If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Tobaru, Akari (The University of Electro-Communications) | Uwano, Fumito (The University of Electro-Communications) | Iwase, Takuya (The University of Electro-Communications) | Matsumoto, Kazuma (The University of Electro-Communications) | Takano, Ryo (The University of Electro-Communications) | Tajima, Yusuke (The University of Electro-Communications) | Umenai, Yuta (The University of Electro-Communications) | Takadama, Keiki (The University of Electro-Communications)
This paper described that proposing a novel method to estimate the sleep stage by biological data obtained with a non-contact sensor devices and that investigating its effectiveness. Proposed method focused on circadian rhythm to consider of a day biological rhythm in overall sleeping in addition to employ the Harada’s method. To verify the effectiveness of the proposed method, we derived the subject experiment that compared with the evaluation accuracy by the previous method with adjustment of circadian rhythm. As the experimental results, the following implications have been revealed: (1) the accuracy of the sleep stage estimation in 5 days out of that in 9 days were improved by proposed method in comparison with Harada’s method; (2) the parameter β (which determines the discount rate of curve of circadian rhythm) should be set around 60%, meaning that a raw circadian rhythm (i.e., no discounted rhythm) strongly affected the sleep stage while the highly discounted circadian rhythm (e.g. 30% discounted rhythm) does not contribute to accurately estimating the sleep stage.
Tajima, Yusuke (The University of Electro-Communications) | Murata, Akinori (The University of Electro-Communications) | Harada, Tomohiro (Ritsumeikan University) | Takadama, Keiki (Ritsumeikan University)
This paper focuses on a sleep cycle, and improves the problem which an estimation accuracy of Real-Time Sleep Stage Estimation Method(RSSE) when it estimates a sleep stage on real time. Concretely, the proposed method re-estimates the sleep stage immediately after first sleep cycle since going to bed for the problem which decreases the correct rate of the sleep stage estimated by RSSE as time passes since going to bed. From the human subject experiments, the following implications have been revealed: (1) the correct rate improved by re-estimation in 8 cases out of 9 cases. (2) when the sleep cycle is long, it is possible to calculate the sleep cycle from the same subject's past sleeping information and if it is used, the estimation accuracy is improved for all cases.
This paper proposes new heart rate estimation method for biological data from sleep monitor sensor toward estimating sleep stage accurately. Concretely, we employed two heart rate estimation methods, and integrated the two methods as weak estimator. One of the two methods calculates power spectrum from the biological data by FFT, and selects the frequency with maximum spectrum as heart rate (HR). The other calculates power spectrum as a same manner of the former method, and selects the frequency which indicates the half size of all power spectrum as HR. To validate the effectiveness of EHEM, this paper applies EHEM to pressure data from sleep monitor sensor. From the result, EHEM can extract HR accurately, and prevent from outliers generated by HEM-FFT. We are going to research (1) what method gives good influence to EHEM, and (2) how to integrate the HRs extracted from the methods.
Takano, Ryo (The University of Electro-Communications) | Hasegawa, Satoshi (The University of Electro-Communications) | Umenai, Yuta (The University of Electro-Communications) | Tatsumi, Takato (The University of Electro-Communications) | Takadama, Keiki (The University of Electro-Communications) | Shimuta, Toru (Murata Manufacturing Company, Ltd.) | Yabe, Toru (Murata Manufacturing Company, Ltd.) | Matsumoto, Hideo (Murata Manufacturing Company, Ltd.)
The purpose of this study is to find novel knowledge to clarify the relationship between the sleep quality and the degree of the mental stress. For this purpose, we focus on not only these two indices (the quality of sleep and the degree of the mental stress), but also the human circadian rhythm as the new index for analysis. Through three types of data measured during the night-time sleep and during the day, we tried to inspect the usefulness of the human circadian rhythm for the index of the analysis. In this paper, data of these three indices were measured by the single subject experiment of about two weeks and analyzed comprehensively. In the analysis, we categorize good / middle / bad for each index every few days, and investigating the relationship between the three indices by summarizing the transition of the categories of the three indices. As a result, by comparing three types of data of ten-odd days in parallel, we obtained the following findings: (1) These three indices have been moving with a similar trend in units of days; (2) those trends coincide details from the simple diary written by the subject. As a result, by comparing three types of data of ten-odd days in parallel, these data were related to each other.
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.
Takadama, Keiki (The University of Electro-Communications)
This paper focuses on care support knowledge (especially focuses on the sleep related knowledge) and tackles its cognitive bias and humanity aspects from machine learning perspective through discussion of whether machine learning can correct commonly accepted knowledge and provide understandable knowledge in care support domain. For this purpose, this paper starts by introducing our data mining method (based on association rule learning) that can provide only necessary number of understandable knowledge without probabilities even if its accuracy slightly becomes worse, and shows its effectiveness in care plans support systems for aged persons as one of healthcare systems. The experimental result indicates that (1) our method can extract a few simple knowledge as understandable knowledge that clarifies what kinds of activities (e.g., rehabilitation, bathing) in care house contribute to having a deep sleep, but (2) the apriori algorithm as one of major association rule learning methods is hard to provide such knowledge because it needs calculate all combinations of activities executed by aged persons.
Bohg, Jeannette (Max Planck Institute for Intelligent Systems) | Boix, Xavier (Massachusetts Institute of Technology) | Chang, Nancy (Google) | Churchill, Elizabeth F. (Google) | Chu, Vivian (Georgia Institute of Technology) | Fang, Fei (Harvard University) | Feldman, Jerome (University of California at Berkeley) | González, Avelino J. (University of Central Florida) | Kido, Takashi (Preferred Networks in Japan) | Lawless, William F. (Paine College) | Montaña, José L. (University of Cantabria) | Ontañón, Santiago (Drexel University) | Sinapov, Jivko (University of Texas at Austin) | Sofge, Don (Naval Research Laboratory) | Steels, Luc (Institut de Biologia Evolutiva) | Steenson, Molly Wright (Carnegie Mellon University) | Takadama, Keiki (University of Electro-Communications) | Yadav, Amulya (University of Southern California)
This paper proposes an improved method to estimate the sleep stage in real-time by heat rate obtained with a non- contact biological sensor device and investigates its effective- ness. The current version of real-time sleep stage estimation decreases its estimation accuracy in early term of sleeping and cannot follow rapid change of the sleep stage. To improve the estimation accuracy of the improved method, this paper introduces sleep feature of different day into the estimation model as user’s pre-information and introduces WAKE and REM sleep stage heuristic classifiers.
Amato, Christopher (University of New Hampshire) | Amir, Ofra (Harvard University) | Bryson, Joanna (University of Bath) | Grosz, Barbara (Harvard University) | Indurkhya, Bipin (Jagiellonian University) | Kiciman, Emre (Microsoft Research) | Kido, Takashi (Rikengenesis) | Lawless, W. F. (Massachusetts Institute of Technology) | Liu, Miao (University of Southern California) | McDorman, Braden (Semio) | Mead, Ross (University of Amsterdam) | Oliehoek, Frans A. (University of Pennsylvania) | Specian, Andrew (American University in Paris) | Stojanov, Georgi (University of Electro-Communications) | Takadama, Keiki
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.
Takadama, Keiki (The University of Electro-Communications)
This paper proposes the concept of Well-being computing which is an information technology for improving not only our health as physical aspect but also our happiness as psy-chological aspect, and shows its potential from the sleep perspective. Concretely, this paper introduces “our personal-ized sleep monitoring system” as the well-being computing technologies and shows the following implications as its ef-fectiveness: (1) from the viewpoint of the service based on the real-time sleep, (1-a) good health is provided through a stable sleep of aged person in care house by reducing their sleep disturbance which may be occurred in diaper exchange, while happiness is provided by the smooth diaper exchange when aged person have a deep/light sleep; (1-b) good health is provided through a sufficient sleep time acquired by a fast falling asleep, while happiness is provided by releasing from anxiety of the insufficient sleep such as insomnia; and (2) from the viewpoint of the service based on the long-term sleep, (2-a) good health is provided through a deep sleep by continuing the daytime activities (such as a walking) which contribute to deriving a deep sleep, while happiness is provided by achieving a deep sleep through a change of life style; (2-b) good health is provided through a good sleep by keeping good bed condition (e.g., a change of a pillow or mattress when cotton/spring is deteriorated), while happiness is provided through a discovery of suitable bedding (such as suitable pillow or bed).