WBAI Incentive Awards

Here present the past awardees of the WBAI Incentive Award, which is given to persons who achieved (potentially) prolific results in promoting the development of brain-inspired artificial general intelligence (AGI).

2019 Awardees

Naoto Yoshida

He conducted experiments in a 3D virtual environment to show that homeostatic properties such as food intake, poison avoidance, and energy gain emerge from the reinforcement learning algorithm, which is considered to be the problem of maximizing the probability of survival, and published the result on the Journal of Artificial General Intelligence.  The emergence of behaviors for homeostasis will contribute the brain-inspired AGI development in terms of realizing autonomous survival, creating the basis for modeling emotions, and discussing the convergence of instrumental subgoals.

Haruo Mizutani

He launched a new research approach called “connectome informatics” to extract knowledge of the essential structure from neural circuits (connectome) for brain-inspired artificial intelligence, and made academic presentations in Japan and overseas. Connectome informatics continues as a research activity within WBAI also with a large exterior effect.  He also created and operated the Nico Nico AI School, a hands-on meeting and online course, contributing to the fostering of brain-inspired artificial intelligence developers.

2018 Awardees

Masahiko Osawa

He proposed extended model of restricted Boltzmann machine (RBM), inspired by the hippocampus and based on the accumulator-based arbitration model (ABAM), a multi-module arbitration method inspired by the prefrontal cortex, and it was appraised in both inside and outside Japan.  Both methods were made public and contributed to the public interest.

Akira Taniguchi

He proposed a model (SpCoSLAM) that integrates self-localization and map generation functions corresponding to the hippocampus, and word segmentation from speech information corresponding to the neocortex and clustering based on multimodal information such as positions, images and words.  It was proposed and implemented, presented at academic conferences in Japan and overseas, and the source code has been released publicly.

2017 Awardee

Masayoshi Nakamura

He developed the AI learning environment called Life in Silico (LIS), which combines Unity the game engine and DQN-based agents in a form easy for general users to use.  He organized a LIS hackathon with about 100 engineers and posted the result on numerous web media.  LIS has a tutorial in a book, has been recognized as a tool for beginners, and has spread to the game industry as he gave a talk at the game conference CEDEC 2016.  This has made a significant contribution to the promotion of technology development for the whole brain architecture.