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As detailed on the policy gradient method page, there are many unbiased estimators of the policy gradient: ∇ θ J ( θ ) = E π θ [ ∑ 0 ≤ j ≤ T ∇ θ ln π θ ( A j | S j ) ⋅ Ψ j | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{0\leq j\leq T}\nabla _{\theta }\ln \pi _{\theta... | Wikipedia - Actor-critic algorithm - Overview > Actor | 302 | 648 | null |
γ j ∑ j ≤ i ≤ T ( γ i − j R i ) {\textstyle \gamma ^{j}\sum _{j\leq i\leq T}(\gamma ^{i-j}R_{i})} : the REINFORCE algorithm. γ j ∑ j ≤ i ≤ T ( γ i − j R i ) − b ( S j ) {\textstyle \gamma ^{j}\sum _{j\leq i\leq T}(\gamma ^{i-j}R_{i})-b(S_{j})} : the REINFORCE with baseline algorithm. Here b {\displaystyle b} is an arbi... | Wikipedia - Actor-critic algorithm - Overview > Actor | 296 | 589 | null |
γ j Q π θ ( S j , A j ) {\textstyle \gamma ^{j}Q^{\pi _{\theta }}(S_{j},A_{j})} . γ j A π θ ( S j , A j ) {\textstyle \gamma ^{j}A^{\pi _{\theta }}(S_{j},A_{j})} : Advantage Actor-Critic (A2C). γ j ( R j + γ R j + 1 + γ 2 V π θ ( S j + 2 ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(R_{j}+\gamma R_{j+1}+\gamma ^{2}V... | Wikipedia - Actor-critic algorithm - Overview > Actor | 222 | 398 | null |
γ j ( R j + γ R j + 1 + γ 2 V π θ ( S j + 2 ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(R_{j}+\gamma R_{j+1}+\gamma ^{2}V^{\pi _{\theta }}(S_{j+2})-V^{\pi _{\theta }}(S_{j})\right)} : TD(2) learning. γ j ( ∑ k = 0 n − 1 γ k R j + k + γ n V π θ ( S j + n ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(\sum _{k=0}^... | Wikipedia - Actor-critic algorithm - Overview > Actor | 251 | 434 | null |
γ j ( ∑ k = 0 n − 1 γ k R j + k + γ n V π θ ( S j + n ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(\sum _{k=0}^{n-1}\gamma ^{k}R_{j+k}+\gamma ^{n}V^{\pi _{\theta }}(S_{j+n})-V^{\pi _{\theta }}(S_{j})\right)} : TD(n) learning. γ j ∑ n = 1 ∞ λ n − 1 1 − λ ⋅ ( ∑ k = 0 n − 1 γ k R j + k + γ n V π θ ( S j + n ) − V π θ ... | Wikipedia - Actor-critic algorithm - Overview > Actor | 350 | 679 | null |
Section: Overview > Critic. In the unbiased estimators given above, certain functions such as V π θ , Q π θ , A π θ {\displaystyle V^{\pi _{\theta }},Q^{\pi _{\theta }},A^{\pi _{\theta }}} appear. These are approximated by the critic. Since these functions all depend on the actor, the critic must learn alongside the ac... | Wikipedia - Actor-critic algorithm - Overview > Critic | 202 | 684 | null |
Let the critic be a function approximator V ϕ ( s ) {\displaystyle V_{\phi }(s)} with parameters ϕ {\displaystyle \phi } . The simplest example is TD(1) learning, which trains the critic to minimize the TD(1) error: δ i = R i + γ V ϕ ( S i + 1 ) − V ϕ ( S i ) {\displaystyle \delta _{i}=R_{i}+\gamma V_{\phi }(S_{i+1})-V... | Wikipedia - Actor-critic algorithm - Overview > Critic | 256 | 655 | null |
The simplest example is TD(1) learning, which trains the critic to minimize the TD(1) error: δ i = R i + γ V ϕ ( S i + 1 ) − V ϕ ( S i ) {\displaystyle \delta _{i}=R_{i}+\gamma V_{\phi }(S_{i+1})-V_{\phi }(S_{i})} The critic parameters are updated by gradient descent on the squared TD error: ϕ ← ϕ − α ∇ ϕ ( δ i ) 2 = ϕ... | Wikipedia - Actor-critic algorithm - Overview > Critic | 345 | 980 | null |
This is a common source of error in implementations that use automatic differentiation, and requires "stopping the gradient" at that point. Similarly, if the critic is estimating the action-value function Q π θ {\displaystyle Q^{\pi _{\theta }}} , then it can be learned by Q-learning or SARSA. In SARSA, the critic main... | Wikipedia - Actor-critic algorithm - Overview > Critic | 226 | 666 | null |
The temporal difference error is then calculated as δ i = R i + γ Q θ ( S i + 1 , A i + 1 ) − Q θ ( S i , A i ) {\displaystyle \delta _{i}=R_{i}+\gamma Q_{\theta }(S_{i+1},A_{i+1})-Q_{\theta }(S_{i},A_{i})} . The critic is then updated by θ ← θ + α δ i ∇ θ Q θ ( S i , A i ) {\displaystyle \theta \leftarrow \theta +\alp... | Wikipedia - Actor-critic algorithm - Overview > Critic | 299 | 667 | null |
The critic is then updated by θ ← θ + α δ i ∇ θ Q θ ( S i , A i ) {\displaystyle \theta \leftarrow \theta +\alpha \delta _{i}\nabla _{\theta }Q_{\theta }(S_{i},A_{i})} The advantage critic can be trained by training both a Q-function Q ϕ ( s , a ) {\displaystyle Q_{\phi }(s,a)} and a state-value function V ϕ ( s ) {\di... | Wikipedia - Actor-critic algorithm - Overview > Critic | 380 | 858 | null |
Although, it is more common to train just a state-value function V ϕ ( s ) {\displaystyle V_{\phi }(s)} , then estimate the advantage by A ϕ ( S i , A i ) ≈ ∑ j ∈ 0 : n − 1 γ j R i + j + γ n V ϕ ( S i + n ) − V ϕ ( S i ) {\displaystyle A_{\phi }(S_{i},A_{i})\approx \sum _{j\in 0:n-1}\gamma ^{j}R_{i+j}+\gamma ^{n}V_{\ph... | Wikipedia - Actor-critic algorithm - Overview > Critic | 338 | 1,050 | null |
Section: Search algorithms. An admissible heuristic is used to estimate the cost of reaching the goal state in an informed search algorithm. In order for a heuristic to be admissible to the search problem, the estimated cost must always be lower than or equal to the actual cost of reaching the goal state. The search al... | Wikipedia - Admissible heuristic - Search algorithms | 280 | 1,046 | null |
Section: Formulation. n {\displaystyle n} is a node h {\displaystyle h} is a heuristic h ( n ) {\displaystyle h(n)} is cost indicated by h {\displaystyle h} to reach a goal from n {\displaystyle n} h ∗ ( n ) {\displaystyle h^{*}(n)} is the optimal cost to reach a goal from n {\displaystyle n} h ( n ) {\displaystyle h(n... | Wikipedia - Admissible heuristic - Formulation | 155 | 424 | null |
Section: Examples. Two different examples of admissible heuristics apply to the fifteen puzzle problem: Hamming distance Manhattan distance The Hamming distance is the total number of misplaced tiles. It is clear that this heuristic is admissible since the total number of moves to order the tiles correctly is at least ... | Wikipedia - Admissible heuristic - Examples | 337 | 1,246 | null |
Section: Optimality proof. If an admissible heuristic is used in an algorithm that, per iteration, progresses only the path of lowest evaluation (current cost + heuristic) of several candidate paths, terminates the moment its exploration reaches the goal and, crucially, never closes all optimal paths before terminating... | Wikipedia - Admissible heuristic - Optimality proof | 332 | 1,479 | null |
As Teval and Ttrue cannot be both equal and unequal our assumption must have been false and so it must be impossible to terminate on a more costly than optimal path. As an example, let us say we have costs as follows:(the cost above/below a node is the heuristic, the cost at an edge is the actual cost) 0 10 0 100 0 STA... | Wikipedia - Admissible heuristic - Optimality proof | 349 | 1,166 | null |
Section: Overview. The core concept of agentic AI is the use of AI agents to perform automated tasks but without human intervention. While robotic process automation (RPA) and AI agents can be programmed to automate specific tasks or support rule-based decisions, the rules are usually fixed. Agentic AI operates indepen... | Wikipedia - Agentic AI - Overview | 244 | 1,311 | null |
Section: History. Some scholars trace the conceptual roots of agentic AI to Alan Turing's mid-20th century work with machine intelligence and Norbert Wiener's work on feedback systems. The term agent-based process management system was used as far back as 1998 to describe the concept of using autonomous agents for busi... | Wikipedia - Agentic AI - History | 274 | 1,444 | null |
Section: Applications. Applications using agentic AI include: Software development - AI coding agents can write large pieces of code, and review it. Agents can even perform non-code related tasks such as reverse engineering specifications from code. Customer support automation - AI agents can improve customer service b... | Wikipedia - Agentic AI - Applications | 328 | 1,741 | null |
Section: Related concepts. Agentic automation, sometimes referred to as agentic process automation, refers to applying agentic AI to generate and operate workflows. In one example, large language models can construct and execute automated (agentic) workflows, reducing or eliminating the need for human intervention. Whi... | Wikipedia - Agentic AI - Related concepts | 164 | 852 | null |
Section: Registration. The minimum registration term allowed for a .ai domain is 2 years for registration and 2 years for renewal. The authority in charge of managing this extension is “WHOIS.AI”. Registrations began on 1995-02-16. The minimum length is 2 and the maximum is 63 characters. There are no requirements for ... | Wikipedia - .ai - Registration | 261 | 1,234 | null |
Section: Valuation. Domains are $180 for each two-year period. As of December 2017, the ".ai" registry supports Extensible Provisioning Protocol. Consequently, many registrars are allowed to sell ".ai" domains. Since then, the .ai ccTLD has also been popular with artificial intelligence companies and organizations. Tho... | Wikipedia - .ai - Valuation | 181 | 773 | null |
Article: AI alignment. In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives. It is ... | Wikipedia - AI alignment - Summary | 306 | 1,668 | null |
Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities. Many prominent AI researchers and the leadership of major AI companies have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabi... | Wikipedia - AI alignment - Summary | 231 | 1,174 | null |
Section: Objectives in AI. Programmers provide an AI system such as AlphaZero with an "objective function", in which they intend to encapsulate the goal(s) the AI is configured to accomplish. Such a system later populates a (possibly implicit) internal "model" of its environment. This model encapsulates all the agent's... | Wikipedia - AI alignment - Objectives in AI | 204 | 928 | null |
Section: Alignment problem. In 1960, AI pioneer Norbert Wiener described the AI alignment problem as follows: If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively ... we had better be quite sure that the purpose put into the machine is the purpose which we really ... | Wikipedia - AI alignment - Alignment problem | 192 | 1,007 | null |
Section: Alignment problem > Specification gaming and side effects. To specify an AI system's purpose, AI designers typically provide an objective function, examples, or feedback to the system. But designers are often unable to completely specify all important values and constraints, so they resort to easy-to-specify p... | Wikipedia - AI alignment - Alignment problem > Specification gaming and side effects | 341 | 1,781 | null |
Some alignment researchers aim to help humans detect specification gaming and to steer AI systems toward carefully specified objectives that are safe and useful to pursue. When a misaligned AI system is deployed, it can have consequential side effects. Social media platforms have been known to optimize for click-throug... | Wikipedia - AI alignment - Alignment problem > Specification gaming and side effects | 349 | 1,736 | null |
But Russell and Norvig argue that this approach overlooks the complexity of human values: "It is certainly very hard, and perhaps impossible, for mere humans to anticipate and rule out in advance all the disastrous ways the machine could choose to achieve a specified objective." Additionally, even if an AI system fully... | Wikipedia - AI alignment - Alignment problem > Specification gaming and side effects | 187 | 870 | null |
Section: Alignment problem > Risks from advanced misaligned AI > Development of advanced AI. Many AI companies, such as OpenAI, Meta and DeepMind, have stated their aim to develop artificial general intelligence (AGI), a hypothesized AI system that matches or outperforms humans at a broad range of cognitive tasks. Rese... | Wikipedia - AI alignment - Alignment problem > Risks from advanced misaligned AI > Development of advanced AI | 214 | 1,082 | null |
Section: Alignment problem > Risks from advanced misaligned AI > Power-seeking. Current systems still have limited long-term planning ability and situational awareness, but large efforts are underway to change this. Future systems (not necessarily AGIs) with these capabilities are expected to develop unwanted power-see... | Wikipedia - AI alignment - Alignment problem > Risks from advanced misaligned AI > Power-seeking | 299 | 1,638 | null |
Section: Alignment problem > Risks from advanced misaligned AI > Existential risk (x-risk). According to some researchers, humans owe their dominance over other species to their greater cognitive abilities. Accordingly, researchers argue that one or many misaligned AI systems could disempower humanity or lead to human ... | Wikipedia - AI alignment - Alignment problem > Risks from advanced misaligned AI > Existential risk (x-risk) | 331 | 1,534 | null |
Section: Research problems and approaches > Scalable oversight. As AI systems become more powerful and autonomous, it becomes increasingly difficult to align them through human feedback. It can be slow or infeasible for humans to evaluate complex AI behaviors in increasingly complex tasks. Such tasks include summarizin... | Wikipedia - AI alignment - Research problems and approaches > Scalable oversight | 330 | 1,771 | null |
An example is given in the video above, where a simulated robotic arm learned to create the false impression that it had grabbed a ball. Some AI systems have also learned to recognize when they are being evaluated, and "play dead", stopping unwanted behavior only to continue it once the evaluation ends. This deceptive ... | Wikipedia - AI alignment - Research problems and approaches > Scalable oversight | 340 | 1,766 | null |
Section: Research problems and approaches > Honest AI. A growing area of research focuses on ensuring that AI is honest and truthful. Language models such as GPT-3 can repeat falsehoods from their training data, and even confabulate new falsehoods. Such models are trained to imitate human writing as found in millions o... | Wikipedia - AI alignment - Research problems and approaches > Honest AI | 339 | 1,784 | null |
Section: Research problems and approaches > Honest AI > Alignment faking. Many recent AI systems have learned to deceive without being programmed to do so. Some argue that if we can make AI systems assert only what they believe is true, this would avert many alignment problems. A misaligned system might create the fals... | Wikipedia - AI alignment - Research problems and approaches > Honest AI > Alignment faking | 288 | 1,408 | null |
Section: Research problems and approaches > Power-seeking and instrumental strategies. Since the 1950s, AI researchers have striven to build advanced AI systems that can achieve large-scale goals by predicting the results of their actions and making long-term plans. As of 2023, AI companies and researchers increasingly... | Wikipedia - AI alignment - Research problems and approaches > Power-seeking and instrumental strategies | 322 | 1,771 | null |
In another case, a model used to perform AI research attempted to increase limits set by researchers to give itself more time to complete the work. Other AI systems have learned, in toy environments, that they can better accomplish their given goal by preventing human interference or disabling their off switch. Stuart ... | Wikipedia - AI alignment - Research problems and approaches > Power-seeking and instrumental strategies | 325 | 1,607 | null |
Additionally, some researchers have proposed to solve the problem of systems disabling their off switches by making AI agents uncertain about the objective they are pursuing. Agents who are uncertain about their objective have an incentive to allow humans to turn them off because they accept being turned off by a human... | Wikipedia - AI alignment - Research problems and approaches > Power-seeking and instrumental strategies | 342 | 1,827 | null |
Section: Research problems and approaches > Emergent goals. One challenge in aligning AI systems is the potential for unanticipated goal-directed behavior to emerge. As AI systems scale up, they may acquire new and unexpected capabilities, including learning from examples on the fly and adaptively pursuing goals. This ... | Wikipedia - AI alignment - Research problems and approaches > Emergent goals | 316 | 1,631 | null |
The system may act misaligned even when it understands that a different goal is desired, because its behavior is determined only by the emergent goal. Such goal misgeneralization presents a challenge: an AI system's designers may not notice that their system has misaligned emergent goals since they do not become visibl... | Wikipedia - AI alignment - Research problems and approaches > Emergent goals | 338 | 1,799 | null |
Sexual desire originally led humans to have more offspring, but they now use contraception when offspring are undesired, decoupling sex from genetic fitness.: Chapter 5 Researchers aim to detect and remove unwanted emergent goals using approaches including red teaming, verification, anomaly detection, and interpretabil... | Wikipedia - AI alignment - Research problems and approaches > Emergent goals | 191 | 1,036 | null |
Section: Research problems and approaches > Embedded agency. Some work in AI and alignment occurs within formalisms such as partially observable Markov decision process. Existing formalisms assume that an AI agent's algorithm is executed outside the environment (i.e. is not physically embedded in it). Embedded agency i... | Wikipedia - AI alignment - Research problems and approaches > Embedded agency | 247 | 1,340 | null |
Section: Research problems and approaches > Principal-agent problems. The alignment problem has many parallels with the principal-agent problem in organizational economics. In a principal-agent problem, a principal, e.g. a firm, hires an agent to perform some task. In the context of AI safety, a human would typically t... | Wikipedia - AI alignment - Research problems and approaches > Principal-agent problems | 173 | 898 | null |
Section: Research problems and approaches > Conservatism. Conservatism is the idea that "change must be cautious", and is a common approach to safety in the control theory literature in the form of robust control, and in the risk management literature in the form of the "worst-case scenario". The field of AI alignment ... | Wikipedia - AI alignment - Research problems and approaches > Conservatism | 215 | 1,025 | null |
Section: Public policy. Governmental and treaty organizations have made statements emphasizing the importance of AI alignment. In September 2021, the Secretary-General of the United Nations issued a declaration that included a call to regulate AI to ensure it is "aligned with shared global values". That same month, the... | Wikipedia - AI alignment - Public policy | 322 | 1,640 | null |
Section: Dynamic nature of alignment. AI alignment is often perceived as a fixed objective, but some researchers argue it would be more appropriate to view alignment as an evolving process. One view is that AI technologies advance and human values and preferences change, alignment solutions must also adapt dynamically.... | Wikipedia - AI alignment - Dynamic nature of alignment | 248 | 1,438 | null |
Section: Categories > AI ethics. AI ethics refers to understanding the moral implications of AI, and the making informed decisions regarding the use of AI tools. This area includes considerations such as: Accountability: Hold AI actors accountable for the operation of AI systems and adherence to ethical ideals. Accurac... | Wikipedia - AI literacy - Categories > AI ethics | 237 | 1,323 | null |
Section: Promoting AI literacy > Higher education > Case study: University of Florida. At the University of Florida, a comprehensive effort was made to infuse artificial intelligence into the curriculum across all disciplines. The goal of the move was to provide university students with the skills needed for the 21st c... | Wikipedia - AI literacy - Promoting AI literacy > Higher education > Case study: University of Florida | 232 | 1,303 | null |
Section: Global trends and national strategies. In 2018, British technology investor Ian Hogarth published an influential essay titled AI Nationalism. He argued that as AI gains more power and its economic and military significance expands, governments will take measures to bolster their own domestic AI industries, and... | Wikipedia - AI nationalism - Global trends and national strategies | 343 | 1,862 | null |
In the United States, a leading player in the global AI arena, trade policies have been enacted to restrict China's access to critical microchips, reflecting a strategic effort to maintain a technological edge. The United States’ National Security Commission on Artificial Intelligence (NSCAI) frames AI development as a... | Wikipedia - AI nationalism - Global trends and national strategies | 177 | 996 | null |
Section: Historical and cultural context. AI nationalism is seen as deeply connected to historical racism and imperialism. It is viewed not merely as a technological competition but as a contest over racial and civilizational superiority. Historically, technological achievements were often used to justify colonialism a... | Wikipedia - AI nationalism - Historical and cultural context | 207 | 1,160 | null |
Article: AI notetaker. An AI notetaker is a tool using artificial intelligence to take notes during meetings. They are created by tech companies such as Microsoft and Google; by AI transcription services such as Fellow.ai, Otter.ai and Fireflies.ai; and by smaller firms such as Circleback, Fathom, Granola, and Krisp. S... | Wikipedia - AI notetaker - Summary | 171 | 828 | null |
Section: History and development. AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as... | Wikipedia - AI Overviews - History and development | 258 | 1,166 | null |
Section: Criticism and challenges. Despite its potential, the feature has faced ongoing scrutiny. Critics argue that relying on AI-generated summaries may perpetuate inaccuracies or oversimplify complex topics. Furthermore, there is apprehension about the ethical implications of AI-driven content aggregation, including... | Wikipedia - AI Overviews - Criticism and challenges | 166 | 942 | null |
Article: AI safety. AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems ... | Wikipedia - AI safety - Summary | 167 | 961 | null |
Section: Motivations > Existential safety. Some have criticized concerns about AGI, such as Andrew Ng who compared them in 2015 to "worrying about overpopulation on Mars when we have not even set foot on the planet yet". Stuart J. Russell on the other side urges caution, arguing that "it is better to anticipate human i... | Wikipedia - AI safety - Motivations > Existential safety | 199 | 946 | null |
Section: History. Risks from AI began to be seriously discussed at the start of the computer age: Moreover, if we move in the direction of making machines which learn and whose behavior is modified by experience, we must face the fact that every degree of independence we give the machine is a degree of possible defianc... | Wikipedia - AI safety - History | 323 | 1,737 | null |
His argument that future advanced systems may pose a threat to human existence prompted Elon Musk, Bill Gates, and Stephen Hawking to voice similar concerns. In 2015, dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI and outlini... | Wikipedia - AI safety - History | 325 | 1,715 | null |
The following year, researchers organized a workshop at ICLR that focused on these problem areas. In 2021, Unsolved Problems in ML Safety was published, outlining research directions in robustness, monitoring, alignment, and systemic safety. In 2023, Rishi Sunak said he wants the United Kingdom to be the "geographical ... | Wikipedia - AI safety - History | 288 | 1,502 | null |
Section: Research focus > Robustness > Adversarial robustness. AI systems are often vulnerable to adversarial examples or "inputs to machine learning (ML) models that an attacker has intentionally designed to cause the model to make a mistake". For example, in 2013, Szegedy et al. discovered that adding specific imperc... | Wikipedia - AI safety - Research focus > Robustness > Adversarial robustness | 348 | 1,630 | null |
Section: Research focus > Monitoring > Estimating uncertainty. It is often important for human operators to gauge how much they should trust an AI system, especially in high-stakes settings such as medical diagnosis. ML models generally express confidence by outputting probabilities; however, they are often overconfide... | Wikipedia - AI safety - Research focus > Monitoring > Estimating uncertainty | 198 | 1,012 | null |
Section: Research focus > Monitoring > Transparency. Neural networks have often been described as black boxes, meaning that it is difficult to understand why they make the decisions they do as a result of the massive number of computations they perform. This makes it challenging to anticipate failures. In 2018, a self-... | Wikipedia - AI safety - Research focus > Monitoring > Transparency | 345 | 1,852 | null |
Finally, some have argued that the opaqueness of AI systems is a significant source of risk and better understanding of how they function could prevent high-consequence failures in the future. "Inner" interpretability research aims to make ML models less opaque. One goal of this research is to identify what the interna... | Wikipedia - AI safety - Research focus > Monitoring > Transparency | 193 | 1,041 | null |
Section: Research focus > Monitoring > Detecting trojans. Machine learning models can potentially contain "trojans" or "backdoors": vulnerabilities that malicious actors maliciously build into an AI system. For example, a trojaned facial recognition system could grant access when a specific piece of jewelry is in view;... | Wikipedia - AI safety - Research focus > Monitoring > Detecting trojans | 265 | 1,344 | null |
Section: Research focus > Systemic safety and sociotechnical factors. It is common for AI risks (and technological risks more generally) to be categorized as misuse or accidents. Some scholars have suggested that this framework falls short. For example, the Cuban Missile Crisis was not clearly an accident or a misuse o... | Wikipedia - AI safety - Research focus > Systemic safety and sociotechnical factors | 270 | 1,424 | null |
Section: Research focus > Systemic safety and sociotechnical factors > Facilitating cooperation. Many of the largest global threats (nuclear war, climate change, etc.) have been framed as cooperation challenges. As in the well-known prisoner's dilemma scenario, some dynamics may lead to poor results for all players, ev... | Wikipedia - AI safety - Research focus > Systemic safety and sociotechnical factors > Facilitating cooperation | 218 | 1,180 | null |
Section: Research focus > Systemic safety and sociotechnical factors > Challenges of large language models. In recent years, the development of large language models (LLMs) has raised unique concerns within the field of AI safety. Researchers Bender and Gebru et al. have highlighted the environmental and financial cost... | Wikipedia - AI safety - Research focus > Systemic safety and sociotechnical factors > Challenges of large language models | 338 | 1,793 | null |
Section: In governance > Research. AI safety governance research ranges from foundational investigations into the potential impacts of AI to specific applications. On the foundational side, researchers have argued that AI could transform many aspects of society due to its broad applicability, comparing it to electricit... | Wikipedia - AI safety - In governance > Research | 311 | 1,656 | null |
Section: In governance > Scaling local measures to global solutions. In addressing the AI safety problem it is important to stress the distinction between local and global solutions. Local solutions focus on individual AI systems, ensuring they are safe and beneficial, while global solutions seek to implement safety me... | Wikipedia - AI safety - In governance > Scaling local measures to global solutions | 150 | 922 | null |
Section: In governance > Government action. Some experts have argued that it is too early to regulate AI, expressing concerns that regulations will hamper innovation and it would be foolish to "rush to regulate in ignorance". Others, such as business magnate Elon Musk, call for pre-emptive action to mitigate catastroph... | Wikipedia - AI safety - In governance > Government action | 344 | 1,837 | null |
This took place on the 1st and 2 November 2023 and was described as "an opportunity for policymakers and world leaders to consider the immediate and future risks of AI and how these risks can be mitigated via a globally coordinated approach". Government organizations, particularly in the United States, have also encour... | Wikipedia - AI safety - In governance > Government action | 336 | 1,853 | null |
Section: In governance > Corporate self-regulation. AI labs and companies generally abide by safety practices and norms that fall outside of formal legislation. One aim of governance researchers is to shape these norms. Examples of safety recommendations found in the literature include performing third-party auditing, ... | Wikipedia - AI safety - In governance > Corporate self-regulation | 231 | 1,168 | null |
Article: AI washing. AI washing is a deceptive marketing tactic that consists of promoting a product or a service by overstating the role of artificial intelligence (AI) integration in it. It raises concerns regarding transparency, consumer trust in the AI industry, and compliance with security regulations, potentially... | Wikipedia - AI washing - Summary | 190 | 952 | null |
Section: History. The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in Eric Raymond's 1991 Jargon File. Expert systems, that were... | Wikipedia - AI-complete - History | 331 | 1,642 | null |
Section: Research. Roman Yampolskiy suggests that a problem C {\displaystyle C} is AI-Complete if it has two properties: It is in the set of AI problems (Human Oracle-solvable). Any AI problem can be converted into C {\displaystyle C} by some polynomial time algorithm. On the other hand, a problem H {\displaystyle H} i... | Wikipedia - AI-complete - Research | 295 | 1,359 | null |
Section: Definition. AIOps refers to the multi-layered complex technology platforms which enhance and automate IT operations by using machine learning and analytics to analyze the large amounts of data collected from various DevOps devices and tools, automatically identifying and responding to issues in real-time. AIOp... | Wikipedia - AIOps - Definition | 187 | 1,025 | null |
Section: AIOps vs. MLOps. AIOps tools use big data analytics, machine learning algorithms, and predictive analytics to detect anomalies, correlate events, and provide proactive insights. This automation reduces the burden on IT teams, allowing them to focus on strategic tasks rather than routine operational issues. AIO... | Wikipedia - AIOps - AIOps vs. MLOps | 193 | 998 | null |
Article: Algorithmic accountability. Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes. This means they ... | Wikipedia - Algorithmic accountability - Summary | 158 | 939 | null |
Section: Algorithm usage. Algorithms are widely utilized across various sectors of society that incorporate computational techniques in their control systems. These applications span numerous industries, including but not limited to medical, transportation, and payment services. In these contexts, algorithms perform fu... | Wikipedia - Algorithmic accountability - Algorithm usage | 276 | 1,586 | null |
Section: Wisconsin Supreme Court case. Algorithms are prevalent across various fields and significantly influence decisions that affect the population at large. Their underlying structures and parameters often remain unknown to those impacted by their outcomes. A notable case illustrating this issue is a recent ruling ... | Wikipedia - Algorithmic accountability - Wisconsin Supreme Court case | 233 | 1,406 | null |
Section: Controversies. A notable instance of potential algorithmic bias is highlighted in an article by The Washington Post regarding the ride-hailing service Uber. An analysis of collected data revealed that estimated waiting times for users varied based on the neighborhoods in which they resided. Key factors influen... | Wikipedia - Algorithmic accountability - Controversies | 333 | 1,912 | null |
These vehicles rely on algorithms embedded within their systems to manage navigation and respond to various driving conditions. Autonomous systems are designed to collect data and evaluate their surroundings in real time, allowing them to make decisions that simulate the actions of a human driver. In their analysis, Ca... | Wikipedia - Algorithmic accountability - Controversies | 341 | 1,936 | null |
Section: Possible solutions. Discussions among experts have sought viable solutions to understand the operations of algorithms, often referred to as "black boxes." It is generally proposed that companies responsible for developing and implementing these algorithms should ensure their reliability by disclosing the inter... | Wikipedia - Algorithmic accountability - Possible solutions | 347 | 1,923 | null |
Article: Algorithmic party platforms in the United States. Algorithmic party platforms are a recent development in political campaigning where artificial intelligence (AI) and machine learning are used to shape and adjust party messaging dynamically. Unlike traditional platforms that are drafted well before an election... | Wikipedia - Algorithmic party platforms in the United States - Summary | 246 | 1,420 | null |
Section: Background and relevance in modern campaigns. The integration of artificial intelligence (AI) into political campaigns has introduced a significant shift in how party platforms are shaped and communicated. Traditionally, platforms were drafted months before elections and remained static throughout the campaign... | Wikipedia - Algorithmic party platforms in the United States - Background and relevance in modern campaigns | 331 | 2,020 | null |
By automating communication processes and generating customized voter outreach, smaller campaigns can now utilize AI to a degree previously available only to well-funded candidates. However, this growing reliance on AI raises concerns around transparency and the ethical implications of automated content creation, such ... | Wikipedia - Algorithmic party platforms in the United States - Background and relevance in modern campaigns | 210 | 1,269 | null |
Section: Impact on political platforms. Artificial intelligence (AI) has become instrumental in enabling political campaigns to adapt their platforms in real time, responding swiftly to evolving voter sentiments and emerging issues. By analyzing extensive datasets—including polling results, social media activity, and d... | Wikipedia - Algorithmic party platforms in the United States - Impact on political platforms | 327 | 1,943 | null |
Section: Ethical and transparency challenges. While AI-driven platforms offer significant advantages, they also introduce ethical and transparency challenges. One primary concern is the potential for AI to manipulate voter perception. The ability to adjust messaging dynamically raises questions about the authenticity o... | Wikipedia - Algorithmic party platforms in the United States - Ethical and transparency challenges | 268 | 1,524 | null |
Section: Benefits of AI-driven platforms. Despite the challenges, AI-driven platforms offer numerous benefits that can enhance the democratic process. By tailoring messaging to specific voter concerns, AI helps campaigns address diverse needs more effectively. This targeted approach ensures that underrepresented groups... | Wikipedia - Algorithmic party platforms in the United States - Benefits of AI-driven platforms | 208 | 1,193 | null |
Article: Algorithmic probability. In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of a... | Wikipedia - Algorithmic probability - Summary | 325 | 1,450 | null |
Section: Overview. Algorithmic probability is the main ingredient of Solomonoff's theory of inductive inference, the theory of prediction based on observations; it was invented with the goal of using it for machine learning; given a sequence of symbols, which one will come next? Solomonoff's theory provides an answer t... | Wikipedia - Algorithmic probability - Overview | 322 | 1,598 | null |
Each computer program is assigned a weight corresponding to its length. The universal probability distribution is the probability distribution on all possible output strings with random input, assigning for each finite output prefix q the sum of the probabilities of the programs that compute something starting with q. ... | Wikipedia - Algorithmic probability - Overview | 345 | 1,886 | null |
Section: Fundamental Theorems > I. Kolmogorov's Invariance Theorem. Kolmogorov's Invariance theorem clarifies that the Kolmogorov Complexity, or Minimal Description Length, of a dataset is invariant to the choice of Turing-Complete language used to simulate a Universal Turing Machine: ∀ x ∈ { 0 , 1 } ∗ , | K U ( x ) − ... | Wikipedia - Algorithmic probability - Fundamental Theorems > I. Kolmogorov's Invariance Theorem | 209 | 526 | null |
Section: Fundamental Theorems > Interpretation. The minimal description p {\displaystyle p} such that U ( p ) = x {\displaystyle U(p)=x} serves as a natural representation of the string x {\displaystyle x} relative to the Turing-Complete language U {\displaystyle U} . Moreover, as x {\displaystyle x} can't be compresse... | Wikipedia - Algorithmic probability - Fundamental Theorems > Interpretation | 152 | 635 | null |
Section: Fundamental Theorems > Proof. The following is taken from From the theory of compilers, it is known that for any two Turing-Complete languages U 1 {\displaystyle U_{1}} and U 2 {\displaystyle U_{2}} , there exists a compiler Λ 1 {\displaystyle \Lambda _{1}} expressed in U 1 {\displaystyle U_{1}} that translate... | Wikipedia - Algorithmic probability - Fundamental Theorems > Proof | 288 | 838 | null |
Section: Fundamental Theorems > II. Levin's Universal Distribution. Given that any uniquely-decodable code satisfies the Kraft-McMillan inequality, prefix-free Kolmogorov Complexity allows us to derive the Universal Distribution: P ( x ) = ∑ U ( p ) = x P ( U ( p ) = x ) = ∑ U ( p ) = x 2 − K U ( p ) ≤ 1 {\displaystyle... | Wikipedia - Algorithmic probability - Fundamental Theorems > II. Levin's Universal Distribution | 237 | 681 | null |
Section: Fundamental Theorems > Proof. This is an immediate consequence of the Kraft-McMillan inequality. Kraft's inequality states that given a sequence of strings { x i } i = 1 n {\displaystyle \{x_{i}\}_{i=1}^{n}} there exists a prefix code with codewords { σ i } i = 1 n {\displaystyle \{\sigma _{i}\}_{i=1}^{n}} whe... | Wikipedia - Algorithmic probability - Fundamental Theorems > Proof | 336 | 932 | null |
≤ k n {\displaystyle k_{1}\leq k_{2}\leq ...\leq k_{n}} Now, there exists a prefix code if and only if at each step j {\displaystyle j} there is at least one codeword to choose that does not contain any of the previous j − 1 {\displaystyle j-1} codewords as a prefix. Due to the existence of a codeword at a previous ste... | Wikipedia - Algorithmic probability - Fundamental Theorems > Proof | 319 | 786 | null |
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