Artificial+Intelligence
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Experts to Help Asia Navigate the Post-COVID-19 and 4IR Eras
Risk Quotient 2020, an international conference co-hosted by KAIST and the National University of Singapore (NUS), will bring together world-leading experts from academia and industry to help Asia navigate the post-COVID-19 and Fourth Industrial Revolution (4IR) eras.
The online conference will be held on October 29 from 10 a.m. Korean time under the theme “COVID-19 Pandemic and A Brave New World”. It will be streamed live on YouTube at https://www.youtube.com/c/KAISTofficial and https://www.youtube.com/user/NUScast.
The Korea Policy Center for the Fourth Industrial Revolution (KPC4IR) at KAIST organized this conference in collaboration with the Lloyd's Register Foundation Institute for the Public Understanding of Risk (IPUR) at NUS.
During the conference, global leaders will examine the socioeconomic impacts of the COVID-19 pandemic on areas including digital innovation, education, the workforce, and the economy. They will then highlight digital and 4IR technologies that could be utilized to effectively mitigate the risks and challenges associated with the pandemic, while harnessing the opportunities that these socioeconomic effects may present. Their discussions will mainly focus on the Asian region.
In his opening remarks, KAIST President Sung-Chul Shin will express his appreciation for the Asian populations’ greater trust in and compliance with their governments, which have given the continent a leg up against the coronavirus. He will then emphasize that by working together through the exchange of ideas and global collaboration, we will be able to shape ‘a brave new world’ to better humanity. Welcoming remarks by Prof. Sang Yup Lee (Dean, KAIST Institutes) and Prof. Tze Yun Leong (Director, AI Technology at AI Singapore) will follow.
For the keynote speech, Prof. Lan Xue (Dean, Schwarzman College, Tsinghua University) will share China’s response to COVID-19 and lessons for crisis management. Prof. Danny Quah (Dean, Lee Kuan Yew School of Public Policy, NUS) will present possible ways to overcome these difficult times. Dr. Kak-Soo Shin (Senior Advisor, Shin & Kim LLC, Former Ambassador to the State of Israel and Japan, and Former First and Second Vice Minister of the Ministry of Foreign Affairs of the Republic of Korea) will stress the importance of the international community’s solidarity to ensure peace, prosperity, and safety in this new era.
Panel Session I will address the impact of COVID-19 on digital innovation. Dr. Carol Soon (Senior Research Fellow, Institute of Policy Studies, NUS) will present her interpretation of recent technological developments as both opportunities for our society as a whole and challenges for vulnerable groups such as low-income families. Dr. Christopher SungWook Chang (Managing Director, Kakao Mobility) will show how changes in mobility usage patterns can be captured by Kakao Mobility’s big data analysis. He will illustrate how the data can be used to interpret citizen’s behaviors and how risks can be transformed into opportunities by utilizing technology. Mr. Steve Ledzian’s (Vice President, Chief Technology Officer, FireEye) talk will discuss the dangers caused by threat actors and other cyber risk implications of COVID-19. Dr. June Sung Park (Chairman, Korea Software Technology Association (KOSTA)) will share how COVID-19 has accelerated digital transformations across all industries and why software education should be reformed to improve Korea’s competitiveness.
Panel Session II will examine the impact on education and the workforce. Dr. Sang-Jin Ban (President, Korean Educational Development Institute (KEDI)) will explain Korea’s educational response to the pandemic and the concept of “blended learning” as a new paradigm, and present both positive and negative impacts of online education on students’ learning experiences. Prof. Reuben Ng (Professor, Lee Kuan Yew School of Public Policy, NUS) will present on graduate underemployment, which seems to have worsened during COVID-19. Dr. Michael Fung’s presentation (Deputy Chief Executive (Industry), SkillsFuture SG) will introduce the promotion of lifelong learning in Singapore through a new national initiative known as the ‘SkillsFuture Movement’. This movement serves as an example of a national response to disruptions in the job market and the pace of skills obsolescence triggered by AI and COVID-19.
Panel Session III will touch on technology leadership and Asia’s digital economy and society. Prof. Naubahar Sharif (Professor, Division of Social Science and Division of Public Policy, Hong Kong University of Science and Technology (HKUST)) will share his views on the potential of China in taking over global technological leadership based on its massive domestic market, its government support, and the globalization process. Prof. Yee Kuang Heng (Professor, Graduate School of Public Policy, University of Tokyo) will illustrate how different legal and political needs in China and Japan have shaped the ways technologies have been deployed in responding to COVID-19. Dr. Hayun Kang (Head, International Cooperation Research Division, Korea Information Society Development Institute (KISDI)) will explain Korea’s relative success containing the pandemic compared to other countries, and how policy leaders and institutions that embrace digital technologies in the pursuit of public welfare objectives can produce positive outcomes while minimizing the side effects.
Prof. Kyung Ryul Park (Graduate School of Science and Technology Policy, KAIST) will be hosting the entire conference, whereas Prof. Alice Hae Yun Oh (Director, MARS Artificial Intelligence Research Center, KAIST), Prof. Wonjoon Kim (Dean, Graduate School of Innovation and Technology Management, College of Business, KAIST), Prof. Youngsun Kwon (Dean, KAIST Academy), and Prof. Taejun Lee (Korea Development Institute (KDI) School of Public Policy and Management) are to chair discussions with the keynote speakers and panelists.
Closing remarks will be delivered by Prof. Chan Ghee Koh (Director, NUS IPUR), Prof. So Young Kim (Director, KAIST KPC4IR), and Prof. Joungho Kim (Director, KAIST Global Strategy Institute (GSI)).
“This conference is expected to serve as a springboard to help Asian countries recover from global crises such as the COVID-19 pandemic through active cooperation and joint engagement among scholars, experts, and policymakers,” according to Director So Young Kim.
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2020.10.22 View 16620 -
KAIST Joins IBM Q Network to Accelerate Quantum Computing Research and Foster Quantum Industry
KAIST has joined the IBM Q Network, a community of Fortune 500 companies, academic institutions, startups, and research labs working with IBM to advance quantum computing for business and science.
As the IBM Q Network’s first academic partner in Korea, KAIST will use IBM's advanced quantum computing systems to carry out research projects that advance quantum information science and explore early applications. KAIST will also utilize IBM Quantum resources for talent training and education in preparation for building a quantum workforce for the quantum computing era that will bring huge changes to science and business. By joining the network, KAIST will take a leading role in fostering the ecosystem of quantum computing in Korea, which is expected to be a necessary enabler to realize the Fourth Industrial Revolution.
Professor June-Koo Rhee who also serves as Director of the KAIST Information Technology Research Center (ITRC) of Quantum Computing for AI has led the agreement on KAIST’s joining the IBM Q Network. Director Rhee described quantum computing as "a new technology that can calculate mathematical challenges at very high speed and low power” and also as “one that will change the future.”
Director Rhee said, “Korea started investment in quantum computing relatively late, and thus requires to take bold steps with innovative R&D strategies to pave the roadmap for the next technological leap in the field”. With KAIST joining the IBM Q Network, “Korea will be better equipped to establish a quantum industry, an important foundation for securing national competitiveness,” he added.
The KAIST ITRC of Quantum Computing for AI has been using the publicly available IBM Quantum Experience delivered over the IBM Cloud for research, development and training of quantum algorithms such as quantum artificial intelligence, quantum chemical calculation, and quantum computing education.
KAIST will have access to the most advanced IBM Quantum systems to explore practical research and experiments such as diagnosis of diseases based on quantum artificial intelligence, quantum computational chemistry, and quantum machine learning technology. In addition, knowledge exchanges and sharing with overseas universities and companies under the IBM Q Network will help KAIST strengthen the global presence of Korean technology in quantum computing.
About IBM Quantum
IBM Quantum is an industry-first initiative to build quantum systems for business and science applications. For more information about IBM's quantum computing efforts, please visit www.ibm.com/ibmq.
For more information about the IBM Q Network, as well as a full list of all partners, members, and hubs, visit https://www.research.ibm.com/ibm-q/network/
©Thumbnail Image: IBM.
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2020.09.29 View 9864 -
Deep Learning Helps Explore the Structural and Strategic Bases of Autism
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis.
However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult.
But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery.
A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access.
Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition.
These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations.
“There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine.
“And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.”
So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions).
The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory.
Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings.
“Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.”
The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism.
“Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said.
“Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.”
-ProfileProfessor Sang Wan LeeDepartment of Bio and Brain EngineeringLaboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/
KAIST
2020.09.23 View 12512 -
Education, a Silver Lining in the Dark COVID-19 Cloud
If there is a silver lining behind the COVID-19 pandemic clouds engulfing the world in darkness, it would be ‘education’. The disruption caused by the pandemic has reminded us of the skills that students need in this unpredictable world and raised public awareness of guaranteeing continuous, fair, and quality learning opportunities. Educational innovation can become a positive and powerful catalyst to transform the world for a better future in the post-COVID era.
According to the speakers at the virtual forum co-hosted by the Global Strategy Institute (GSI) and Korea Policy Center for the Fourth Industrial Revolution (KPC4IR) at KAIST on June 24, the recent transition to remote education amplifies the existing socio-economic disparities between the haves and the have-nots, and narrowing the digital divide is the most urgent challenge that should be addressed in this ever-evolving technology-dominating era. They also called for students to be resilient despite the numerous uncertainties ahead of them and prepare new skill sets to better adjust to new environments.
KAIST launched the GSI as its think tank in February of this year. The GSI aims to identify global issues proactively and help make breakthroughs well aligned with solid science and technology-based policies. The second forum of the KAIST GSI, following its inaugural forum in April, was held under the theme “Envisioning the Future of Education for a Non-Contact Society in the Post-Coronavirus Era”.
In his opening remarks, KAIST President Sung-Chul Shin stressed that “distance teaching and learning will eventually become integral components of our future education system”. He then called for close collaboration between the public and private sectors to better shape the future of digital education. President Shin said that global cooperation is also needed to continue offering inclusive, quality education that can equally benefit every student around the world. “We should never let a crisis go to waste, and the COVID-19 pandemic is no exception,” he added.
CEO of Minerva Schools Ben Nelson described the current coronavirus crisis as “an earthquake happening deep down on the ocean floor – we don’t feel it, but it can cause a devastating tsunami.” He continued, “Online learning can totally change the current education system forever.”
Saying that blended education, which combines online and offline classes, will be the new norm in the post-coronavirus era, Coursera CEO Jeff Maggioncalda anticipates that institutions will have to offer more and more online courses and credentials, and should at the same time prepare to drive down the cost of education as students expect to pay much less in tuition and fees for online learning options. “With the economy slumping and unemployment soaring, job-relevant education will also be a must,” Maggioncalda said.
National University of Singapore President Tan Eng Chye further pointed out that future education systems should prepare students to be creative lifelong learners. President Tan encouraged students to be able to integrate knowledge and technical skills from multiple disciplines for complex problem solving, and be adaptable and resilient with bigger appetites for risks and a higher tolerance for failures. He also mentioned digital competency, empathy, and social responsibility as virtues that students in the post-coronavirus era should possess.
Rebecca Winthrop, Co-Director of the Center for Universal Education at the Brookings Institution, raised concerns over the ever-growing digital disparities caused by the recent shift to online teaching and learning, claiming that insufficient infrastructures for low-income families in developing nations are already causing added educational disparities and provoking the inequity issue around the world. “New approaches to leapfrog inequality and provide quality education equally through faster and more effective means should be studied,” she said.
In response to this, Vice President of Microsoft Anthony Salcito introduced the Microsoft Education Transformation Framework, which provides practical advice to develop strategies for digital education transformation with a holistic, long-term view implemented in discrete phases that the global community can begin today. The Framework reportedly shows how emerging technologies, such as artificial intelligence, support new approaches to building efficient and effective physical and digital infrastructure, modernizing teaching and learning, empowering research, and managing student success.
The GSI will host two more forums in September and November.
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2020.06.24 View 15849 -
Professor Alice Haeyun Oh to Join GPAI Expert Group
Professor Alice Haeyun Oh will participate in the Global Partnership on Artificial Intelligence (GPAI), an international and multi-stakeholder initiative hosted by the OECD to guide the responsible development and use of AI. In collaboration with partners and international organizations, GPAI will bring together leading experts from industry, civil society, government, and academia.
The Korean Ministry of Science and ICT (MSIT) officially announced that South Korea will take part in GPAI as one of the 15 founding members that include Canada, France, Japan, and the United States.
Professor Oh has been appointed as a new member of the Responsible AI Committee, one of the four committees that GPAI established along with the Data Governance Committee, Future of Work Committee, and Innovation and Commercialization Committee.
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2020.06.22 View 9409 -
Research on the Million Follower Fallacy Receives the Test of Time Award
Professor Meeyoung Cha’s research investigating the correlation between the number of followers on social media and its influence was re-highlighted after 10 years of publication of the paper.
Saying that her research is still as relevant today as the day it was published 10 years ago, the Association for the Advancement of Artificial Intelligence (AAAI) presented Professor Cha from the School of Computing with the Test of Time Award during the 14th International Conference on Web and Social Media (ICWSM) held online June 8 through 11.
In her 2010 paper titled ‘Measuring User Influence in Twitter: The Million Follower Fallacy,’ Professor Cha proved that number of followers does not match the influential power. She investigated the data including 54,981,152 user accounts, 1,963,263,821 social links, and 1,755,925,520 Tweets, collected with 50 servers.
The research compares and illustrates the limitations of various methods used to measure the influence a user has on a social networking platform. These results provided new insights and interpretations to the influencer selection algorithm used to maximize the advertizing impact on big social networking platforms.
The research also looked at how long an influential user was active for, and whether the user could freely cross the borders between fields and be influential on different topics as well. By analyzing cases of who becomes an influencer when new events occur, it was shown that a person could quickly become an influencer using several key tactics, unlike what was previously claimed by the ‘accidental influential theory’.
Professor Cha explained, “At the time, data from social networking platforms did not receive much attention in computer science, but I remember those all-nighters I pulled to work on this project, fascinated by the fact that internet data could be used to solve difficult social science problems. I feel so grateful that my research has been endeared for such a long time.”
Professor Cha received both her undergraduate and graduate degrees from KAIST, and conducted this research during her postdoctoral course at the Max Planck Institute in Germany. She now also serves as a chief investigator of a data science group at the Institute for Basic Science (IBS).
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2020.06.22 View 8202 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 13827 -
Professor Jong Chul Ye Appointed as Distinguished Lecturer of IEEE EMBS
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was appointed as a distinguished lecturer by the International Association of Electrical and Electronic Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS). Professor Ye was invited to deliver a lecture on his leading research on artificial intelligence (AI) technology in medical video restoration. He will serve a term of two years beginning in 2020.
IEEE EMBS's distinguished lecturer program is designed to educate researchers around the world on the latest trends and technology in biomedical engineering. Sponsored by IEEE, its members can attend lectures on the distinguished professor's research subject.
Professor Ye said, "We are at a time where the importance of AI in medical imaging is increasing.” He added, “I am proud to be appointed as a distinguished lecturer of the IEEE EMBS in recognition of my contributions to this field.”
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2020.02.27 View 12750 -
Professor Minsoo Rhu Recognized as Facebook Research Scholar
Professor Minsoo Rhu from the School of Electrical Engineering was selected as the recipient of the Systems for Machine Learning Research Awards presented by Facebook.
Facebook launched the award last year with the goal of funding impactful solutions in the areas of developer tookits, compilers and code generation, system architecture, memory technologies, and machine learning accelerator support.
A total of 167 scholars from 100 universities representing 26 countries submitted research proposals, and Facebook selected final 10 scholars. Professor Rhu made the list with his research topic ‘A Near-Memory Processing Architecture for Training Recommendation Systems.’ He will receive 5,000 USD in research funds at the award ceremony which will take place during this year’s AI Systems Faculty Summit at the Facebook headquarters in Menlo Park, California.
Professor Rhu’s submission was based on research on ‘Memory-Centric Deep Learning System Architecture’ that he carried out for three years under the auspices of Samsung Science and Technology Foundation from 2017. It was an academic-industrial cooperation research project in which leading domestic companies like Samsung Electronics and SK Hynix collaborated to make a foray into the global memory-centric smart system semiconductor market.
Professor Rhu who joined KAIST in 2018 has led various systems research projects to accelerate the AI computing technology while working at NVIDIA headquarters from 2014.
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2020.02.21 View 11411 -
New Insights into How the Human Brain Solves Complex Decision-Making Problems
A new study on meta reinforcement learning algorithms helps us understand how the human brain learns to adapt to complexity and uncertainty when learning and making decisions. A research team, led by Professor Sang Wan Lee at KAIST jointly with John O’Doherty at Caltech, succeeded in discovering both a computational and neural mechanism for human meta reinforcement learning, opening up the possibility of porting key elements of human intelligence into artificial intelligence algorithms. This study provides a glimpse into how it might ultimately use computational models to reverse engineer human reinforcement learning.
This work was published on Dec 16, 2019 in the journal Nature Communications. The title of the paper is “Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning.”
Human reinforcement learning is an inherently complex and dynamic process, involving goal setting, strategy choice, action selection, strategy modification, cognitive resource allocation etc. This a very challenging problem for humans to solve owing to the rapidly changing and multifaced environment in which humans have to operate. To make matters worse, humans often need to often rapidly make important decisions even before getting the opportunity to collect a lot of information, unlike the case when using deep learning methods to model learning and decision-making in artificial intelligence applications.
In order to solve this problem, the research team used a technique called 'reinforcement learning theory-based experiment design' to optimize the three variables of the two-stage Markov decision task - goal, task complexity, and task uncertainty. This experimental design technique allowed the team not only to control confounding factors, but also to create a situation similar to that which occurs in actual human problem solving.
Secondly, the team used a technique called ‘model-based neuroimaging analysis.’ Based on the acquired behavior and fMRI data, more than 100 different types of meta reinforcement learning algorithms were pitted against each other to find a computational model that can explain both behavioral and neural data. Thirdly, for the sake of a more rigorous verification, the team applied an analytical method called ‘parameter recovery analysis,’ which involves high-precision behavioral profiling of both human subjects and computational models.
In this way, the team was able to accurately identify a computational model of meta reinforcement learning, ensuring not only that the model’s apparent behavior is similar to that of humans, but also that the model solves the problem in the same way as humans do.
The team found that people tended to increase planning-based reinforcement learning (called model-based control), in response to increasing task complexity. However, they resorted to a simpler, more resource efficient strategy called model-free control, when both uncertainty and task complexity were high. This suggests that both the task uncertainty and the task complexity interact during the meta control of reinforcement learning. Computational fMRI analyses revealed that task complexity interacts with neural representations of the reliability of the learning strategies in the inferior prefrontal cortex.
These findings significantly advance understanding of the nature of the computations being implemented in the inferior prefrontal cortex during meta reinforcement learning as well as providing insight into the more general question of how the brain resolves uncertainty and complexity in a dynamically changing environment. Identifying the key computational variables that drive prefrontal meta reinforcement learning, can also inform understanding of how this process might be vulnerable to break down in certain psychiatric disorders such as depression and OCD. Furthermore, gaining a computational understanding of how this process can sometimes lead to increased model-free control, can provide insights into how under some situations task performance might break down under conditions of high cognitive load.
Professor Lee said, “This study will be of enormous interest to researchers in both the artificial intelligence and human/computer interaction fields since this holds significant potential for applying core insights gleaned into how human intelligence works with AI algorithms.”
This work was funded by the National Institute on Drug Abuse, the National Research Foundation of Korea, the Ministry of Science and ICT, Samsung Research Funding Center of Samsung Electronics.
Figure 1 (modified from the figures of the original paper doi:10.1038/s41467-019-13632-1). Computations implemented in the inferior prefrontal cortex during meta reinforcement learning. (A) Computational model of human prefrontal meta reinforcement learning (left) and the brain areas whose neural activity patterns are explained by the latent variables of the model. (B) Examples of behavioral profiles. Shown on the left is choice bias for different goal types and on the right is choice optimality for task complexity and uncertainty. (C) Parameter recoverability analysis. Compared are the effect of task uncertainty (left) and task complexity (right) on choice optimality.
-Profile
Professor Sang Wan Lee
sangwan@kaist.ac.kr
Department of Bio and Brain Engineering
Director, KAIST Center for Neuroscience-inspired AI
KAIST Institute for Artificial Intelligence (http://aibrain.kaist.ac.kr)
KAIST Institute for Health, Science, and Technology
KAIST (https://www.kaist.ac.kr)
2020.01.31 View 6475 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 11634 -
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject.
Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum.
Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
2019.12.04 View 16329