Our Policy

Our Policy for FY 2016

Towards WBA co-production infrastructure

Looking Back: the Initial Policy

WBAI had the following objectives at its inauguration in August 2015.

  1. Sustainability: retaining its objectives towards an expected advent of AGI (beyond 2030).
  2. Publicness: publishing research results and developing software with an open license.
  3. Cooperation with academia: promoting interactions among academic areas such as neuroscience, cognitive science and machine learning, with AI as the pivotal area.
  4. Human resource development: fostering multi-domain knowledge required for WBAI research.
  5. Infrastructure research: creating platforms for machine learners, evaluation methods for AGI, and research environments for simulation and data preparation.
  6. Social activity: making advanced AI technologies transparent for the public to create the future with AI.

Through discussions, it has been made clear that WBAI should encourage researches by the WBA approach rather than doing research itself. It is to avoid rivaling with other research entities and to be consistent with our policy for open technology.

Major recent trends with regard to policy making for FY 2016

The following are three major trends with regard to policy making after our inauguration.

    1. Technological acceleration: AI researches accelerate along with the advancement of deep learning. DL has been applied not only to image processing, but also to motion control and multi-modal information processing as well as to the domains of creativity and language understanding. This trend calls for short-term acceleration of technological development as well as long-term human resource development.
    2. Increase in AI investment in Japan: Following suite of MITI, MEXT and MIC started AI investments while those by enterprises and universities increase. This makes AI researchers and engineers quite busy for making relatively short-term results.
    3. The advent of OpenAI: the NPO was founded in December 2015 to promote open AI development with initial investment up to US$1 billion in commitments. It furthers the situation where AI technologies get more open and accessible.


In the WBA approach, we believe co-productive development of WBA by a community will be an important option for hastening the reach to AGI (Fig. 2). With the central hypothesis of WBA, development in a community becomes possible by decomposing the entire R&D into the R&D of brain modules and that of brain-like cognitive architecture. To support this development, micro and macro level neuroscientific knowledge is required. Knowledge in AI and cognitive sciences is also required for the construction for cognitive architecture.

Forming an Open AI Development Community (Fig. 3 α)

With regard to the acceleration of technology, we set out for forming an open AI R&D community as large as having the population of 10K people to promote rather short-term R&D. The aim here is to expand faster open AI R&D by combining engineers and experts across the boundary of institutions. We have already formed a SIG where hackathons with activities around Deep Predictive Network and LIS (Life in Silico) the learning environment simulator for AI. By this means, AGI development from the WBA approach will be accelerated and the shared knowledge could be passed on to the organizations affiliated to the participants for the source of competitiveness.


Facilitative R&D(Fig. 3 β・Fig. 4)

WBAI carries out various R&D. We continue developing BriCA, the Brain-inspired Computing Architecture, as a technology for massive distributed processing in brain-like architecture. It also develops BiCAmon, the tool for monitoring cognitive architecture’s activity while mapping it onto connectome, and LIS(Life in Silico: framework that makes intelligent agents live and learn in a virtual environment). The results have been used in the open development community as well as public and private research projects. We expect that these tools will be useful in areas where brain-like AI are applied.


Supporting Specific Researches (Fig 3 γ)

Though various machine learning techniques are getting open, it requires knowledge and skills to integrate them into WBA. As this task is generally not so easy for engineers interested in AI, it would be valuable to have support from researchers by, for example, breaking down the construction of the entire architecture into development themes.
Instead of employing PIs (principal investigators), we are going to financially support researchers who recently started to work on the WBA approach.

Summary for FY 2016 Business

In FY 2016, we shall be mainly engaged in the support of specific researches with PIs, entry level researchers and students (Fig. 3 γ), an open AI community activity centered around engineers (Fig. 3 α), and facilitative R&D lead by WBAI (Fig. 3 β), to accumulate R&D result in an open repository, so that we could pass it generally on to the society as well as to our stakeholders.