Human-Compatible Artificial Intelligence

2021 ◽  
pp. 3-23
Author(s):  
Stuart Russell
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Standard Model ◽  
Real World ◽  
Technical Aspects ◽  
Alan Turing ◽  
Wide Range ◽  
The Standard Model ◽  
Formal Class

Following the analysis given by Alan Turing in 1951, one must expect that AI capabilities will eventually exceed those of humans across a wide range of real-world-decision making scenarios. Should this be a cause for concern, as Turing, Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real: we have to work out how to design AI systems that are far more powerful than ourselves while ensuring that they never have power over us. I believe the technical aspects of this problem are solvable. Whereas the standard model of AI proposes to build machines that optimize known, exogenously specified objectives, a preferable approach would be to build machines that are of provable benefit to humans. I introduce assistance games as a formal class of problems whose solution, under certain assumptions, has the desired property.

scholarly journals Artificial Intelligence and the Problem of Control

2021 ◽  
pp. 19-24
Author(s):  
Stuart Russell
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Standard Model ◽  
Real World ◽  
New Model ◽  
The Real ◽  
Ability To Act ◽  
The Standard Model ◽  
Philosophy And Economics

AbstractA long tradition in philosophy and economics equates intelligence with the ability to act rationally—that is, to choose actions that can be expected to achieve one’s objectives. This framework is so pervasive within AI that it would be reasonable to call it the standard model. A great deal of progress on reasoning, planning, and decision-making, as well as perception and learning, has occurred within the standard model. Unfortunately, the standard model is unworkable as a foundation for further progress because it is seldom possible to specify objectives completely and correctly in the real world. The chapter proposes a new model for AI development in which the machine’s uncertainty about the true objective leads to qualitatively new modes of behavior that are more robust, controllable, and deferential to humans.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

scholarly journals Awareness, Knowledge, and Utility of RCT Data vs RWE: Results From a Survey of US Cardiologists: Real-world Evidence in Clinical Decision Making

2020 ◽  
Vol 14 ◽  
pp. 117954682095341 ◽  
Author(s):  
Todd C Villines ◽  
Mark J Cziraky ◽  
Alpesh N Amin
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Real World ◽  
Randomized Clinical Trials ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
The Body ◽  
Representative Sampling ◽  
Real World Evidence ◽  
Wide Range

Real-world evidence (RWE) provides a potential rich source of additional information to the body of data available from randomized clinical trials (RCTs), but there is a need to understand the strengths and limitations of RWE before it can be applied to clinical practice. To gain insight into current thinking in clinical decision making and utility of different data sources, a representative sampling of US cardiologists selected from the current, active Fellows of the American College of Cardiology (ACC) were surveyed to evaluate their perceptions of findings from RCTs and RWE studies and their application in clinical practice. The survey was conducted online via the ACC web portal between 12 July and 11 August 2017. Of the 548 active ACC Fellows invited as panel members, 173 completed the survey (32% response), most of whom were board certified in general cardiology (n = 119, 69%) or interventional cardiology (n = 40, 23%). The survey results indicated a wide range of familiarity with and utilization of RWE amongst cardiologists. Most cardiologists were familiar with RWE and considered RWE in clinical practice at least some of the time. However, a significant minority of survey respondents had rarely or never applied RWE learnings in their clinical practice, and many did not feel confident in the results of RWE other than registry data. These survey findings suggest that additional education on how to assess and interpret RWE could help physicians to integrate data and learnings from RCTs and RWE to best guide clinical decision making.

scholarly journals Resurrecting low-mass axion dark matter via a dynamical QCD scale

2021 ◽  
Vol 2021 (12) ◽  
Author(s):  
Lucien Heurtier ◽  
Fei Huang ◽  
Tim M.P. Tait
Keyword(s):  
Dark Matter ◽  
Standard Model ◽  
Temperature Dependent ◽  
Axion Field ◽  
Wide Range ◽  
The Standard Model ◽  
Low Mass ◽  
Higher Temperature ◽  
Relic Abundance ◽  
Dependent Mass

Abstract In the framework where the strong coupling is dynamical, the QCD sector may confine at a much higher temperature than it would in the Standard Model, and the temperature-dependent mass of the QCD axion evolves in a non-trivial way. We find that, depending on the evolution of ΛQCD, the axion field may undergo multiple distinct phases of damping and oscillation leading generically to a suppression of its relic abundance. Such a suppression could therefore open up a wide range of parameter space, resurrecting in particular axion dark-matter models with a large Peccei-Quinn scale fa ≫ 1012 GeV, i.e., with a lighter mass than the standard QCD axion.

scholarly journals Participatory Budgeting with Project Interactions

2020 ◽  
Author(s):  
Pallavi Jain ◽  
Krzysztof Sornat ◽  
Nimrod Talmon
Keyword(s):  
Computational Complexity ◽  
Standard Model ◽  
Real World ◽  
Participatory Budgeting ◽  
Aggregation Methods ◽  
The Standard Model ◽  
Public Money ◽  
Complementarity Effects

Participatory budgeting systems allow city residents to jointly decide on projects they wish to fund using public money, by letting residents vote on such projects. While participatory budgeting is gaining popularity, existing aggregation methods do not take into account the natural possibility of project interactions, such as substitution and complementarity effects. Here we take a step towards fixing this issue: First, we augment the standard model of participatory budgeting by introducing a partition over the projects and model the type and extent of project interactions within each part using certain functions. We study the computational complexity of finding bundles that maximize voter utility, as defined with respect to such functions. Motivated by the desire to incorporate project interactions in real-world participatory budgeting systems, we identify certain cases that admit efficient aggregation in the presence of such project interactions.

Emotion: A Very Short Introduction

2019 ◽  
Author(s):  
Dylan Evans
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Human Nature ◽  
Middle Ages ◽  
Emotional Impact ◽  
Neural Basis ◽  
Short Introduction ◽  
New Developments ◽  
Biological Basis ◽  
Wide Range

Was love invented by European poets in the Middle Ages or is it part of human nature? Will winning the lottery really make you happy? Is it possible to build robots that have feelings? Emotion: A Very Short Introduction explores the latest thinking about emotions, drawing on a wide range of scientific research, from anthropology and psychology to neuroscience and artificial intelligence. It discusses the evolution of emotions and their biological basis, the science of happiness, and the role that emotions play in memory and decision-making. This new edition has been updated to incorporate new developments in our understanding of emotions, including the neural basis of empathy and the emotional impact of films.

Computational Intelligence Applications in Business

2011 ◽  
pp. 1806-1820
Author(s):  
Kevin E. Voges ◽  
Nigel K. Ll. Pope
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Rough Sets ◽  
Multi Agent Systems ◽  
Alan Turing ◽  
Business Applications ◽  
Wide Range ◽  
Intelligent Behavior ◽  
Multi Agent ◽  
Business Functions

We present an overview of the literature relating to computational intelligence (also commonly called artificial intelligence) and business applications, particularly the journal-based literature. The modern investigation into artificial intelligence started with Alan Turing who asked in 1948 if it would be possible for “machinery to show intelligent behaviour.” The computational intelligence discipline is primarily concerned with understanding the mechanisms underlying intelligent behavior, and consequently embodying these mechanisms in machines. The term “artificial intelligence” first appeared in print in 1955. As this overview shows, the 50 years of research since then have produced a wide range of techniques, many of which have important implications for many business functions, including finance, economics, production, operations, marketing, and management. However, gaining access to the literature can prove difficult for both the computational intelligence researcher andthe business practitioner, as the material is contained in numerous journals and discipline areas. The chapter provides access to the vast and scattered literature by citing reviews of the main computational intelligence techniques, including expert systems, artificial neural networks, fuzzy systems, rough sets, evolutionary algorithms, and multi-agent systems.

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