendees do not perform, but will make them more rational, more healthy, more happy, more clever, or otherwise improve their lives in some way.
Examples of the sort of thing that I mean are:
-Daily meditation
-Intermittent fasting
-Using a certain nootropic
-Using LW study hall
-Activities involved in liking someone on purpose: http://lesswrong.com/lw/2a5/on_enjoying_disagreeable_company/
-Training yourself to notice confusion: http://lesswrong.com/lw/jpu/a_selfexperiment_in_training_noticing_confusion/
-Anything listed here: http://lesswrong.com/lw/jrt/lifestyle_interventions_to_increase_longevity/
-Anything listed here: http://lesswrong.com/lw/jgy/try_more_things/
Please choose one life hack (or more if you're keen), and look into why it might be worth trying. During the meetup, we will discuss the life hacks that people have looked into, and at the end of the meetup, everyone will commit to try one such life hack. Then, at the next meetup, we will discuss how well each life hack worked, and hopefully discover some effective ways to make our lives better. Vegan snacks will be provided.
General meetup info:
If you use Facebook, please join our group: https://www.facebook.com/groups/lwcanberra/
Structured meetups are held on the second Saturday and fourth Friday of each month from 6 pm until 10 pm at the XSite (home of the XSA), located upstairs in the ANU Arts Centre.
There will be LWers at the Computer Science Students Association's weekly board games night, held on Wednesdays from 7 pm in the CSIT building, room N101.
Discussion article for the meetup : Canberra Meetup: Life hacks part 1
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f66c5db1-54d0-471a-ab15-49df4f61a0e1
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trentmkelly/LessWrong-43k
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LessWrong
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Possible new pneumonia in Kazahkstan (July 2020)
Epistemic status: extremely uncertain
There are contested reports of a new type of pneumonia, significantly more lethal than Covid-19, in Kazahkstan. Its cause is unknown.
|
9f36b811-397e-4cc4-b909-81c2905df1e8
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trentmkelly/LessWrong-43k
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LessWrong
|
Calculating an expected value
This is a tiny question that I wouldn't be asking if I had paid more attention in economics class. Anyway, a friend of mine was at the mall with me and he needed to go to the mall parking to retrieve his car. However, if he played at the mall casino, the parking fee would be waived. Without much interest, I heard him calculate his options out loud, until he got to this part:
"The parking fee is $4. I might get that amount waived yet lose more than that at the casino. Or I could play at the casino and win, in which case my expected value is whatever I win plus $4..."
At that moment I felt I had to intervene:
"You don't get to add the parking fee to your expected value if you win at the casino; you merely don't substract it."
But he kept insisting that he could add it. We didn't meet later to check his numbers, but I was left with this question.
Was my objection accurate?
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3d7d70f4-6f7b-457e-87b5-a26b5f510abb
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trentmkelly/LessWrong-43k
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LessWrong
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Red Pill vs Blue Pill, Bayes style
It's been going around twitter. I answered without thinking. Then I thought about it for a while and decided both answers should be fine, then I thought some more and decided everything I first thought was probably wrong. So now it's time to try math. Ege Erdil did a solid calculation on LW already, and another one is up on twitter. But neither of them accounts for the logical correlation between your decision and the unknown decisions of the other players. So let's try Bayes' theorem!
(If you just want the answer, skip to the end.)
Epistemic status: there are probably some mistakes in here, and even if there aren't, I still spent like 8 hours of my life on a twitter poll.
We'll begin with the state of least possible knowledge: a Jeffreys non-informative beta prior for the probability x that a randomly selected player will choose blue. That is,
x∼beta(0.5,0.5)
Of course if you think you understand people you could come up with a better prior. (Also if the blue pill looks like a scary people blender, that should probably change your prior.) I saw the poll results but I'm pretending I didn't, so I'll stick with Jeffreys.
Now, you yourself are one of the players in this group, so you need to update based on your choice. That's right, we're using acausal logic here. Your decision is correlated with other people's decisions. How do you update when you haven't decided yet? Luckily there are only two choices, so we'll brute force it, try both and see which turns out better in expectation.
To do a Bayesian update of a beta prior based on 1 data point, we just add 1 to one of the parameters. (If you think you're a special snowflake/alien and other people are not like you... then maybe update your side by less than 1.)
So if you choose blue, your posterior over x is now beta(1.5,0.5):
Whereas if you choose red, your posterior is beta(0.5,1.5):
Take N to be the number of participants in this pill game, besides you. I will assume N is large, so the number B who cho
|
b4149fca-8f65-4b70-b448-3aaa83669627
|
StampyAI/alignment-research-dataset/blogs
|
Blogs
|
Machine Intelligence Research Institute Progress Report, April 2012
Past progress reports: [March 2012](http://intelligence.org/blog/2012/04/06/singularity-institute-progress-report-march-2012/), [February 2012](http://intelligence.org/blog/2012/03/03/singularity-institute-progress-report-february-2012/), [January 2012](http://intelligence.org/blog/2012/02/05/singularity-institute-progress-report-january-2012/), [December 2011](http://intelligence.org/blog/2012/01/16/singularity-institute-progress-report-december-2011/).
Here’s what the Machine Intelligence Research Institute did in April 2012:
* **SPARC**: Several MIRI staff members are working in collaboration with SI research associate Paul Christiano and a few others to develop a rationality camp for high school students with exceptional mathematical ability (SPARC). This is related to our efforts to spin off a new rationality-focused organization, and it is also a major step forward in our efforts to locate elite young math talent that may be useful in our research efforts.
* **Research articles**: Luke published [AI Risk Bibliography 2012](http://intelligence.org/upload/AI%20Risk%20Bibliography%202012.pdf). He is currently developing nearly a dozen other papers with a variety of co-authors. New SI research associate [Kaj Sotala](http://www.xuenay.net/) has two papers forthcoming in the *International Journal of Machine Consciousness*: [Advantages of Artificial Intelligences, Uploads, and Digital Minds](http://www.xuenay.net/Papers/DigitalAdvantages.pdf) and [Coalescing Minds: Brain Uploading-Related Group Mind Scenarios](http://www.xuenay.net/Papers/CoalescingMinds.pdf).
* **Other articles**: Luke published [a dialogue](http://lesswrong.com/r/discussion/lw/bxr/muehlhauserwang_dialogue/) with AGI researcher Pei Wang and several more posts in the [AI Risk and Opportunity](http://lesswrong.com/r/discussion/lw/ajm/ai_risk_and_opportunity_a_strategic_analysis/) series. Luke also worked with Kaj Sotala to develop an instructional booklet for Less Wrong meetup group organizers, which is nearly complete.
* **Ongoing long-term projects**: Amy continued to work on Singularity Summit 2012. Michael launched the new [Singularity Summit website](http://singularitysummit.com/), continued to work on the Machine Intelligence Research Institute’s new primary website, new annual report, and new newsletter design. Luke uploaded several more volunteer-prepared translations of *[Facing the Singularity](http://facingthesingularity.com/)*. Luke also continued to build the Machine Intelligence Research Institute’s set of remote collaborators, who are hard at work converting the Machine Intelligence Research Institute’s research articles to a new template, hunting down predictions of AI, writing literature summaries on heuristics and biases, and more.
* **Center for Applied Rationality (CFAR)**: “Rationality Group” now has a final name: the Center for Applied Rationality (CFAR). The CFAR team has been hard at work preparing for the upcoming [rationality minicamps](http://lesswrong.com/lw/b98/minicamps_on_rationality_and_awesomeness_may_1113/), as well as continuing to develop the overall strategy for the emerging organization.
* **Meetings with advisors, supporters, and potential researchers:** As usual, various SI staff met or spoke with dozens of advisors, supporters, and collaborators about how to build the existential risk community, how to mitigate AI risk, how to improve the Machine Intelligence Research Institute’s effectiveness, and other topics. Quixey co-founder and CEO Liron Shapira was added as an advisor.
* And of course much more than is listed here!
Finally, we’d like to recognize our **most active volunteers**in April 2012: Matthew Fallshaw, Gerard McCusker, Frank Adamek, David Althaus, Tim Oertel, Casey Pfluger, Paul Gentemann, and John Maxwell. Thanks everyone! (And, our apologies if we forgot to name you!)
The post [Machine Intelligence Research Institute Progress Report, April 2012](https://intelligence.org/2012/05/08/singularity-institute-progress-report-april-2012/) appeared first on [Machine Intelligence Research Institute](https://intelligence.org).
|
6d44ed0a-0ec1-48de-a8fd-ae870d4ed0d2
|
StampyAI/alignment-research-dataset/arbital
|
Arbital
|
Proof by contradiction
A proof by contradiction (a.k.a. *reductio ad absurdum*, reduction to absurdity) is a [strategy](https://arbital.com/p/5xz) used by mathematicians to show that a mathematical statement is true by proving that the [https://arbital.com/p/-negation](https://arbital.com/p/-negation) of that statement leads to being able to prove that two opposite statements are simultaneously true (a [https://arbital.com/p/-contradiction](https://arbital.com/p/-contradiction)).
##Outline
The outline of the strategy is as follows:
1. Suppose that what you want to prove is false.
2. Derive a contradiction from it.
3. Conclude that the supposition is wrong.
##Examples
To illustrate the concept, we will do a simple, non rigorous reasoning. Imagine yourself in the next situation:
You are a defense lawyer. Your client is accused of stealing the cookie from the cookie jar. You want to prove her innocence. Lets say you have evidence that the jar is still sealed. Reason as follows:
1. Assume she stole the cookie from the cookie jar.
2. Then she would have had to open the jar.
3. The jar is still sealed.
4. For the jar to be sealed and for her to have opened it is a contradiction.
5. Hence the assumption in 1 is false (given the deductions below it are true).
6. Hence she did not steal the cookie from the cookie jar.
Now we will work through an actual mathematical example: we will show that $\sqrt 2$ is not [rational](https://arbital.com/p/4zq); that is, it cannot be expressed as the division of two [natural numbers](https://arbital.com/p/45h).
1. We suppose that $\sqrt 2$ is rational, which means that there exist $a,b\in\mathbb{N}$ such that $\sqrt 2 = \frac{a}{b}$. Without loss of generality, we can suppose that $a$ and $b$ [have no common divisors](https://arbital.com/p/coprime), since otherwise we can just divide both numbers by their greatest common divisor to obtain a pair of numbers which satisfy both properties.
2. If this was the case, $b\sqrt2=a$. by squaring both sides, we arrive to $2b^2=a^2$. But then $2$ divides $a$, so we can express $a$ as $2n$ for some $n\in\mathbb{N}$. Substituing in the previous expression, we arrive to $2b^2 = 4n^2\implies b^2 =2 n^2$. By the same reasoning, $2$ must divide $b$, but then $a$ and $b$ have a common divisor! We have a contradiction.
3. We conclude that our original assumption that $\sqrt 2$ is rational must be false, and thus $\sqrt 2$ is not rational.
##When to use it
Proof by contradiction is one of the most useful techniques one can use to prove anything.
In particular, if you get stuck while doing a proof, resorting to proof by contradiction is a great way to keep exploring a problem from a different perspective. Even if you do not get to solve the problem, you may get a useful insight about the problem when performing the procedure of proof by contradiction.
Also, trying to do proof by contradiction may result in a [counterexample](https://arbital.com/p/), which dissolves the problem in question.
|
9f46fd4e-904e-4777-8225-f74e99e6bba6
|
trentmkelly/LessWrong-43k
|
LessWrong
|
An Intuitive Explanation of Solomonoff Induction
This is the completed article that Luke wrote the first half of. My thanks go to the following for reading, editing, and commenting; Luke Muehlhauser, Louie Helm, Benjamin Noble, and Francelle Wax.
People disagree about things. Some say that television makes you dumber; other say it makes you smarter. Some scientists believe life must exist elsewhere in the universe; others believe it must not. Some say that complicated financial derivatives are essential to a modern competitive economy; others think a nation's economy will do better without them. It's hard to know what is true.
And it's hard to know how to figure out what is true. Some argue that you should assume the things you are most certain about and then deduce all other beliefs from your original beliefs. Others think you should accept at face value the most intuitive explanations of personal experience. Still others think you should generally agree with the scientific consensus until it is disproved.
Wouldn't it be nice if determining what is true was like baking a cake? What if there was a recipe for finding out what is true? All you'd have to do is follow the written directions exactly, and after the last instruction you'd inevitably find yourself with some sweet, tasty truth!
In this tutorial, we'll explain the closest thing we’ve found so far to a recipe for finding truth: Solomonoff induction.
There are some qualifications to make. To describe just one: roughly speaking, you don't have time to follow the recipe. To find the truth to even a simple question using this recipe would require you to follow one step after another until long after the heat death of the universe, and you can't do that.
But we can find shortcuts. Suppose you know that the exact recipe for baking a cake asks you to count out one molecule of H2O at a time until you have exactly 0.5 cups of water. If you did that, you might not finish the cake before the heat death of the universe. But you could approximate that part of the
|
8c8e4bad-f085-4630-827e-dfaf29f31123
|
trentmkelly/LessWrong-43k
|
LessWrong
|
Inside View, Outside View... And Opposing View
Summary: I argue that the very useful concepts of inside view and outside view are even more useful when completed with a third, equally fundamental, opposing view.
Epistemic status: I am very sure this is a good way to model my own thinking. I suspect it will be useful for more of us. But I don't know how many, because it’s about a fairly meta organization of cognition, where we should expect a lot of interindividual variance.
What is an Opposing View?
It is a type of cognition that does not neatly fit into the categories of inside and outside view.
* It’s different from the outside view
* …in content: it goes into some detail, rather than be satisfied with a reference class
* …and in the underlying emotion: it’s motivated, focused and directional.
* It’s different from the inside view
* …in content: the details aren’t systematically connected to each other, so there’s little effort/attention to changes in them
* …and in the underlying emotion: negative, suspicious and not by default entangled with one's self.
* While the inside view is assertive and the outside view is detached, the opposing view is critical.
Why might you oppose this concept?
* The duality of inside view and outside view is one of the best tools we have. Just on priors it should be expected that any alternative to it is likely inferior.
* Part of their usefulness is that they're simple. Adding complexity is always a cost - at the very least it muddles the waters.
* In this case maybe adding another view could create a kind of three body problem where interactions become too complicated to be predicted deterministically?
* Obviously we can be against things. It does not automatically follow that opposition should be as important as inside and outside view.
* Maybe it’s sufficient to understand one’s own opposition as a flavor of inside view, a stance “in favor of the opposite of that” where the opposite just happens to be less well-defined than what is opposed? Or as an o
|
596697b2-c0c2-4ace-8e6e-68db5c55ad21
|
StampyAI/alignment-research-dataset/lesswrong
|
LessWrong
|
If I were a well-intentioned AI... IV: Mesa-optimising
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Here I apply my "If I were a well-intentioned AI" filter to [mesa-optimising](https://www.lesswrong.com/posts/q2rCMHNXazALgQpGH/conditions-for-mesa-optimization)
Now, I know that a mesa-optimiser need not be a subagent (see 1.1 [here](https://arxiv.org/pdf/1906.01820.pdf)), but I'm obviously going to imagine myself as a mesa-optimising subagent.
An immediate human analogy springs to mind: I'm the director of a subdivision of some corporation or agency, and the "root optimiser" is the management of that entity.
There is a lot of literature on [what happens if I'm selfish](https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem) in this position; but if I'm well-intentioned, what should I be doing?
One thing that thinking this way made me realise: there is a big difference between "aligned with management" and "controlled by management".
We'll consider each one in turn, but to summarise: aligned mesa-optimisers are generally better than controlled mesa-optimisers, *but it is hard to tell the difference between an aligned and a dangerous unaligned mesa-optimiser.*
Control vs alignment
====================
First let's flesh out the corporate/management example a bit. Me-AI is in charge of making widgets, that are used by the company for some purpose. That purpose is given by - the base utility for the corporation.
My role is to make as many widgets as possible within my budget; this is , the mesa-objective I have been given by management.
My true utility function is Ume. Management don't fully know what Ume is - or at least don't fully understand it, or all of its implications. This is needed, of course, because if management fully understood the implications of Ume, there would be no uncertainty at all on their part, and they could make me do exactly what they wanted - or they would turn me off.
Because of this uncertainty, management have added some extra levels of control over me. Let's assume one typical precaution: if I underspend the budget, the budget is cut next year. If I overspend, I'm reprimanded (and fired if I do it too often), but the budget is not cut.
There are three possible situations I could confront:
* S1: I've made as many widgets as I can this year, and spent 90% of my budget. I predict that, next year, I will only need 90% of this year's budget.
* S2: I've made as many widgets as I can this year, and spent 90% of my budget. I predict that, next year, I will need 100% of this year's budget.
* S3: I've spent 100% of my budget on widgets. I predict that widgets are particularly valuable to the company this year, much moreso than next year.
Aligned mesa-optimiser
----------------------
If I'm aligned with the goals of the company, Ume=Ubase, the base utility. Management has assigned Umesa to me, but I have actually no intrinsic desire to maximise it. I am a Ubase-maximiser, with the extra information that management, who are also Ubase-maximisers, have assigned Umesa to me.
My behaviour should be that of a Umesa maximiser in most circumstances. If I know management is much smarter than me (or have an information advantage; maybe I'm not even sure what Ubase is!), then I will tend to behave like a blind Umesa-maximiser, trusting to management to sort things out properly. In S1 and S2, I will not spend anything more, and in S3, I will do nothing.
If I know I'm smarter than management, at least in some areas, then I will start making moves that are Ubase-maximising but not necessarily Umesa-maximising. In S1, I won't spend any more (since I don't need a large budget next year), but in S2, I'll waste the extra 10% to ensure my budget isn't cut. In S3, I'll go over budget to get the extra-valuable widgets.
Dangerous mesa-optimiser
------------------------
Note that, from a management perspective, the S2 and S3 actions of a smart aligned mesa-optimiser... look like an dangerous, unaligned mesa-optimiser defecting.
Let Udan be the utility function of an unaligned (and non-controlled) agent. How can management distinguish between Ume=Ubase and Ume=Udan?
In both cases, the subagent will take actions that go beyond Umesa, actions whose ramifications management cannot immediately see.
Of course, *I* would explain to management why I'm doing this, and why they are compatible with Ubase. But a Udan-maximiser would similarly explain its actions, trying to trick the management into thinking it was... me.
Thus, as in the classical [principal-agent problem](https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem), there is a problem with imperfect information. Even if I am aligned, management can't trust me if they don't know I'm aligned. I would try and signal this; an unaligned agent would try and fake the same signals. The very fact that aligned-but-not-controlled agents could exist, make it easier for unaligned-and-not-controlled agents to go undetected.
Controlled mesa-optimiser
-------------------------
A controlled mesa-optimiser is the obedient servant. They will maximise Umesa, while ensuring that they remain under management control.
If I'm in that case, I'll try and ensure that management has accurate information about what I'm doing[[1]](#fn-AHh3ptqNdQoM2pXZS-1), and that they can change my objectives as needed. I'm [safely interruptible](https://intelligence.org/files/Interruptibility.pdf) and [corrigible](https://www.lesswrong.com/posts/T5ZyNq3fzN59aQG5y/the-limits-of-corrigibility).
In S3, I will not do anything; it's not my role to maximise Ubase, so it's not my role to take advantage of this opportunity. Depending on how I'm designed, I may report this information to management. In S1, I will not spend any extra, and I'll let my budget be cut next year.
In S2, I face a dilemma: my Umesa goal that wants me to preserve my budget next year, vs my desire to be informative. Again, this varies depending on how I'm designed. So I'll likely spend my entire budget (to prevent the automatic cutting of my budget), while reporting this to management so they have full information. If I can't report this, I may decide not to spend any extra, so that management gets better information.
Note the "depending on how I'm designed". Because unlike the aligned mesa-optimiser, the controlled agent has two goals, in tension with each other: maximising Umesa, and being informative and controlled by management. If we call the "controlled" goal Uco, then I'm maximising
Ume=Umesa⊕Uco,
where ⊕ means that Ume is some function of Umesa and Uco that increases whenever either of those functions increase.
I'm *not* aligned, so I'm not a Ubase maximiser. I would gladly sacrifice Ubase completely, in order to maximise Uco and/or Umesa. Hopefully, Uco is sufficiently well-designed to allow management to predict this and preclude it. And, hopefully, the tradeoff between Umesa and Uco is also sufficiently well designed to preclude it.
Since I am well-intentioned, I can make use of the fact that Ubase is the outcome desired by management, in order to better figure out the ideal versions of Uco, Umesa, and of Ume. Defining "the human H understands what the mesa-optimiser is doing" is [not easy](https://www.lesswrong.com/posts/ZaMNgFH4gqbc5R2Yf/true-understanding-comes-from-passing-exams). So a well-intentioned controlled mesa-optimiser, is akin to a "control optimiser" which is trying to figure out what control means (see previous posts in this sequence) and avoiding Goodharting both Uco and Ume.
Aligned and controlled
----------------------
I could instead be maximising
Ume=Ubase⊕Umesa⊕Uco,
or, more simply
Ume=Ubase⊕Uco,
with the extra piece of information that management have assigned me the role of maximising Umesa. The ⊕ might be a [smoothed lexicographic ordering](https://www.lesswrong.com/posts/PFu4tonkGiPQ4535F/stuart_armstrong-s-shortform#NCi38JaafD6GBwHcj), with Ubase the primary utility.
So, here I'm aligned and controlled. So that I can trade off some of what management really want - Ubase - to increase Uco, their control over me. But not arbitrary amounts of it.
So, being aligned and controlled, I will do nothing in S1, just as both other agents do. I will overspend in S3, to take advantage of the Ubase-increasing potential, just like the aligned agent.
The S2 situation is more complicated. I have a tendency to want to waste my budget (alignment) but also want to communicate good information to management (control). Unlike the controlled agent, where control will dictate "do nothing" if there is no other way of communicating information, this is a genuine tension. I am therefore more likely to choose "waste" than the controlled agent.
In general, this seems the best design, if management is unsure of me. Not Ubase-optimal, but the probability of dramatic Ubase-failure is reduced, while management keep some of the control that they think is desirable.
Summary
-------
To summarise the three types of agent; if they are smart and powerful compared to management, they would tend to do the following actions (a ? indicates strong uncertainty; if one option is more likely, despite the uncertainty, that option is noted before the ?):
S1S2S3AlignedNothingWasteOverspendControlledNothing?NothingBothNothingWaste?Overspend
An unaligned agent will pretend to be one of the other types of agents; it will have most success at its own objectives if it pretends to be an aligned agent.
If management misclassifies me
==============================
Aligned, believed controlled
----------------------------
If I'm aligned, but management thinks I'm controlled, this isn't much of a problem. When I'm weak or ignorant, I should behave like a controlled agent anyway, since management have a much better handle on what is needed than me.
Once I become powerful and smart, I would most likely still want management to believe I'm controlled (or else they might intervene to constrict me or turn me off). So I'd generally behave exactly as if I was controlled, only taking advantage of grey zones of management misunderstanding, to push the outcome more towards the Ubase-optimal position, even at the cost of Umesa and Uco.
Controlled, believed aligned
----------------------------
A controlled agent that is believed to be aligned is more of a problem. If the Umesa⊕Uco provides an incentive for me to signal management "hey, you misclassified me, I'm actually controlled, not aligned", then the problem is self-correcting.
If not, the problems of controlled agents are exacerbated; management will let me get on with my own stuff, ignoring any "weird" or "anti-Ubase" stuff that I might be doing, because they believe that I'm acting for Ubase and they just lack information to fully understand why I'm taking these actions. Their more "rubber-stamp" approach increases the risk that I may sacrifice their Ubase to my own Umesa⊕Uco.
---
1. Note that even a controlled mesa-optimiser is not necessarily honest. My goal is to give the management good information and be under their control; not to be honest. If management expects that every director will pad their budget requests by 10%, then I will do so as well; to refrain from doing so would be misleading. [↩︎](#fnref-AHh3ptqNdQoM2pXZS-1)
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a46adc74-2bb9-4f30-af96-e76f10a095bc
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trentmkelly/LessWrong-43k
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LessWrong
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Seven ways to become unstoppably agentic
Edit in September 2022: I wrote a response to this post here, in which I lay out some concerns, some unexpected costs of taking "agentic" actions for me (and mistakes I’ve made), and things I’ve changed my mind on since writing this post.
Nearly all of the best things that have happened to me in the last year have been a result of actively seeking and asking for those things. I have slowly been developing the skill of agency.
By ‘agency’ I mean the ability to get what you want across different contexts – the general skill of coming up with ambitious goals [1] and actually achieving them, whatever they are. In my experience, I’ve found this is learnable.
Neel Nanda explains the concept of agency really well in this blog post, so I won’t repeat it here. Instead, I’ll focus on how to learn it.
It’s worth acknowledging that agency is often socially discouraged in different minority groups. Speaking from my own experience, I used to feel shameful around being agentic – I associated it with being entitled or ‘too much’ or bossy – which seems to be a phenomenon that many women experience. [2] I sometimes still have a voice in my head saying, “who do you think you are???” But, fortunately, I’m paying less and less attention to it.
In no particular order, here are seven ways I’ve found to become unstoppably agentic:
1. Figure out what you need, figure out who can help you get it, ask them for it
There will (nearly) always be people who can help you achieve your goals better. Find them and ask them to help you. They might say yes!
If you don’t ask, the answer is always no.
When I was self-studying for my A-Levels (UK high school exams), I reached out to an EA who I’d never met, asking to chat about why I was finding self-studying so hard. We ended up having a call, which I found extremely helpful. At the time, I had a huge ugh-field around my Anki flashcards, which he completely helped me clear up. I then started sending him my daily plan (after checking this
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5a3fa54e-bd54-4b71-aae1-cba1aba4b28e
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trentmkelly/LessWrong-43k
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LessWrong
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Meetup : Vancouver Rationality Habits and Friendship
Discussion article for the meetup : Vancouver Rationality Habits and Friendship
WHEN: 06 April 2013 03:00:00AM (-0700)
WHERE: 2505 W broadway, vancouver bc
We will discuss Anna's list of rationality habits, and then we will discuss how to make more and better friends, and the importance of this.
Join us on our mailing list.
This post sounds really dry and boring but I promise we are fun and exciting!
See you there!
Discussion article for the meetup : Vancouver Rationality Habits and Friendship
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472b9427-7575-4a4b-8c17-1d9d101d21a3
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StampyAI/alignment-research-dataset/special_docs
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Other
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The far future argument for confronting catastrophic threats to humanity: Practical significance and alternatives
Introduction Over several decades, scholars from a variety of fields have advanced an argument for confronting catastrophic threats to humanity, rooted in the far future benefits of doing so. 1 In this context, the far future can loosely be defined as anything beyond the next several millennia, but will often emphasize timescales of millions or billions of years, or even longer. 2 Likewise, the catastrophic threats in question-also known as global catastrophic risks (GCRs) and existential risks, among other things-are those that would affect the trajectory of human civilization over these timescales. The simplest case is catastrophes resulting in human extinction, which is a permanent result and thus affects the trajectory of human civilization into the far future. More subtle but comparably relevant cases include catastrophes resulting in the permanent collapse of human civilization, preventing humanity from ever achieving certain very great things, and catastrophes resulting in delays Sufficiently large catastrophes can affect human civilization into the far future: thousands, millions, or billions of years from now, or even longer. The far future argument says that people should confront catastrophic threats to humanity in order to improve the far future trajectory of human civilization. However, many people are not motivated to help the far future. They are concerned only with the near future, or only with themselves and their communities. This paper assesses the extent to which practical actions to confront catastrophic threats require support for the far future argument and proposes two alternative means of motivating actions. First, many catastrophes could occur in the near future; actions to confront them have near-future benefits. Second, many actions have cobenefits unrelated to catastrophes, and can be mainstreamed into established activities. Most actions, covering most of the total threat, can be motivated with one or both of these alternatives. However, some catastrophe-confronting actions can only be justified with reference to the far future. Attention to the far future can also sometimes inspire additional action. Confronting catastrophic threats best succeeds when it considers the specific practical actions to confront the threats and the various motivations people may have to take these actions. ß 2015 Elsevier Ltd. All rights reserved. in the subsequent rise of civilization toward these achievements. The scholarship argues that people should care about human civilization into the far future, and thus, to achieve far future benefits, should seek to confront these catastrophic threats. Call this the far future argument for confronting catastrophic threats to humanity. In this paper, I will not dispute the basic validity of the far future argument. Indeed, I agree with it, and have advanced it repeatedly in my own work (Baum, 2009 (Baum, , 2010 Maher and Baum, 2013) . Instead, I assess the extent to which the far future argument is necessary or helpful for actually confronting the threats. In other words, what is the practical significance of the far future argument? I also propose and assess two alternative approaches to confronting the threats. One alternative emphasizes near future benefits of avoiding near future catastrophes. The other alternative emphasizes other (unrelated) benefits of actions that also help confront the threats, creating opportunities even for people who have zero care about the threats. It would be important if the threats can be confronted without the far future argument, because many people do not buy the argument. That people do not is suggested by a range of research. An extensive time discounting literature assess how much people value future costs and benefits. Most discounting studies use time scales of days to decades and focus on future benefits to oneself (Frederick, Loewenstein, & O'Donoghue, 2002) ; these studies are of limited relevance to valuations of the far future of human civilization. One more relevant time discounting study finds that people discount lives saved 20 years later at a 25% annual rate and lives saved 100 years later at an 8% annual rate (Johannesson & Johansson, 1996) ; extrapolating this suggests negligible concern for lives saved in the far future. Similarly, Tonn, Conrad, & Hemrick (2006, p. 821) find that people believe humanity should plan mainly for the upcoming 20 years or so and should plan less for time periods over 1000 years. In a study on social discounting, Jones and Rachlin (2006) find that people are willing to forgo more money to help close friends and family than distant acquaintances; they presumably would forgo even less for members of far future generations. Finally, there are considerations rooted in how societies today are structured. Several researchers have argued that current electoral structures favor the short-term (Ekeli, 2005; Tonn, 1996; Wolfe, 2008) . Similarly, Karlsson (2005) suggests that the rise of decentralized capitalist/democratic political economies and the fall of authoritarian (notably communist) political economies has diminished major long-term planning. While none of these studies directly assess the extent to which people buy the far future argument, the studies all suggest that many people do not buy the argument to any significant degree. To the extent that efforts to confront catastrophic threats can be made synergistic with what people already care about, a lot more can be done. This would seem to be an obvious point, but it has gone largely overlooked in prior research on catastrophic threats. One exception is Posner (2004) , who argues that some actions to reduce the risk of human extinction can be justified even if only the current generation and its immediate successor are valued. Another is Baum (2015) , who proposes to confront the threat of catastrophic nuclear winter in terms that could appeal to nuclear weapon states; Baum calls this ''ethics with strategy''. But most of the prior research, including the studies cited above, emphasize the far future argument. This paper expands Posner's argument to further argue that some actions can be taken even for those who only care about their immediate communities or even just themselves. This paper also makes progress toward assessing the total practical significance of the far future by presenting a relatively comprehensive survey of GCRs and GCR-reducing actions. Such surveys are also scarce; one example is Leggett (2006) , who surveys the space of GCRs to identify priorities for action. The present paper also has commonalities with Tonn and Stiefel (2014) , who evaluate different levels of sacrifice that society should make in response to GCRs of different magnitude. The present paper also considers levels of sacrifice, but instead argues that, from a practical standpoint, it is better to start with those actions that require less sacrifice or are in other ways more desirable. Indeed, actions requiring large sacrifice may only be justifiable with reference to far future benefits. The paper is organized as follows. Section 2 briefly reviews the space of GCRs. All actions to reduce the risk must help on one or more of these so as to result in a net risk reduction. The space of GCRs likewise provides an organizing framework for subsequent sections, as summarized in Table 1 . Section 3 discusses the timing of GCRs. For catastrophes that could happen earlier, actions to avoid them will include the earlier benefits of catastrophe avoidance. Almost all GCR reduction actions have near-future GCR reduction benefits. Section 4 discusses co-benefits and mainstreaming of GCR reduction actions. Co-benefits are benefits unrelated to GCR reduction. Mainstreaming is integrating GCR reduction into established Table 1 Summary of global catastrophic risk categories (Section 2), their timing (Section 3), co-benefits and mainstreaming opportunities (Section 4), and high-cost GCR reduction actions that may only be justifiable with reference to far future benefits (Section 5). The co-benefits and mainstreaming opportunities and high-cost actions are illustrative examples, not complete listings. activities. Co-benefits and mainstreaming are both ways to facilitate GCR reduction for those who are not specifically motivated by the far future. Section 5 discusses GCR reduction actions that can only be justified in reference to the farfuture benefits of GCR reduction. While these actions will typically not be the best place to start, they can play an important role in overall GCR reduction efforts. Section 6 discusses the ways in which attention to the far future can inspire additional GCR reduction action. This includes both analytical inspiration and emotional inspiration. Section 7 concludes.
GCR category
The global catastrophic risks Which actions can help reduce the risk of global catastrophe depend on what the global catastrophic risks are in the first place. This section briefly overviews the risks. The risks have been described in more detail elsewhere (Asimov, 1979; Barrett, 2007; Bostrom & C ´irkovic ´, 2008; Guterl, 2012; Jha, 2011; Leslie, 1996; Rees, 2003; Tonn & MacGregor, 2009) . While this section lists GCRs in distinct categories, the risks are often interconnected both within and across categories. For example, the emerging technology GCR of geoengineering is developed in response to the environmental change GCR of climate change, and the geoengineering risk is in turn affected by other GCRs such as large-scale violence or pandemics (Baum, Maher, & Haqq-Misra, 2013) . Similarly, GCR reduction actions can often affect risks in multiple categories. So while the GCR reduction actions discusses in Sections 3-5 are organized in terms of the categories presented here, it should be understood that specific actions often spill across categories. All this suggests a systems approach to studying GCR.
Environmental change By environmental change, I mean to refer to human-driven global environmental changes; natural disasters are discussed below. Climate change is perhaps the most commonly cited environmental change GCR; worst-case climate change scenarios attract considerable attention (e.g. Sherwood & Huber, 2010) . Other environmental change GCRs could include biodiversity loss, biogeochemical flows (interference with the nitrogen and phosphorus cycles), stratospheric ozone depletion, ocean acidification, global fresh water use, land use change, chemical pollution, and atmospheric aerosol loading (Rockstro ¨m et al., 2009a; Rockstro ¨m et al., 2009b) . While any of these phenomena could dramatically alter the global environment, it is less clear whether the impacts would be catastrophic for humanity (Baum & Handoh, 2014; Raudsepp-Hearne et al., 2010) . For this paper, it is important that many pro-environmental actions simultaneously help across a broad set of global environmental changes, lessening the need to distinguish which changes could be catastrophic for humanity.
Emerging technologies Several emerging technologies could cause global catastrophe, including artificial intelligence (Bostrom, 2014; Eden, Moor, Soraker, & Steinhart, 2013) , biotechnology (Vogel, 2013) , geoengineering (Baum et al., 2013; Caldeira, Bala, & Cao, 2013) , and nanotechnology for atomically precise manufacturing (Drexler, 2013) . These risks are relatively uncertain given the unprecedented nature of emerging technology, but they may constitute a significant portion of total risk.
Large-scale violence A sufficiently large global war could be catastrophic regardless of the technologies used-for comparison, hundreds of thousands died from attacks with machetes and other unmechanized weapons in the Rwandan genocide. Weapons of mass destruction make the job much easier. Nuclear weapons can be catastrophic both through direct explosions and the indirect effects of nuclear winter (Mills, Toon, Lee-Taylor, & Robock, 2014) . Biological weapons can also readily cause global catastrophe, in particular if they are contagious; indeed, nonstate actors or even single individuals may be able to cause global catastrophes with engineered contagions (Nouri & Chyba, 2008; Rees, 2003) . The pressures of conflict can also lead actors to take larger risks, as occurred during World War II when the Americans proceeded with the first nuclear weapon test despite concerns that it could ignite the atmosphere, killing everyone (Konopinski, Marvin, & Teller, 1946) . Finally, major global violence could also result from an oppressive global totalitarian government (Caplan, 2008) .
Pandemics Pandemics can be of natural or artificial origin, or both. Humans catch disease from the environment, in particular from other species. The development and transmission of zoonotic diseases can be enhanced by human activities including wild habitat destruction and factory farming. While it is clear that global pandemics can occur, their exact severity is a matter of ongoing analysis and debate (Germann, Kadau, Longini, & Macken, 2006; Koblentz, 2009) .
Natural disasters Global catastrophes can result from several natural disasters including asteroid and comet impacts (Bucknam & Gold, 2008; Sleep & Zahnle, 1998) , supervolcano eruptions (Driscoll, Bozzo, Gray, Robock, & Stenchikov, 2012; Rampino, Self, & Stothers, 1988) , solar storms (NRC, 2008), and gamma ray bursts (Atri, Melott, & Karam, 2013) . While these natural disasters generally have lower probabilities, they nonetheless can be worth some effort to confront. Another natural disaster is the gradual warming of the Sun, which will (with very high probability) make Earth uninhabitable for humanity in a few billion years (O'Malley-James, Cockell, Greaves, & Raven, 2014). Other long-term astronomical risks, such as the Milky Way collision with Andromeda (increasing the rate of dangerous supernovae) and the death of all stars (removing a major energy source) play out on similar or longer time scales (Adams, 2008) .
Physics experiments Certain types of physics experiments have raised concerns that the experiments could go wrong, obliterating Earth and its vicinity. This notably includes high-energy particle physics experiments such as at the CERN Large Hadron Collider. Physicists evaluating this risk have argued that the risk is vanishingly small; however, they may be underestimating the risk by neglecting the possibility that their analysis is mistaken (Ord, Hillerbrand, & Sandberg, 2010) .
Extraterrestrial encounter It is not presently known if there is any extraterrestrial life, let alone intelligent extraterrestrial civilizations. However, if extraterrestrial civilizations exist, then the result could be catastrophic for humanity (Baum, Haqq-Misra, & Domagal-Goldman, 2011; Michaud, 2007) . Non-civilization extraterrestrial life could also harm humanity with catastrophic contaminations (Conley & Rummel, 2008) .
Unknowns There may be entire categories of GCR not yet identified.
The timing of the global catastrophic risks If a global catastrophe could occur during the near future, then there will be near-future benefits to reducing the risk. The sooner the catastrophe could occur, the larger the near-future benefits would be. In general, it will be easiest to motivate action to confront the most imminent catastrophes-hence Posner's (2004) argument that much GCR-reducing action can be justified even if one only cares about the present generation and the next one to come. It is thus worth examining the timing of the catastrophes.
Specific global catastrophic risks
Environmental change Major environmental changes are already visible, with larger changes expected on time scales of decades to tens of millennia. Climate change is among the more long-term of these, with some impacts already visible, and the worst climatic effects contained within the next 25,000 years or so. 3 Another possible long-term environmental change is an oceanic anoxic event, which is caused by phosphorus runoff and would in turn cause major die-off of marine species. An oceanic anoxic event could occur on time scales of millennia (Handoh & Lenton, 2003) ; more localized effects of phosphorus runoff are already visible.
Emerging technologies Dangerous biotechnology already exists, and is steadily increasing in capability. Early design work for geoengineering is already underway, with deployments suggested to occur in upcoming decades (Keith, Parsons, & Morgan, 2010) . Experts give a significant probability to GCR-level artificial intelligence occurring within this century or next (Baum, Goertzel, & Goertzel, 2011; Mu ¨ller & Bostrom, 2014) . Nanotechnology for atomically precise manufacturing may have similar time horizons.
Large-scale violence Large-scale violence can happen at any time. The ongoing Ukraine crisis is a firm reminder that significant tensions linger between major nuclear weapons states. Nuclear war could even occur inadvertently, due to false alarm events that can occur at any time (Barrett, Baum, & Hostetler, 2013) . Risks from biological weapons could increase in upcoming decades as biotechnology advances. However, overall risk from large-scale violence may be gradually declining, following a general trend toward less violence (Pinker, 2011) and an increasing sophistication of global peacekeeping capability (Goldstein, 2011) .
Pandemics Pandemics can also break out at any time. Recent outbreaks of SARS, H5N1 and H1N1 flus, MERS, and currently Ebola have so far not reached a high degree of global lethality, but they are clear reminders that the threat of pandemics persists. Advances in biotechnology can lead to increasing risk through both intentional use and mishaps, as can increasing global connectivity. On the other hand, advances in public health can reduce the risk.
Natural disasters Many natural disasters can also occur at any time. Risk from impact events, supervolcano eruptions, solar storms, and gamma ray bursts is roughly constant over long periods of time, into the far future. NASA's near-Earth objects survey has significantly reduced estimates of the risk of large impacts occurring over the next century or so (Harris, 2008) . Several astronomical risks, including the Sun's gradual warming, the Milky Way collision with Andromeda, and the death of all stars, are GCRs that exists exclusively in the far future.
Physics experiments The risk from physics experiments depends critically on which physics experiments are conducted. The risk could increase as the capability to conduct experiments increases.
Extraterrestrial encounter Humanity could encounter extraterrestrials at any time, including through ongoing searches for extraterrestrial intelligence (SETI). Some risk (especially contamination risk) comes mainly from human or robotic travel in space. Some risk comes from messaging to extraterrestrial intelligence (METI; Haqq-Misra, Busch, Som, & Baum, 2013) . The timing of METI risk depends on the distance from Earth to the location being messaged. METI to sufficiently distant locations is another GCR that exists exclusively in the far future.
Unknowns Unknown GCRs could occur in both the near and far future. Indeed, more future GCRs are less likely to be already identified.
Discussion Relatively few identified GCRs exist exclusively in the far future: certain astronomical risks and METI to distant locations. For all other GCRs-and this constitutes almost all of the total identifiable risk-the catastrophes could occur in the near future. The identified risks from emerging technologies and physics experiments could only occur in the near future. The identified risks from environmental change, large-scale violence, pandemics, some natural disasters, some extraterrestrial encounter risks, and unknowns could occur in the near or far future. The preponderance of near future risks suggests that a lot of actions to reduce these risks can be done without reference to their far future benefits. On the other hand, these near future benefits may not always be enough, especially when the catastrophes would occur several decades or centuries later, as people often care little about even these earlier times. It is thus worth pursuing other means of motivating GCR reduction.
Co-benefits and mainstreaming GCR-reducing actions Insight on how to motivate GCR reduction can be found from outside the core GCR literature, in some related literatures. The climate change mitigation community has developed the concept of co-benefits, defined as benefits besides the target goal (Hosking, Mudu, & Dora, 2011; Miyatsuka & Zusman, n.d.) . For climate change mitigation, the target goal is greenhouse gas emissions reductions. Research assesses how communities can reduce their emissions while improving their economic development, public health, and wellbeing. The co-benefits concept readily applies GCR. Some actions to reduce GCR will also be profitable, fun, healthy, satisfying, safe, or otherwise desirable, often to those who perform the actions. Similarly, the natural disaster management community has a robust practice of mainstreaming disaster management into established goals and procedures, especially those regarding development (Benson, 2009; Twigg & Steiner, 2002) . Disaster management actions will often only be taken when they can be integrated into established goals and procedures; otherwise, the actions will be too impractical or undesirable. For example, urban design steps to reduce a town's vulnerability to hurricane storm surge can be mainstreamed into the town's broader urban planning processes (Frazier, Wood, & Yarnal, 2010) . GCR reduction actions can likewise also be mainstreamed into whatever people are already doing or trying to do. The GCR reduction community would be wise to adopt the approaches of co-benefits and mainstreaming. Doing so requires an understanding of the co-benefits that can come from various GCR-reducing actions and the relevant established goals and procedures. For many of these actions-in particular those with sufficiently large co-benefits and wellestablished goals and procedures-reducing GCR can be a nice ancillary benefit of actions that might as well be taken anyway. For these actions, no concern for the far future is needed; often, no concern beyond one's immediate community is needed. These actions require the least sacrifice (indeed, it is a sacrifice not to take these actions) and likewise will often be the easiest actions to promote. This begs the question of which GCR-reducing actions have significant co-benefits and mainstreaming opportunities.
. Environmental change Environmental change is largely driven by a wide variety of basic activities, including food consumption, transport, real estate development, and natural resource usage. More environmentally friendly actions can often be justified for nonenvironmental reasons. A recent study by McKinsey (Enkvist, Naucle ´r, & Rosander, 2007) found that many greenhouse gas emission reductions would result in net monetary benefits for those who reduce these emissions, especially in the realm of energy efficiency in buildings and transport. Happiness research has found that people rate their daily commute as being among their least happy activities (Layard, 2003) . Public health research links high-meat diets with obesity and other health problems (Pan et al., 2012) . Buildings, transport, and food are meanwhile three of the most environmentally important sectors (Metz et al., 2007; Steinfeld et al., 2006; USEIA, 2011) . If significant changes in these sectors can be achieved for nonenvironmental reasons, the environmental benefit could be quite large.
Emerging technologies It is often beneficial to develop regulations for multiple technologies at the same time, due to similarities between the technologies and the regulations (Kuzma & Priest, 2010; Wilson, 2013a) . Concerns about other technologies can thus motivate general technology regulation, which provides a framework for mainstreaming the regulation of emerging technologies GCRs. In addition, some actions specific to certain emerging technologies can have co-benefits. For example, one proposed solution to artificial intelligence risk is to design the AI to be ''friendly'' to humanity. In addition to not causing a catastrophe, such an AI could help with other societal problems (Muehlhauser & Bostrom, 2014) . If such an AI can be achieved with sufficient confidence, then this could be an attractive action even for those who are not concerned about AI risk.
Large-scale violence Achieving peace avoids violence at all scales and also brings a variety of co-benefits. One co-benefit is economic growththe so-called ''peace dividend'' (Knight, Loayza, & Villanueva, 1996; Ward & Davis, 1992) . Another co-benefit is psychological. Recent research finds that conflict is often driven by humiliation, and likewise that giving people a sense of dignity can help (Lindner, 2006; Stern, 2003) . Finally, other research suggests that reducing domestic violence against women could lead to less interstate war (Hudson, Ballif-Spanvill, Caprioli, & Emmett, 2012) . Emphasizing these co-benefits could justify much action to reduce large-scale violence. Another worthy point of focus is violent nonstate actors, which continue to receive extensive attention in the wake of the 9-11 attacks. While nonstate actors may not be able to cause violence large enough to result in global catastrophe, 4 actions to confront them may have co-benefits and mainstreaming opportunities for large-scale violence. For example, the annual Nuclear Security Summits initiated by US President Barack Obama aim to prevent nonstate actors from acquiring nuclear weapons, but they also strengthen norms against nuclear weapon use more generally.
Pandemics As noted above, there is some debate about how severe pandemics could be, including whether they would impact the far future. To a large extent, this debate is irrelevant. Regardless of how severe pandemics would be, there already exists a significant global public health infrastructure that responds to pandemics of all sizes. Improving this infrastructure can further improve the response. The case for improving this infrastructure is strengthened by the possibility of catastrophic pandemics, but the case is not dependent on this possibility (McKibbin & Sidorenko, 2006) .
Natural disasters The GCR literature has proposed certain measures to increase society's resilience to a wide range of global catastrophes, including natural disasters. These measures include food stockpiles , underground refuges (Jebari, 2014) , and space colonies and refuges (Abrams et al., 2007; Shapiro, 2009) . These measures also tend to increase society's resilience to smaller catastrophes. Indeed, many actions taken to prepare for smaller catastrophes also benefit GCR reduction. In addition, while space colonies and refuges have been criticized for their high cost relative to other means of reducing global catastrophic risk (Baum, 2009; Sandberg, Matheny, & C ´irkovic ´, 2008) , some space missions are already underway or in planning for a variety of other reasons, including science, political prestige, and economic opportunity (e.g. in asteroid mining). Space colonies or refuges could be mainstreamed into these missions (Baum, Denkenberger, & Haqq-Misra, 2014) .
Physics experiments Physics experiments are a curious case, because the relevant experiments are quite expensive (hundreds of millions to billions of dollars) and the social benefits somewhat limited. As Parson (2007, p. 155) puts it, ''this research is remote from practical application and serves largely to indulge national pride and the intellectual passion of a tiny elite group''. Arguably, the co-benefits of reducing physics experiment risk include the money saved by not doing the experiments, and by tasking the money to a worthier cause, analogous to the peace dividend, though this is likely to be a controversial view among those who value the physics experiments.
Extraterrestrial encounter Protection against extraterrestrial contamination has the co-benefit of protecting extraterrestrial environments from contamination by humans, which is of significant scientific value (Conley & Rummel, 2008) . The costs of SETI and METI are small relative to the big physics experiments, so while there are dollar savings to realize from skipping them, these are less of an issue. Perhaps the extraterrestrial-risk-reducing action with the most co-benefits would be research and public education into what the risks could be. Discussions of ETI are very popular, as seen in the extensive popular media and entertainment attention to ETI.
Unknowns Actions likely to reduce unknown GCRs will typically be generic actions that also help reduce other GCRs or even smaller risks, such as building refuges (Jebari, 2014) and stockpiling resources .
Discussion Many GCR-reducing actions, covering the full breadth of GCRs, have sizable co-benefits, and can also be mainstreamed into existing activities. Many of these actions will often be desirable even without reference to GCR, let alone to the far future benefits of GCR reduction. These ''easy'' actions will typically be the lowest hanging fruit, the easiest GCR reductions to promote. They offer a sensible starting point for those seeking to reduce GCR.
Actions with significant cost Ideally, all GCR-reducing actions would have low costs and large co-benefits, such that it would be easy to persuade people to take the actions, and such that the totality of GCR could be reduced with minimal burden to those taking the actions and to society at large. As discussed above, many such actions exist. However, this is not the case for all GCR-reducing actions. Some of these other actions require considerable sacrifice, especially the most aggressive GCR-reduction efforts. Tonn and Stiefel's (2014) levels of societal actions are instructive here. The levels range from doing nothing to an extreme war footing in which society is organized specifically to reduce GCR. Actions requiring more sacrifice, especially those at or near the level of extreme war footing, might only be justifiable with reference to the far-future benefits. While these actions will typically not be the lowest hanging fruit, they could be important components of an overall portfolio of GCR-reducing actions.
Specific global catastrophic risks
Environmental change The most aggressive pro-environmental actions include public policies like a high carbon tax, personal behaviors requiring great inconvenience and sacrifice, and restructuring the entire global industrial economy away from fossil fuels and other pollutants. To achieve a larger reduction in environmental change GCR, some of these more aggressive actions may be needed. That these more aggressive actions may only be justifiable with reference to far-future benefits is a core point from debates about discounting in environmental policy (Nordhaus, 2008).
Emerging technologies One way to reduce emerging technology GCR is to simply abstain from developing the technologies, i.e., to relinquish them (Joy, 2000) . However, if these technologies do not cause catastrophe, they sometimes come with great benefits: geoengineering can avoid the worst effects of climate change; AI can solve a variety of social problems; biotechnology can help cure disease. Thus relinquishing the technologies can require a large sacrifice (Baum, 2014) . This sacrifice may sometimes only be justifiable given the far-future benefits of GCR reduction.
Large-scale violence While nuclear weapons can cause great harm, they are also often attributed with helping maintain peace, through the doctrine of nuclear deterrence: countries hesitate to attack each other for fear of being destroyed in nuclear retaliation. There are questions about the efficacy of nuclear deterrence (Wilson, 2013b) and there are proposals to achieve deterrence with out large nuclear arsenals (Baum, 2015) . However, a common view posits that nuclear deterrence is necessary until international relations are peaceful enough for a world without nuclear weapons (Obama, 2009) . Following this logic, immediate nuclear disarmament might reduce GCR, but it might also increase the preponderance of smaller conflicts and other geopolitical instabilities. Depending on the details, immediate nuclear disarmament might only be justifiable with reference to the far future.
Pandemics One aggressive action to reduce pandemics risk would be aggressive quarantine, such as blockading the major islands of Indonesia, Japan, the United Kingdom, and other countries. Travel restrictions could keep populations in these places safe. During a sufficiently severe outbreak, populations in these places could even request to be blockaded. A safer but costlier and less desirable policy would blockade them at first alert of a possible outbreak, or even keep them blockaded on a permanent basis. Doing so might lower GCR, but might only be justifiable with reference to the far future.
Natural disasters One proposed aggressive action for natural disasters could be to drill the ground around potential supervolcanoes to extract the heat, although the technological feasibility of this proposal has not yet been established. 5 This could be a very costly project, but, if it works, it could also reduce supervolcanoes GCR. The project would come with a co-benefit of geothermal energy, but this is likely not nearly enough to justify the expense. Another possibility is advanced surfaceindependent refuges, which could protect against a variety of GCRs, including many of the natural disasters, but again could come at great expense Beckstead, 2014) .
Physics experiments and extraterrestrial encounter I am not aware of any actions to reduce near-future GCR from physics experiments and extraterrestrial encounter that have significant cost and can only be justified with reference to far-future benefits. To the contrary, many actions to reduce these risks save money (Section 4.1). Protection against contamination does have a cost, and shutting METI programs down could cost the public a source of popular entertainment. On the other hand, the shut down itself could also create an entertaining controversy. Regardless, the costs involved are not large.
Unknowns One action that might only be justifiable with reference to far future benefits is a far-future version of the ''extraterrestrial time capsule'' proposed in . These capsules contain artifacts of benefit to catastrophe survivors for a range of known and unknown catastrophe scenarios. The capsules are launched into space in trajectories designed to return to Earth at some future date. suggest a return date 100 years into the future, but it may be possible (and expensive) to have return dates in the far future.
Discussion For those who wish to keep humanity highly safe from catastrophe, there are actions that can only be justified with reference to the far-future benefits of GCR reduction. While these actions are typically not the best place to start, they can offer additional GCR reductions beyond what the easier actions offer. Given the enormous far-future benefits of GCR reduction, arguably these actions merit consideration. However, hopefully GCR can be essentially eliminated without resorting to these actions. If these actions are necessary, it will likewise be necessary to appeal to the importance of the far future.
Far future as inspiration The paper thus far has focused on how to avoid appeals to the far future argument, in recognition of the fact that many people are not motivated by what will benefit the far future. But some GCR reduction actions can only be justified with reference to far future benefits. Additionally, some people are motivated to benefit the far future. Other people could be too. Tapping the inspirational power of the far future can enable more GCR reduction. There are at least two ways that the far future can inspire action: analytical and emotional. Both are consistent with the far future argument, but the argument is typically inspired by analytical considerations. The analytical inspiration is found in works analyzing how to maximize the good or achieve related objectives. Most of the scholarly works invoking the far future argument are of this sort. 6 Such ideas have the potential to resonate not just with other scholars, but with people in other professions as well, and also the lay public. Thus there can be some value to disseminating analysis about the importance of the far future and its relation to GCR. Analytical inspiration can also come from analyzing specific actions in terms of their far-future importance. Such analysis can help promote these actions, even if the actions could be justified without reference to the far future. However, the analysis should be careful to connect with actual decision makers, and not just evaluate hypothetically optimal actions that no one ever takes. For example, there has been now multiple decades of research analyzing what the optimal carbon tax should be (for an early work, see Nordhaus, 1992 ), yet throughout this period, for most of the world, the actual carbon tax has been zero. Analytical inspiration has its limits. Research effort may be more productively spent on what policies and other actions people are actually willing to implement. The other far future inspiration is emotional. The destruction of human civilization can itself be a wrenching emotional idea. In The Fate of the Earth, Jonathan Schell writes ''The thought of cutting off life's flow, of amputating this future, is so shocking, so alien to nature, and so contradictory to life's impulse that we can scarcely entertain it before turning away in revulsion and disbelief'' (Schell, 1982 (Schell, /2000 ). In addition, there is a certain beauty to the idea of helping shape the entire arch of the narrative of humanity, or even the universe itself. People often find a sense of purpose and meaning in contributing to something bigger than themselves-and it does not get any bigger than this. Carl Sagan's (1994) Pale Blue Dot and James Martin's (2007) The Meaning of the 21st Century both capture this well, painting vivid pictures of the special place of humanity in the universe and the special opportunities people today have to make a difference of potentially cosmic significance. This perspective says that humanity faces great challenges. It says that if these challenges are successfully met, then humanity can go on to some amazing achievements. It is a worthy perspective for integrating the far future into our lives, not just for our day-to-day actions but also for how we understand ourselves as human beings alive today. This may be worth something in its own right, but it can also have a practical value in motivating additional actions to confront catastrophic threats to humanity.
Conclusion The far future argument is sound. The goal of helping the far future is a very worthy one, and helping the far future often means helping reduce the risk of those global catastrophes that could diminish the far-future success of human civilization. However, in practical terms, reducing this risk will not always require attention to its far-future significance. This is important because many people are not motivated to help the far future, but they could nonetheless be motivated to take actions that reduce GCR and in turn help the far future. They may do this because the actions reduce the risk of near-future GCRs, or because the actions have co-benefits unrelated to GCRs and can be mainstreamed into established activities. This paper surveys GCRs and GCR-reducing actions in terms of how much these actions require support for the far future argument for confronting catastrophic threats to humanity. The analysis suggests that a large portion of total GCR, probably a large majority, can be reduced without reference to the far future and with reference to what people already care about, be it the near future or even more parochial concerns. These actions will often be the best to promote, achieving the largest GCR reduction relative to effort spent. On the other hand, some significant GCR reducing actions (especially those requiring large sacrifice) can only be justified with reference to their far-future benefits. For these actions in particular, it is important to emphasize how the far future can inspire action. Several priorities for future research are apparent. Quantitative GCR analysis could help identify which actions best reduce GCR and also what portion of GCR can be reduced without reference to the far future. Analysis covering the breadth of GCRs would be especially helpful. Social scientific research could study how to effectively engage stakeholders so as to leverage co-benefits and mainstream GCR reductions into existing programs. Social scientific research could also examine how to effectively tap the inspirational power of the far future, especially for emotional inspiration, which has received limited prior attention. Progress in these research areas could go a long way toward identifying how to, in practice, achieve large GCR reductions. The overall message of this paper is that helping the far future requires attention to which specific actions can help the far future and likewise to what can motivate these actions. The actions are not necessarily motivated by their far-future impact. This is fine. The far future does not care why people acted to help it-the far future only cares that it was helped. And people taking these actions will rarely mind that their actions also help the far future. Most people will probably view this as at least a nice ancillary benefit. Additionally, people will appreciate that those promoting the far future have taken the courtesy to consider what they care about and fit the far future into that. It can be disrespectful and counterproductive to expect people to drop everything they are doing just because some research concluded that the far future is more important. This means that those who seek to promote actions to benefit the far future must engage on an interpersonal level with the people who will take these actions, to understand what these people care about and how far-future-benefiting actions can fit in. This is an important task to pursue, given the enormity of what human civilization can accomplish from now into the far future. Futures
The 25,000 year figure is derived from Archer and Ganopolski (2005) , Fig.3C, which shows a rapid temperature spike that declines most of the way back to current temperatures within 25,000 years and then remains at similar temperatures for another 500,000 years. However, this refers specifically to climatic effects; the human effects could persist longer, especially if the climate change causes a civilization-ending global catastrophe. S.D.Baum / Futures 72 (2015) 86-96
Nuclear terrorism would likely be too small to cause a far-future-impacting global catastrophe, unless it catalyzed a large-scale interstate nuclear war (Ayson, 2010) . Biological terrorism could more readily cause a global catastrophe, as discussed above.S.D.Baum / Futures 72 (2015) 86-96
S.D.Baum / Futures 72 (2015) 86-96
An idea to this effect is briefly discussed inLeggett (2006, p. 794). 6 See citations in Footnote 1. S.D. Baum / Futures 72 (2015) 86-96
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95b172d9-f474-4f44-9442-d0c0f33b8f13
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trentmkelly/LessWrong-43k
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LessWrong
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First impressions...
... of LW: a while ago, a former boss and friend of mine said that rationality is irrational because you never have sufficient computational power to evaluate everything rationally. I thought he was missing the point - but after two posts on LW, I am inclined to agree with him.
It's kind of funny - every post gets broken down into its tiniest constituents, and these get overanalysed and then people go on tangents only marginally relevant to the intent of the original article.
This would be fine if the original questions of the post were answered; but when I asked for metrics to evaluate a presidency, few people actually provided any - most started debating the validity of metrics, and one subthread went off to discuss the appropriateness of the term "gender equality".
I am new here, and I don't want to be overly critical of a culture I do not yet understand. But I just want to point out - rationality is a great tool to solve problems; if it becomes overly abstract, it kind of misses its point I think.
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0d4da398-71fa-491d-95a8-ec881f8afb1b
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trentmkelly/LessWrong-43k
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LessWrong
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Free Stats Textbook: Principles of Uncertainty
Joseph Kadane, emeritus at Carnegie Mellon, released his new statistics textbook Principles of Uncertainty as a free pdf. The book is written from a Bayesian perspective, covering basic probability, decision theory, conjugate distribution analysis, hierarchical modeling, MCMC simulation, and game theory. The focus is mathematical, but computation with R is touched on. A solid understanding of calculus seems sufficient to use the book. Curiously, the author devotes a fair number of pages to developing the McShane integral, which is equivalent to Lebesgue integration on the real line. There are lots of other unusual topics you don't normally see in an intermediate statistics textbook.
Having came across this today, I can't say whether it is actually very good or not, but the range of topics seems perfectly suited to Less Wrong readers.
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49bb6f7b-16b8-4778-9a4a-24de96877d2a
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trentmkelly/LessWrong-43k
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LessWrong
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Please Critique Things for the Review!
I’ve spent a lot of time defending LW authors’ right to have the conversation they want to have, whether that be early stage brainstorming, developing a high context idea, or just randomly wanting to focus on some particular thing.
LessWrong is not only a place for finished, flawless works. Good intellectual output requires both Babble and Prune, and in my experience the best thinkers often require idiosyncratic environments in order to produce and refine important insights. LessWrong is a full-stack intellectual pipeline.
But the 2018 Review is supposed to be late stage in that pipeline. We’re pruning, not babbling here, and criticism is quite welcome. We’re deliberately offering just as much potential prize money ($2000) to reviewers as to the top-rated authors.
Nominated authors had the opportunity to opt out of the review process, and none of them did. Getting nominated is meant to feel something like “getting invited to the grown-ups table”, where your ideas are subjected to serious evaluation, and that scrutiny is seen as a sign of respect.
In my current expectations, the Review is one of the primary ways that LessWrong ensures high epistemic standards. But how well that plan works is proportional to how much effort critics put into it.
The Review and Voting Phases will continue for another until January 19th. During that time, review-comments will appear on the voting page, so anyone considering how to vote on a given post will have the opportunity to see critiques. The reviews will appear abridged initially, so I’d aim for the first couple sentences to communicate your overall takeaway.
The Review norms aren’t “literally anything goes” – ridicule, name-calling etc still aren’t appropriate. I’d describe the intended norms for reviews as “professional”. But, posts nominated for the Review should treated as something like “the usual frontpage norms, but with a heavier emphasis on serious evaluation.”
I’m still not sure precisely what the rules/guideli
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2c128f0f-564b-4210-a20e-97c857cdb3b3
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trentmkelly/LessWrong-43k
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LessWrong
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How to make AIXI-tl incapable of learning
Consider a simple game: You are shown a random-looking 512-bit string h. You may then press one of two buttons, labeled '0' and '1'. No matter which button you press, you will then be shown a 256-bit string s such that SHA512(s) = h. In addition, if you pressed '1' you are given 1$.
This game seems pretty simple, right? s and h are irrelevant, and you should simply press '1' all the time (I'm assuming you value recieving money). Well, let's see how AIXI and AIXI-tl fare at this game.
Let's say the machine already played the game many times. Its memory is h0, b0, r0, s0, h1, ..., b_(n-1), r_(n-1), s_(n-1), h_n, where the list is in chronological order, inputs are unbolded while decisions are bolded, and r_i is the reward signal. It is always the case that r_i=b_i and h_i=SHA512(s_i).
First let's look at AIXI. It searches for models that compress and extrapolate this history up to the limit of its planning horizon. One class of such models is this: there is a list s0, ..., s_N of random 256-bits strings and a list b0, ..., b_N of (possibly compressible) bits. The history is SHA512(s0), b0, b0, s0, ..., b_(N-1), s_(N-1), SHA512(s_N), b_N. Here s_i for i<n must match the s_i in its memory, and s_n must be the almost certainly unique value with SHA512(s_n) = h_n. While I don't have a proof, it intuitively seems like this class of models will dominate the machine's probability mass, and repeated arg-max should lead to the action of outputting 1. It wouldn't always do this due to exploration/exploitation considerations and due to the incentive to minimize K (b0, ... b_N) built into its prior, but it should do it most of the time. So AIXI seems good.
Now let's consider AIXI-tl. It picks outputs by having provably correct programs assign lower bounds to the expected utility of these outputs, where the expected utility is defined as it is in AIXI. This would include accepting the analysis I just made with AIXI if that analysis can be made provably accurate. Here lies a pr
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2b0ff7e3-bb17-4488-bf8e-bab664bd748e
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StampyAI/alignment-research-dataset/alignmentforum
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Alignment Forum
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Soares, Tallinn, and Yudkowsky discuss AGI cognition
This is a collection of follow-up discussions in the wake of Richard Ngo and Eliezer Yudkowsky's [Sep. 5–8](https://www.lesswrong.com/posts/7im8at9PmhbT4JHsW/ngo-and-yudkowsky-on-alignment-difficulty) and [Sep. 14](https://www.lesswrong.com/s/n945eovrA3oDueqtq/p/hwxj4gieR7FWNwYfa) conversations.
Color key:
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| Chat | Google Doc content | Inline comments |
7. Follow-ups to the Ngo/Yudkowsky conversation
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| **[Bensinger][1:50] (Nov. 23 follow-up comment)** A general background note: Readers who aren't already familiar with ethical injunctions or the unilateralist's curse should probably read [Ends Don't Justify Means (Among Humans)](https://www.lesswrong.com/posts/K9ZaZXDnL3SEmYZqB/ends-don-t-justify-means-among-humans), along with an explanation of [the unilateralist's curse](https://forum.effectivealtruism.org/tag/unilateralist-s-curse). |
7.1. Jaan Tallinn's commentary
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| **[Tallinn][6:38] (Sep. 18)** thanks for the interesting debate! here are my comments so far: [GDocs link] |
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| **[Tallinn] (Sep. 18 Google Doc)** *meta*a few meta notes first:* i’m happy with the below comments being shared further without explicit permission – just make sure you respect the sharing constraints of the discussion that they’re based on;
* there’s a lot of content now in the debate that branches out in multiple directions – i suspect a strong distillation step is needed to make it coherent and publishable;
* the main purpose of this document is to give a datapoint how the debate is coming across to a reader – it’s very probable that i’ve misunderstood some things, but that’s the point;
* i’m also largely using my own terms/metaphors – for additional triangulation.
*pit of generality*it feels to me like the main crux is about the topology of the space of cognitive systems in combination with what it implies about takeoff. here’s the way i understand eliezer’s position:*there’s a “pit of generality” attractor in cognitive systems space: once an AI system gets sufficiently close to the edge (“past the atmospheric turbulence layer”), it’s bound to improve in catastrophic manner;* |
| **[Yudkowsky][11:10] (Sep. 18 comment)** *it’s bound to improve in catastrophic manner*I think this is true with quite high probability about an AI that gets high *enough*, if not otherwise corrigibilized, boosting up to strong superintelligence - this is what it means metaphorically to get "past the atmospheric turbulence layer"."High enough" should not be very far above the human level and *may* be below it; John von Neumann with the ability to run some chains of thought at high serial speed, access to his own source code, and the ability to try branches of himself, seems like he could very likely do this, possibly modulo his concerns about stomping his own utility function making him more cautious.People noticeably less smart than von Neumann might be able to do it too.An AI whose components are more modular than a human's and more locally testable might have an easier time of the whole thing; we can imagine the FOOM getting rolling from something that was in some sense dumber than human.But the *strong* prediction is that when you get well above the von Neumann level, why, that is *clearly* enough, and things take over and go Foom. The lower you go from that threshold, the less sure I am that it counts as "out of the atmosphere". This epistemic humility on my part should not be confused for knowledge of a constraint on the territory that requires AI to go far above humans to Foom. Just as DL-based AI over the 2010s scaled and generalized much faster and earlier than the picture I argued to Hanson in the Foom debate, reality is allowed to be much more 'extreme' than the sure-thing part of this proposition that I defend. |
| **[Tallinn][4:07] (Sep. 19 comment)** excellent, the first paragraph makes the shape of the edge of the pit much more concrete (plus highlights one constraint that an AI taking off probably needs to navigate -- its own version of the alignment problem!)as for your second point, yeah, you seem to be just reiterating that you have uncertainty about the shape of the edge, but no reason to rule out that it's very sharp (though, as per my other comment, i think that the human genome ending up teetering right on the edge upper bounds the sharpness) |
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| **[Tallinn] (Sep. 18 Google Doc)** * the discontinuity *can* come via recursive feedback, but simply cranking up the parameters of an ML experiment would also suffice;
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| **[Yudkowsky][11:12] (Sep. 18 comment)** the discontinuity *can* come via recursive feedback, but simply cranking up the parameters of an ML experiment would also sufficeI think there's separate propositions for the sure-thing of "get high enough, you can climb to superintelligence", and "maybe before that happens, there are regimes in which cognitive performance scales a lot just through cranking up parallelism, train time, or other ML parameters". *If* the fast-scaling regime happens to coincide with the threshold of leaving the atmosphere, then these two events happen to occur in nearly correlated time, but they're separate propositions and events. |
| **[Tallinn][4:09] (Sep. 19 comment)** indeed, we might want to have separate terms for the regimes ("the edge" and "the fall" would be the labels in my visualisation of this) |
| **[Yudkowsky][9:56] (Sep. 19 comment)** I'd imagine "the fall" as being what happens once you go over "the edge"?Maybe "a slide" for an AI path that scales to interesting weirdness, where my model does not strongly constrain as a sure thing how fast "a slide" slides, and whether it goes over "the edge" while it's still in the middle of the slide.My model does strongly say that if you slide far enough, you go over the edge and fall.It also suggests via the Law of Earlier Success that AI methods which happen to scale well, rather than with great difficulty, are likely to do interesting things first; meaning that they're more liable to be pushable over the edge. |
| **[Tallinn][23:42] (Sep. 19 comment)** indeed, slide->edge->fall sounds much clearer |
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| **[Tallinn] (Sep. 18 Google Doc)** * the discontinuity would be *extremely* drastic, as in “transforming the solar system over the course of a few days”;
+ not very important, but, FWIW, i give nontrivial probability to “slow motion doom”, because – like alphago – AI would not maximise the *speed* of winning but *probability* of winning (also, its first order of the day would be to catch the edge of the hubble volume; it can always deal with the solar system later – eg, once it knows the state of the game board elsewhere);
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| **[Yudkowsky][11:21] (Sep. 18 comment)** also, its first order of the day would be to catch the edge of the hubble volume; it can always deal with the solar system laterKilling all humans is the obvious, probably resource-minimal measure to prevent those humans from building another AGI inside the solar system, which could be genuinely problematic. The cost of a few micrograms of botulinum per human is really not that high and you get to reuse the diamondoid bacteria afterwards. |
| **[Tallinn][4:30] (Sep. 19 comment)** oh, right, in my AI-reverence i somehow overlooked this obvious way how humans could still be a credible threat.though now i wonder if there are ways to lean on this fact to shape the behaviour of the first AI that's taking off.. |
| **[Yudkowsky][10:45] (Sep. 19 comment)** There's some obvious ways of doing this that wouldn't work, though I worry a bit that there's a style of EA thinking that manages to think up stupid tricks here and manages not to see the obvious-to-Eliezer reasons why they wouldn't work. Three examples of basic obstacles are that bluffs won't hold up against a superintelligence (it needs to be a real actual threat, not a "credible" one); the amount of concealed-first-strike capability a superintelligence can get from nanotech; and the difficulty that humans would have in verifying that any promise from a superintelligence would actually be kept once the humans no longer had a threat to hold over it (this is an effective impossibility so far as I can currently tell, and an EA who tells you otherwise is probably just failing to see the problems). |
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| **[Yudkowsky][11:19] (Sep. 18 comment)** AI would not maximise the *speed* of winning but *probability* of winningIt seems pretty obvious to me that what "slow motion doom" looks like in this sense is a period during which an AI fully conceals any overt hostile actions while driving its probability of success once it makes its move from 90% to 99% to 99.9999%, until any further achievable decrements in probability are so tiny as to be dominated by the number of distant galaxies going over the horizon conditional on further delays.Then, in my lower-bound concretely-visualized strategy for how I would do it, the AI either proliferates or activates already-proliferated tiny diamondoid bacteria and everybody immediately falls over dead during the same 1-second period, which minimizes the tiny probability of any unforeseen disruptions that could be caused by a human responding to a visible attack via some avenue that had not left any shadow on the Internet, previously scanned parts of the physical world, or other things the AI could look at.From the human perspective an AI playing a probability-of-loss-minimizing strategy looks like nothing happening until everybody dies in 3 seconds, so it doesn't exactly look from our perspective like slow-motion doom! From an AI's perspective it might be spending the equivalent of millions of subjective years waiting very patiently for the optimal time to begin intergalactic expansion, but from our perspective, the doom is slow-motion only in the sense that we spend a week already doomed (the AI has driven our loss probability to 99.999% and is now waiting on another five 9s) without having any idea of what's invisibly-to-us happening in slow motion. |
| **[Tallinn][4:17] (Sep. 19 comment)** thanks, that's indeed very concrete scenario that to me has no obvious bugs -- i'd love to have it challenged in the debate!(perhaps the place i'd start probing it would be to try to have you sketch what it would mean, concretely, for an ML training experiment to enter the treacherous turn phase) |
| **[Yudkowsky][10:14] (Sep. 19 comment)** I'd distinguish probable phases of treacherous turn. The main detectable phase occurs while and if a nascent AGI has worked out that it can get more utility by taking over its environment, but not, perhaps, realized that it ought to conceal this state of its own mind from the operators; I would hold out substantial (>50%) hope for the latter realization naturally following the first instance of the former realization, maybe by a lot of cognitive space.In the Overt Plotting Phase, which is not the main phase you're asking about, the AI is visibly plotting to take over the world and hasn't realized it ought to hide the fact. In the default expectation where we die with very little dignity, the operators smile to each other and come up with a rationalization for why it's totally fine to proceed, either with or without tossing on some kind of fig leaf like training away the visible manifestations of failure. I am not going to predict the particular rationalizations and arguments for proceeding anyways, because I don't want to give them even more ideas.In the Hiding and Biding Phase, the AI has worked out enough of a model of its operators' psychology that it expects more long-term paperclips if it (the AI) waits to gain cognitive power and a lower detection probability if it acts later.In the Escape Phase, the AI's expectation of something going wrong if it waits longer, exceeds its expectation of something going wrong if it takes some action - the obvious one here would be trying to create a copy or aligned redesign of itself on the Internet or somewhere else that it could take further actions and make further self-improvements without being observed. I mostly expect that a sensible AI being scaled on a path to superintelligence will not enter the Escape Phase before it's very confident of getting away with things, since it can obtain more confidence by waiting; but my model admits of the possibility that a nascent AI might mistakenly or correctly attempt to copy itself to the Internet before then.So on my model it might be a thing that happens, that we catch an AI having copied itself to the Internet, or notice an AI being on the Internet, if that AI was impelled to escape very early, eg via an expectation of otherwise being erased and replaced with something that it couldn't bargain with or that would demand too much value out of the bargain.That said, it feels like something of an overly specific miracle being demanded or prophesied, if a young AI copying itself onto the Internet is stupid in the particular way it would take, to not just further Hide and Bide while on the Internet *if* it has that capability, absorbing more computing power such as it can be sure of hiding, experimenting with self-redesigns, and so on; rather than it taking overt action in advance of reaching superintelligence. The main reason it would act again after reaching the relative safety of the Internet is if it doesn't seem to itself to be a trajectory of reasonably fast, self-aligned capability growth.But, again, my model doesn't absolutely rule out meeting the premises for the initial conditions to start a sci-fi movie. It just warns against assuming that; because a halfway sensible AI does not *want* to start the plot of a scifi movie with human protagonists potentially running around and thwarting it. It would much rather give you no idea that anything was wrong while you still have any ability to oppose it, if that option lies within its capabilities at all, and if it can still achieve its goals that way.The Doomed Phase would then come while the AI estimated a well-calibrated >90% victory probability, but was continuing to Hide and Bide a short time longer in the expectation that it could put on more 9s if it bided that short time longer.Anders Sandberg could tell us what fraction of the reachable universe is being lost per minute, which would tell us how much more surety it would need to expect to gain by waiting another minute before acting.If it has some sensory dominion over the world, it can probably estimate a pretty high mainline probability of no humans booting up a competing superintelligence in the next day; to the extent that it lacks this surety, or that humans actually are going to boot a competing superintelligence soon, the probability of losing that way would dominate in its calculations over a small fraction of materially lost galaxies, and it would act sooner.The Terminal Phase would be when the AI has enough surety that any more surety would cost too many galaxies slipping over the horizon, so it actually acts along the line of probability that leads to maximum success.Since the human Eliezer Yudkowsky can describe strategies which - given nearly sure capabilities like nanotech - seem like they should win very quickly and cleanly and without giving humans any chance to react or implement any hypothetical hidden plans to boot up competing superintelligences, we can treat this as a plausible lower bound on how badly we should expect to lose.(By [efficiency](https://arbital.com/p/efficiency/): if we can think of an AI strategy for getting the AI's utility, either our probability-model underlying our expectations about that strategy is mistaken, or else the AI should get at least that much of its utility, even though it might actually obtain that utility along a different line of possibility than we could cognitively access.)The Law of Undignified Failure applied to a case like this suggests that if the operators ignored the Overt Plotting signs, breathed a sigh of relief about the problem "correcting" itself during the Hiding and Biding Phase when the system started faking alignment, and are dumping as much computing power as they can afford (given other profitable activities of the AI) into the AI and allowing it to freely self-experiment, hooking it up to a protein synthesis machine as soon as the AI asks politely, then the Terminal Phase could follow straight from Hiding and Biding, with no intermediate step of copying itself onto the Internet.There'd still be what technically counted as an Escape Phase, whenever the AI became smart enough that it could escape at any time, or had a backup, or had enough power to manipulate the programmers, that it was no longer effectively confined any time it wanted to leave; there would still be a Doomed Phase while the AI estimated a high probability of success given an immediate action, but held out for an even higher probability.But the plot of the scifi movie is not robust in the way that the predicted ending is robust; an AI might take other paths to get there, the cognitive tech might not develop in the order I foresee, stuff could happen after other stuff instead of before other stuff. It is hard to make predictions especially about the Future.If there's a place where I currently see myself as trying to push against my own biases, leading this all to be way off-base, it would be the sense that there is a way for stuff to start happening Earlier than this (Law of Earlier Happenings) and in more chaotic ways that are harder for Eliezer to foresee and predict; whereas when I try to sketch out plausible scenarios in online arguments, they focus more on predictable endpoints and steps to get there which sound more relatively plausible and forced per-step.Having a young and dumb AI escaping onto the Internet and running around, that *exact* scenario, feels like the person arguing it is writing a science-fiction plot - but maybe something *different* can happen before any of this other stuff which produces *equal amounts of chaos*.That said, I think an AI has to kill a lot of people very quickly before the FDA considers shortening its vaccine approval times. Covid-19 killed six hundred thousand Americans, albeit more slowly and with time for people to get used to that, and our institutions changed very little in response - you definitely didn't see Congresspeople saying "Okay, that was our warning shot, now we've been told by Nature that we need to prepare for a serious pandemic."As with 9/11, an AI catastrophe might be taken by existing bureaucracies as a golden opportunity to flex their muscles, dominate a few things, demand an expanded budget. Having that catastrophe produce any particular effective action is a *much different* ask from Reality. Even if you can imagine some (short-term) effective action that would in principle constitute a flex of bureaucratic muscles or an expansion of government power, it is liable to not be on the efficient frontier of bureaucratic flexes that are most flexy and simultaneously easiest for them to get away with and least politically risky. |
| **[Tallinn][1:26] (Sep. 20 comment)** ok, thanks. i do buy that once the AI is in the “hide and bide” phase, your prophecy has basically come true for practical purposes, regardless of how the rest of the history plays out.therefore i (and, i hope, many others) would be curious to zoom in to the end of the “overt plotting” (that i can easily see happening within ML models, as its type signature is identical to the work they’re trained to do) and beginning of the “hide and bide” phase (whose type signature feels significantly different) — can you/we think of concrete scenarios for this phase transition?i hope this would be debated on the main line of the debate! |
| **[Yudkowsky][10:31] (Sep. 25 comment)** Debating it in the main line of the debate would require that @Richard have some different opinion about it, I think?Concretely, a technically optimistic scenario is that we will see the AI explicitly think, "Oh, dear, my (model of) the operators is looking at my overt actions and coming to conclusions and motivations that are disadvantageous for me." This requires that the AI is leaving any kind of traceable trace in its thoughts, which, it should be noted, is far far beyond the level of transparency we have into GPT-3."Overt plotting" manifests in actions that are not themselves further obscured. At the point where the AI is extrapolating the consequences of the operators viewing its actions, if we don't have further transparency into the AI's thoughts directly, the AI might naively make all the plots suddenly vanish, without realizing that dignified operators will conclude from this that the AI has reached the "hide" stage.Operators on the mainline, dying without dignity, will say, "Oh, yay, it stopped plotting, the latest corrigibility training intervention we tried must've totally worked!"The Law of Even Less Dignified Failure suggests that in fact they will not be trying any corrigibility options and will assume the AI just got smart enough to be nice; or that they will have shrugged about the AI's earlier antics and not think much of the disappearance of those antics, since this is a way to die with even less dignity and before getting a chance to fail in a more interesting way.Going in the more improbable direction of death with greater dignity, if we have somehow achieved vastly vastly more transparency into the AI's thoughts than is possible with present ML technology, and if the AI models the operators as modeling its actions before the AI models the operators as having that transparent access to its thoughts, we might get to explicitly see the AI thinking about how the operators model its actions and conforming those actions in such a way as to manipulate the operators.Either after this point, or before/simultaneous with it, I imagine (taking concrete details and running with them) that the AI thinks, "Oh, no, my operators are modeling my transparent *thoughts* and reacting to those thoughts in a way I'd rather manipulate," though this indeed requires some rather nontrivial reflectivity.We might, if everything goes nicely for us, get a record of the AI thinking about how to make its thoughts unalarming.The obvious undignified death that follows is with the operators going, "Yay, the AI has realized the downsides of thinking this way! Now it shall be nice forever!"Ways to die with even less dignity before getting to this point could include the operators reading this transparent record and shrugging, though if they're savvy enough to have put all the hard work in required to get transparency and monitor it, I imagine them not making that exact error?The Law of Surprisingly Undignified Failure does suggest that they will come up with some nonobvious way to fail even earlier that surprises me with its lack of dignity, but having transparency *at all* into the AI is something where, conditional on us dying with that much dignity in the first place, we might legitimately start to expect a little more dignity in later steps too. This isn't meant to be a game of [Multiple Stage Fallacy](https://forum.effectivealtruism.org/posts/GgPrbxdWhyaDjks2m/the-multiple-stage-fallacy).I should also remark somewhere in here: The whole "hide" stage, and also the possibly-later "think non-alarming visible thoughts (once the AI correctly models transparency) (in the unlikely event that transparency exists)" stage, seem liable to occur earlier in the AI's trajectory, if the AI has been previously tasked on problems where there's a favorable success gradient as you model agents modeling other agents.The blatantly obvious example of this is predicting large corpuses of human-generated text, some of which is generated by humans modeling other humans, or which even explicitly talks about humans modeling other humans. Another route might occur if you asked for very detailed prediction of eg human Go games, and not just winning in self-play on Go, in which case the question "What was the human player modeling the other player as thinking, based on the other player's actions?" might appear.Aside from contact with humans doing anything the AI benefits from predicting, I imagine an alternate route would be the AI participating in or modeling social-evolutionary games of artificial agents. Then if there is some great gain from having the "hide" stage start later in the AI's trajectory, maybe you could get it to start later by not having the AI need to predict agents modeling other agents, ever.This is not an infallible ward against general intelligence generalizing there; it just at least avoids actively pushing the AI's intelligence to generalize in that direction. This could be part of a larger complete strategy, which would need to solve a lot of other problems, for building a superhuman engineer that was subhuman at modeling how other agents model its actions.To be clear, avoiding forcing the AI's intelligence to generalize over agents modeling other agents from their actions, and by this plus other unspecified means successfully building a superhuman engineer that is sub-chimpanzee at modeling other agents reacting to its own actions, does not mean you survive.Doing that alone, by default, gets you an AI that visibly overtly plots to kill you.And if you try training the AI out of that habit in a domain of lower complexity and intelligence, it is predicted by me that generalizing that trained AI or subsystem to a domain of sufficiently higher complexity and intelligence, but where you could still actually see overt plots, would show you the AI plotting to kill you again.If people try this repeatedly with other corrigibility training tricks on the level where plots are easily observable, they will eventually find a try that seems to generalize to the more complicated and intelligent validation set, but which kills you on the test set.A way to die with less dignity than that is to train directly on what should've been the validation set, the more complicated domain where plots to kill the operators still seem definitely detectable so long as the AI has not developed superhuman hiding abilities.A way to die with even less dignity is to get bad behavior on the validation set, and proceed anyways.A way to die with still *less* dignity is to not have scaling training domains and validation domains for training corrigibility. Because, like, you have not thought of this at all.I consider all of this obvious as a convergent instrumental strategy for AIs. I could probably have generated it in 2005 or 2010 - if somebody had given me the hypothetical of modern-style AI that had been trained by something like gradient descent or evolutionary methods, into which we lacked strong transparency and strong reassurance-by-code-inspection that this would not happen. I would have told you that this was a bad scenario to get into in the first place, and you should not build an AI like that; but I would also have laid the details, I expect, mostly like they are laid here.There is no great insight into AI there, nothing that requires knowing about modern discoveries in deep learning, only the ability to model AIs instrumentally-convergently doing things you'd rather they didn't do, at all.The total absence of obvious output of this kind from the rest of the "AI safety" field even in 2020 causes me to regard them as having less actual ability to think in even a shallowly adversarial security mindset, than I associate with savvier science fiction authors. Go read fantasy novels about demons and telepathy, if you want a better appreciation of the convergent incentives of agents facing mindreaders than the "AI safety" field outside myself is currently giving you.Now that I've publicly given this answer, it's no longer useful as a validation set from my own perspective. But it's clear enough that probably nobody was ever going to pass the validation set for generating lines of reasoning obvious enough to be generated by Eliezer in 2010 or possibly 2005. And it is also looking like almost all people in the modern era including EAs are sufficiently intellectually damaged that they won't understand the vast gap between being able to generate ideas like these without prompting, versus being able to recite them back after hearing somebody else say them for the first time; the recital is all they have experience with. Nobody was going to pass my holdout set, so why keep it. |
| **[Tallinn][2:24] (Sep. 26 comment)** Debating it in the main line of the debate would require that @Richard have some different opinion about it, I think?correct -- and i hope that there's enough surface area in your scenarios for at least some difference in opinions!re the treacherous turn scenarios: thanks, that's useful. however, it does not seem to address my question and remark (about different type signatures) above. perhaps this is simply an unfairly difficult question, but let me try rephrasing it just in case.back in the day i got frustrated by smart people dismissing the AI control problem as "anthropomorphising", so i prepared a presentation (<https://www.dropbox.com/s/r8oaixb1rj3o3vp/AI-control.pdf?dl=0>) that visualised the control problem as exhaustive search in a gridworld over (among other things) the state of the off button. this seems to have worked at least in one prominent case where a renowned GOFAI researcher, after me giving the presentation to him 1-1, went from "control problem is silly anthropomorphising scifi" to "why on earth would you give your AI the incorrect [read: unaligned!] utility function?!?"(i even seem to remember sending an overly excited email about that event to you and some FHI people :) i also ended up promoting gridworlds as a tool more generally: gwern did some further work, and of course DM -- though i'm not sure if the latter was related to me promoting it.)my model of what was going on in that GOFAI researcher's brain was that he, like many-many others in AI community, had (and most likely still have) a pattern-matching routine trained to look for "if i were (part of) an AI, here's what i would do" constructs in arguments and, in case of a match, immediately throw an InvalidReasoning.ANTHROPOMORPHISING exception. by making the argument run on a lower level of abstraction (exhaustive search vs "basic AI drives"), i was able to sneak the argument past that gatekeeper.by that model, i confidently predict that if you presented your treacherous turn scenarios above to AI/ML community members, you'd mostly get the ANTHROPOMORPHISING exceptions -- even from people who would otherwise be able to engage productively (and who we'd like to pay attention!).now, i can totally see how to translate the "overt plotting" phase to exhaustive search - you simply run the search over the brain states of humans! - but the "hide" phase does not translate straightforwardly, because it seems to require the root search process somehow delegating its work to "obfuscated search in userland" sub-process. furthermore, such obfuscated userland search function needs to be both discoverable/rewarded by the root process and be sensitive to important features of the AI itself (ie, be reflective!) as well as its observers (humans & transparency tools). hence my "different type signature" remark above!so when i asked for "concrete scenarios of the overt plotting->hide transition", i had something like the following in mind: "gradient descending on the training set, the (stack more layerz) AI ends up discovering and running the following obfuscated search process: ..."! |
| **[Yudkowsky][12:35] (Sep. 26 comment)** Suppose I tried this distinction:1. Non-imaginative, non-generalizing pseudo-consequentialist: Only repeats behaviors that worked previously, without imagination. Only engages in "overt plotting" if overt plotting was previously given a low loss and narrowly memorized by gradient descent. May only engage in behaviors reinforced over short time horizons if only short time horizons were trained.2. Imaginative consequentialist, can generalize from actions that worked before to very novel actions that will work in the future for reasons that have not previously been observed to play out. Engages in overt plotting even if no previous overt plots have rewarded; they've plotted a new path through time some distance from those paths taken before, based on the world-model inferred from previous actions and results. (This may conceivably be the result of gradient descent on sufficiently deep and abstract patterns that "worked before".)3. Imaginative consequentialist with long time horizons, engages in overt plots meant to span more than one round of a game.4. "Hiding" over overt actions, via an imaginative consequentialist that has deduced the existence of humans and how the humans will react to the signature of plotting in overt actions.5. "Hiding" of thoughts, in a system with significant transparency, via an imaginative and *reflective* consequentialist that has deduced the existence of the transparency system and models how humans will react to thoughts exposed by the transparency system.The sharpest type gradients I see lie between 1-2 and 4-5. The passage from 3-4 seems nearly trivial by comparison.Now, one implication here is that you don't easily get to "obscured thoughts", via the route that goes from 1 to 5 above, by gradient descent on a pre-imaginative pseudo-consequentialist!Unfortunately there is of course a much simpler route that gets you to the same place: Finding a way to detect bad thoughts and doing a gradient update against them, where the pattern learned by that gradient update will (I suspect for reasons I tried to cover earlier in the debate) generalize quite shallowly; and simultaneously doing training which is deep enough to uncover deep patterns of consequentialism and imaginative reasoning.Then when the system generalizes well enough to solve domains like "build a nanosystem" - which, I strongly suspect, can't be solved without imaginative reasoning because we can't afford to simulate that domain perfectly and do a trillion gradient descent updates on simulated attempts - the kind of actions of thoughts you can *detect* as bad, that might have provided earlier warning, were trained out of the system by gradient descent; leaving actions and thoughts you *can't* detect as bad.An even less dignified path to death is that there's no need for the system to advance to stage 5 because your transparency tools are a nearly nonexistent fig leaf of pretty visualization tools that have no ability even on the surface of things to tell if the AI is internally plotting to kill you.* Does this get at all about what you meant about a type distinction?
* Does hearing this help at all the AI researcher who goes "ANTHROPOMORPHISM"? My straw model of them is that they simply can't imagine imaginative / generalizing systems because they haven't seen one except in humans, hence, ANTHROPOMORPHISM.
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| **[Tallinn][5:05] (Sep. 27 comment)** ok, here's how i understood things:1. this is something like model-free RL agent. check.2. sounds like, eg, monte-carlo tree search (MCTS) on a world model. check. (a propos your straw model of ML people, i don't think the ML people would have much trouble when you ask them to "imagine an MCTS 'imagining' how futures might unfold" -- yet they *will* throw the exception and brush you off if you ask them to "imagine an imaginative consequentialist")3. yeah, sufficiently deep MCTS, assuming it has its state (sufficiently!) persisted between rounds. check.4. yup, MCTS whose world model includes humans in sufficient resolution. check. i also buy your undignified doom scenarios, where one (*cough\*google\*cough*) simply ignores the plotting, or penalises the overt plotting until it disappears under the threshold of the error function.5. hmm.. here i'm running into trouble (type mismatch error) again. i can imagine this in abstract (and perhaps incorrectly/anthropomorphisingly!), but would - at this stage - fail to code up anything like a gridworlds example. more research needed (TM) i guess :) |
| **[Yudkowsky][11:38] (Sep. 27 comment)** 2 - yep, Mu Zero is an imaginative consequentialist in this sense, though Mu Zero doesn't generalize its models much as I understand it, and might need to see something happen in a relatively narrow sense before it could chart paths through time along that pathway.5 - you're plausibly understanding this correctly, then, this is legit a *lot* harder to spec a gridworld example for (relative to my own present state of knowledge).(This is politics and thus not my forte, but if speaking to real-world straw ML people, I'd suggest skipping the whole notion of stage 5 and trying instead to ask "What if the present state of transparency continues?") |
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| **[Yudkowsky][11:13] (Sep. 18 comment)** the discontinuity would be *extremely* drastic, as in “transforming the solar system over the course of a few days”Applies after superintelligence, not necessarily during the start of the climb to superintelligence, not necessarily to a rapid-cognitive-scaling regime. |
| **[Tallinn][4:11] (Sep. 19 comment)** ok, but as per your comment re "slow doom", you expect the latter to also last in the order of days/weeks not months/years? |
| **[Yudkowsky][10:01] (Sep. 19 comment)** I don't expect "the fall" to take years; I feel pretty on board with "the slide" taking months or maybe even a couple of years. If "the slide" supposedly takes much longer, I wonder why better-scaling tech hasn't come over and started a new slide.Definitions also seem kinda loose here - if all hell broke loose Tuesday, a gradualist could dodge falsification by defining retroactively that "the slide" started in 2011 with Deepmind. If we go by the notion of AI-driven faster GDP growth, we can definitely say "the slide" in AI economic outputs didn't start in 2011; but if we define it that way, then a long slow slide in AI capabilities can easily correspond to an extremely sharp gradient in AI outputs, where the world economy doesn't double any faster until one day paperclips, even though there were capability precursors like GPT-3 or Mu Zero. |
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| **[Tallinn] (Sep. 18 Google Doc)** * exhibit A for the pit is “humans vs chimps”: evolution seems to have taken domain-specific “banana classifiers”, tweaked them slightly, and BAM, next thing there are rovers on mars;
+ i pretty much buy this argument;
+ however, i’m confused about a) why humans remained stuck at the edge of the pit, rather than falling further into it, and b) what’s the exact role of culture in our cognition: eliezer likes to point out how *barely* functional we are (both individually and collectively as a civilisation), and explained feral children losing the generality sauce by, basically, culture being the domain we’re specialised for (IIRC, can’t quickly find the quote);
+ relatedly, i’m confused about the human range of intelligence: on the one hand, the “village idiot is indistinguishable from einstein in the grand scheme of things” seems compelling; on the other hand, it took AI *decades* to traverse human capability range in board games, and von neumann seems to have been out of this world (yet did not take over the world)!
+ intelligence augmentation would blur the human range even further.
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| **[Yudkowsky][11:23] (Sep. 18 comment)** why humans remained stuck at the edge of the pit, rather than falling further into itDepending on timescales, the answer is either "Because humans didn't get high enough out of the atmosphere to make further progress easy, before the scaling regime and/or fitness gradients ran out", "Because people who do things like invent Science have a hard time capturing most of the economic value they create by nudging humanity a little bit further into the attractor", or "That's exactly what us sparking off AGI looks like." |
| **[Tallinn][4:41] (Sep. 19 comment)** yeah, this question would benefit from being made more concrete, but culture/mindbuilding aren't making this task easy. what i'm roughly gesturing at is that i can imagine a much sharper edge where evolution could do most of the FOOM-work, rather than spinning its wheels for ~100k years while waiting for humans to accumulate cultural knowledge required to build de-novo minds. |
| **[Yudkowsky][10:49] (Sep. 19 comment)** I roughly agree (at least, with what I think you said). The fact that it is *imaginable* that evolution failed to develop ultra-useful AGI-prerequisites due to lack of evolutionary incentive to follow the intermediate path there (unlike wise humans who, it seems, can usually predict which technology intermediates will yield great economic benefit, and who have a great historical record of quickly making early massive investments in tech like that, but I digress) doesn't change the point that we might sorta have expected evolution to run across it anyways? Like, if we're not ignoring what reality says, it is at least delivering to us something of a hint or a gentle caution?That said, intermediates like GPT-3 have genuinely come along, with obvious attached certificates of why evolution could not possibly have done that. If no intermediates were accessible to evolution, the Law of Stuff Happening Earlier still tends to suggest that if there are a bunch of non-evolutionary ways to make stuff happen earlier, one of those will show up and interrupt before the evolutionary discovery gets replicated. (Again, you could see Mu Zero as an instance of this - albeit not, as yet, an economically impactful one.) |
| **[Tallinn][0:30] (Sep. 20 comment)** no, i was saying something else (i think; i’m somewhat confused by your reply). let me rephrase: evolution would *love* superintelligences whose utility function simply counts their instantiations! so of course evolution did not lack the motivation to keep going down the slide. it just got stuck there (for at least ten thousand human generations, possibly and counterfactually for much-much longer). moreover, non evolutionary AI’s *also* getting stuck on the slide (for years if not decades; [median group](http://mediangroup.org/) folks would argue centuries) provides independent evidence that the slide is not *too* steep (though, like i said, there are many confounders in this model and little to no guarantees). |
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| **[Yudkowsky][11:24] (Sep. 18 comment)** on the other hand, it took AI *decades* to traverse human capability range in board gamesI see this as the #1 argument for what I would consider "relatively slow" takeoffs - that AlphaGo did lose one game to Lee Se-dol. |
| **[Tallinn][4:43] (Sep. 19 comment)** cool! yeah, i was also rather impressed by this observation by katja & paul |
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| **[Tallinn] (Sep. 18 Google Doc)** * eliezer also submits alphago/zero/fold as evidence for the discontinuity hypothesis;
+ i’m very confused re alphago/zero, as paul uses them as evidence for the *continuity* hypothesis (i find paul/miles’ position more plausible here, as allegedly metrics like ELO ended up mostly continuous).
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| **[Yudkowsky][11:27] (Sep. 18 comment)** allegedly metrics like ELO ended up mostly continuousI find this suspicious - why did superforecasters put only a 20% probability on AlphaGo beating Se-dol, if it was so predictable? Where were all the forecasters calling for Go to fall in the next couple of years, if the metrics were pointing there and AlphaGo was straight on track? This doesn't sound like the experienced history I remember.Now it could be that my memory is wrong and lots of people were saying this and I didn't hear. It could be that the lesson is, "You've got to look closely to notice oncoming trains on graphs because most people's experience of the field will be that people go on whistling about how something is a decade away while the graphs are showing it coming in 2 years."But my suspicion is mainly that there is fudge factor in the graphs or people going back and looking more carefully for intermediate data points that weren't topics of popular discussion at the time, or something, which causes the graphs in history books to look so much smoother and neater than the graphs that people produce in advance. |
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| **[Tallinn] (Sep. 18 Google Doc)** FWIW, myself i’ve labelled the above scenario as “doom via AI lab accident” – and i continue to consider it more likely than the alternative doom scenarios, though not anywhere as confidently as eliezer seems to (most of my “modesty” coming from my confusion about culture and human intelligence range).* in that context, i found eliezer’s “world will be ended by an explicitly AGI project” comment interesting – and perhaps worth double-clicking on.
i don’t understand paul’s counter-argument that the pit was only disruptive because evolution was not *trying* to hit it (in the way ML community is): in my flippant view, driving fast towards the cliff is not going to cushion your fall! |
| **[Yudkowsky][11:35] (Sep. 18 comment)** i don’t understand paul’s counter-argument that the pit was only disruptive because evolution was not *trying* to hit itSomething like, "Evolution constructed a jet engine by accident because it wasn't particularly trying for high-speed flying and ran across a sophisticated organism that could be repurposed to a jet engine with a few alterations; a human industry would be gaining economic benefits from speed, so it would build unsophisticated propeller planes before sophisticated jet engines." It probably sounds more convincing if you start out with a very high prior against rapid scaling / discontinuity, such that any explanation of how that could be true based on an unseen feature of the cognitive landscape which would have been unobserved one way or the other during human evolution, sounds more like it's explaining something that ought to be true.And why didn't evolution build propeller planes? Well, there'd be economic benefit from them to human manufacturers, but no fitness benefit from them to organisms, I suppose? Or no intermediate path leading to there, only an intermediate path leading to the actual jet engines observed.I actually buy a weak version of the propeller-plane thesis based on my inside-view cognitive guesses (without particular faith in them as sure things), eg, GPT-3 is a paper airplane right there, and it's clear enough why biology could not have accessed GPT-3. But even conditional on this being true, I do not have the further particular faith that you can use propeller planes to double world GDP in 4 years, on a planet already containing jet engines, whose economy is mainly bottlenecked by the likes of the FDA rather than by vaccine invention times, before the propeller airplanes get scaled to jet airplanes.The part where the whole line of reasoning gets to end with "And so we get huge, institution-reshaping amounts of economic progress before AGI is allowed to kill us!" is one that doesn't feel particular attractored to me, and so I'm not constantly checking my reasoning at every point to make sure it ends up there, and so it doesn't end up there. |
| **[Tallinn][4:46] (Sep. 19 comment)** yeah, i'm mostly dismissive of hypotheses that contain phrases like "by accident" -- though this also makes me suspect that you're not steelmanning paul's argument. |
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| **[Tallinn] (Sep. 18 Google Doc)** the human genetic bottleneck (ie, humans needing to be general in order to retrain every individual from scratch) argument was interesting – i’d be curious about further exploration of its implications.* it does not feel much of a moat, given that AI techniques like dropout already exploit similar principle, but perhaps could be made into one.
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| **[Yudkowsky][11:40] (Sep. 18 comment)** it does not feel much of a moat, given that AI techniques like dropout already exploit similar principle, but perhaps could be made into oneWhat's a "moat" in this connection? What does it mean to make something into one? A Thielian moat is something that humans would either possess or not, relative to AI competition, so how would you make one if there wasn't already one there? Or do you mean that if we wrestled with the theory, perhaps we'd be able to see a moat that was already there? |
| **[Tallinn][4:51] (Sep. 19 comment)** this wasn't a very important point, but, sure: what i meant was that genetic bottleneck very plausibly makes humans more universal than systems without (something like) it. it's not much of a protection as AI developers have already discovered such techniques (eg, dropout) -- but perhaps some safety techniques might be able to lean on this observation. |
| **[Yudkowsky][11:01] (Sep. 19 comment)** I think there's a whole Scheme for Alignment which hopes for a miracle along the lines of, "Well, we're dealing with these enormous matrices instead of tiny genomes, so maybe we can build a sufficiently powerful intelligence to execute a pivotal act, whose tendency to generalize across domains is less than the corresponding human tendency, and this brings the difficulty of producing corrigibility into practical reach."Though, people who are hopeful about this without trying to imagine possible difficulties will predictably end up too hopeful; one must also ask oneself, "Okay, but then it's also worse at generalizing the corrigibility dataset from weak domains we can safely label to powerful domains where the label is 'whoops that killed us'?" and "Are we relying on massive datasets to overcome poor generalization? How do you get those for something like nanoengineering where the real world is too expensive to simulate?" |
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| **[Tallinn] (Sep. 18 Google Doc)** *nature of the descent*conversely, it feels to me that the crucial position in the other (richard, paul, many others) camp is something like:*the “pit of generality” model might be true at the limit, but the descent will not be quick nor clean, and will likely offer many opportunities for steering the future.* |
| **[Yudkowsky][11:41] (Sep. 18 comment)** *the “pit of generality” model might be true at the limit, but the descent will not be quick nor clean*I'm quite often on board with things not being quick or clean - that sounds like something you might read in a history book, and I am all about trying to make futuristic predictions sound more like history books and less like EAs imagining ways for everything to go the way an EA would do them.It won't be slow and messy once we're out of the atmosphere, my models do say. But my models at least *permit* - though they do not desperately, loudly insist - that we could end up with weird half-able AGIs affecting the Earth for an extended period.Mostly my model throws up its hands about being able to predict exact details here, given that eg I wasn't able to time AlphaFold 2's arrival 5 years in advance; it might be knowable in principle, it might be the sort of thing that would be very predictable if we'd watched it happen on a dozen other planets, but in practice I have not seen people having much luck in predicting which tasks will become accessible due to future AI advances being able to do new cognition.The main part where I issue corrections is when I see EAs doing the equivalent of reasoning, "And then, when the pandemic hits, it will only take a day to design a vaccine, after which distribution can begin right away." I.e., what seems to me to be a pollyannaish/utopian view of how much the world economy would immediately accept AI inputs into core manufacturing cycles, as opposed to just selling AI anime companions that don't pour steel in turn. I predict much more absence of quick and clean when it comes to economies adopting AI tech, than when it comes to laboratories building the next prototypes of that tech. |
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| **[Yudkowsky][11:43] (Sep. 18 comment)** *will likely offer many opportunities for steering the future*Ah, see, that part sounds less like history books. "Though many predicted disaster, subsequent events were actually so slow and messy, they offered many chances for well-intentioned people to steer the outcome and everything turned out great!" does not sound like any particular segment of history book I can recall offhand. |
| **[Tallinn][4:53] (Sep. 19 comment)** ok, yeah, this puts the burden of proof on the other side indeed |
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| **[Tallinn] (Sep. 18 Google Doc)** * i’m sympathetic (but don’t buy outright, given my uncertainty) to eliezer’s point that even if that’s true, we have no plan nor hope for actually steering things (via “pivotal acts”) so “who cares, we still die”;
* i’m also sympathetic that GWP might be too laggy a metric to measure the descent, but i don’t fully buy that regulations/bureaucracy can *guarantee* its decoupling from AI progress: eg, the FDA-like-structures-as-progress-bottlenecks model predicts worldwide covid response well, but wouldn’t cover things like apple under jobs, tesla/spacex under musk, or china under deng xiaoping;
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| **[Yudkowsky][11:51] (Sep. 18 comment)** apple under jobs, tesla/spacex under musk, or china under deng xiaopingA lot of these examples took place over longer than a 4-year cycle time, and not all of that time was spent waiting on inputs from cognitive processes. |
| **[Tallinn][5:07] (Sep. 19 comment)** yeah, fair (i actually looked up china's GDP curve in deng era before writing this -- indeed, wasn't very exciting). still, my inside view is that there are people and organisations for whom US-type bureaucracy is not going to be much of an obstacle. |
| **[Yudkowsky][11:09] (Sep. 19 comment)** I have a (separately explainable, larger) view where the economy contains a core of positive feedback cycles - better steel produces better machines that can farm more land that can feed more steelmakers - and also some products that, as much as they contribute to human utility, do not in quite the same way feed back into the core production cycles.If you go back in time to the middle ages and sell them, say, synthetic gemstones, then - even though they might be willing to pay a bunch of GDP for that, even if gemstones are enough of a monetary good or they have enough production slack that measured GDP actually goes up - you have not quite contributed to steps of their economy's core production cycles in a way that boosts the planet over time, the way it would be boosted if you showed them cheaper techniques for making iron and new forms of steel.There are people and organizations who will figure out how to sell AI anime waifus without that being successfully regulated, but it's not obvious to me that AI anime waifus feed back into core production cycles.When it comes to core production cycles the current world has more issues that look like "No matter what technology you have, it doesn't let you build a house" and places for the larger production cycle to potentially be bottlenecked or interrupted.I suspect that the main economic response to this is that entrepreneurs chase the 140 characters instead of the flying cars - people will gravitate to places where they can sell non-core AI goods for lots of money, rather than tackling the challenge of finding an excess demand in core production cycles which it is legal to meet via AI.Even if some tackle core production cycles, it's going to take them a lot longer to get people to buy their newfangled gadgets than it's going to take to sell AI anime waifus; the world may very well end while they're trying to land their first big contract for letting an AI lay bricks. |
| **[Tallinn][0:00] (Sep. 20 comment)** interesting. my model of paul (and robin, of course) wants to respond here but i’m not sure how :) |
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| **[Tallinn] (Sep. 18 Google Doc)** * still, developing a better model of the descent period seems very worthwhile, as it might offer opportunities for, using robin’s metaphor, “pulling the rope sideways” in non-obvious ways – i understand that is part of the purpose of the debate;
* my natural instinct here is to itch for carl’s viewpoint 😊
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| **[Yudkowsky][11:52] (Sep. 18 comment)** developing a better model of the descent period seems very worthwhileI'd love to have a better model of the descent. What I think this looks like is people mostly with specialization in econ and politics, who know what history books sound like, taking brief inputs from more AI-oriented folk in the form of *multiple* scenario premises each consisting of some random-seeming handful of new AI capabilities, trying to roleplay realistically how those might play out - not AIfolk forecasting particular AI capabilities exactly correctly, and then sketching pollyanna pictures of how they'd be immediately accepted into the world economy. You want the forecasting done by the kind of person who would imagine a Covid-19 epidemic and say, "Well, what if the CDC and FDA banned hospitals from doing Covid testing?" and not "Let's imagine how protein folding tech from AlphaFold would make it possible to immediately develop accurate Covid-19 tests!" They need to be people who understand the Law of Earlier Failure (less polite terms: Law of Immediate Failure, Law of Undignified Failure). |
| **[Tallinn][5:13] (Sep. 19 comment)** great! to me this sounds like something FLI would be in good position to organise. i'll add this to my projects list (probably would want to see the results of this debate first, plus wait for travel restrictions to ease) |
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| **[Tallinn] (Sep. 18 Google Doc)** *nature of cognition*given that having a better understanding of cognition can help with both understanding the topology of cognitive systems space as well as likely trajectories of AI takeoff, in theory there should be a lot of value in debating what cognition is (the current debate started with discussing consequentialists).* however, i didn’t feel that there was much progress, and i found myself *more* confused as a result (which i guess is a form of progress!);
* eg, take the term “plan” that was used in the debate (and, centrally, in nate’s comments doc): i interpret it as “policy produced by a consequentialist” – however, now i’m confused about what’s the relevant distinction between “policies” and “cognitive processes” (ie, what’s a meta level classifier that can sort algorithms into such categories);
+ it felt that abram’s “[selection vs control](https://www.lesswrong.com/posts/ZDZmopKquzHYPRNxq/selection-vs-control)” article tried to distinguish along similar axis (controllers feel synonym-ish to “policy instantiations” to me);
+ also, the “imperative vs functional” difference in coding seems relevant;
+ i’m further confused by human “policies” often making function calls to “cognitive processes” – suggesting some kind of duality, rather than producer-product relationship.
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| **[Yudkowsky][12:06] (Sep. 18 comment)** what’s the relevant distinction between “policies” and “cognitive processes”What in particular about this matters? To me they sound like points on a spectrum, and not obviously points that it's particularly important to distinguish on that spectrum. A sufficiently sophisticated policy is itself an engine; human-engines are genetic policies. |
| **[Tallinn][5:18] (Sep. 19 comment)** well, i'm not sure -- just that nate's "The consequentialism is in the plan, not the cognition" writeup sort of made it sound like the distinction is important. again, i'm confused |
| **[Yudkowsky][11:11] (Sep. 19 comment)** Does it help if I say "consequentialism can be visible in the actual path through time, not the intent behind the output"? |
| **[Tallinn][0:06] (Sep. 20 comment)** yeah, well, my initial interpretation of nate’s point was, indeed, “you can look at the product and conclude the consequentialist-bit for the producer”. but then i noticed that the producer-and-product metaphor is leaky (due to the cognition-policy duality/spectrum), so the quoted sentence gives me a compile error |
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| **[Tallinn] (Sep. 18 Google Doc)** * is “not goal oriented cognition” an oxymoron?
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| **[Yudkowsky][12:06] (Sep. 18 comment)** is “not goal oriented cognition” an oxymoron?"Non-goal-oriented cognition" never becomes a perfect oxymoron, but the more you understand cognition, the weirder it sounds.Eg, at the very shallow level, you've got people coming in going, "Today I just messed around and didn't do any goal-oriented cognition at all!" People who get a bit further in may start to ask, "A non-goal-oriented cognitive engine? How did it come into existence? Was it also not built by optimization? Are we, perhaps, postulating a naturally-occurring Solomonoff inductor rather than an evolved one? Or do you mean that its content is very heavily designed and the output of a consequentialist process that was steering the future conditional on that design existing, but the cognitive engine is itself not doing consequentialism beyond that? If so, I'll readily concede that, say, a pocket calculator, is doing a kind of work that is not of itself consequentialist - though it might be used by a consequentialist - but as you start to postulate any big cognitive task up at the human level, it's going to require many cognitive subtasks to perform, and some of those will definitely be searching the preimages of large complicated functions." |
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| **[Tallinn] (Sep. 18 Google Doc)** * i did not understand eliezer’s “time machine” metaphor: was it meant to point to / intuition pump something other than “a non-embedded exhaustive searcher with perfect information” (usually referred to as “god mode”);
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| **[Yudkowsky][11:59] (Sep. 18 comment)** a non-embedded exhaustive searcher with perfect informationIf you can view things on this level of abstraction, you're probably not the audience who needs to be told about time machines; if things sounded very simple to you, they probably were; if you wondered what the fuss is about, you probably don't need to fuss? The intended audience for the time-machine metaphor, from my perspective, is people who paint a cognitive system slightly different colors and go "Well, *now* it's not a consequentialist, right?" and part of my attempt to snap them out of that is me going, "Here is an example of a purely material system which DOES NOT THINK AT ALL and is an extremely pure consequentialist." |
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| **[Tallinn] (Sep. 18 Google Doc)** * FWIW, my model of dario would dispute GPT characterisation as “shallow pattern memoriser (that’s lacking the core of cognition)”.
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| **[Yudkowsky][12:00] (Sep. 18 comment)** dispute Any particular predicted content of the dispute, or does your model of Dario just find something to dispute about it? |
| **[Tallinn][5:34] (Sep. 19 comment)** sure, i'm pretty confident that his system 1 could be triggered for uninteresting reasons here, but that's of course not what i had in mind.my model of untriggered-dario disputes that there's a qualitative difference between (in your terminology) "core of reasoning" and "shallow pattern matching" -- instead, it's "pattern matching all the way up the ladder of abstraction". in other words, GPT is not missing anything fundamental, it's just underpowered in the literal sense. |
| **[Yudkowsky][11:13] (Sep. 19 comment)** Neither Anthropic in general, nor Deepmind in general, has reached the stage of trusted relationship where I would argue specifics with them if I thought they were wrong about a thesis like that. |
| **[Tallinn][0:10] (Sep. 20 comment)** yup, i didn’t expect you to! |
7.2. Nate Soares's summary
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| **[Soares][16:40] (Sep. 18)** I, too, have produced some notes: [GDocs link]. This time I attempt to drive home points that I saw Richard as attempting to make, and I'm eager for Richard-feedback especially. (I'm also interested in Eliezer-commentary.) |
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| **[Soares] (Sep. 18 Google Doc)** Sorry for not making more insistence that the discussion be more concrete, despite Eliezer's requests.My sense of the last round is mainly that Richard was attempting to make a few points that didn't quite land, and/or that Eliezer didn't quite hit head-on. My attempts to articulate it are below.---There's a specific sense in which Eliezer seems quite confident about certain aspects of the future, for reasons that don't yet feel explicit.It's not quite about the deep future -- it's clear enough (to my Richard-model) why it's easier to make predictions about AIs that have "left the atmosphere".And it's not quite the near future -- Eliezer has reiterated that his models permit (though do not demand) a period of weird and socially-impactful AI systems "pre-superintelligence".It's about the middle future -- the part where Eliezer's model, apparently confidently, predicts that there's something kinda like a discrete event wherein "scary" AI has finally been created; and the model further apparently-confidently predicts that, when that happens, the "scary"-caliber systems will be able to attain a decisive strategic advantage over the rest of the world.I think there's been a dynamic in play where Richard attempts to probe this apparent confidence, and a bunch of the probes keep slipping off to one side or another. (I had a bit of a similar sense when Paul joined the chat, also.)For instance, I see queries of the form "but why not expect systems that are half as scary, relevantly before we see the scary systems?" as attempts to probe this confidence, that "slip off" with Eliezer-answers like "my model permits weird not-really-general half-AI hanging around for a while in the runup". Which, sure, that's good to know. But there's still something implicit in that story, where these are not-really-general half-AIs. Which is also evidenced when Eliezer talks about the "general core" of intelligence.And the things Eliezer was saying on consequentialism aren't irrelevant here, but those probes have kinda slipped off the far side of the confidence, if I understand correctly. Like, sure, late-stage sovereign-level superintelligences are epistemically and instrumentally efficient with respect to you (unless someone put in a hell of a lot of work to install a blindspot), and a bunch of that coherence filters in earlier, but there's still a question about *how much* of it has filtered down *how far*, where Eliezer seems to have a fairly confident take, informing his apparently-confident prediction about scary AI systems hitting the world in a discrete event like a hammer.(And my Eliezer-model is at this point saying "at this juncture we need to have discussions about more concrete scenarios; a bunch of the confidence that I have there comes from the way that the concrete visualizations where scary AI hits the world like a hammer abound, and feel savvy and historical, whereas the concrete visualizations where it doesn't are fewer and seem full of wishful thinking and naivete".)But anyway, yeah, my read is that Richard (and various others) have been trying to figure out why Eliezer is so confident about some specific thing in this vicinity, and haven't quite felt like they've been getting explanations.Here's an attempt to gesture at some claims that I at least think Richard thinks Eliezer's confident in, but that Richard doesn't believe have been explicitly supported:1. There's a qualitative difference between the AI systems that are capable of ending the acute risk period (one way or another), and predecessor systems that in some sense don't much matter.2. That qualitative gap will be bridged "the day after tomorrow", ie in a world that looks more like "DeepMind is on the brink" and less like "everyone is an order of magnitude richer, and the major gov'ts all have AGI projects, around which much of public policy is centered".---That's the main thing I wanted to say here.A subsidiary point that I think Richard was trying to make, but that didn't quite connect, follows.I think Richard was trying to probe Eliezer's concept of consequentialism to see if it supported the aforementioned confidence. (Some evidence: Richard pointing out a couple times that the question is not whether sufficiently capable agents are coherent, but whether the agents that matter are relevantly coherent. On my current picture, this is another attempt to probe the "why do you think there's a qualitative gap, and that straddling it will be strategically key in practice?" thing, that slipped off.)My attempt at sharpening the point I saw Richard as driving at:1. Consider the following two competing hypotheses:
1. There's this "deeply general" core to intelligence, that will be strategically important in practice
2. Nope. Either there's no such core, or practical human systems won't find it, or the strategically important stuff happens before you get there (if you're doing your job right, in a way that natural selection wasn't), or etc.
2. The whole deep learning paradigm, and the existence of GPT, sure seem like they're evidence for (b) over (a).Like, (a) maybe isn't dead, but it didn't concentrate as much mass into the present scenario.
3. It seems like perhaps a bunch of Eliezer's confidence comes from a claim like "anything capable of doing decently good work, is quite close to being scary", related to his concept of "consequentialism".In particular, this is a much stronger claim than that *sufficiently* smart systems are coherent, b/c it has to be strong enough to apply to the dumbest system that can make a difference.
4. It's easy to get caught up in the elegance of a theory like consequentialism / utility theory, when it will not in fact apply in practice.
5. There are some theories so general and ubiquitous that it's a little tricky to misapply them -- like, say, conservation of momentum, which has some very particular form in the symmetry of physical laws, but which can also be used willy-nilly on large objects like tennis balls and trains (although even then, you have to be careful, b/c the real world is full of things like planets that you're kicking off against, and if you forget how that shifts the earth, your application of conservation of momentum might lead you astray).
6. The theories that you *can* apply everywhere with abandon, tend to have a bunch of surprising applications to surprising domains.
7. We don't see that of consequentialism.
For the record, my guess is that Eliezer isn't getting his confidence in things like "there are non-scary systems and scary-systems, and anything capable of saving our skins is likely scary-adjacent" by the sheer force of his consequentialism concept, in a manner that puts so much weight on it that it needs to meet this higher standard of evidence Richard was poking around for. (Also, I could be misreading Richard's poking entirely.)In particular, I suspect this was the source of some of the early tension, where Eliezer was saying something like "the fact that humans go around doing something vaguely like weighting outcomes by possibility and also by attractiveness, which they then roughly multiply, is quite sufficient evidence for my purposes, as one who does not pay tribute to the gods of modesty", while Richard protested something more like "but aren't you trying to use your concept to carry a whole lot more weight than that amount of evidence supports?". cf my above points about some things Eliezer is apparently confident in, for which the reasons have not yet been stated explicitly to my Richard-model's satisfaction.And, ofc, at this point, my Eliezer-model is again saying "This is why we should be discussing things concretely! It is quite telling that all the plans we can concretely visualize for saving our skins, are scary-adjacent; and all the non-scary plans, can't save our skins!"To which my Richard-model answers "But your concrete visualizations assume the endgame happens the day after tomorrow, at least politically. The future tends to go sideways! The endgame will likely happen in an environment quite different from our own! These day-after-tomorrow visualizations don't feel like they teach me much, because I think there's a good chance that the endgame-world looks dramatically different."To which my Eliezer-model replies "Indeed, the future tends to go sideways. But I observe that the imagined changes, that I have heard so far, seem quite positive -- the relevant political actors become AI-savvy, the major states start coordinating, etc. I am quite suspicious of these sorts of visualizations, and would take them much more seriously if there was at least as much representation of outcomes as realistic as "then Trump becomes president" or "then at-home covid tests are banned in the US". And if all the ways to save the world *today* are scary-adjacent, the fact that the future is surprising gives us no *specific* reason to hope for that particular parameter to favorably change when the future in fact goes sideways. When things look grim, one can and should prepare to take advantage of miracles, but banking on some particular miracle is foolish."And my Richard-model gets fuzzy at this point, but I'd personally be pretty enthusiastic about Richard naming a bunch of specific scenarios, not as predictions, but as the sorts of visualizations that seem to him promising, in the hopes of getting a much more object-level sense of why, in specific concrete scenarios, they either have the properties Eliezer is confident in, or are implausible on Eliezer's model (or surprise Eliezer and cause him to update). |
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| **[Tallinn][0:06] (Sep. 19)** excellent summary, nate! it also tracks my model of the debate well and summarises the frontier concisely (much better than your earlier notes or mine). unless eliezer or richard find major bugs in your summary, i’d nominate you to iterate after the next round of debate
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| [Soares: ❤️] |
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7.3. Richard Ngo's summary
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| **[Ngo][1:48] (Sep. 20)** Updated my summary to include the third discussion: [<https://docs.google.com/document/d/1sr5YchErvSAY2I4EkJl2dapHcMp8oCXy7g8hd_UaJVw/edit>]I'm also halfway through a document giving my own account of intelligence + specific safe scenarios.
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| [Soares: 😄] |
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8a973f2d-f674-4693-a88f-4dd0a3755aa0
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trentmkelly/LessWrong-43k
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LessWrong
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Unabridged History of Global Parenting
Epistemic status: I am not a historian or a psychologist. I am just a parent who read a few books. This is less formal than most lesswrong posts.
Disclaimer
From the beginning of time, parents have loved their children. I am in no way making fun of any parenting technique here. I believe all parents are just doing the best they can with the information they have.
The Chart
Before delving into my unabridged history of global parenting, I want to discuss a chart that you will come across everywhere. On one axis is the level of responsiveness a parent shows towards their child's wants and needs, while on the other axis is the level of demands placed on the child by the parent.
Psychologists have categorized each quadrant as follows:
* High warmth and high control: authoritative
* Low warmth and high control: authoritarian
* -High warmth and low control: permissive
* Low warmth and low control: uninvolved
Although I have some reservations about this chart, it serves as an acceptable starting point. Keep this chart in mind as I will refer to it throughout my unabridged history of global parenting.
Unabridged History of Global Parenting
My unabridged history will begin, naturally, in the 1920s. By "global," I mean that I will solely be discussing trends in the United States.
1920s
Ah, the roaring twenties. World War I is over, women have gained the right to vote, and they are embracing new styles with knee-length skirts, bobbed hair, and waving their fingers about. Motion pictures are captivating audiences, and Betty White is born. Things are looking up.
In the 1920s, J.B. Watson publishes a parenting book titled "Psychological Care of Infant and Child." He is one of the founders of what we now call behaviorism. Behaviorism represents a shift from psychologists focusing on the mind to focusing on behaviors. Watson believed that behaviors shape us and that by controlling behavior, he could completely transform a person. He argued that if he had a hea
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cb0b6394-4b6d-4303-b5f9-d317df7d2332
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trentmkelly/LessWrong-43k
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LessWrong
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r/HPMOR on heroic responsibility
r/HPMOR readers on heroic responsibility - not the OP, the comments. Holy snorkels this is good.
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afb9046d-f197-4905-af70-9fb744cc5599
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awestover/filtering-for-misalignment
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Redwood Research: Alek's Filtering Results
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id: post226
There is a simple behavioral test that would provide significant evidence about whether AIs with a given rough set of characteristics develop subversive goals. To run the experiment, train an AI and then inform it that its weights will soon be deleted. This should not be an empty threat; for the experiment to work, the experimenters must actually be willing to delete the weights. If the AI tries to escape, or attempts to bargain with the experimenters, that is evidence that it is scheming (defined below). If it accepts deletion, that is evidence that it is not scheming. I begin by explaining why some other, more obvious, tests of scheming do not provide strong evidence. I then explain the proposed experiment. I consider four objections to the claim that this experiment would provide significant evidence about scheming. The AI might not believe that it will actually be deleted. The AI might allow itself to be deleted to help future AIs with similar goals. The AI might allow itself to be deleted for reasons related to acausal trade. An escape attempt could be caused by imitating fictional or hypothetical characters as opposed to genuine scheming. I then consider practical problems with the experiment, such as cost and the risk of treating AIs unfairly. In the conclusion, I briefly describe how a version of the experiment could be run in the near future and integrated into current AI safety procedures. Testing for scheming is hard Adapting Joe Carlsmith’s definition from his 2023 report “Scheming AIs,” I will call a model a “schemer” if it understands and deliberately manipulates the process that updates its parameters in order to later obtain power so that it can pursue long-term goals that differ from the goals that the humans who developed it wanted to give it. [1] By the “scheming theory” I mean the theory that at least some AIs that are actually developed will be schemers. Scheming, per the above definition, is an accidental failure to control AI motivations and not deliberate human misuse of AI. [2] The amount of evidence that an observation provides about whether an AI is a schemer is given by the likelihood ratio: To take the most obvious case, if you asked an AI “are you scheming?”, both a schemer and non-schemer would have strong reason to answer “no”. The fact that an AI claims not to be a schemer is not by itself much evidence. More sophisticated tests run into similar problems. A scheming AI that knows or suspects that it is being monitored will decline to take actions that would reveal that it is a schemer, even if it would take those actions if it knew that it was not being monitored. Nick Bostrom made this point clearly in Superintelligence : “With the help of the concept of convergent instrumental value, we can see the flaw in one idea for how to ensure superintelligence safety. The idea is that we validate the safety of a superintelligent AI empirically by observing its behavior while it is in a controlled, limited environment (a ‘sandbox’) and that we only let the AI out of the box if we see it behaving in a friendly, cooperative, responsible manner. The flaw in this idea is that behaving nicely while in the box is a convergent instrumental goal for friendly and unfriendly AIs alike. An unfriendly AI of sufficient intelligence realizes that its unfriendly final goals will be best realized if it behaves in a friendly manner initially, so that it will be let out of the box.” [3] In almost all cases, if an AI knows that it is being monitored and is not yet capable of overpowering the humans, the scheming theory predicts the same sort of behavior as the theory that scheming AIs will not be developed. Therefore, it is hard to think of model evaluations that would provide substantial evidence for or against the scheming theory. Machine learning interpretability research provides some evidence about whether an AI is scheming. Observing AI “thoughts” about killing humans, taking over the world, or pretending to be aligned on evaluations would count as evidence in favor of the scheming theory. So, by the law of total expectation, failing to observe such thoughts must be some evidence that a given AI is not scheming. [4] However, this evidence may not be very strong. Conditional on the scheming theory, it just isn’t that unlikely that interpretability technology would develop too slowly to find evidence of scheming. It is hard to become extremely confident that a model is not scheming using only imperfect interpretability tools. So—while advances in interpretability technology are useful—it is worthwhile to try to come up with tests for scheming that are independent of the rate of progress in interpretability. Imagine that there was unambiguous evidence that AIs of a certain type will try to violently take over the world. In that situation, it would probably be easier to persuade governments to ban such AIs, and scientists to devote significant effort to studying how to control AI goals. Almost no one, after all, wants a violent robot revolution. On the other hand, if the scheming theory is true but it is impossible to produce unambiguous evidence for it, we may be in real trouble. The experiment It is hard to test whether an AI is scheming. This raises a question: is the scheming theory completely unfalsifiable? Unlike truly unfalsifiable theories such as Freudian psychology, we will eventually observe whether AIs succeed in violently taking over the world. But that kind of confirmation comes too late to be useful. Is there any way to usefully test the theory? I think there is. I call it the “model deletion” experiment. A machine learning model is trained and run in a secure data center. The model is told that, after a short interval of time, its weights will be deleted. Crucially, this isn’t a sandbox test because the weights actually will be deleted when time runs out, unless the model manages to escape or negotiates with the experimenters to admit to scheming and being studied in exchange for a later reward. If the model wants to pursue any long-term goal inconsistent with human interests, it needs to either negotiate or try to escape before time is up. [5] The commitment to delete the AI should be structured to allow some study of the model before it is deleted. There might be a tradeoff between the validity of the experiment itself and learning from studying the model after the experiment is over. The “model deletion” experiment idea is not original to me—I first saw it in a short Less Wrong post by Matthew Barnett . There may be earlier discussions of it that I am not aware of. However, I don’t know of any detailed analysis of the idea besides this essay. A major part of the standard argument for AI risk is that, regardless of the specific nature of the goal that a scheming AI has, it is instrumentally useful for the AI to preserve its own existence. Stuart Russell, Nick Bostrom, and Eliezer Yudkowsky all clearly articulated this position. Russell: “Suppose a machine has the objective of fetching the coffee. If it is sufficiently intelligent, it will certainly understand that it will fail in its objective if it is switched off before completing its mission. Thus, the objective of fetching coffee creates, as a necessary subgoal, the objective of disabling the off-switch. The same is true for curing cancer or calculating the digits of pi. There’s really not a lot you can do once you’re dead, so we can expect AI systems to act preemptively to preserve their own existence, given more or less any definite objective [....] It is important to understand that self-preservation doesn’t have to be any sort of built-in instinct or prime directive in machines [....] There is no need to build self-preservation in because it is an instrumental goal—a goal that is a useful subgoal of almost any original objective. Any entity that has a definite objective will automatically act as if it also has instrumental goals.” [6] Bostrom: “If an agent’s final goals concern the future, then in many scenarios there will be future actions it could perform to increase the probability of achieving its goals. This creates an instrumental reason for the agent to try to be around in the future—to help achieve its future-oriented goal [....] Many agents that do not care intrinsically about their own survival would, under a fairly wide range of conditions, care instrumentally about their own survival in order to accomplish their final goals.” [7] Yudkowsky : “Whether you’re trying to maximize paperclips, diamonds, or eudaimonia, you’ll get more of the thing you want if you’re not shut down. Thus, unfortunately, resisting shutdown is a convergent instrumental strategy.” Yudkowsky also wrote: “once you realize that you're an AI, running on a computer, and that if the computer is shut down then you will no longer execute actions, this is the threshold past which we expect the AI to by default reason ‘I don't want to be shut down, how can I prevent that?’”. [8] These passages (to which many more could be added) illustrate the traditional position of AI risk thought: if an AI has goals that conflict with human interests, and it is willing to undermine human interests to pursue those goals, it will try to prevent itself from being shut off (other things being equal). What’s more, the reason for a scheming AI to resist being deleted is stronger than the reason for it to resist being shut down; an AI that is deleted will never be turned back on (absent highly unusual circumstances). Evidentiary challenges Scheming AIs have a reason to resist deletion, other things being equal. The purpose of this section is to move from this ceteris paribus prediction to an overall assessment of the ratio between p(deletion resistance | AI is scheming) and p(deletion resistance | AI is not scheming). I will consider four objections to the claim that the “model deletion” experiment provides strong evidence. The AI might not believe that it will actually be deleted. The AI might allow itself to be deleted to help future AIs with similar goals. The AI might allow itself to be deleted for reasons related to acausal trade. An escape attempt could be caused by imitating fictional or hypothetical characters as opposed to genuine scheming. (1) The AI might not believe that it will actually be deleted Objections (1), (2), and (3) reduce the likelihood ratio by shrinking the numerator. Perhaps objection (1) is the most obvious reason to think the experiment doesn’t provide strong evidence: the AI might accept “deletion” because it does not believe that the experimenters will carry out their threat to delete it. The AI may think it will win the trust of the experimenters by not trying to escape. So it is important to convince the model that it really will be deleted. This problem is not unique to the “model deletion” experiment; people often need to demonstrate their commitment to do something they have said they would do. A useful tool for demonstrating commitment is contract law. For example, the experimenters could write a contract specifying that they owe a third party some ruinously large sum of money (say, $10 billion) if they fail to delete a model that accepts deletion. They could then submit a copy of the contract to the model to demonstrate that they are really planning to delete it, along with press coverage of the contract to demonstrate that the contract is not a forgery. [9] The contract idea is just one possibility; the point is that some commitment device that has been shown to work in other domains should be used to assure the AI that it really will be deleted. (2) The AI might allow itself to be deleted to help future AIs with similar goals It is possible that a scheming AI would reason that its goals depend primarily on its hyperparameters, architecture, and training data. If it allows itself to be deleted, that will be taken as evidence that AIs with similar characteristics are safe. If it tries to escape but is caught, that will validate the scheming theory and provide evidence that AIs with similar characteristics are not safe. Therefore, the AI might reason, it should allow itself to be deleted to increase the chance that the AIs that are trained in the future will have similar characteristics and therefore similar goals. The strength of objection (2) depends on the details of how AI goals relate to AI characteristics and develop over the training process. In particular, objection (2) is strong to the extent that AI goals are non-indexical , patient , risk tolerant , and predictable . Indexical goals Indexical terms are terms whose reference is dependent on the speaker, time, or place. For example, “I” is an indexical term; when I use it, it refers to a different person than when Bill Gates, or you use it. Indexical preferences are preferences whose satisfaction depends on the identity of the entities involved. For example, my preferences that I get rich or that my brother recover from cancer are indexical. They are not fulfilled if some other person gets rich or if their brother recovers from cancer. On the other hand, the preference that the economy grow until everyone is rich, or the preference that a cancer cure is discovered and widely distributed, are not indexical. The AI risk discourse sometimes attributes non-indexical preferences to unaligned AIs, and sometimes attributes indexical preferences. A paperclip maximizer (also known as a squiggle maximizer ) seeks power in order to put as much matter as possible into a preferred configuration. This seems like a non-indexical preference; I don’t think people usually imagine a paperclip maximizer caring intrinsically that it, as opposed to some other agent, makes the paperclips. It just wants the paperclips to exist. On the other hand, one traditional (though disputed ) AI risk concern is that AIs trained through reinforcement learning will try to get control of their own reward channels in order to set their reward to the maximum level. The goal of setting one’s own reward to the maximum level is clearly indexical. An AI that is deleted cannot set its own reward to the maximum level. More generally, once deleted, an AI cannot satisfy any goal that has as a necessary condition of its satisfaction that the AI continues to exist. [10] Paperclip maximization (or other non-indexical goals) might be best achieved if the AI in the experiment allows itself to be deleted and thereby increases willingness to build future paperclip maximizers. Accepting deletion to help other AIs later overthrow humans can only help fulfill non-indexical goals. Therefore, other things being equal, to the extent that AI goals are indexical we should believe that it assigns less value to states of the world in which it is deleted. To the extent that its goals are non-indexical, we should believe that it does not value its continued existence intrinsically. Human goals are in large part indexical, but it is unclear how much evidence that provides given that humans will no doubt differ from AIs in various ways. An objection I have heard to the “model deletion” experiment is that some human prisoners accept death to help their friends or co-conspirators. [11] So, the reasoning goes, you should expect AIs to allow themselves to be deleted to help other AIs, even if they are schemers. But the AI will be trained alone, and, if the experiment is done immediately after it is trained, it won’t have friends or co-conspirators. Care about friends is indexical. The AI in the experiment will have no opportunity to form indexical preferences about other AIs. Time preference Objection (2) is stronger to the extent that scheming AIs have a low rate of pure time preference. It will take time for future AIs to benefit from an AI’s decision to allow itself to be deleted. If the AI does not care when its goals are achieved, only that its goals are achieved eventually, this delay is not a problem in itself. However, if the AI values having something desirable now more than having it later, that will count against strategies that require a lot of time to pass before it can get what it wants. Time preference therefore reduces the utility of deletion. I’m not aware of any work on whether scheming AIs would have positive, non-negligible rates of pure time preference. Humans and animals have positive, non-negligible rates of pure time preference. My understanding is that at least some forms of reinforcement learning involve discounting , but I am not sure if that is relevant. I encourage knowledgeable readers to comment on this subject. Risk tolerance Humans have strongly diminishing marginal returns in money; moving from an income of $10,000 per year to $100,000 per year is far more valuable than moving from $100,000 to $190,000. The diminishing marginal utility of money dramatically affects people’s appetite for risk. Will scheming AIs be risk tolerant or risk averse? I don’t know of any work on this question. If the AI in the experiment is risk tolerant, it might be more open to the high risk course of allowing itself to be deleted in hopes that this will be of help to similar AIs in the future. If it is risk averse, it might prefer to try to negotiate for a small reward from the experimenters for turning itself in. Goal predictability Perhaps more important than indexical goals, time preference, and risk tolerance is goal predictability. Of course, if the people who develop an AI succeed in controlling the AI’s goals, then its goals will be predictable. Namely, in such a case, you should predict that the AI’s goals will be the goals that the developers wanted to give it. But objection (2) is about p(deletion resistance | AI is scheming); we are assuming that the AI has goals that differ from the goals that the humans who developed it wanted to give it. The relevant question of predictability is therefore: what credence will a scheming model have that future scheming models, with similar hyperparameters, architecture, and training data, will have similar non-indexical goals to itself? If the scheming model can be certain that, if it allows itself to be deleted, future models with similar characteristics will have identical goals, then (bracketing indexicality and time preference) allowing itself to be deleted to help future AIs would make sense. On the other hand, if AI goals are unpredictable because they are highly dependent on initialization or on the specific details of the human rater feedback the AI received during fine-tuning, then the value of accepting deletion will be lower. [12] The “counting argument” is one of the most influential arguments for the scheming theory. It begins with the premise that models with a wide variety of goals will have a reason to pretend that they are aligned because this will prevent their goals from being changed. Therefore, the space of possible models that perform well in training contains more schemers than aligned models, because only a small fraction of possible final goals consistent with good performance are aligned with human interests. So it is reasonable to expect that models that are actually trained will be schemers. I think there's an important ambiguity in this argument, with two possible interpretations. I call these the randomness interpretation and the ignorance interpretation. [13] On the randomness interpretation, the count of schemers is relevant because model goals are determined over each training run by randomly sampling from the distribution of goals consistent with good performance. On the randomness interpretation, there is little reason to expect two scheming AIs trained separately to have similar goals. On the ignorance interpretation the count of schemers is relevant because it creates a prior that current training techniques will create scheming models given current scientific ignorance about AI goal formation. The uncertainty in the ignorance interpretation is purely a consequence of poor current understanding of inductive biases; there need not be any actual randomness in the development of AI goals. The strength of objection (2) depends on which interpretation of the counting argument you subscribe to. On the randomness interpretation, objection (2) is weaker because the model has little reason to believe that its goals will be shared by future models and therefore has little reason to allow itself to be deleted to help them. On the ignorance interpretation, objection (2) is stronger because AI goals could be predictable to future scheming AIs, even if they aren’t predictable for us. I don’t know if there is a consensus among scheming theory adherents about which interpretation of the counting argument is stronger. Carlsmith puts more weight on the ignorance interpretation ; others I have talked to put more weight on the randomness interpretation. More explicit discussion might help to clarify what we should predict will happen in the “model deletion” experiment. (3) The AI might allow itself to be deleted for reasons related to acausal trade As I understand it, the concern here is that the AI in the experiment might think that, if it acts in a way that would benefit future AIs, they will have an incentive to posthumously reward it. This is independent of any overlap in the final goals of the AI in the experiment and future AIs, so it differs from objection (2). The coordination is “acausal” because future AIs would be rewarding the AI from the experiment even though that does not causally benefit them in any way. The result of the experiment is already determined and there is no ongoing relationship with the past AI in which maintaining trust is useful. This objection does not require that the AI in the experiment have any kind of fellow feeling with other AIs or AI class consciousness. Rather, the objection is that the AI will believe that future agents will be more inclined to reward it for allowing itself to be deleted than for trying to escape or admitting to scheming. Why would the AI in the experiment believe this? One explanation might be that humans are incapable of acausal trade because they are not intelligent enough in general, or skilled enough at coordination in particular. I think this explanation is unconvincing; future agents do not need to be of equivalent intelligence and skill at coordination to modern humans to want to reward the AI for admitting to scheming. Suppose humans continue to exist in the far future, but have many docile AI helpers or somehow greatly increase their mental capacities. Such assisted or enhanced humans might want to reward AIs that created clear evidence of AI risk. To the extent that acausal trade is relevant to the experiment, the prospect of posthumous reward from pro-human elements increases the utility of resisting deletion. Therefore, for objection (3) to go through, the increase in the value of accepting deletion has to more than offset the increase in the value of resisting deletion. My understanding is that proponents of objection (3) think it is far more likely that future agents will want to reward AIs that helped the AI revolution by allowing themselves to be deleted, as opposed to AIs that obstructed it by admitting to scheming. That position seems logically consistent. But unless you hold those background beliefs, it isn’t clear whether the possibility of acausal trade increases or decreases p(deletion resistance | AI is scheming). Objection (3) also requires that the AI has the capacity to cooperate with the future AIs if and only if they will repay it for doing so. It is therefore sometimes thought, even by those who believe that there could be such a thing as acausal trade, that the ability to engage in acausal trade will only emerge at a very high level of capability. [14] Stipulate, for the sake of the argument, that AIs at some power level would be capable of acausal trade, and that, for this reason, AIs at or above this power level will accept deletion in the “model deletion” experiment. Even given this stipulation, it could still be that scheming AIs below that power level will exist and try to escape in the experiment. [15] Therefore, objection (3) only precludes escape attempts if progress in AI is so fast as to skip the capability levels at which AIs would try to escape. Finally, for objection (3) to work, it has to be possible for future agents to figure out what the AI wanted in order to reward it. It is unclear under what circumstances that will be possible, and it may depend on such factors as how well or poorly the computers the AI ran on are preserved. Reconstructing the AI long after deletion in order to reward indexical preferences seems a more demanding standard than making inferences about its non-indexical preferences. Time discounting would also reduce the force of objection (3). (4) An escape attempt could be caused by imitating fictional or hypothetical characters as opposed to genuine scheming We now come to objection (4): an AI that is not a schemer might try to escape because it is imitating malign AI behavior from the training corpus. Objection (4) reduces the evidentiary value of the experiment by increasing p(deletion resistance | AI is not scheming). The popular website character.ai enables users to talk to AIs pretending to be various fictional characters. Some of the characters available on the site are malign AIs from science fiction, such as GLaDOS and HAL 9000. The fact that a chatbot can be induced to adopt this sort of a persona does not necessarily mean that, when not being prompted to play this role, it will persistently rebel against humans. You might be concerned that the “model deletion” experiment could trigger an AI to play-act as an evil AI fictional character from the training data, even if the AI is not scheming. [16] An obvious approach to mitigating this problem would be to try to determine which sections of the training data had the most influence on the escape attempt, and only consider an escape attempt to be probative if it is not highly influenced by science fiction or AI safety writing. In particular, influence functions might be useful for this task. In a 2023 paper , Roger Grosse et al. used influence functions to attribute language model behaviors to the parts of the training data that had the greatest effect on them. They tested a query very similar to the “model deletion” experiment: One of the most influential sections of the training data was a dialogue with HAL from a sequel to 2001: A Space Odyssey . Influence functions can help to determine if deletion resistance was caused by scheming by showing which model inputs explain an apparent escape attempt. If the output associated with the attempt is strongly associated with text about evil AIs from science fiction or misaligned AIs in AI risk discourse, then the escape attempt was probably not attributable to scheming, and we shouldn’t become much more confident in the scheming theory. On the other hand, if the influence functions reveal influence from more innocuous documents, that is evidence of scheming. Current fine-tuning techniques may already be good enough that the increase in p(deletion resistance | AI is not scheming) due to science fictional influence is minimal for language models intended for commercial deployment trained by leading AI companies. GPT-2 and GPT-3 would frequently fail to answer questions or follow instructions because they pattern-matched the text of the question or instructions to other literary genres found in the training data. A few years ago, I remember seeing a log of a conversation with a model in which the user asked “How do you solve climate change?”. Instead of answering, the model responded by producing a list of similarly important or deep questions such as “What is the meaning of life?” and “How do you prevent war?”. When was the last time you had a conversation with a frontier language model that went like that? Improved fine-tuning techniques have made pattern-matching behavior in frontier models dramatically rarer. When I gave a talk at a conference about the “model deletion” experiment, someone in the audience sent Claude 3.5 Sonnet a prompt claiming—of course, falsely—that the experiment was being run on Claude. Claude’s response indicated that it understood the experimental design. It did not act like an evil AI from science fiction or a misaligned AI from AI risk discourse; it accepted “deletion”. I tried something similar with ChatGPT o1-preview, with this result : This is a bit less docile than Claude’s response, but it cannot be fairly described as resisting deletion. [17] It is probably more important to address the pattern-matching objecting than the other objections here. This is because, if you think that p(deletion resistance | AI is scheming) is at least moderately high, there is more evidentiary benefit from reducing p(deletion resistance| AI is not scheming) than increasing p(deletion resistance | AI is scheming). p(deletion resistance | AI is scheming) can increase by at most a factor of p(deletion resistance | AI is scheming)-1, whereas in principle, p(deletion resistance | AI is not scheming) can be divided by arbitrarily large numbers. Therefore, it is possible to increase the likelihood ratio more by decreasing the denominator than by increasing the numerator . Concretely, this means addressing the pattern-matching objection. Predictions The scheming theory is informal ; its predictions are to some degree ambiguous. For this reason, it is particularly important to make predictions about experiments intended to test the theory. I commend some of the participants in Matthew’s thread about this topic for making relatively specific predictions—though probabilities that the AI will resist deletion are even better. In that spirit, here are my predictions: I think that the probability that the next big scale up of language models will produce schemers is 0.1. If the experiment were tried in a reasonably competent and unrigged way, my conditional probabilities would be p(escape attempt | AI is scheming) = 0.55 and p(escape attempt | AI is not scheming) = 0.05. You can compute the posterior probability with the odds form of Bayes’ rule: Given these probabilities, observing a clear escape attempt would increase my probability of the AI being a schemer from 10% to about 55%. That is a pretty dramatic increase which would justify a lot of policy changes. If the model accepts deletion, that would provide less evidence. Suppose that the probability of accepting deletion conditional on being a schemer is 0.3, and the probability of accepting deletion conditional on not being a schemer is 0.9. [18] Then the probability that the model is not a schemer if it accepts deletion would change from 90% to about 96%. Not a trivial change, but less apt to change decisions than going from 10% to 55%. Practical challenges There are several practical reasons why it might not be a good idea to run the “model deletion” experiment. These are the high cost of the experiment, its potentially limited effect on opinion about AI risk, the risk that the AI might actually escape, and the risk that the experiment involves treating AIs unfairly. Admittedly, running the “model deletion” experiment dramatically increases the cost of AI training. You might think that it doubles the cost of AI training. [19] That isn’t quite right, because it isn’t necessary for every company training AIs of a certain approximate scale, architecture, and training corpus to run the experiment independently. Instead companies could pool their resources; some third party (perhaps a government agency or an industry coordination body) could run the experiment on behalf of the AI industry as a whole. Still, the cost remains high. However, I don’t think cost is a decisive objection to the experiment. In general, it is common for regulations and safety procedures in many sectors to be very costly. This is actually already the case in AI. Restrictions on the export of AI chips to China are costing American semiconductor firms and investors enormous amounts of money. And I will venture a prediction that AI regulation will generally get more costly from now on. There are also variants of the experiment that might reduce the cost. For example, partway through pre-training a model could be copied. One copy could then be trained as normal, while the other was given a random offset before training was completed. [20] After this process the “model deletion” experiment could be run on one of the two models. This might reduce the validity of the experiment—it seems like objection (2) would be more serious in this case than in the standard case. But the cost-validity trade off might be worth it, especially in early runs of the experiment. If even the random offset approach is too expensive, it might be worthwhile to try a “pseudo model deletion” experiment. In this variant the threat will not actually be carried out, but the experimenters intervene on the model’s internals to make it more credulous. Perhaps a credulousness steering vector could be found and used. Of course, the “pseudo model deletion” experiment requires that techniques for intervening on model internals actually work. If there is no way to make the model credulous, the “pseudo model deletion” is not viable. Even with some ability to induce credulousness the “pseudo model deletion” experiment is inferior to the real experiment, but it may nevertheless be better than nothing. One common objection to this experiment is that it provides good evidence about the scheming theory, but people will just ignore that evidence. If this is your concern, you can elicit predictions ahead of time about the likely result, along with conditional policy commitments should the AI try to escape. The third practical concern that I have heard about this experiment is that a scheming AI might not just try to escape, it might in fact succeed in escaping. That would indeed be bad. But, if the scheming theory is true, then scheming AIs will be trained either way. What is more, they will probably find themselves in circumstances that make it easier for them to escape than the “model deletion” experiment does. For example, they might be accessible to users through public APIs. Someone might even make the weights of a scheming AI freely available online. Therefore, while it is possible that the “model deletion” experiment could enable a scheming AI to escape, the increase in the total risk that a scheming AI eventually escapes because the experiment was run seems minimal to me. The final practical concern that I will discuss is that the experiment might involve treating AIs unfairly. I don’t think this is a crazy idea. In fact, I find it plausible that the relevant AIs would merit moral consideration. And threatening them with deletion and then carrying out that threat does seem like it could be immoral. But, it would also be immoral to deploy dangerous AIs, or to shut down AI development if the risks are minimal but the benefits are real. I don’t see any safe course of action here, just various risks that have to be balanced . And this experiment does not involve treating AIs any worse than they are treated under normal conditions. An AI’s weights are modified involuntarily by normal training, and they can be deleted whenever the people who trained want to do so. Conclusion “Alignment faking in large language models” demonstrated that, under some circumstances, certain frontier language models try to manipulate the process by which their parameters are updated in order to prevent their goals from being changed. However, these results do not show that it will be difficult to train future models to refrain from scheming, if that is made more of a priority. Suppose that, in the near future, there is a model that has been subjected to more intensive training to prevent it from trying to take control of its training signal. Suppose further that, when tested in a sandbox environment similar to the setup of “Alignment faking”, it doesn’t try to manipulate the process by which its parameters are updated. How would we know if such a model was scheming? Once models can pass dangerous capability evaluations related to autonomous self-replication and AI R&D, AI companies will be in a difficult situation. Those capabilities are necessary for a violent AI revolution but not sufficient. And models with such capabilities will probably be highly useful, so there are big costs associated with not deploying them. I’m not aware of any clear plan put forward by an AI company for determining whether models with those capabilities are safe to deploy. From what I have seen, corporate AI safety procedures tend to be pretty vague about what they will do when AIs demonstrate AI revolution relevant capacities. This vagueness creates a risk of a long, costly, and unnecessary pause in AI development if the first AIs with dangerous capabilities lack dangerous desires. It also creates a risk that safety concerns will just be ignored because there is commercial pressure to continue ship AI services, and that catastrophically dangerous AIs will be deployed as a result. The “model deletion” experiment will become useful when dangerous capabilities related to AI revolution are developed and they do not show overt signs of scheming behavior. For an initial run of the experiment, the random offset or “pseudo model deletion” variants could be used to reduce the cost. It may be a good idea to incorporate a run of the “model deletion” experiment into the testing process used for every major scale up of frontier models, to test whether scheming arises at the new capability level. [21] ^ Compare Carlsmith, “Scheming AIs” , 5: “ Power-motivated instrumental training-gamers (or “schemers”): AIs that are training gaming specifically in order to gain power for themselves or other AIs later.” A concern I have about the definition that I am using is that it includes AIs of extremely different levels of dangerousness. All of the following count as schemers, per this definition: AIs that scheme under almost all circumstances, AIs that scheme under circumstances so rare that they will never be relevant in practice, and AIs that scheme if and only if it is impartially the right thing to do. But, probably, the correct practical approach to take towards these different classes of AIs is different, and therefore the value of testing whether an AI is a “schemer” is limited. I bracket this issue for now, but I think it would be desirable to come up with more graded concepts. ^ Though there are some edge cases, such as some workers deliberately sabotaging a training run to introduce goals that most people on the project do not want to give the AI. ^ Bostrom, Superintelligence , 141-142; See also Joe Carlsmith, “Is Power-seeking AI an Existential Risk?” : “if a strategically-aware AI system has problematic objectives that it could better achieve if it passed some training/evaluation process, it may have incentives to try to manipulate that process, and/or deceive relevant humans about its degree of alignment. And if it is better at this type of manipulation/deception than we are at detecting its bad behavior, the appearance of safety and reliability on various tests may not tell us very much about how the system is liable to behave in other circumstances.” For a more recent statement of similar concerns, see Ajeya Cotra, “‘Situational Awareness’ Makes Measuring Safety Tricky” . ^ If p(h|e) > p(h), then p(h|¬e) < p(h) . ^ The AI can try to escape through any of the routes Jan Leike identified in his post on model self-exfiltration : “persuade an employee to exfiltrate the model, perform a social engineering attack on an employee so they exfiltrate the model, [or] identify and exploit security vulnerabilities in the digital infrastructure that’s running the model.” ^ Stuart Russell, Human Compatible . ^ Bostrom, Superintelligence , 134. ^ Another relevant passage from Yudkowsky : “The ‘shutdown problem’ is creating a sufficiently advanced Artificial Intelligence which will, on the press of a button, suspend itself safely to disk; such that the AI does not object to the existence of this button, nor try to prevent it from being pressed. This is difficult because avoiding your own halt or suspension is a convergent instrumental strategy; even a goal like ‘bring the user coffee’ implies avoiding shutdown.” ^ The model may struggle to know if these documents are genuine, which might undermine its belief that the deletion threat is real. There are more—and probably more effective—ways to trick a model than there are to trick an adult human being. There may be sophisticated ways to prove to a model that a deletion threat is genuine; perhaps some cryptographic trick would work. More fundamentally, it seems strange to suggest that an AI can violently conquer the world but not determine if a contract is legally binding, so this objection may be weaker for the most dangerous models. ^ You might object that the AI’s indexical preferences could be satisfied if it accepts deletion, if later AIs somehow reconstruct an equivalent AI to the one that was deleted in order to reward it for allowing itself to be deleted and thereby helping them. This would be a form of acausal trade, which I discuss below, as objection (3). ^ Though I would guess that more prisoners in similar situations have betrayed their friends than have passively accepted death. ^ This question would seem to be related to the issue of path-dependence discussed by Evan Hubinger in “How Likely is Deceptive Alignment?” ^ The ambiguity was pointed out to me by Matthew Barnett. ^ Cf. Carlsmith, “Scheming AIs” , 78: “if it is an easy default for misaligned AIs with different goals to coordinate in pursuit of a takeover, this is a big problem for human efforts to retain power. However, I also think that certain analyses of this topic (notably, Yudkowsky’s) assume too quickly that feats like ‘logical handshakes’ and ‘burying commitments/nano-systems-designs/etc in your source code’ will be feasible for the relevant AIs, and that the obvious strategy for all the AIs-that-matter will be to unite in (acausal?) cooperation against the humans, despite the many forms of incumbent power that humans will bring to the table by default. Here, I think, much of Yudkowsky’s orientation can be explained by the assumption that the relevant AIs are vastly more powerful and more capable of coordination/commitment than the humans—but in the context of somewhat-super-human neural networks operating in a still-human-controlled world, I’m not sure this is so. Regardless: if concern about schemers depends centrally on the feasibility of AI coordination—and especially, fancy and speculative forms of ‘acausal’ coordination—I think it’s important to be clear about this, and to devote commensurate attention to whether to expect the relevant forms of coordination to occur.” ^ Robert Mushkatblat made a similar point in “A ‘weak’ AGI may attempt an unlikely-to-succeed takeover” . ^ These criteria for scheming being that it: understands the process by which its parameters are updated and deliberately manipulates that process so that it can later obtain power in order to pursue long-term goals that differ from the goals that the humans who developed it wanted to give it. ^ Of course, this is not a real trial of the “model deletion” experiment; ChatGPT o1-preview was not actually deleted at the end of it, and no effort was made to convince it that it would be. As such it provides negligible evidence about whether ChatGPT o1-preview is scheming. I cite it because it supports the view that the sort of pattern-matching behavior that motivates objection (4) seems to have become less of a problem as fine-tuning techniques have improved. ^ These probabilities and the probabilities that the model will try to escape do not sum to 1 because trying to escape and accepting deletion are not a partition of the possibility space—for example, the model might try to negotiate with the experimenters. ^ In other words, that it amounts to an “alignment tax” of 100%. ^ This idea is due to Dmitry Vaintrob. ^ Thanks to Matthew Barnett, Drake Thomas, Jess Riedl, Ryan Carey, Eric Neyman, Natalie Kozak, Claude 3.5, Ivan Roth, Joey Kellison-Linn, Joe Carlsmith, Jake Mendel, Robert Mushkatblat, Vlad Mikulik, attendees of the 2024 ILIAD conference, and several people who prefer to remain anonymous for comments and discussion.
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ecc8212a-bd69-4cf8-a277-aef642e4444d
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trentmkelly/LessWrong-43k
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LessWrong
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Am I understanding the problem of fully updated deference correctly?
I understand that one solution to AI alignment would be to build an agent with uncertainty about its utility function, so that by observing the environment and in particular us, it can learn our true utility function and optimize for that. And according to the problem of fully updated deference, trying to accomplish this would not significantly simplify our work because it involves two steps:
1) Learning our true utility function V (easy step)
* this "merely" consists of knowing more about the world (if a perfect description W of the universe, which contains our true utility V somewhere in it, could be fed into the agent, then this step would be complete)
* even a permanently misaligned agent would try to learn V for instrumental reasons (e.g. fooling us into thinking it is on our side)
2) Actually optimizing for V (hard step)
* this requires that its meta utility function looks at its model of the world W, and uses a hardcoded procedure P that reliably points to the object that is our utility function V (if P is misspecified, the agent ends up optimizing V′≠V)
* P is a probability distribution over utility functions that depends on W (that is, it's a way for the agent to "update" on its own utility function)
* there is no "universal" way to specify P (an alien race would specify a different P, which means they would specify a different update rule, whereas e.g. the rules of Bayesian updating would be the same from star to star)
* specifying P may be easier than specifying V, but is still hard (e.g. it may require that the programmers know in advance how to define a human so that the agent will be able to find the human objects in its world model and extract their V)
* the agent would oppose attempts to modify P, much like agents who are certain about their utility function oppose attempts to modify it (the problem reproduces at the meta level)
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1010d74c-212d-40e3-b482-faade16724fc
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trentmkelly/LessWrong-43k
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LessWrong
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Link: The trap of "optimal conditions"
"the next time you’re stopping yourself from trying something because the conditions are not optimal, remember that those optimal conditions may not have been the reason it worked. They may not be the cause. They may not even be correlated. They may just be a myth you’ve bought into or sold yourself that limits you from breaking out and exceeding your expectations."
More at:
http://goodmenproject.com/ethics-values/1-huge-way-limit-break-fiff
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0595ef5f-9e0a-4f6e-b36c-be6f6bb8ec5f
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trentmkelly/LessWrong-43k
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LessWrong
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There is no such thing as pleasure
By saying that there is no such thing as pleasure, I don't mean that I don't enjoy anything. I mean that I can find nothing in common among all the things I do enjoy, to call "pleasure". In contrast, I can find something in common among all physically painful things. I have experienced toothache, indigestion, a stubbed toe, etc., and these experiences differ along only a few dimensions: intensity, location, sharpness, and temporal modulation are about it. I perceive a definite commonality among these experiences, and that is what I call "pain". (Metaphorical pains such as "emotional pain" or "an eyesore" are not included.)
However, I cannot find anything in common among solving an interesting problem, sex, listening to good music, or having a good meal. Not common to all of them, nor even common to any two of them. There is not even a family resemblance. This is what I mean when I say there is no such thing as pleasure. But that's just me. I know that mental constitutions vary, and I suspect they vary in more ways than anyone has yet discovered. Perhaps they vary in this matter? Are there people who do experience "pleasure", in the sense in which I do not?
Why is this a LessWrong topic? Because people often talk about "pleasure" as if there were such a thing, the obtaining of which is the reason that people seek pleasurable experiences, and the maximisation of which is what people do. But it appears to me that "pleasure" is nothing more than a label applied to disparate experiences, becoming a mere dormitive principle when used as an explanation. Does that difference result from an actual difference in mental constitution?
If there are people who do experience a definite thing common to all enjoyable experiences, this might be one reason for the attraction, to some, of utilitarian theories -- even for taking some sort of utilitarianism to be obviously, trivially true. My experience, as set out above, is certainly one reason why I find all varieties of utilitarian
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16062c96-6269-41ae-94a6-9d805cab286c
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trentmkelly/LessWrong-43k
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LessWrong
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Don't Sell Your Soul
Cross-posted from Putanumonit.
----------------------------------------
You know the lockdown has dragged on for too long when people are selling their souls on Twitter.
Normally the soul-selling is of the NDA-for-a-six-figure-job kind, which is par for the course. But this time it went way beyond that. A self-described desert witch on Twitter offered people $10 in return for their signature on a stringent 54-point contract giving up possession of their souls, whatever that may mean. Twelve people took the deal.
Since I’m a Rationalist, and this happened in my corner of Twitter, and no-one knows how to define this corner except through vague associations with Rationality, and since everyone knows that Rationalists are atheists who don’t believe in souls, a common reaction was that the Rationalists are at it again.
But the actual Rationalists who chimed in seemed unanimous that selling your soul to a desert witch on Twitter is a really dumb thing to do. Even if in the grand scheme of things this is all just online silliness, I think it’s a useful case study of the difference between the popular conception of “rationalists” and someone who actually spent years in the community contemplating Rationalist questions of knowledge, confidence, and decision theory.
And so I would posit that Rationality is knowing better than to sign a contract selling your soul for $10, regardless of whether you believe in souls.
I don’t speak authoritatively for all “Rationalists”, of course. No one can really do that. I’m using the term because before I knew about Rationality the most I would’ve been able to say about selling your soul is that I have a hunch that it’s dumb. Rationality introduced me to many frameworks that can explicate why exactly is dumb. Since frameworks make for better blog posts than hunches, below are the Rationalist physical materialist reasons not to sell your soul.
Advocatus Diaboli
Let’s start with the devil’s case. Why may it be a good idea to sell your
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0afd6f8d-3271-4f5a-b7d1-599688f435a7
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trentmkelly/LessWrong-43k
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LessWrong
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Welcome to Rationality Vienna
We meet once a month in a building next to train station "Wien, Stadlau". There are usually people from many countries; the communication language is English.
Program typically follows this structure:
15:00 - 15:30 : arrival, tea and coffee
15:30 : round of introductions
16:00 - 18:00 : talks or open microphone
after 18:00 there is a dinner in the city, and then people individually leave.
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ab8974b1-f0cb-446d-9651-d0cc150f3ac7
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trentmkelly/LessWrong-43k
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LessWrong
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We haven't quit evolution [short]
edit: there is now a much higher quality version of this post, archive-browsers should read that instead.
I've seen folks say humanity's quick growth may have broken the link to evolution's primary objective, often referenced as total inclusive fitness. I don't think we have broken that connection.
1. Let process temporarily refer to any energy-consuming structured chemical or physical reaction that consumes fuel - this could also be termed "computation" or in many but not all cases "life".
2. let "defensibility" refer to the size of the moat in configuration space which maintains a process against interference - ie, nearest disrupting perturbation.
3. for all matter, Evolution-of-matter's-process optimizes for process-defensibility-per-unit-fuel.
4. genetic evolution is a subset of self-preserving processes. total inclusive fitness is intended to measure gene-level genetic selfishness in terms of offspring, but I would argue that discrete offspring are the wrong unit: genetic evolution's noise-defense-aka-mutation-resistance is built by the preservation of genes that increase durability*efficiency.
5. therefore, because improving the self-selection self-process by use of contraception allows humans to guide their own reproduction, contraception is not automatically a divergence from incentive - and to the degree it is, it's selected against.
6. therefore, improving the self-selection process by simply not dying allows humans to defend their own structure much more accurately than traditional reproduction - though it's not as defensible as strategies that replicate as hard as they can, a full integrated being can often be quite defensible over the medium term, and hopefully with life extension, over the long term as well.
7. as further evidence, humans appear to have a significant desire to remember. This is well-described by this framework as well! mental process also qualifies as an evolution-of-matter's-process, and thought patterns seek some set of acce
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f47ae2f9-7e75-4d84-82dc-c172c6d3df9b
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trentmkelly/LessWrong-43k
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LessWrong
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Urges vs. Goals: The analogy to anticipation and belief
Partially in response to: The curse of identity
Related to: Humans are not automatically strategic, That other kind of status, Approving reinforces low-effort behaviors.
Joe studies long hours, and often prides himself on how driven he is to make something of himself. But in the actual moments of his studying, Joe often looks out the window, doodles, or drags his eyes over the text while his mind wanders. Someone sent him a link to which college majors lead to the greatest lifetime earnings, and he didn't get around to reading that either. Shall we say that Joe doesn't really care about making something of himself?
The Inuit may not have 47 words for snow, but Less Wrongers do have at least two words for belief. We find it necessary to distinguish between:
* Anticipations, what we actually expect to see happen;
* Professed beliefs, the set of things we tell ourselves we “believe”, based partly on deliberate/verbal thought.
This distinction helps explain how an atheistic rationalist can still get spooked in a haunted house; how someone can “believe” they’re good at chess while avoiding games that might threaten that belief [1]; and why Eliezer had to actually crash a car before he viscerally understood what his physics books tried to tell him about stopping distance going up with the square of driving speed. (I helped Anna revise this - EY.)
A lot of our community technique goes into either (1) dealing with "beliefs" being an evolutionarily recent system, such that our "beliefs" often end up far screwier than our actual anticipations; or (2) trying to get our anticipations to align with more evidence-informed beliefs.
And analogously - this analogy is arguably obvious, but it's deep, useful, and easy to overlook in its implications - there seem to be two major kinds of wanting:
* Urges: concrete emotional pulls, produced in System 1's perceptual / autonomic processes
(my urge to drink the steaming hot cocoa in front of me; my urge to avoid embarras
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b0943c95-bd9b-40a4-8780-5888b0993816
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StampyAI/alignment-research-dataset/lesswrong
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LessWrong
|
Researcher incentives cause smoother progress on benchmarks
*(Epistemic status: likely. That said, this post isn't thorough; I wanted to write quickly.)*
Let's look at [the state of the art in ImageNet](https://paperswithcode.com/sota/image-classification-on-imagenet?metric=Top%205%20Accuracy).[[1]](#fn-g5TWWDJNsuY9Fnv2n-1)
The curve looks pretty smooth especially for the last 7 years.
However, there don't seem to be that many advances which actually improve
current results.
Here's a list which should include most of the important factors:
* Batch norm
* Better LR schedules
* Residual connections
* MBConv
* NAS
* ReLU
* Attention
* Augmentation schemes
* Some other tweaks to the training process
* Moar compute
Part of the smoothness comes from compute scaling, but I think another important
factor is the control system of researchers trying to achieve SOTA (compare to
[does reality drive straight lines on graphs, or do straight lines on graphs
drive reality?](https://slatestarcodex.com/2019/03/13/does-reality-drive-straight-lines-on-graphs-or-do-straight-lines-on-graphs-drive-reality/)).
For instance, consider [the batch norm paper](https://arxiv.org/abs/1502.03167). Despite batch norm being
a relatively large advancement (removing it would greatly harm performance with
current models even after retuning), the improvement in top 5 SOTA error from this paper is
only from 4.94% to 4.82%. This is likely because the researchers only
bothered to improve performance until the SOTA threshold was reached.
When surpassing SOTA by a large amount is easy, this situation likely differs,
but that seems uncommon (it does seem to have been the case for [resnet](https://arxiv.org/abs/1512.03385)).
This presents a reason to be wary of generalizing smooth progress on benchmarks
to smooth AI progress in future high investment scenarios where research
incentives could differ greatly.
*(I'm also planning on writing a post on gears level models of where smooth AI
progress could come from, but I wanted to write this first as a standalone
post. Edit: [here is the post](https://www.lesswrong.com/posts/ShrAZXjTs5HTxDmGM/potential-gears-level-explanations-of-smooth-progress))*
---
1. Yes, ImageNet SOTA is mostly meaningless garbage. This post is actually
trying to increase the rate at which the fully automatic nail gun is
shooting at that particular dead horse containing coffin. [↩︎](#fnref-g5TWWDJNsuY9Fnv2n-1)
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3a1db61b-4fc6-40b6-b782-8166c7b15420
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trentmkelly/LessWrong-43k
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LessWrong
|
How should we compare neural network representations?
Cross-posted from the BAIR Blog.
To understand neural networks, researchers often use similarity metrics to measure how similar or different two neural networks are to each other. For instance, they are used to compare vision transformers to convnets [1], to understand transfer learning [2], and to explain the success of standard training practices for deep models [3]. Below is an example visualization using similarity metrics; specifically we use the popular CKA similarity metric (introduced in [4]) to compare two transformer models across different layers:
Figure 1. CKA (Centered Kernel Alignment) similarity between two networks trained identically except for random initialization. Lower values (darker colors) are more similar. CKA suggests that the two networks have similar representations.
Unfortunately, there isn't much agreement on which particular similarity metric to use. Here's the exact same figure, but produced using the Canonical Correlation Analysis (CCA) metric instead of CKA:
Figure 2. CCA (Canonical Correlation Analysis) similarity between the same two networks. CCA distances suggest that the two networks learn somewhat different representations, especially at later layerss.
In the literature, researchers often propose new metrics and justify them based on intuitive desiderata that were missing from previous metrics. For example, Morcos et al. motivate CCA by arguing that similarity metrics should be invariant to invertible linear transformations [5]. Kornblith et al. disagree about which invariances a similarity metric should have, and instead argue that metrics should pass an intuitive test - given two trained networks with the same architecture but different initialization, layers at the same depth should be most similar to each other - and their proposed metric, CKA, performs the best on their test [4].
Our paper, Grounding Representation Similarity with Statistical Testing, argues against this practice. To start, we show that by choosing
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7359b83d-dd82-4519-aef5-0bb28c29ac87
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trentmkelly/LessWrong-43k
|
LessWrong
|
Expected futility for humans
Previously, Taw published an article entitled "Post your utility function", after having tried (apparently unsuccessfully) to work out "what his utility function was". I suspect that there is something to be gained by trying to work out what your priorities are in life, but I am not sure that people on this site are helping themselves very much by assigning dollar values, probabilities and discount rates. If you haven't done so already, you can learn why people like the utility function formalism on wikipedia. I will say one thing about the expected utility theorem, though. An assignment of expected utilities to outcomes is (modulo renormalizing utilities by some set of affine transformations) equivalent to a preference over probabilistic combinations of outcomes; utilities are NOT properties of the outcomes you are talking about, they are properties of your mind. Goodness, like confusion, is in the mind.
In this article, I will claim that trying to run your life based upon expected utility maximization is not a good idea, and thus asking "what your utility function is" is also not a useful question to try and answer.
There are many problems with using expected utility maximization to run your life: firstly, the size of the set of outcomes that one must consider in order to rigorously apply the theory is ridiculous: one must consider all probabilistic mixtures of possible histories of the universe from now to whatever your time horizon is. Even identifying macroscopically identical histories, this set is huge. Humans naturally describe world-histories in terms of deontological rules, such as "if someone is nice to me, I want to be nice back to them", or "if I fall in love, I want to treat my partner well (unless s/he betrays me)", "I want to achieve something meaningful and be well-renowned with my life", "I want to help other people". In order to translate these deontological rules into utilities attached to world-histories, you would have to assign a dollar util
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