This year, GECCO 2018 offers 3 enticing keynote talks and a SIGEVO Lecture.
Kazuo YanoFellow, Corporate Officer, Hitachi, Ltd., Tokyo, Japan
AI for Happiness of People
There is little discussion on why AI is necessary. The reason for requiring AI is not because technology has been advanced. It is that demand requires flexibility to change and diversity, whereas standardization of work since Taylor cannot meet this. This is called from "rule-oriented" to "outcome-oriented." We have developed AI ("multipurpose AI") which enables flexible action according to circumstances for given outcome. It has already been utilized in more than 60 business cases in areas, such as financial, distribution, energy, and transportation. To enable flexible action, a consistent objective is required. The higher is the level of the objective, the higher its value and consistency. "The happiness" is positioned at the top in any problem. We have discovered a method to quantify the happiness of people from movements of the body using accelerometer. We also have developed AI that supports enhancement of people's happiness. This paves a way to the new capitalism that judges everything for human happiness. From a society seeking to follow uniform rules in the past, it is possible to realize a society in which flowers are bloomed at the place where the person is located. The whole picture of this new society in introduced.
Kazuo Yano received the B. S., M. S., and Ph. D degrees from Waseda University, Japan, in 1982, 1984, 1993, respectively. From 1991 to 1992 he was a Visiting Scientist at the Arizona State University.
He is now a Fellow, Corporate Officer, Hitachi Ltd. He is known for the pioneering works in semiconductor field, such as the world-first room-temperature single-electron memories in 1993. In 2003, he has pioneered the measurement and analysis of social big data. The wearable sensor for this purpose has been introduced in a Harvard Business Review. He has succeeded quantifying the happiness, which has been used in more than 30 companies. His recent work is on the multi-purpose artificial intelligence, which has been applied to over 60 cases. He has applied over 350 patents and his papers are cited by over 2500 papers. His book, "The New Invisible Hand," is cited as one of top-10 business books in Japan in 2014.
Tatsuya OkabeAI R&I Division and Value Innovation Division, DENSO Co., Ltd.
Exploitation of Bio Signal Data to Understand Human State
Around 2010, brain machine interface (or brain computer interface), which can control a machine (a computer) by human thought, was one of hot topics in computer science area. The basic technical components of brain machine interface are sensing bio signal, i.e. brain activity, analyzing bio signal data and controlling a machine based on the analyzed data. Many institutes and companies joined competitions to show the results of brain machine interface and its applications. However, a few products were released based on these competitions. The main reasons are insufficient performance against product level and no proper applications. After these competitions, the progress of exploitation of bio signal data was little bit saturated.
Recently, exploitation of bio signal data to estimate human state is becoming a hot topic again because of technological aspect and social needs. The performance of machine learning including deep learning proposed by Hinton is dramatically improving and the progress of computer resources, ex. GPGPU, allows us to treat huge data and carry out huge calculation. After showing the surprising performance of deep learning in the field of computer vision, the exploitation of bio signal data is revisited from machine learning point of view.
Furthermore, since possibility of autonomous driving and advanced driver assistant system based on AI technology is becoming more realistic, estimation of human state, i.e. a driver, is starting to gather our attention because except for L4/L5 autonomous driving, transfer of responsibility for control of car from a car to a driver or vise versa, so called 'takeover' or 'handover', should be considered. In order to transfer the right properly, we have to consider several strategies according to a human (human) state, ex. normal transfer, warning, or emergency stop without transfer.
In my presentation, the history of exploitation of bio signal data including brain machine interface will be explained and several hot topics around bio signal exploitation, ex. combination of AI and brain machine interface, will be shown. Finally, we would like to discuss the future topics around bio signal exploitation.
BiosketchTatsuya Okabe was born in Osaka in 1970 and learned material science in Osaka university and the graduated school of Osaka university with master thesis. In 1995, I joined West Japan Railway co., Ltd. and developed a total train planning system as a leader. After releasing the system, I started to work for Honda R&D as a fundamental researcher in 1999. I researched for optimization, parallel computing, machine learning, brain machine interface, robotics and signal processing. From 2000 to 2004, I worked for Honda Research Institute Europe in Germany and received doctor degree from Bielefeld university supervised by Prof. Ritter, Prof. Koerner and Prof. Sendhoff in 2004. From 2006 to 2009, I was sent to Advanced Telecommunications Research Institute International in Kyoto to develop a brain machine interface and reached highest performance of brain machine interface and succeeded to control a humanoid robot by human thought. From 2016 to 2017, I was assigned as a responsible manager to establish Honda Innovation Lab. Tokyo. After successfully establishing the laboratory, I was a deputy large project leader of AI research in Honda Innovation Lab. Tokyo. In 2017, I joined DENSO to research for IoT and human state estimation in more attractive environment. Now, I am working in DENSO as a general manager of AI R&I div. and Value Innovation div. in Tokyo.
Naoko YamazakiThe University of Tokyo, former JAXA astronaut
Connecting Human and Technologies in Space
Based on the experience onboard the International Space Station (ISS) and Space Shuttle Discovery, the way we are trained on the ground and we live in space will be introduced.
In recent space vehicles, human-machine interfaces have been developing. A validity of robots has been already researched and verified in some cases, such as, becoming a conversation partner in spaceship, supporting to take a video, doing extravehicular activity, and robots will eventually play more important roles.
To make robots cooperate with humans on an equal basis or to replace humans for robots, considering sharing responsibilities, what we need is not only an improvement, but also an innovation. When trying to make an innovation, it is important to look ahead beyond a few generations. Even the technologies that seem to be absurd dreams may cause breakthroughs. Since I serve as an Executive Committee Member of ``World Robot Summit'' held in Japan in 2020, I would like to introduce its challenges as well.
I would like to emphasize the importance of wide ``teamwork'' including human beings, robots and computers and their interfaces.
Naoko Yamazaki was born in Chiba, Japan and earned a Master of Science degree majored in Aerospace Engineering from the University of Tokyo in 1996, and then started working for Japan Aerospace Exploration Agency (JAXA). In 1999, she was selected as an astronaut candidate and was qualified as a Soyuz-TMA Flight Engineer in 2004 and NASA Mission Specialist in 2006.
In April, 2010, Yamazaki was onboard Space Shuttle Discovery on the crew of STS-131, an assembly & resupply mission to the International Space Station (ISS) and operated remote manipulator systems for both of Space Shuttle and ISS. She retired from JAXA in 2011 and has been serving as a member of Japan Space Policy Committee and an adviser of Young Astronaut Club (YAC), a visiting professor at Ritsumeikan University and Joshibi University of Art and Design, etc.
David E. GoldbergThreeJoy Associates, Big Beacon, and University of Illinois (Emeritus)
SIGEVO Lecture - On Becoming a Reflective Practitioner
The tension between theory, experiment, and practice plays out in genetic and evolutionary computation (GEC) as it plays out in other areas of science and technology. Back in the 80s, 90s, and 00s, I was always compelled to mix theory, experiment, and practical application in vigorous ways to achieve both understanding and effective computation, but my methodology often seemed to irritate more people than it satisfied. Theoreticians didn't think the work was quite "proper theory", and experimentalists/practitioners didn't think the work was sufficiently "real worldly." Although these concerns were always present in my GEC work, I haven't been thinking about them specifically over the last few years. Since resigning my tenure in 2010, I've been on a global quest to improve engineering education, a quest described in the book, A Whole New Engineer (www.wholenewengineer.org), and partially as a result of that journey, I think I can now better articulate some of the intuitions that led to the methodology of my earlier GEC career.
I start philosophically by sharing some of Don Schön's thoughts about the epistemology of practice. He asks, how is it, that practitioners, whether they be physicians, architects, engineers, accounts, computer scientists, or even physical scientists, know things in practice? The conventional wisdom, Schön claims, is that practitioners know things by first, mastering a body of well understood and accepted theory, then applying that theory in practice. Schön calls this theory of practical knowing, technical rationality, and he claims that it (1) is the dominant paradigm of epistemology of practice and that (2) it is largely mistaken (or at least, incomplete and misleading). As an alternative, he suggests that practitioners come to know through a process of reflection-in-action, and the talk discusses some of the key ideas behind this model of practice.
Thereafter, I revisit two case studies in early GEC work, the idea and use of deception and the idea and use of approximate little models through the lenses of technical rationality and reflection-in-action. The aim of this examination is to better understand the objections to and the intentions of the work, both. These are found to line up nicely along Schön's lines. Thereafter, I introduce Barry Johnson's notion of a polarity, and frame technical rationality and reflection-in-action. Johnson suggests that polarities are often regarded as solutions, but suggests that the appropriate stance is that poles must be managed. Here I suggest that the complexity of GEC demands the development of a population of reflective practitioners who actively manage the polarities of technical rationality and reflection-in-action, both. The talk discusses some of the key practices, particularly conversational practices, that can help do this.
The talk concludes with some theoretical and practical observations regarding the education of A Whole New Engineer and what these might offer the educators and education of the next generation of genetic algorithmists and evolutionary computationers.
David E. Goldberg (Dave) is a trained civil engineer (Michigan, 75, 76, 83) in hydraulics and hydrology, a registered engineer (PA), and a trained leadership coach (Georgetown, 2011). He taught engineering at Michigan, Alabama, and Illinois for 26 years, and as an academic, was known for his work in artificial intelligence, particularly genetic algorithms and evolutionary algorithms, amassing an h-index h=102, including 5 authored texts and a number of edited volumes, including the highly cited Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, 1989). During his career he has co-founded a Silicon Valley startup (www.sharethis.com), 3 academic conferences, including one combining philosophy & engineering, and an educational transformation incubator (iFoundry at UIUC). He now heads www.ThreeJoy.com, a coaching & change leadership firm for higher education, and www.BigBeacon.org, a 501(c3) non-profit corporation devoted to transforming higher education.
In 2010, Dave resigned his tenure and distinguished professorship at the University of Illinois to help transform higher education in alignment with the creativity imperative of the 21st century. Specifically, he traveled to Asia, South America, Europe, and back to North America to unlock the keys to authentic transformation and thereby unleash a new generation of students, faculty, and educational leaders. Today, Dave travels the globe to help co-create more transformative educational institutions and organizations. His most recent book, A Whole New Engineer: The Coming Revolution in Engineering Education, is available in hardcover and e-book formats (www.wholenewengineer.org).