Artificial+Intelligence
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KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master's and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi >
- Professor Shinhyun Choi and Professor Young-Gyu Yoon’s Joint Research Team from the School of Electrical Engineering developed a computing chip that can learn, correct errors, and process AI tasks
- Equipping a computing chip with high-reliability memristor devices with self-error correction functions for real-time learning and image processing
Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.
KAIST (President Kwang Hyung Lee) announced on the 17th of January that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.
< Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). >
What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.
This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team's main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.
The research team has developed the world's first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.
< Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). >
At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells.
*Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past.
The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.
This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient.
“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”
The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master's and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025.
*Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array ( https://doi.org/10.1038/s41928-024-01318-6 )
This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project and PIM AI Semiconductor Core Technology Development Project of the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the Institute of Information & communications Technology Planning & Evaluation.
2025.01.17 View 1811 -
KAIST Wins CES 2025 Innovation Award, Showcasing Innovative Technologies
KAIST will showcase innovative technologies at the world’s largest technology fair, the Consumer Electronics Show (CES 2025). In addition, KAIST startups VIRNECT Inc., Standard Energy Inc., A2US Inc., and Panmnesia, Inc. won the 2025 CES Innovation Awards.
< Image 1. 3D-Graphical Profile of CES 2025 KAIST Exhibition Booth >
KAIST (President Kwang-Hyung Lee) announced on the 31st that it will operate a 140㎡ standalone booth at CES Eureka Park, which will be held in Las Vegas, USA from January 7th to 10th next year, to showcase KAIST's innovative technologies to global companies and investors.
KAIST startups VIRNECT, Standard Energy, A2US, and Panmnesia, Inc. won the 2025 CES Innovation Awards. ▴VIRNECT won the Innovation Award in the ‘Industrial Equipment and Machinery’ category for ‘VisionX’, an AI-based smart glass for industrial sites; ▴Standard Energy Co., Ltd. won the Innovation Award in the ‘Smart City’ category for developing the world’s first vanadium-ion battery; ▴A2US won the Innovation Award in the ‘Environment & Energy’ category for its portable air purifier that eliminates bacteria, odors, and fine dust in the air with just water droplets; ▴Panmnesia, Inc. won the Innovation Award in the ‘Computer Peripherals and Accessories’ category for its ‘CXL-based GPU Memory Expansion Kit’ that can drastically reduce the cost of building AI infrastructure.
< Image 2. (From left on the top row) VIRNECT, Standard Energy, (From left on the bottom row) A2US, Panmnesia, Inc. >
This exhibition will feature 15 startups that are standing out in cutting-edge technologies such as artificial intelligence (AI), robotics, mobility, and sustainability. In particular, AI-based deep tech startups in various industries such as logistics, architecture, and medicine will take up half of the total, showcasing the companies’ innovative AI technologies.
Polyphenol Factory Co.,Ltd introduces ‘Grabity’, a hair loss shampoo launched domestically, which applies the patented ingredient ‘LiftMax 308™’ that forms an instantaneous protective layer on the hair during the shampooing process. A real-time demonstration will be held at this exhibition hall so that visitors can experience the effects of the ingredient directly, and plans to enter the global market starting with the launch on Amazon in the US in January 2025.
VIRNECT will present ‘VisionX’, a prototype that won the Innovation Award this time. The product provides a chatbot AI through an AI voice interface, and has a function that allows users to check the status of the equipment in real time through conversations with the AI and receive troubleshooting guidance through voice conversations, so users can experience it directly at the KAIST Hall.
‘Standard Energy’ plans to exhibit ‘Energy Tile’, an indoor ESS that utilizes the world’s first vanadium ion battery (hereinafter referred to as VIB). VIB is absolutely safe from fire and has high installation flexibility, so it can be applied to smart cities and AI data centers.
‘A2US’ is the only company in the world that has hydroxyl radical water production technology, and won the Innovation Award for its first product, an air purifier. In the future, it is expected to be widely commercialized in air and water purification, smart farms, food tech, and semiconductor cleaning using safe and environmentally friendly hydroxyl radical water.
Panmnesia, Inc. won the CES Innovation Award for its GPU memory expansion solution equipped with its CXL 3.1 IP. By connecting a memory expansion device using Panmnesia’s CXL IP, the GPU’s memory capacity can be expanded to the terabyte level. Following the Innovation Award for ‘CXL-equipped AI Accelerator’ at CES 2024 last year, it is the only company to have won the Innovation Award for its AI-oriented CXL solution for two consecutive years.
In addition, technologies from a total of 15 companies will be introduced, including ▴Omelet ▴NEXTWAVE ▴Planby Technologies ▴Cosmo Bee ▴ImpactAI ▴Roen Surgical ▴DIDEN Roboticss ▴Autopedia ▴OAQ ▴HydroXpand ▴BOOKEND ▴Sterri.
On the central stage of the KAIST Hall, KAIST students selected as CES Student Supporters will conduct interviews with participating companies and promote the companies' innovative technologies and solutions. On the 8th, from 5 PM to 7 PM, a KAIST NIGHT event will be held where pre-invited investors and participating companies can network.
Keon Jae Lee, the head of the Institute of Technology Value Creation, said, “Through CES 2025, we will showcase innovative technologies and solutions from startups based on KAIST’s deep science and deep tech, and lead commercialization in cutting-edge technology fields such as AI, robotics, mobility, and environment/energy. KAIST plans to further promote technology commercialization by supporting the growth and marketing of innovative startups through the Institute of Technology Value Creation and by strengthening global networks and expanding cooperation opportunities.”
2024.12.31 View 1382 -
KAIST Proposes a New Way to Circumvent a Long-time Frustration in Neural Computing
The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables much faster and more accurate learning when exposed to actual data by pre-learning random information in a brain-mimicking artificial neural network, and is expected to be a breakthrough in the development of brain-based artificial intelligence and neuromorphic computing technology in the future.
KAIST (President Kwang-Hyung Lee) announced on the 16th of December that Professor Se-Bum Paik 's research team in the Department of Brain Cognitive Sciences solved the weight transport problem*, a long-standing challenge in neural network learning, and through this, explained the principles that enable resource-efficient learning in biological brain neural networks.
*Weight transport problem: This is the biggest obstacle to the development of artificial intelligence that mimics the biological brain. It is the fundamental reason why large-scale memory and computational work are required in the learning of general artificial neural networks, unlike biological brains.
Over the past several decades, the development of artificial intelligence has been based on error backpropagation learning proposed by Geoffery Hinton, who won the Nobel Prize in Physics this year. However, error backpropagation learning was thought to be impossible in biological brains because it requires the unrealistic assumption that individual neurons must know all the connected information across multiple layers in order to calculate the error signal for learning.
< Figure 1. Illustration depicting the method of random noise training and its effects >
This difficult problem, called the weight transport problem, was raised by Francis Crick, who won the Nobel Prize in Physiology or Medicine for the discovery of the structure of DNA, after the error backpropagation learning was proposed by Hinton in 1986. Since then, it has been considered the reason why the operating principles of natural neural networks and artificial neural networks will forever be fundamentally different.
At the borderline of artificial intelligence and neuroscience, researchers including Hinton have continued to attempt to create biologically plausible models that can implement the learning principles of the brain by solving the weight transport problem.
In 2016, a joint research team from Oxford University and DeepMind in the UK first proposed the concept of error backpropagation learning being possible without weight transport, drawing attention from the academic world. However, biologically plausible error backpropagation learning without weight transport was inefficient, with slow learning speeds and low accuracy, making it difficult to apply in reality.
KAIST research team noted that the biological brain begins learning through internal spontaneous random neural activity even before experiencing external sensory experiences. To mimic this, the research team pre-trained a biologically plausible neural network without weight transport with meaningless random information (random noise).
As a result, they showed that the symmetry of the forward and backward neural cell connections of the neural network, which is an essential condition for error backpropagation learning, can be created. In other words, learning without weight transport is possible through random pre-training.
< Figure 2. Illustration depicting the meta-learning effect of random noise training >
The research team revealed that learning random information before learning actual data has the property of meta-learning, which is ‘learning how to learn.’ It was shown that neural networks that pre-learned random noise perform much faster and more accurate learning when exposed to actual data, and can achieve high learning efficiency without weight transport.
< Figure 3. Illustration depicting research on understanding the brain's operating principles through artificial neural networks >
Professor Se-Bum Paik said, “It breaks the conventional understanding of existing machine learning that only data learning is important, and provides a new perspective that focuses on the neuroscience principles of creating appropriate conditions before learning,” and added, “It is significant in that it solves important problems in artificial neural network learning through clues from developmental neuroscience, and at the same time provides insight into the brain’s learning principles through artificial neural network models.”
This study, in which Jeonghwan Cheon, a Master’s candidate of KAIST Department of Brain and Cognitive Sciences participated as the first author and Professor Sang Wan Lee of the same department as a co-author, was presented at the 38th Neural Information Processing Systems (NeurIPS), the world's top artificial intelligence conference, on December 14th in Vancouver, Canada. (Paper title: Pretraining with random noise for fast and robust learning without weight transport)
This study was conducted with the support of the National Research Foundation of Korea's Basic Research Program in Science and Engineering, the Information and Communications Technology Planning and Evaluation Institute's Talent Development Program, and the KAIST Singularity Professor Program.
2024.12.16 View 2934 -
KAIST Awarded Presidential Commendation for Contributions in Software Industry
- At the “25th Software Industry Day” celebration held in the afternoon on Monday, December 2nd, 2024 at Yangjae L Tower in Seoul
- KAIST was awarded the “Presidential Commendation” for its contributions for the advancement of the Software Industry in the Group Category
- Korea’s first AI master’s and doctoral degree program opened at KAIST Kim Jaechul Graduate School of AI
- Focus on training non-major developers through SW Officer Training Academy "Jungle", Machine Learning Engineer Bootcamp, etc., talents who can integrate development and collaboration, and advanced talents in the latest AI technologies.
- Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI received Prime Minister’s Commendation for his contributions for the advancement of the software industry.
< Photo 1. Professor Kyung-soo Kim, the Senior Vice President for Planning and Budget (second from the left) and the Manager of Planning Team, Mr. Sunghoon Jung, stand at the stage after receiving the Presidential Commendation as KAIST was selected as one of the groups that contributed to the advancement of the software industry at the "25th Software Industry Day" celebration. >
“KAIST has been leading the way in achieving the grand goal of fostering 1 million AI talents in Korea by services that pan from providing various educational opportunities, from developing the capabilities of experts with no computer science specialty to fostering advanced professionals. I would like to thank all members of KAIST community who worked hard to achieve the great feat of receiving the Presidential Commendations.” (KAIST President Kwang Hyung Lee)
KAIST (President Kwang Hyung Lee) announced on December 3rd that it was selected as a group that contributed to the advancement of the software industry at the “2024 Software Industry Day” celebration held at the Yangjae El Tower in Seoul on the 2nd of December and received a presidential commendation.
The “Software Industry Day”, hosted by the Ministry of Science and ICT and organized by the National IT Industry Promotion Agency and the Korea Software Industry Association, is an event designed to promote the status of software industry workers in Korea and to honor their achievements.
Every year, those who have made significant contributions to policy development, human resource development, and export growth for industry revitalization are selected and awarded the ‘Software Industry Development Contribution Award.’
KAIST was recognized for its contribution to developing a demand-based, industrial field-centric curriculum and fostering non-major developers and convergence talents with the goal of expanding software value and fostering excellent human resources.
< Photo 2. Senior Vice President for Planning and Budget Kyung-soo Kim receiving the commendation as the representative of KAIST >
Specifically, it first opened the SW Officer Training Academy "Jungle" to foster convergent program developers equipped with the abilities to handle both the computer coding and human interactions for collaborations. This is a non-degree program that provides intensive study and assignments for 5 months for graduates and intellectuals without prior knowledge of computer science.
KAIST Kim Jaechul Graduate School of AI opened and operated Korea’s first master's and doctoral degree program in the field of artificial intelligence. In addition, it planned a “Machine Learning Engineers’ Boot Camp” and conducted lectures and practical training for a total of 16 weeks on the latest AI technologies such as deep learning basics and large language models. It aims to strengthen the practical capabilities of start-up companies while lowering the threshold for companies to introduce AI technology.
Also, KAIST was selected to participate in the 1st and 2nd stages of the Software-centered University Project and has been taking part in the project since 2016. Through this, it was highly evaluated for promoting curriculum based on latest technology, an autonomous system where students directly select integrated education, and expansion of internships.
< Photo 3. Professor Minjoon Seo of Kim Jaechul Graduate School of AI, who received the Prime Minister's Commendation for his contribution to the advancement of the software industry on the same day >
At the awards ceremony that day, Professor Minjoon Seo of KAIST Kim Jaechul Graduate School of AI also received the Prime Minister's Commendation for his contribution to the advancement of the software industry. Professor Seo was recognized for his leading research achievements in the fields of AI and natural language processing by publishing 28 papers in top international AI conferences over the past four years.
At the same time, he was noted for his contributions to enhancing the originality and innovation of language model research, such as △knowledge encoding, △knowledge access and utilization, and △high-dimensional inference performance, and for demonstrating leadership in the international academic community.
President Kwang Hyung Lee of KAIST stated, “Our university will continue to do its best to foster software talents with global competitiveness through continuous development of cutting-edge curriculum and innovative degree systems.”
2024.12.03 View 1939 -
KAIST’s RAIBO2 becomes the World’s First Robo-dog to Successfully Complete a Full-course Marathon
KAIST's quadrupedal walking robot "RAIBO", which can run seamlessly on sandy beaches, has now evolved into "RAIBO2"and achieved the groundbreaking milestone by becomeing the world's first quadrupedal robot to successfully complete a full-course marathon in an official event.
< Photo 1. A group photo of RAIBO2 and the team after completing the full-course marathon >
KAIST (President Kwang Hyung Lee) announced on the 17th of November that Professor Je Min Hwangbo's research team of the Department of Mechanical Engineering participated in the 22nd Sangju Dried-Persimmon Marathon and completed the full-course race (42.195 km) with a time of 4 hours 19 minutes and 52 seconds.
< Photo 2. RAIBO2 after completing the full-course marathon with its official record presented on the photo wall >
The Sangju Dried Persimmon Marathon is known for its challenging course featuring two 50 m elevation climbs, each at the 14 km and 28 km marks, making it defficult for amateur runners. This made it an especially demanding challenge for the walking robot, as unexpected losses in efficiency could occur.
< Photo 3. RAIBO2 with the completion medal around its neck >
To prepare RAIBO2, Professor Hwangbo's team developed a walking controller using reinforcement learning algorithms within their proprietary simulation environment "RaiSim". This simulator allowed the team to simlate diverse terrains such as slopes, stairs, and icy roads to ensure stable walking performance.
In particular, RAIBO2's high torque transparency joint mechanism enable the robot to efficiently harvest energy on the downhill slopes to regain some of the energy used in climbing up steep hill.
In addition, the stability of the robot was greatly improved through the collaboration with RAION ROBOTICS Inc., a company founded by the researchers from Professor Hwangbo’s lab.
< Figure 1. Conceptual diagram of power flow employed by the quadrupedal robot >
< Figure 2. The process of leg posture change of RAIBO2 walking at the most efficient walking speed of 3 m/s. By reducing the ground contact speed of the feet, the collision energy loss was reduced, and by minimizing the slipperiness of the foot upon contact, the body's kinetic energy was maintained towards the direction of the movement. >
Due to the nature of walking, pedal robots must employ highly complex systems that can withstand periodic vibrations from the frequent impacts that occur upon contact with the ground surface. Immediately after development, high efficiency was already recorded in short-distance experiments in the laboratory at the beginning of the year, but the manufacturing technology of RAION ROBOTICS significantly bolstered RAIBO's performance in running safely for a prolonged time of more than 4 hours among random pack of people in an actual marathon.
Compared to previous studies on improving walking efficiency, where external parts or software could not be changed and only limited improvements were made in some areas, Professor Hwangbo’s research team cited the fact that they were able to comprehensively solve problems by developing all steps and parts in-house, including mechanism design, electrical design, software, and artificial intelligence, as a key factor in improving efficiency.
Following the development of RAIBO1, the research team developed RAIBO2 and optimized all aspects of the robot. In particular, the team integrated the motor driver circuitry directly into the robot to minimize actuator losses and increase the control bandwidth, greatly improving walking efficiency and stability.
< Photo 4. RAIBO2 running the full-course marathon along human participants >
Choongin Lee, a Ph.D. Student that co-first author of the studies on RAIBO, said, “Through the marathon project, we demonstrated that RAIBO2 has the walking performance to stably execute services such as delivery and patrol in urban environments with many people and random objects,” and “In follow-up research, we will add autonomous navigation functions to RAIBO and strive to achieve the world’s best walking performance in mountainous and disaster environments.”
< Photo 5. RAIBO2 and co-first authors of the related research at the Ph.D. program of the Department of Mechanical Engineering at KAIST. (From left) Choongin Lee, Donghoon Youm, and Jeongsoo Park >
This research was conducted with the support of Samsung Electronics Future Technology Promotion Center and RAION ROBOTICS Inc.
2024.11.17 View 3852 -
KAIST Proposes AI Training Method that will Drastically Shorten Time for Complex Quantum Mechanical Calculations
- Professor Yong-Hoon Kim's team from the School of Electrical Engineering succeeded for the first time in accelerating quantum mechanical electronic structure calculations using a convolutional neural network (CNN) model
- Presenting an AI learning principle of quantum mechanical 3D chemical bonding information, the work is expected to accelerate the computer-assisted designing of next-generation materials and devices
The close relationship between AI and high-performance scientific computing can be seen in the fact that both the 2024 Nobel Prizes in Physics and Chemistry were awarded to scientists for their AI-related research contributions in their respective fields of study. KAIST researchers succeeded in dramatically reducing the computation time for highly sophisticated quantum mechanical computer simulations by predicting atomic-level chemical bonding information distributed in 3D space using a novel AI approach.
KAIST (President Kwang-Hyung Lee) announced on the 30th of October that Professor Yong-Hoon Kim's team from the School of Electrical Engineering developed a 3D computer vision artificial neural network-based computation methodology that bypasses the complex algorithms required for atomic-level quantum mechanical calculations traditionally performed using supercomputers to derive the properties of materials.
< Figure 1. Various methodologies are utilized in the simulation of materials and materials, such as quantum mechanical calculations at the nanometer (nm) level, classical mechanical force fields at the scale of tens to hundreds of nanometers, continuum dynamics calculations at the macroscopic scale, and calculations that mix simulations at different scales. These simulations are already playing a key role in a wide range of basic research and application development fields in combination with informatics techniques. Recently, there have been active efforts to introduce machine learning techniques to radically accelerate simulations, but research on introducing machine learning techniques to quantum mechanical electronic structure calculations, which form the basis of high-scale simulations, is still insufficient. >
The quantum mechanical density functional theory (DFT) calculations using supercomputers have become an essential and standard tool in a wide range of research and development fields, including advanced materials and drug design, as they allow fast and accurate prediction of material properties.
*Density functional theory (DFT): A representative theory of ab initio (first principles) calculations that calculate quantum mechanical properties from the atomic level.
However, practical DFT calculations require generating 3D electron density and solving quantum mechanical equations through a complex, iterative self-consistent field (SCF)* process that must be repeated tens to hundreds of times. This restricts its application to systems with only a few hundred to a few thousand atoms.
*Self-consistent field (SCF): A scientific computing method widely used to solve complex many-body problems that must be described by a number of interconnected simultaneous differential equations.
Professor Yong-Hoon Kim’s research team questioned whether recent advancements in AI techniques could be used to bypass the SCF process. As a result, they developed the DeepSCF model, which accelerates calculations by learning chemical bonding information distributed in a 3D space using neural network algorithms from the field of computer vision.
< Figure 2. The deepSCF methodology developed in this study provides a way to rapidly accelerate DFT calculations by avoiding the self-consistent field process (orange box) that had to be performed repeatedly in traditional quantum mechanical electronic structure calculations through artificial neural network techniques (green box). The self-consistent field process is a process of predicting the 3D electron density, constructing the corresponding potential, and then solving the quantum mechanical Cohn-Sham equations, repeating tens to hundreds of times. The core idea of the deepSCF methodology is that the residual electron density (δρ), which is the difference between the electron density (ρ) and the sum of the electron densities of the constituent atoms (ρ0), corresponds to chemical bonding information, so the self-consistent field process is replaced with a 3D convolutional neural network model. >
The research team focused on the fact that, according to density functional theory, electron density contains all quantum mechanical information of electrons, and that the residual electron density — the difference between the total electron density and the sum of the electron densities of the constituent atoms — contains chemical bonding information. They used this as the target for machine learning.
They then adopted a dataset of organic molecules with various chemical bonding characteristics, and applied random rotations and deformations to the atomic structures of these molecules to further enhance the model’s accuracy and generalization capabilities. Ultimately, the research team demonstrated the validity and efficiency of the DeepSCF methodology on large, complex systems.
< Figure 3. An example of applying the deepSCF methodology to a carbon nanotube-based DNA sequence analysis device model (top left). In addition to classical mechanical interatomic forces (bottom right), the residual electron density (top right) and quantum mechanical electronic structure properties such as the electronic density of states (DOS) (bottom left) containing information on chemical bonding are rapidly predicted with an accuracy corresponding to the standard DFT calculation results that perform the SCF process. >
Professor Yong-Hoon Kim, who supervised the research, explained that his team had found a way to map quantum mechanical chemical bonding information in a 3D space onto artificial neural networks. He noted, “Since quantum mechanical electron structure calculations underpin materials simulations across all scales, this research establishes a foundational principle for accelerating material calculations using artificial intelligence.”
Ryong-Gyu Lee, a PhD candidate in the School of Electrical Engineering, served as the first author of this research, which was published online on October 24 in Npj Computational Materials, a prestigious journal in the field of material computation. (Paper title: “Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints”)
This research was conducted with support from the KAIST High-Risk Research Program for Graduate Students and the National Research Foundation of Korea’s Mid-career Researcher Support Program.
2024.10.30 View 2319 -
A heated battle of science and sports, who is the winner of this year's KA-PO War?
< Photos from KAIST-POSTECH Science War (photographed by Student Junhyeok Park of KAIST Freshman Course) >
The future leaders of science at KAIST and POSTECH (President Seong Keun Kim) held their annual science and sporting event at POSTECH for two days from September 20th to 21st. The 'KAIST-POSTECH Science War (hereafter KA-PO War)' is a festival consisting of science and sports games and various side events to promote exchange and cooperation between the two universities. It is also known by the nickname 'Science War'.
KA-PO War consists of △Science Games △e-Sports △Athletics, and the two universities compete in a total of 7 events including hacking competitions, artificial intelligence programming (AI), science quizzes, League of Legends (LOL), baseball, basketball, and soccer. In particular, the 9-hour ‘hacking competition’ and the ‘AI programming’ competition, which pits the AI design strategies of the two universities against each other, are famous for its competitions that are not easily seen at other universities.
The future science leaders of KAIST and POSTECH competed with their brains and physical strength even in the rain, and in the competition where the university that wins more than 4 out of 7 events wins, KAIST won with a score of 6 to 1 after fierce matches.
In addition, for this KAIST competition, Byeong-cheol Kim, the CEO of POSTECH Holdings and an alumnus of the Department of Industrial Management at POSTECH, donated 10 million won for the preparation of this event.
< Photos from KA-PO War site (photographed by Student Junhyeok Park of KAIST Freshman Course) >
KA-PO War Director Henry Kwon (KAIST Department of Electrical and Electronic Engineering) said, “I would like to thank the planning team and supporters who worked hard to make it a successful event. This year’s KA-PO War shined even brighter because of the students from both universities who cheered passionately and played games despite the hot weather and rain. I hope this will be an opportunity to further strengthen the bond and sense of belonging among engineering students.”
KA-PO War Preparatory Committee Chairman Sa-joon Hong (POSTECH Department of Physics) said, “As if to manifest this year’s motto, ‘BLAST,’ the intense heat swept through the competition, and regardless of the outcome, the students from both universities took away unforgettable and precious memories.”
As a kind of student festival jointly held between the two universities, which have been held annually since 2002, KAIST-POSTECH Science Wars is held under a different name each year depending on the venue. This year, it was held at POSTECH, thus called ‘KA-PO War.’
2024.09.19 View 1399 -
KAIST and NYU set out to Install Korea's First Joint Degree Program in AI
< (From left) New York University President Linda Mills and President Kwang-Hyung Lee >
KAIST (President Kwang-Hyung Lee) and New York University (NYU, President Linda G. Mills) signed an MOU in the afternoon of the 9th to introduce a graduate program for a joint degree in the field of artificial intelligence.
This agreement was promoted based on the consensus between the two universities that strengthening capabilities in the field of AI and fostering global talent are essential elements that can lead to great development in the entire future society beyond simple technical education.
The two universities have been operating joint research groups in various industrial fields related to AI and convergence with it, and based on this agreement, they plan to establish an operating committee within this year to design a joint degree program for graduate school courses related to artificial intelligence.
A KAIST official said, “If the joint degree program in AI is implemented, it is expected to be an unprecedented innovative experiment in which KAIST and NYU join forces to create ‘a single AI degree.’
The committee will consist of an equal number of faculty members from both schools, and will discuss the overall strategic planning of the joint degree program, including ▴curriculum structure and course composition ▴course completion roadmap ▴calculation of faculty and student population ▴calculation of budget size ▴calculation of operating facility size and details ▴legal matters regarding certification. In addition, the development of a new logo symbolizing the joint degree of KAIST and NYU in AI will also be carried out.
The two schools expect that the joint degree program being promoted this time will contribute to advancing education and research capabilities in the field of artificial intelligence, jointly discovering and fostering talent in related fields that are currently lacking worldwide, and will become an exemplary case of global education and research cooperation.
The faculty members of both schools, who possess excellent capabilities, will provide innovative and creative education in the field of artificial intelligence. Students will receive support to gain top-level research experience by participating in various international joint research projects promoted by the faculty members of both schools. Through this, the core of this joint degree program promoted by both schools is to continuously cultivate excellent human resources who will lead the future global society.
Since signing a cooperation agreement for the establishment of a joint campus in June 2022, KAIST and NYU have been promoting campus sharing, joint research, and joint bachelor's degree programs. Including this, they are developing an innovative joint campus model and establishing an active international cooperation model.
In particular, the exchange student system for undergraduate students will be implemented starting from the second semester of the 2023 academic year. 30 students from KAIST and 11 students from NYU were selected through a competitive selection process and are participating. In the case of KAIST students, if they complete one of the six minor programs at NYU, they will receive a degree that states the completion of the minor upon graduation.
Based on the performance of the undergraduate exchange student operation, the two schools have also agreed to introduce a dual degree system for master's and doctoral students, and specific procedures are currently in progress.
In addition, from 2023 to the present, we are carrying out future joint research projects in 15 fields that are integrated with AI, and we plan to begin international joint research in 10 fields centered on AI and bio from the fourth quarter of this year.
NYU President Linda Mills said, “AI technology can play a significant role in addressing various social challenges such as climate change, health care, and education inequality,” and added that, “The global talent cultivated through our two schools will also go on to make innovative contributions to solving these social problems.”
Kwang-Hyung Lee, the president of KAIST, said, “In the era of competition for global hegemony in technology, the development of AI technology is an essential element for countries and companies to secure competitiveness,” and “Through long-term cooperation with NYU, we will take the lead in fostering world-class, advanced talents who can innovatively apply and develop AI in various fields.”
The signing ceremony held at the Four Seasons Hotel in Seoul was attended by KAIST officials including President Kwang-Hyung Lee, Hyun Deok Yeo, the Director of G-School, NYU officials including President Linda Mills, Kyunghyun Cho, a Professor of Computer Science and Data Science, and Dr. Karin Pavese, the Executive Director of NYU-KAIST Innovation Research Institute, amid attendance by other key figures from the industries situated in Korea. (End)
2024.09.10 View 3048 -
KAIST presents strategies for Holotomography in advanced bio research
Measuring and analyzing three-dimensional (3D) images of live cells and tissues is considered crucial in advanced fields of biology and medicine. Organoids, which are 3D structures that mimic organs, are particular examples that significantly benefits 3D live imaging. Organoids provide effective alternatives to animal testing in the drug development processes, and can rapidly determine personalized medicine. On the other hand, active researches are ongoing to utilize organoids for organ replacement.
< Figure 1. Schematic illustration of holotomography compared to X-ray CT. Similar to CT, they share the commonality of measuring the optical properties of an unlabeled specimen in three dimensions. Instead of X-rays, holotomography irradiates light in the visible range, and provides refractive index measurements of transparent specimens rather than absorptivity. While CT obtains three-dimensional information only through mechanical rotation of the irradiating light, holotomography can replace this by applying wavefront control technology in the visible range. >
Organelle-level observation of 3D biological specimens such as organoids and stem cell colonies without staining or preprocessing holds significant implications for both innovating basic research and bioindustrial applications related to regenerative medicine and bioindustrial applications.
Holotomography (HT) is a 3D optical microscopy that implements 3D reconstruction analogous to that of X-ray computed tomography (CT). Although HT and CT share a similar theoretical background, HT facilitates high-resolution examination inside cells and tissues, instead of the human body. HT obtains 3D images of cells and tissues at the organelle level without chemical or genetic labeling, thus overcomes various challenges of existing methods in bio research and industry. Its potential is highlighted in research fields where sample physiology must not be disrupted, such as regenerative medicine, personalized medicine, and infertility treatment.
< Figure 2. Label-free 3D imaging of diverse live cells. Time-lapse image of Hep3B cells illustrating subcellular morphology changes upon H2O2 treatment, followed by cellular recovery after returning to the regular cell culture medium. >
This paper introduces the advantages and broad applicability of HT to biomedical researchers, while presenting an overview of principles and future technical challenges to optical researchers. It showcases various cases of applying HT in studies such as 3D biology, regenerative medicine, and cancer research, as well as suggesting future optical development. Also, it categorizes HT based on the light source, to describe the principles, limitations, and improvements of each category in detail. Particularly, the paper addresses strategies for deepening cell and organoid studies by introducing artificial intelligence (AI) to HT.
Due to its potential to drive advanced bioindustry, HT is attracting interest and investment from universities and corporates worldwide. The KAIST research team has been leading this international field by developing core technologies and carrying out key application researches throughout the last decade.
< Figure 3. Various types of cells and organelles that make up the imaging barrier of a living intestinal organoid can be observed using holotomography. >
This paper, co-authored by Dr. Geon Kim from KAIST Research Center for Natural Sciences, Professor Ki-Jun Yoon's team from the Department of Biological Sciences, Director Bon-Kyoung Koo's team from the Institute for Basic Science (IBS) Center for Genome Engineering, and Dr. Seongsoo Lee's team from the Korea Basic Science Institute (KBSI), was published in 'Nature Reviews Methods Primers' on the 25th of July. This research was supported by the Leader Grant and Basic Science Research Program of the National Research Foundation, the Hologram Core Technology Development Grant of the Ministry of Science and ICT, the Nano and Material Technology Development Project, and the Health and Medical R&D Project of the Ministry of Health and Welfare.
2024.07.30 View 2566 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 3528 -
KAIST President Kwang-Hyung Lee receives honorary doctorate from Université de Montréal
KAIST (President Kwang-Hyung Lee) announced on June 16th that President Kwang-Hyung Lee received an honorary doctorate on the 15th, local time, from the Université de Montréal in Canada, one of the largest French-speaking universities in North America.
< Image. (from left) Mr. Pierre Lassonde, Chairman of the Board of Polytechnique Montréal, President Maud Cohen of Polytechnique Montréal, President Kwang-Hyung Lee of KAIST, Chancellor Frantz Saintellemy of Université de Montréal and Mr. Alexandre Chabot, Secretary General of Université de Montéal. >
President Lee was selected as the recipient of the honorary doctorate from the Université de Montréal upon the recommendation of Polytechnique Montréal in recognition of his contributions in pioneering the multidisciplinary approach to integrate a number of fields studies including computer science, biology, and nanotechnology.
Polytechnique Montréal is a university in affiliation with the University of Montréal and is one of the largest engineering education and research institutions of Canada. President Lee's honorary doctorate was awarded at the Convocation Ceremony of Polytechnique Montréal held for the Class of 2024. On this day, Mr. Serge Gendron, a businessman, a philanthropist and an alum of Polytechnique Montréal, also had the honor of receiving an honorary doctorate along with President Lee.
President Kwang-Hyung Lee is internationally recognized for his contributions in various fields, including engineering education, multidisciplinary research, strategy establishment, and future prospects. President Lee is also well known to have had significant influence on the first-generation venture entrepreneurs, a large portion of which are from KAIST, who have now grown into full-fledged entrepreneurs. For these activities, President Lee received numerous decorations and commendations within Korea, including the National Order of Civil Merit - Camellia Medal, and in 2003, he received the ‘Légion d’Honneur Chevalier’ from the French government as well.
Through his speech at the ceremony, KAIST President Kwang-Hyung Lee expressed his gratitude to the Université de Montréal and Polytechnique Montréal, while congratulating and encouraging the graduates who are poised to start anew as they part from the school. “Hold on to your dreams, try looking at the world from a different perspective, and enjoy the challenges without being afraid of failures.” With these three pieces of advice, President Lee cheered on the graduates saying, “The future belongs to those of you who challenge them.”
Maud Cohen, the President of Polytechnique Montréal, commented on President Kwang-Hyung Lee's honorary doctorate, that Polytechnique Montréal is proud to award an honorary doctorate to Mr. Lee for his exceptional career path, his holistic, multidisciplinary and undeniably forward-looking vision, which strongly echoes the values of Polytechnique Montréal, and for his involvement in and commitment to education, research and the future of the next generation.
* Established in 1873, Polytechnique Montréal is one of Canada’s largest engineering education and research universities, and is located on the Université de Montréal campus – North America’s largest Francophone university campus. Joshua Bengio, who won the Turing Award for establishing the foundations of deep learning, is gaining international recognition in artificial intelligence and other related fields at Polytechnique Montréal. Polytechnique Montréal chose KAIST as the first Korean university establish partnership with and has continued to build up close cooperative relationship since 1998.
* The Université de Montréal (UdeM) is a public university founded in 1878. It is located in Montréal, in the French-speaking province of Québec, Canada. It is one of Canada's five major universities, and the second largest in terms of student enrollment. The Université de Montréal is the largest in the French-speaking world in terms of both student enrollment and research. The Université de Montréal enjoys an excellent reputation as one of the best French-language post-secondary institutions. Its rector is Mr. Daniel Jutras.
2024.06.16 View 3912 -
KAIST appoints K-Pop Star “G-Dragon” Kwon Ji-yong as a visiting professor
KAIST (President Kwang Hyung Lee) announced on June 5th that its Department of Mechanical Engineering has appointed Kwon Ji-yong, a K-Pop star better known as G-Dragon, as a visiting professor. The appointment was officiated at the “Innovate Korea 2024” event held at the KAIST Sports Complex in the morning of the 5th.
This appointment was promoted to expand the global competitiveness of Korean culture by applying the latest scientific technology developed from KAIST to K-content and the cultural industry.
An official from Galaxy Corporation, Mr. Kwon’s agency, said, “Through this appointment, we expect to contribute to market innovation and to the global spread and growth of K-culture through research and development of ‘entertech’*.”
* Entertech: A compound word of ‘entertainment’ and ‘technology’ refering to an industry that creates new added value by combining ICT with intellectual property rights (IP) and contents, which are the core values of entertainment.
The newly appointed Visiting Professor Kwon Ji-yong is scheduled to give a special lecture on leadership to undergraduate and graduate students. The purpose is to share his experience and life as a world-class artist to provide KAIST students with vision and insight into the world, as well as the courage and inspiration to pioneer within their own fields of expertise.
In addition, cultural events are planned to be held to help facilitate communication with KAIST members and provide artistic experiences necessary to grow into creative and convergent talents.
Joint research that applies KAIST's various technologies to art and cultural contents will also be pursued. ‘(temporarily named) KAIST-Galaxy Corporation Entertech Research Center’ will be established within the Department of Mechanical Engineering, conducting ▴research on Digital Twin technology for Korean Wave artists, starting with G-Dragon himself ▴convergent research on science and technology encompassing artificial intelligence, robots, and metaverse and K-Culture and ▴research on the development of differentiated avatars of artists using the latest technologies such as volumetrics, motion capture, and haptics.
Galaxy Corporation produces highly talked about televised shows including 'Physical: 100 Season 2', which achieved the feat of ranking first in the Netflix’s ‘Global Top 10 Non-English TV Shows’ category, as well as 'Street Woman Fighter', '2 Days & 1 Night', 'Let's Play Soccer: Mungchyeoya Chanda' and 'Mr. Trot 2', and expanded its IP (intellectual property rights) to all aspects of entertainment, from broadcasting to the music industry by recruiting G-Dragon in December last year.
It is an 'entertech' company that is pioneering a new content market by combining IP with the latest digital technologies such as metaverse, avatar, and artificial intelligence (AI). Based on these achievements, it recently signed a partnership with a global investment bank (IB). Through global investment, the company is soon to be the first entertech startup to become a unicorn (corporate value of 1 trillion won).
Kwon Ji-yong said, “It is an honor to be a visiting professor at KAIST, where countless scientific geniuses come out of. I am hoping the best of science and technology experts and my expertise in the entertainment business will come together and a great synergy, a ‘big bang’ will be ensue as a result.
“More and more musicians are working with artificial intelligence and these cutting-edge technologies enable more diverse forms of creative work,” he continued. He also expressed his specific wish. “Through the AI avatar developed jointly by Galaxy Corporation and KAIST, I wish to communicate more closely with my fans around the world that I cannot meet so often otherwise.”
President Kwang-Hyung Lee said, “Considering that KAIST is a university that has always explored new things and pioneered unknown territories since its establishment, I believe that Kwon Ji-yong also shares KAIST's DNA in that he is a leader and pioneer who has attained world-class achievements in the world of culture and arts.”
President Lee continued expressing his expectations saying, “Kwon Ji-yong will not only contribute to taking KAIST’s science and technology to spread and grow K-culture on the global stage, through his activities but also in sharing his experience and spirit as the trend leader of the world, he will be an asset to the members of KAIST which aims to become a top-tier university.”
The period of Kwon Ji-yong’s professorship is two years from the 4th of this month to June of 2026. In addition, Professor Kwon Ji-yong has been appointed as KAIST’s global ambassador to help strengthen KAIST's international presence.
Professor Kwon Ji-yong, who began his career as G-Dragon, has established himself as an icon symbolizing Korean pop culture over 18 years as a member and the leader of the K-Pop idol group 'Big Bang', which debuted in 2006 and gained global popularity. While being recognized for his outstanding ability in all aspects of his music, from writing lyrics, composing, and producing to performing, he has also been active in fashion, becoming the first Asian man to be selected as a global ambassador for Chanel in 2016, spreading Korean music and fashion to the world.
In June, 2017, he released his 3rd solo album 'Kwon Ji Yong' on USB instead of the existing CD format, for the first time in Korea. He also attended CES, the world's largest home appliance and IT exhibition held in Las Vegas, USA in January of this year, extending his strides as a 'tech-tainer'.
2024.06.05 View 12108