Before Alpha-Go: interpretation though improvisation and LISP

This poster outlines the process undertaken to write Go/Koan, a composition which uses a ½ point win Game between a mid-level Dan Go player and Zen19, a Go computer, which at the time (2013) was one of the strongest of its kind. Since then, Google’s Alpha-GO has radically changed this landscape with wide reaching implications

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

Peek into the Future: A Breakdown of the Various Implications of Alphago's Success over the Traditional Board Game, Go

This paper aims to present a collection of the several implications of AlphaGo's victory against the top professional Go player, Lee Sedol. The implications were studied through three different aspects: (1) technological, (2) social, and (3) economical/political. The study of the technological implications was done through the lens of understanding what makes AlphaGo work, as well as the differences between AlphaGo and Deep Blue. The social implications are viewed through the perspective of the Go community in general, with a discussion on the fears and optimistic outlooks by different professionals. The economic/political aspect is viewed on a global perspective, integrating business and political mindsets as well as an inquiry into how the Chinese think about issues of war and diplomacy

Can Reinforcement Learning Be Applied to Surgery?

Background: Remarkable progress has recently been made in the field of artificial intelligence (AI).Objective: We sought to investigate whether reinforcement learning could be used in surgery in the future.Methods: We created simple 2D tasks (Tasks 1–3) that mimicked surgery. We used a neural network library, Keras, for reinforcement learning. In Task 1, a Mac OS X with an 8 GB memory (MacBook Pro, Apple, USA) was used. In Tasks 2 and 3, a Ubuntu 14. 04LTS with a 26 GB memory (Google Compute Engine, Google, USA) was used.Results: In the task with a relatively small task area (Task 1), the simulated knife finally passed through all the target areas, and thus, the expected task was learned by AI. In contrast, in the task with a large task area (Task 2), a drastically increased amount of time was required, suggesting that learning was not achieved. Some improvement was observed when the CPU memory was expanded and inhibitory task areas were added (Task 3).Conclusions: We propose the combination of reinforcement learning and surgery. Application of reinforcement learning to surgery may become possible by setting rules, such as appropriate rewards and playable (operable) areas, in simulated tasks

FML-based Prediction Agent and Its Application to Game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.Comment: 6 pages, 12 figures, Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 201