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58-712: AIS may refer to: Medicine [ edit ] Abbreviated Injury Scale , an anatomical-based coding system to classify and describe the severity of injuries Acute ischemic stroke, the thromboembolic type of stroke Androgen insensitivity syndrome , an intersex condition in which there is an inability of many cells in the affected genetic male to respond to androgenic hormones Athens Insomnia Scale , used to measure severity of insomnia Organizations and companies [ edit ] Advanced Info Service , Thai mobile phone operator Akademio Internacia de la Sciencoj San Marino ( International Academy of Sciences San Marino ),

116-532: A Native American tribe living on the Atlantic coast of Florida, U.S. Ais, an alternate spelling of Eyeish , a native American tribe in Texas, U.S. American Indycar Series (1988–2005), a former American auto racing series Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with the title AIS . If an internal link led you here, you may wish to change

174-454: A catastrophic accident that harms everyone involved. Concerns about scenarios like these have inspired both political and technical efforts to facilitate cooperation between humans, and potentially also between AI systems. Most AI research focuses on designing individual agents to serve isolated functions (often in 'single-player' games). Scholars have suggested that as AI systems become more autonomous, it may become essential to study and shape

232-607: A classifier to distinguish anomalous and non-anomalous inputs, though a range of additional techniques are in use. Scholars and government agencies have expressed concerns that AI systems could be used to help malicious actors to build weapons, manipulate public opinion, or automate cyber attacks. These worries are a practical concern for companies like OpenAI which host powerful AI tools online. In order to prevent misuse, OpenAI has built detection systems that flag or restrict users based on their activity. Neural networks have often been described as black boxes , meaning that it

290-1417: A division of the Australian Sports Commission Australian Iron & Steel Aviatsionnaya Ispitatelnaya Stantsiya , Russian World War I naval aviation station and aircraft company Schools [ edit ] Abu Dhabi International School in Abu Dhabi, United Arab Emirates Agnes Irwin School in Pennsylvania, U.S. Ahmadhiyya International School in Malé, Maldives Almaty International School in Almaty, Kazakhstan American International School (disambiguation) , several schools, some known as AIS Antonine International School , in Ajaltoun, Lebanon Antwerp International School , in Antwerp, Belgium Atlanta International School , in Georgia, U.S. Australian International School (disambiguation) , several schools, some known as AIS Software and technology [ edit ] Accounting information system ,

348-456: A higher reward. 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 overconfident, especially in situations that differ from those that they were trained to handle. Calibration research aims to make model probabilities correspond as closely as possible to

406-530: A misuse of technology. Policy analysts Zwetsloot and Dafoe wrote, "The misuse and accident perspectives tend to focus only on the last step in a causal chain leading up to a harm: that is, the person who misused the technology, or the system that behaved in unintended ways… Often, though, the relevant causal chain is much longer." Risks often arise from 'structural' or 'systemic' factors such as competitive pressures, diffusion of harms, fast-paced development, high levels of uncertainty, and inadequate safety culture. In

464-607: A reward model might estimate how helpful a text response is and a language model might be trained to maximize this score. Researchers have shown that if a language model is trained for long enough, it will leverage the vulnerabilities of the reward model to achieve a better score and perform worse on the intended task. This issue can be addressed by improving the adversarial robustness of the reward model. More generally, any AI system used to evaluate another AI system must be adversarially robust. This could include monitoring tools, since they could also potentially be tampered with to produce

522-561: A safe manner until risks can be sufficiently managed". In September 2021, the People's Republic of China published ethical guidelines for the use of AI in China, emphasizing that AI decisions should remain under human control and calling for accountability mechanisms. In the same month, The United Kingdom published its 10-year National AI Strategy, which states the British government "takes

580-607: A scientific association AIS Airlines , Dutch airline Armée islamique du salut , military wing of the Islamic Salvation Front , a former political party in Algeria Asahi India Glass Limited , Indian manufacturing company Association for Information Systems , an international professional organization Australian Information Service , historical Australian government agency (1973–1986) Australian Institute of Sport ,

638-415: A score of 3 or more. The definition was used to harmonize count of serious injuries or serious road injury in different member States (see Killed or Seriously Injured ). Since 2017 Valletta Council conclusions on road safety, States started collecting those numbers. This need use of hospital data rather than police data. Patients often have more than one injury. The Maximum Abbreviated Injury Score (MAIS)

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696-887: A second processor mode in Centaur/VIA C3 x86 CPUs Application Interface Specification , for high-availability application software Artificial immune system , in artificial intelligence AI safety , a field concerned with preventing harmful consequences that could result from artificial intelligence Artificial Intelligence System , a distributed computing project undertaken by Intelligence Realm, Inc. Automotive Industry Standards , vehicle technical specifications of India Automated information system , an assembly of computer hardware, software, firmware, or any combination of these, configured to accomplish specific information-handling operations Others [ edit ] AIS, IATA code for Arorae Island Airport , Kiribati Ais , Etruscan word meaning 'god' Ais people ,

754-444: A security risk, researchers have argued that trojans provide a concrete setting for testing and developing better monitoring tools. In the field of artificial intelligence (AI), AI alignment aims to steer AI systems toward a person's or group's intended goals, preferences, and ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives. It

812-413: A specific trigger is visible. Note that an adversary must have access to the system's training data in order to plant a trojan. This might not be difficult to do with some large models like CLIP or GPT-3 as they are trained on publicly available internet data. Researchers were able to plant a trojan in an image classifier by changing just 300 out of 3 million of the training images. In addition to posing

870-576: A sub-optimal level of caution". A research stream focuses on developing approaches, frameworks, and methods to assess AI accountability, guiding and promoting audits of AI-based systems. 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 measures for all AI systems across various jurisdictions. Some researchers argue for

928-402: A system of collecting, storing and processing financial and accounting data Aeronautical Information Service , distributor of air navigation information Automatic identification system , for tracking marine vessels Alarm indication signal in a telecommunications system Alarm indication signal line (AIS-L) Alarm indication signal path (AIS-P) Alternate Instruction Set ,

986-617: 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 home of global AI safety regulation" and to host the first global summit on AI safety. The AI safety summit took place in November 2023, and focused on

1044-540: Is explainability . It is sometimes a legal requirement to provide an explanation for why a decision was made in order to ensure fairness, for example for automatically filtering job applications or credit score assignment. Another benefit is to reveal the cause of failures. At the beginning of the 2020 COVID-19 pandemic, researchers used transparency tools to show that medical image classifiers were 'paying attention' to irrelevant hospital labels. Transparency techniques can also be used to correct errors. For example, in

1102-691: 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 for risks and enhancing their reliability. The field is particularly concerned with existential risks posed by advanced AI models. Beyond technical research, AI safety involves developing norms and policies that promote safety. It gained significant popularity in 2023, with rapid progress in generative AI and public concerns voiced by researchers and CEOs about potential dangers. During

1160-486: Is approaching human-like ( AGI ) and superhuman cognitive capabilities ( ASI ) and could endanger human civilization if misaligned. These risks remain debated. 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

1218-500: 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-driving car killed a pedestrian after failing to identify them. Due to the black box nature of the AI software, the reason for the failure remains unclear. It also raises debates in healthcare over whether statistically efficient but opaque models should be used. One critical benefit of transparency

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1276-428: Is not the arbitrary code for a deceased patient or fatal injury, but the code for injuries specifically assigned an AIS 6 severity. An AIS-Code of 9 is used to describe injuries for which not enough information is available for more detailed coding, e.g. crush injury to the head . The AIS scale is a measurement tool for single injuries. A universally accepted injury aggregation function has not yet been proposed, though

1334-823: Is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler proxy goals , such as gaining human approval . But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. Misaligned AI systems can malfunction and cause harm. AI systems may find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways ( reward hacking ). They may also develop unwanted instrumental strategies , such as seeking power or survival because such strategies help them achieve their final given goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before

1392-456: Is the highest AIS score of all injuries of a person. A road casualty with a MAIS score of 3 or more is referred to as MAIS3+ Those data can be computed in three different ways: Previously each State had a different definition of a serious injury. It has been estimated that 110,000 people were seriously injured in traffic collisions on the roads of European Union member States in 2019, based on MAIS3+ definition. AI safety AI safety

1450-598: 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 defiance of our wishes. From 2008 to 2009, the Association for the Advancement of Artificial Intelligence ( AAAI ) commissioned a study to explore and address potential long-term societal influences of AI research and development. The panel

1508-497: The injury severity score and its derivatives are better aggregators for use in clinical settings. In other settings such as automotive design and occupant protection, MAIS is a useful tool for the comparison of specific injuries and their relative severity and the changes in those frequencies that may result from evolving motor vehicle design. The European Union defined the MAIS3+ as the maximum abbreviated injury scale (MAIS) with

1566-909: The 2023 AI Safety Summit , the United States and the United Kingdom both established their own AI Safety Institute . However, researchers have expressed concern that AI safety measures are not keeping pace with the rapid development of AI capabilities. Scholars discuss current risks from critical systems failures, bias , and AI-enabled surveillance, as well as emerging risks like technological unemployment , digital manipulation, weaponization, AI-enabled cyberattacks and bioterrorism . They also discuss speculative risks from losing control of future artificial general intelligence (AGI) agents, or from AI enabling perpetually stable dictatorships. 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

1624-601: The US National Security Commission on Artificial Intelligence reported that advances in AI may make it increasingly important to "assure that systems are aligned with goals and values, including safety, robustness and trustworthiness". Subsequently, the National Institute of Standards and Technology drafted a framework for managing AI Risk, which advises that when "catastrophic risks are present – development and deployment should cease in

1682-533: The White House Office of Science and Technology Policy and Carnegie Mellon University announced The Public Workshop on Safety and Control for Artificial Intelligence, which was one of a sequence of four White House workshops aimed at investigating "the advantages and drawbacks" of AI. In the same year, Concrete Problems in AI Safety – one of the first and most influential technical AI Safety agendas –

1740-719: The already imbalanced game between cyber attackers and cyber defenders. This would increase 'first strike' incentives and could lead to more aggressive and destabilizing attacks. In order to mitigate this risk, some have advocated for an increased emphasis on cyber defense. In addition, software security is essential for preventing powerful AI models from being stolen and misused. Recent studies have shown that AI can significantly enhance both technical and managerial cybersecurity tasks by automating routine tasks and improving overall efficiency. The advancement of AI in economic and military domains could precipitate unprecedented political challenges. Some scholars have compared AI race dynamics to

1798-406: The benefit of being able to take perfect measurements and perform arbitrary ablations. ML 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; or a trojaned autonomous vehicle may function normally until

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1856-598: The book Superintelligence: Paths, Dangers, Strategies . He has the opinion that the rise of AGI has the potential to create various societal issues, ranging from the displacement of the workforce by AI, manipulation of political and military structures, to even the possibility of human extinction. 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

1914-492: The broader context of safety engineering , structural factors like 'organizational safety culture' play a central role in the popular STAMP risk analysis framework. Inspired by the structural perspective, some researchers have emphasized the importance of using machine learning to improve sociotechnical safety factors, for example, using ML for cyber defense, improving institutional decision-making, and facilitating cooperation. Some scholars are concerned that AI will exacerbate

1972-451: The cold war, where the careful judgment of a small number of decision-makers often spelled the difference between stability and catastrophe. AI researchers have argued that AI technologies could also be used to assist decision-making. For example, researchers are beginning to develop AI forecasting and advisory systems. Many of the largest global threats (nuclear war, climate change, etc.) have been framed as cooperation challenges. As in

2030-478: The complex challenges posed by advanced AI systems worldwide. 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 catastrophic risks. Outside of formal legislation, government agencies have put forward ethical and safety recommendations. In March 2021,

2088-444: The head of longterm governance and strategy at DeepMind has emphasized the dangers of racing and the potential need for cooperation: "it may be close to a necessary and sufficient condition for AI safety and alignment that there be a high degree of caution prior to deploying advanced powerful systems; however, if actors are competing in a domain with large returns to first-movers or relative advantage, then they will be pressured to choose

2146-400: The link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=AIS&oldid=1238748364 " Category : Disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Abbreviated Injury Scale The first version of the scale

2204-438: The long-term risk of non-aligned Artificial General Intelligence, and the unforeseeable changes that it would mean for ... the world, seriously". The strategy describes actions to assess long-term AI risks, including catastrophic risks. The British government held first major global summit on AI safety. This took place on the 1st and 2 November 2023 and was described as "an opportunity for policymakers and world leaders to consider

2262-408: The model to make a mistake". For example, in 2013, Szegedy et al. discovered that adding specific imperceptible perturbations to an image could cause it to be misclassified with high confidence. This continues to be an issue with neural networks, though in recent work the perturbations are generally large enough to be perceptible. All of the images on the right are predicted to be an ostrich after

2320-456: The necessity of scaling local safety measures to a global level, proposing a classification for these global solutions. This approach underscores the importance of collaborative efforts in the international governance of AI safety, emphasizing that no single entity can effectively manage the risks associated with AI technologies. This perspective aligns with ongoing efforts in international policy-making and regulatory frameworks, which aim to address

2378-532: The need for research projects that contribute positively towards an equitable technological ecosystem. AI governance is broadly concerned with creating norms, standards, and regulations to guide the use and development of AI systems. 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 electricity and

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2436-547: 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 internal neuron activations represent. For example, researchers identified a neuron in the CLIP artificial intelligence system that responds to images of people in spider man costumes, sketches of spiderman, and

2494-697: The paper "Locating and Editing Factual Associations in GPT", the authors were able to identify model parameters that influenced how it answered questions about the location of the Eiffel tower. They were then able to 'edit' this knowledge to make the model respond to questions as if it believed the tower was in Rome instead of France. Though in this case, the authors induced an error, these methods could potentially be used to efficiently fix them. Model editing techniques also exist in computer vision. Finally, some have argued that

2552-616: The perturbation is applied. (Left) is a correctly predicted sample, (center) perturbation applied magnified by 10x, (right) adversarial example. Adversarial robustness is often associated with security. Researchers demonstrated that an audio signal could be imperceptibly modified so that speech-to-text systems transcribe it to any message the attacker chooses. Network intrusion and malware detection systems also must be adversarially robust since attackers may design their attacks to fool detectors. Models that represent objectives (reward models) must also be adversarially robust. For example,

2610-409: The planet yet". Stuart J. Russell on the other side urges caution, arguing that "it is better to anticipate human ingenuity than to underestimate it". AI researchers have widely differing opinions about the severity and primary sources of risk posed by AI technology – though surveys suggest that experts take high consequence risks seriously. In two surveys of AI researchers, the median respondent

2668-974: The risks of misuse and loss of control associated with frontier AI models. During the summit the intention to create the International Scientific Report on the Safety of Advanced AI was announced. In 2024, The US and UK forged a new partnership on the science of AI safety. The MoU was signed on 1 April 2024 by US commerce secretary Gina Raimondo and UK technology secretary Michelle Donelan to jointly develop advanced AI model testing, following commitments announced at an AI Safety Summit in Bletchley Park in November. AI safety research areas include robustness, monitoring, and alignment. AI systems are often vulnerable to adversarial examples or "inputs to machine learning (ML) models that an attacker has intentionally designed to cause

2726-697: The societal impacts of AI and outlining concrete directions. To date, the letter has been signed by over 8000 people including Yann LeCun , Shane Legg , Yoshua Bengio , and Stuart Russell . In the same year, a group of academics led by professor Stuart Russell founded the Center for Human-Compatible AI at the University of California Berkeley and the Future of Life Institute awarded $ 6.5 million in grants for research aimed at "ensuring artificial intelligence (AI) remains safe, ethical and beneficial". In 2016,

2784-430: The steam engine. Some work has focused on anticipating specific risks that may arise from these impacts – for example, risks from mass unemployment, weaponization, disinformation, surveillance, and the concentration of power. Other work explores underlying risk factors such as the difficulty of monitoring the rapidly evolving AI industry, the availability of AI models, and 'race to the bottom' dynamics. Allan Dafoe,

2842-537: The system is deployed and encounters new situations and data distributions . Today, some of these issues affect existing commercial systems such as large language models , robots , autonomous vehicles , and social media recommendation engines . 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, including Geoffrey Hinton , Yoshua Bengio , and Stuart Russell , argue that AI

2900-402: The true proportion that the model is correct. Similarly, anomaly detection or out-of-distribution (OOD) detection aims to identify when an AI system is in an unusual situation. For example, if a sensor on an autonomous vehicle is malfunctioning, or it encounters challenging terrain, it should alert the driver to take control or pull over. Anomaly detection has been implemented by simply training

2958-686: The way they interact. 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 costs associated with training these models, emphasizing that the energy consumption and carbon footprint of training procedures like those for Transformer models can be substantial. Moreover, these models often rely on massive, uncurated Internet-based datasets, which can encode hegemonic and biased viewpoints, further marginalizing underrepresented groups. The large-scale training data, while vast, does not guarantee diversity and often reflects

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3016-493: The well-known prisoner's dilemma scenario, some dynamics may lead to poor results for all players, even when they are optimally acting in their self-interest. For example, no single actor has strong incentives to address climate change even though the consequences may be significant if no one intervenes. A salient AI cooperation challenge is avoiding a 'race to the bottom'. In this scenario, countries or companies race to build more capable AI systems and neglect safety, leading to

3074-432: The word 'spider'. It also involves explaining connections between these neurons or 'circuits'. For example, researchers have identified pattern-matching mechanisms in transformer attention that may play a role in how language models learn from their context. "Inner interpretability" has been compared to neuroscience. In both cases, the goal is to understand what is going on in an intricate system, though ML researchers have

3132-663: The worldviews of privileged demographics, leading to models that perpetuate existing biases and stereotypes. This situation is exacerbated by the tendency of these models to produce seemingly coherent and fluent text, which can mislead users into attributing meaning and intent where none exists, a phenomenon described as 'stochastic parrots'. These models, therefore, pose risks of amplifying societal biases, spreading misinformation, and being used for malicious purposes, such as generating extremist propaganda or deepfakes. To address these challenges, researchers advocate for more careful planning in dataset creation and system development, emphasizing

3190-674: Was generally skeptical of the radical views expressed by science-fiction authors but agreed that "additional research would be valuable on methods for understanding and verifying the range of behaviors of complex computational systems to minimize unexpected outcomes". In 2011, Roman Yampolskiy introduced the term "AI safety engineering" at the Philosophy and Theory of Artificial Intelligence conference, listing prior failures of AI systems and arguing that "the frequency and seriousness of such events will steadily increase as AIs become more capable". In 2014, philosopher Nick Bostrom published

3248-411: Was optimistic about AI overall, but placed a 5% probability on an "extremely bad (e.g. human extinction )" outcome of advanced AI. In a 2022 survey of the natural language processing community, 37% agreed or weakly agreed that it is plausible that AI decisions could lead to a catastrophe that is "at least as bad as an all-out nuclear war". Risks from AI began to be seriously discussed at the start of

3306-411: Was published in 1969 with major updates in 1976, 1980, 1985, 1990, 1998, 2005, 2008 and 2015. The score describes three aspects of the injury using seven numbers written as 12(34)(56).7 Each number signifies Fractures, rupture, laceration, etc. Abbreviated Injury Score-Code is on a scale of one to six, one being a minor injury and six being maximal (currently untreatable). An AIS-Code of 6

3364-654: Was published. In 2017, the Future of Life Institute sponsored the Asilomar Conference on Beneficial AI , where more than 100 thought leaders formulated principles for beneficial AI including "Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards". In 2018, the DeepMind Safety team outlined AI safety problems in specification, robustness, and assurance. The following year, researchers organized

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