I believe that no such bounds can be set. Prescient, gloomy, and a bit rambling article about humans' role in the future, with genetics, robotics, and nanotech. Argues for the importance of thinking about the safety implications of formal models of ASI, to identify both potential problems and lines of research. Overviews engineering challenges, potential risks, and research goals for safe AI. A New York Times Bestseller analyzing many arguments for and against the potential danger of highly advanced AI systems.
Consider a cleaning robot whose only objective is to clean your house, and which is intelligent enough to realize that if you turn it off then it won't be able to clean your house anymore.
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Such a robot has some incentive to resist being shut down or reprogrammed for another task, since that would interfere with its cleaning objective. Loosely speaking, we say that an AI system is incorrigible to the extent that it resists being shut down or reprogrammed by its users or creators, and corrigible to the extent that it allows such interventions on its operation. For example, a corrigible cleaning robot might update its objective function from "clean the house" to "shut down" upon observing that a human user is about to deactivate it.
For an AI system operating highly autonomously in ways that can have large world-scale impacts, corrigibility is even more important; this HCAI research category is about developing methods to ensure highly robust and desirable forms of corrigibility. Corrigibility may be a special case of preference inference.
Describes the open problem of corrigibilitydesigning an agent that doesn't have instrumental incentives to avoid being corrected e. A proposal for training a reinforcement learning agent that doesn't learn to avoid interruptions to episodes, such as the human operator shutting it down. Discusses objections to the convergent instrumental goals thesis, and gives a simple formal model.
Principled approach to corrigibility and the shutdown problem based on cooperative inverse reinforcement learning.
Describes an approach to averting instrumental incentives by "cancelling out" those incentives with artificially introduced terms in the utility function. Proposes an objective function that ignores effects through some channel by performing separate causal counterfactuals for each effect of an action. Theoretical results can also reduce our dependency on trial-and-error methodologies for determining safety, which could be important when testing systems is difficult or expensive to do safely.
Whereas existing theoretical foundations such as probability theory and game theory have been helpful in developing current approaches to AI, it is possible that additional foundations could be helpful in advancing HCAI research specifically. This category is for theoretical research aimed at expanding those foundations, as judged by their expected usefulness for HCAI.
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Motivates the study of decision theory as necessary for aligning smarter-than-human artificial systems with human interests. Uses reflective oracles to define versions of Solomonoff and AIXI which are contained in and have the same type as their environment, and which in particular reason about themselves. Logic, Rationality, and Interaction: 5th International Workshop.
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Introduces a framework for treating agents and their environments as mathematical objects of the same type, allowing agents to contain models of one another, and converge to Nash equilibria. Shows that Legg-Hutter intelligence strongly depends on the universal prior, and some universal priors heavily discourage exploration.
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Describes sufficient conditions for learnability of environments, types of agents and their optimality, the computability of those agents, and the Grain of Truth problem. Proposes a criterion for "good reasoning" using bounded computational resources, and shows that this criterion implies a wide variety of desirable properties.
Proves a version of Lob's theorem for bounded reasoners, and discusses relevance to cooperation in the Prisoner's Dilemma and decision theory more broadly. Overview of an agenda to formalize various aspects of human-compatible "naturalized" embedded in its environment superintelligence. Presents an algorithm that uses Brouwer's fixed point theorem to reason inductively about computations using bounded resources, and discusses a corresponding optimality notion. Provides a model of game-theoretic agents that can reason using explicit models of each other, without problems of infinite regress.
Illustrates how agents formulated in term of provability logic can be designed to condition on each others' behavior in one-shot-games to achieve cooperative equilibria. Agents that only use logical deduction to make decisions may need to "diagonalize against the universe" in order to perform well even in trivially simple environments.
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Incorporating more human oversight and involvement "in the loop" with an AI system creates unique opportunities for ensuring the alignment of an AI system with human interests. This category surveys approaches to achieving interactivity between humans and machines, and how it might apply to developing human compatible AI. Such approaches often require some degree of both transparency and reward engineering for the system, and as such could also be viewed under those headings. Wang, Percy Liang, Christopher D. Manning Association for Computational Linguistics. A framework for posing and solving language-learning goals for an AI, as a cooperative game with a human.
Bradley Knox, Todd Kulesza Case studies demonstrating how interactivity results in a tight coupling between the system and the user, how existing systems fail to account for the user, and some directions for improvement.
Adams, Charles Sutton Producing diverse clusterings of data by elicit experts to reject clusters. Proposes the following objective for HCAI: Estimate the expected rating a human would give each action if she considered it at length. Take the action with the highest expected rating.
The open problem of reinforcing an approval-directed RL agent so that it learns to be robustly aligned at its capability level. If we ask a robot to "keep the living room clean", we probably don't want the robot locking everyone out of the house to prevent them from making a mess there even though that would be a highly effective strategy for the objective, as stated.
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There seem to be an extremely large number of such implicit common sense rules, which we humans instinctively know or learn from each other, but which are currently difficult for us to codify explicitly in mathematical terms to be implemented by a machine. It may therefore be necessary to specify our preferences to AI systems implicitly, via a procedure whereby a machine will infer our preferences from reasoning and training data. This category highlights research that we think may be helpful in developing methods for that sort of preference inference. Feron, V. Balakrishnan Studies in Applied Mathematics.
Overview of inverse optimal control methods for linear dynamical systems. Proposes having AI systems perform value alignment by playing a cooperative game with the human, where the reward function for the AI is known only to the human. Introduces Inverse Reinforcement Learning, gives useful theorems to characterize solutions, and an initial max-margin approach. Good approach to semi-supervised RL and learning reward functions -- one of the few such papers. Recent and important paper on deep inverse reinforcement learning.
IRL with linear feature combinations. Introduces matching expected feature counts as an optimality criterion. Schapire Kalman Journal of Basic Engineering. Seminal paper on inverse optimal control for linear dynamical systems. Psychology is full of fascinating figures rife with intriguing stories and anecdotes.
This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most. In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and different employment options that are available.
In this type of paper, you will provide an in depth analysis of your subject, including a thorough biography. It is also important to note that your paper doesn't necessarily have to be about someone you know personally. In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films. Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.
In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results. Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well subjects you have studied in the past.