Kido, Takashi


The Challenges for Understanding Cognitive Bias and Humanity for Well-Being AI — Beyond Machine Intelligence

AAAI Conferences

In this AAAI Spring symposium 2018, we discuss cognitive bias and humanity in the context of well-being AI. We define “well-being AI” as an AI research paradigm for promoting psychological well-being and maximizing human potential. The goals of well-being AI are (1) to understand how our digital experience affects our health and our quality of life and (2) to design well-being systems that put humans at the center. The important challenges of this research are how to quantify subjective things such as happiness, personal impressions, and personal values, and how to transform them into scientific representations with corresponding computational methods. One of the important keywords in understanding machine intelligence in human health and wellness is cognitive bias. Advances in big data and machine learning should not overlook some new threats to enlightened thought, such as the recent trend of social media platforms and commercial recommendation systems being used to manipulate people's inherent cognitive bias. The second important keyword is humanity. Rational thinking, on which early AI researchers had been focused their efforts, is recently and rapidly replacing human thinking by machines. Many people might have begun to believe that irrational thinking is the root of humanity. Empirical and philosophical discussions on AI and humanity would be welcome. This paper describes the detailed motivation, technical, and philosophical challenges of this symposium proposal.



Reports of the AAAI 2016 Spring Symposium Series

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.


Machine Learning and Personal Genome Informatics Contribute to Happiness Sciences and Wellbeing Computing

AAAI Conferences

Two big recent revolutions: machine learning technologies; such as “deep learning” in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various “citizen science” projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informat-ics might contribute to happiness sciences. We introduce the “Social Intelligence Genomics and Empathy-Building Study” and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We dis-cuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing.


Reports on the 2015 AAAI Spring Symposium Series

AI Magazine

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill?


Reports on the 2015 AAAI Spring Symposium Series

AI Magazine

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill? and Turn-Taking and Coordination in Human-Machine Interaction.The highlights of each symposium are presented in this report.


Ambient Intelligence and Crowdsourced Genetics for Understanding Loss Aversion in Decision Making

AAAI Conferences

The big challenge for Artificial Intelligence is a better understanding of human nature. Our fundamental motivation is to understand the minds of modern people by uncovering mechanisms of the brain, genes, and body, and enhancing our health and cognitive talents with Artificial Intelligence technologies. This paper presents how we can quantify cognitive biases in the decision-making process and understand the evolutionary mechanisms using Ambient Intelligence and crowdsourced genetics technologies. We focus on prospect theory (proposed by Daniel Kahneman), which models how people choose between options involving gains or losses. People perceive losses to hurt more than gains feel good. This “loss aversion” is an important cognitive bias in decision-making. However, little is known about individual differences in loss aversion. We launched a citizen science project to test the hypothesis that mutations in genes related to neural processes are related to individual variation in loss aversion. Our preliminary experiment showed that DRD2 gene mutations may be related to individual variation in loss aversion. This crowdsourced genetics research is probably the first trial to report the possibilities of individual genetic differences in loss aversion behaviors. We discuss the future paradigms in Ambient Intelligence for health and cognitive enhancement.


Reports of the 2013 AAAI Spring Symposium Series

AI Magazine

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.


Reports of the 2013 AAAI Spring Symposium Series

AI Magazine

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.


Exploring the Mind with the Aid of Personal Genome — Citizen Science Genetics to Promote Positive Well-Being

AAAI Conferences

Understanding the human mind and increasing individual happiness are important goals in artificial intelligence (AI) and well-being science. The recent revolution in portable self-tracking devices in the data-driven wellness movement and participatory-driven wellness communities, such as the Quantified Self community, provides us with new opportunities to collect psychological or physiological data for understanding the human mind.  While new technologies make it possible to track our daily behavior and various biological signals such as physiological or genetic data more easily, one of the important remaining challenges is to discover our own truly meaningful personal values. Citizen science, scientific research by crowdsourcing or human-based computation, is a new and challenging framework that promotes interdisciplinary research in the fields of computer science, life/brain science, and social psychological/behavioral science, which may introduce new paradigms to the AI community. We have been working on citizen science projects related to the area of personal genomics and have developed a personal genomics information environment named MyFinder. The developed platform supports the search for our inherited talents and maximizes our potential for a meaningful life. In particular, we are interested in the human mind and the personal genome. In this paper, we introduce our MyFinder Project and present the results of a recent study on “social intelligence genomics and empathy building”, and discuss issues involved in exploring our mind within the context of personal genomics.