Abstract:With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I focus on the problems of public health and wildlife conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I present our deployments from around the world as well as lessons learned that I hope are of use to researchers who are interested in AI for Social Impact. Achieving social impact in these domains often requires methodological advances. I will highlight key research advances in topics such as computational game theory, multi-armed bandits and influence maximization in social networks as well as in integrating machine learning with such advances in the data to deployment pipeline. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.

Abstract:This talk introduces several intelligent systems creating co-experience knowledge and wisdom with human-machine harmonious collaboration. They have been supported by the funding program, CREST, for team-oriented research with the aim of achieving the strategic goals set forth by the government at Japan Science and Technology Agency (JST) in 2014-2022. They are supposed to develop human-machine “harmonious” or “balanced” collaboration gradually coping with the ethical, legal and social issues (ELSI). This talk also focuses on the research area of AI and robotics such as human-robot interaction with natural language and/or robotics, symbol grounding, consensus formation, multi-agent collaboration/coordination, ontology, computer vision, machine learning, integrated information theory, free-energy principle, artificial consciousness. Social impact and trends on situated services for eleven research projects in the CREST are discussed.

Abstract: We develop PRINTEPS, which is a user-centric platform to develop unified AI robot applications by integrating five types of intelligent software modules: knowledge-based reasoning, speech dialog processing, image sensing, motion planning and machine learning. The knowledge-based reasoning systems include workflows, business rules, and ontologies. The spoken dialogue processing has the spoken dialog system with response generation rules, dialogue processing rules and language understanding rules, and then the translation from queries in natural language into SPARQL queries. Image sensing systems have object and human behavior recognition with deep learning and Augmentation Reality. Motion planning systems include obstacle recognition and dynamic/intelligent route planning. Machine leaning systems consists of conventional machine learning and deep learning such as convolutional neural net-works and LSTM and so on. PRINTEPS supports end users to design AI robot applications and to develop and revise them easily. They just design work-flows with business rules related with unified AI robot applications and then PRINTEPS automatically convert them into Python programming codes that can run over ROS. We evaluate how much well go AI unification with the fol-lowing real case studies: Robot Café where multi robots work together, and AI classroom where TA robots teach puplis together with teachers. The real case studies show us the unified AI robot applications go well with PRINTEPS.

Abstract: In this talk, I will present a hypothesis that consciousness has evolved as a platform for general intelligence. Here we define general intelligence as the ability to apply knowledge and models acquired from past experiences to generate solutions to novel problems. We propose three ways to establish general intelligence in AI systems, namely by simulation, combination, and generation. We relate those solutions to putative functions of consciousness put forward, respectively, by the information generation hypothesis (Kanai et al., 2019), the global workspace theory, and a form of higher order theory where qualia are regarded as meta-representations. By linking theories of consciousness to possible architectures of artificial general intelligence, I will discuss the fundamental link between consciousness and intelligence.

References: Kanai, R., Chang, A., Yu, Y., Magrans de Abril, I., Biehl, M., & Guttenberg, N. (2019) Information generation as a functional basis of consciousness, Neuroscience of Consciousness, Volume 2019, Issue 1, 2019, niz016, https://doi.org/10.1093/nc/niz016

Abstract: Online discussion among civilian is important and essential for next-generation democracy. Providing good support is critical for establishing and maintaining coherent discussions. Large-scale online discussion platforms are receiving great attention as potential next-generation methods for smart democratic citizen platforms. Such platforms require support functions that can efficiently achieve a consensus, reasonably integrate ideas, and discourage flaming. Researchers are developing several crowd-scale discussion platforms and conducting social experiments with a local government. One of these studies employed human facilitators in order to achieve good discussion. However, they clarified the critical problem faced by human facilitators caused by the difficulty of facilitating large-scale online discussions. In this work, we propose an automated facilitation agent to manage crowd-scale online discussions. An automated facilitator agent extracts the discussion structure from the texts posted in discussions by people. In this paper, we present our current implementation of D-agree, a crowd-scale discussion support system based on an automated facilitation agent, which extracts discussion structures from text discussions, analyzes them, and posts facilitation messages. We conducted a large-scale social experiment with Nagoya City’s local government. The results present that our automated facilitation agents succeeded to gather more opinions from people. Also, we found that people satisfactions on both discussions by automated facilitation agents and discussions by human facilitators were almost same score. Our main contribution is that this is one of the earliest real trials, in which an automated agent facilitated discussion among people in the real society.