the alignment problem

More recently, growing interest in machine learning and deep learning have helped advance computer vision, speech recognition, and natural language processing, the very fields that symbolic AI struggled at. These cookies do not store any personal information. According to the guidelines, researchers must ensure that AI abides by shared human values, is always under human control, and is not endangering public safety. [2] For example, the aforementioned social media recommender systems have been profitable despite creating unwanted addiction and polarization. An unsolved challenge is specification gaming: when researchers penalize an AI system when they detect it seeking power, the system is thereby incentivized to seek power in ways that are difficult-to-detect,[2] or hidden during training and safety testing (see Scalable oversight and Emergent goals). Reddit and its partners use cookies and similar technologies to provide you with a better experience. It can be slow or infeasible for humans to evaluate complex AI behaviors in increasingly complex tasks. The first three chapters are, in opposition to the title, setting the foundation of the discussion by defining and discussing representation, fairness and transparency. The Alignment Problem stays non-technical, but its a bit too long for a non-technical audience. For the alignment problem in artificial intelligence, see, brianchristian.org/the-alignment-problem/, Human Compatible: Artificial Intelligence and the Problem of Control, Superintelligence: Paths, Dangers, Strategies, "Nonfiction Book Review: The Alignment Problem: Machine Learning and Human Values by Brian Christian. [110] AI systems trained on such data therefore learn to mimic false statements. This is a BETA experience. But each of those achievements also proves that purely pursuing external rewards is not exactly how intelligence works. Such power-seeking behavior is not explicitly programmed but emerges because power is instrumental for achieving a wide range of goals. WebThe problem of AI alignment involves training AI systems to understand and carry out human intent faithfully. Machine learning: mapping inputs to outputs A growing[update] area of research focuses on ensuring that AI is honest and truthful. In the earlier decades of AI research, symbolic systems made remarkable inroads in solving complicated problems that required logical reasoning. Scalable oversight studies how to reduce the time and effort needed for supervision, and how to assist human supervisors. Shaping, chapter 5, is hit and miss. The infants are seeing lots of things. Yet they were terrible at simple tasks that every human learns at a young age, such as detecting objects, people, voices, and sounds. Modeling the world as it is is one thing. [24][26] To minimize the need for human feedback, a helper model is then trained to reward the main model in novel situations for behaviors that humans would reward. Program Equilibrium in the Prisoners Dilemma via Lbs Theorem. Paper presented at the AAAI 2014 Multiagent Interaction without Prior Coordination Workshop. The alignment problem from a deep learning perspective Richard Ngo, Lawrence Chan, Sren Mindermann Within the coming decades, artificial general intelligence (AGI) may surpass human capabilities at a wide range of important tasks. [7] Language models trained with human feedback increasingly[update] object to being shut down or modified and express a desire for more resources, arguing that this would help them achieve their purpose.[58]. [6] Additionally, as AI designers detect and penalize power-seeking behavior, their systems have an incentive to game this specification by seeking power in ways that are not penalized or by avoiding power-seeking before they are deployed.[6]. The Alignment Problem: Machine Learning and Human Values: Christian, Brian: 9780393868333: Amazon.com: Books Books Computers & Technology Computer Science Enjoy fast, FREE delivery, exclusive deals and award-winning movies & TV shows with Prime Try Prime and start saving today with Fast, FREE Delivery Buy new: $19.00 But as soon as you begin using that model, you are changing the world, in ways large and small. As planning over long horizons is often helpful for humans, some researchers argue that companies will automate it once models become capable of it. A number of governmental and treaty organizations have made statements emphasizing the importance of AI alignment. The AI Alignment Problem: Why Its Hard, and Where to Start Eliezer Yudkowsky AI Alignment: Why It's Hard, and Where to Start Watch on What is it? 'Logic' and 'reason' are merely some of the many stories that we tell ourselves as humans, and they are certainly not fundamental particles of the universe. Truthfulness requires that AI systems only make objectively true statements; honesty requires that they only assert what they believe to be true. Until then, well have to tread carefully and beware of how much credit we assign to systems that mimic human intelligence on the surface. [40] Chatbots often produce falsehoods if they are based on language models that are trained to imitate text from internet corpora which are broad but fallible. [50] Researchers who scale modern neural networks observe that they indeed develop increasingly general and unanticipated capabilities. Reinforcement learning offers us a powerful, and perhaps even universal, definition of what intelligence is, Christian writes. Fallenstein and Soares Vingean Reflection is currently the most up-to-date overview of work in goal stability. Imitation can do wonders, especially in problems where the rules and labels are not clear-cut. Any sufficiently capable intelligent system will prefer to ensure its own continued existence and to acquire physical and computational resources not for their own sake, but to succeed in its assigned task. [7][83] In CIRL, AI agents are uncertain about the reward function and learn about it by querying humans. Although power-seeking is not explicitly programmed, it can emerge because agents that have more power are better able to accomplish their goals. [130] The strategy describes actions to assess long term AI risks, including catastrophic risks. For instance, a reinforcement learning model that plays StarCraft 2 at championship level wont be able to play another game with similar mechanics. WebThis conundrum - dubbed 'The Alignment Problem' by experts - is the subject of this timely and important book. [citation needed] Such goal misgeneralization[9] presents a challenge: an AI system's designers may not notice that their system has misaligned emergent goals, since they do not become visible during the training phase. The focus is on how we shape human behavior, and the chapter is weak about how it can be applied to ML. Muehlhauser notes the analogy between computer security and AI alignment research in AI Risk and the Security Mindset. The alignment problem Evans et al. Researchers at OpenAI used this approach to train chatbots like ChatGPT and InstructGPT, which produces more compelling text than models trained to imitate humans. Reduced Impact Artificial Intelligences. Working paper. Papers Human learning isnt dependent solely on labels, but its clearly part of our learning. The only way to get good at writing encryption systems is to break other peoples' systems. Carefully specifying the desired objective is known as outer alignment, and ensuring that emergent goals match the specified goals for the system is known as inner alignment.[3]. His most recent book, The Alignment Problem, explores the history of alignment research, and the technical and philosophical questions that well have to answer if were ever going to safely outsource our reasoning to machines. Simply put, our world is built from stories, not facts. Yudkowsky (2014). MIRI Strategy We need to consider critically not only where we get our training data but where we get the labels that will function in the system as a stand-in for ground truth. Alignment Problem [33], AI alignment is an open problem for modern AI systems[34][35] and a research field within AI. Conversations ChatGPT) and explicitly planning architectures (e.g. Caballero, Ethan; Gupta, Kshitij; Rish, Irina; Krueger, David (2022). Often the ground truth is not the ground truth, Christian warns. Additionally, researchers propose to solve the problem of systems disabling their off-switches by making AI agents uncertain about the objective they are pursuing. The arguments I give for having a utility function are standard, and can be found in e.g. Canon U.S.A Inc. All Rights Reserved. through self-preservation), a behavior that persists across a wide range of environments and goals. Slides without transitions: High-quality, low-quality. OpenAI is committing 20% of its compute to date towards this goal. This website uses cookies to improve your experience. In this case, the model, which was trained on the companys historical hiring data, reflected problems within Amazon itself. The Alignment Problem: How Can Artificial Intelligence Learn Human Other papers cited: Yudkowsky and Herreshoff (2013). Alignment Of particular importance is inverse reinforcement learning, a broad approach for machines to learn the objective function of a human or another agent. "[4] Writing for Nature, Virginia Dignum gave the book a positive review, favorably comparing it to Kate Crawford's Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. [10][51][52] Such models have learned to operate a computer or write their own programs; a single "generalist" network can chat, control robots, play games, and interpret photographs. [9][121][122] Goal misgeneralization arises from goal ambiguity (i.e. [62][63] As a result, their deployment might be irreversible. For more about orthogonal final goals and convergent instrumental strategies, see Bostroms The Superintelligent Will (also reproduced in Superintelligence). The Alignment Problem: Machine Learning and Taylor (2016). In the first section, Christian interweaves discussions of the history of artificial intelligence research, particularly the machine learning approach of artificial neural networks such as the Perceptron and AlexNet, with examples of how AI systems can have unintended behavior. It is easier to invent an encryption system than to break it. "[32][7] Different definitions of AI alignment require that an aligned AI system advances different goals: the goals of its designers, its users or, alternatively, objective ethical standards, widely shared values, or the intentions its designers would have if they were more informed and enlightened. [8]:88[82] Cooperative IRL (CIRL) assumes that a human and AI agent can work together to teach and maximize the human's reward function. But again, imitation paints an incomplete picture of the intelligence puzzle. A popular example is a Google Photos classification algorithm that tagged dark-skinned people as gorillas. Our taste for sugary food (an emergent goal) was originally aligned with inclusive fitness, but now leads to overeating and health problems. Poes argument against the possibility of machine chess is from the 1836 essay Maelzels Chess-Player.. [53] According to surveys, some leading machine learning researchers expect AGI to be created in this decade[update], some believe it will take much longer, and many consider both to be possible. [58] GPT-4 showed the ability to strategically deceive humans. [35], Research on truthful AI includes trying to build systems that can cite sources and explain their reasoning when answering questions, which enables better transparency and verifiability. To increase supervision quality, a range of approaches aim to assist the supervisor, sometimes by using AI assistants. [36] [1] Aligning AI involves two main challenges: carefully specifying the purpose of the system (outer alignment) and ensuring that the system adopts the specification robustly (inner alignment). You also have the option to opt-out of these cookies. Machine learning algorithms scale well with the availability of data and compute resources, which is largely why theyve become so popular in the past decade. The Alignment Problem Machine Learning and Human Values by Brian Christian (Author) Finalist for the Los Angeles Times Book Prize A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Political Polarization-And What Can Be Done About It, "Uber disabled emergency braking in self-driving car: U.S. agency", "2020 Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy", "DeepMind Introduces Gato, a New Generalist AI Agent", "Adept's AI assistant can browse, search, and use web apps like a human", "Viewpoint: When Will AI Exceed Human Performance? When a misaligned AI system is deployed, it can cause consequential side effects. Many of the contributors to this conversation seem to be responding to those arguments and ignoring the more substantial arguments proposed by Omohundro, Bostrom, and others. He tells the story of Julia Angwin, a journalist whose ProPublica investigation of the COMPAS algorithm, a tool for predicting recidivism among criminal defendants, led to widespread criticism of its accuracy and bias towards certain demographics. Our digital butlers are watching closely, Christian writes. For this very reason, research in this field has been limited to a few labs that are backed by very wealthy companies. My printer has a problem of printing the alignment page not staright, it also hasa vertical line on the alignment page and a vertical rainbow-like line when I print full page images. Tiling Agents for Self-Modifying AI, and the Lbian Obstacle. Working paper. The alignment problem : machine learning and human - last edited on The Peter Norvig / Stuart Russell quotation is from Artificial Intelligence: A Modern Approach, the top undergraduate textbook in AI. [98][99] More generally, it can be difficult to evaluate AI that outperforms humans in a given domain. The last three chapters are on imitation, inference, and uncertainty.
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