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Can Deep Blue™ make us happy? Reflections on human and artificial expertise
Sadly, progress in AI has confirmed earlier conclusions, reached using formal domains, about the strict limits of human information processing and has also shown that these limits are only partly remedied by intuition. More positively, AI offers mankind a unique avenue to circumvent its cognitive limits: (1) by acting as a prosthesis extending processing capacity and size of the knowledge base; (2) by offering tools for studying our own cognition; and (3) as a consequence of the previous item, by developing tools that increase the quality and quantity of our own thinking. These ideas are illustrated with chess expertise
Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero
Artificial Intelligence (AI) systems have made remarkable progress, attaining
super-human performance across various domains. This presents us with an
opportunity to further human knowledge and improve human expert performance by
leveraging the hidden knowledge encoded within these highly performant AI
systems. Yet, this knowledge is often hard to extract, and may be hard to
understand or learn from. Here, we show that this is possible by proposing a
new method that allows us to extract new chess concepts in AlphaZero, an AI
system that mastered the game of chess via self-play without human supervision.
Our analysis indicates that AlphaZero may encode knowledge that extends beyond
the existing human knowledge, but knowledge that is ultimately not beyond human
grasp, and can be successfully learned from. In a human study, we show that
these concepts are learnable by top human experts, as four top chess
grandmasters show improvements in solving the presented concept prototype
positions. This marks an important first milestone in advancing the frontier of
human knowledge by leveraging AI; a development that could bear profound
implications and help us shape how we interact with AI systems across many AI
applications.Comment: 61 pages, 29 figure
Scaffolding Human Champions: AI as a More Competent Other
Artifcial intelligence (AI) has surpassed humans in a number of specialised intellectual activities—chess and Go being two of many examples. Amongst the many potential consequences of such a development, I focus on how we can utilise cutting edge AI to promote human learning. The purpose of this article is to explore how a specialised AI can be utilised in a manner that promotes human growth by acting as a tutor to our champions. A framework for using AI as a tutor of human champions based on Vygotsky’s theory of human learning is here presented. It is based on a philosophical analysis of AI capabilities, key aspects of Vygotsky’s theory of human learning, and existing research on intelligent tutoring systems. The main method employed is the theoretical development of a generalised framework for AI powered expert learning systems, using chess and Go as examples. In addition to this, data from public interviews with top professionals in the games of chess and Go are used to examine the feasibility and realism of using AI in such a manner. Basing the analysis on Vygotsky’s socio-cultural theory of development, I explain how AI operates in the zone of proximal development of our champions and how even non-educational AI systems can perform certain scafolding functions. I then argue that AI combined with basic modules from intelligent tutoring systems could perform even more scafolding functions, but that the most interesting constellation right now is scafolding by a group consisting of AI in combination with human peers and instructors.publishedVersio
Considerations for comparing video-game AI agents with humans
Video games are sometimes used as environments to evaluate AI agents’ ability to develop and execute complex action sequences to maximize a defined reward. However, humans cannot match the fine precision of the timed actions of AI agents; in games such as StarCraft, build orders take the place of chess opening gambits. However, unlike strategy games, such as chess and Go, video games also rely heavily on sensorimotor precision. If the “finding” was merely that AI agents have superhuman reaction times and precision, none would be surprised. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans. Here, I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video game play provided by AI agent
Diversifying AI: Towards Creative Chess with AlphaZero
In recent years, Artificial Intelligence (AI) systems have surpassed human
intelligence in a variety of computational tasks. However, AI systems, like
humans, make mistakes, have blind spots, hallucinate, and struggle to
generalize to new situations. This work explores whether AI can benefit from
creative decision-making mechanisms when pushed to the limits of its
computational rationality. In particular, we investigate whether a team of
diverse AI systems can outperform a single AI in challenging tasks by
generating more ideas as a group and then selecting the best ones. We study
this question in the game of chess, the so-called drosophila of AI. We build on
AlphaZero (AZ) and extend it to represent a league of agents via a
latent-conditioned architecture, which we call AZ_db. We train AZ_db to
generate a wider range of ideas using behavioral diversity techniques and
select the most promising ones with sub-additive planning. Our experiments
suggest that AZ_db plays chess in diverse ways, solves more puzzles as a group
and outperforms a more homogeneous team. Notably, AZ_db solves twice as many
challenging puzzles as AZ, including the challenging Penrose positions. When
playing chess from different openings, we notice that players in AZ_db
specialize in different openings, and that selecting a player for each opening
using sub-additive planning results in a 50 Elo improvement over AZ. Our
findings suggest that diversity bonuses emerge in teams of AI agents, just as
they do in teams of humans and that diversity is a valuable asset in solving
computationally hard problems
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