Samsung Electronics today opened the Samsung AI Forum, at which it shared the latest research achievements in artificial intelligence (AI) and computer engineering (CE), contributing to enhancing the company’s next-generation semiconductor technology.

With over 1,000 attendees — including leading academics, industry experts, researchers and students — day 1 of the seventh iteration of the Samsung AI Forum took place at the Suwon Convention Center in Gyeonggi-do, Korea, under the theme of “large-scale AI for a better tomorrow.” A two-day forum, the first day was hosted by the Samsung Advanced Institute of Technology (SAIT), and day 2 will be hosted on November 8 by Samsung Research at the Samsung R&D campus in Seoul, Korea.

Kye Hyun Kyung, President and CEO of Samsung Electronics’ Device Solutions (DS) Division, said in his opening remarks, “The spotlight has recently shifted toward Generative AI technology, as it provides us the potential to unlock new solutions and address long-standing challenges. But the need for in-depth research on the safety, trustworthiness and sustainability of AI is increasing at the same time.” About the event, Kyung added, “We expect this forum — where top global experts have gathered — will be a platform to discuss ways to create a brighter future through AI and semiconductor technologies.”

Safe Direction for AI Research Suggested; Future of LLM-Based Semiconductors Shared

▲ SAIF 2023 keynote speakers: (from left) Yoshua Bengio, professor at University of Montreal, Kye Hyun Kyung President and CEO of Samsung Electronics, Jim Keller, CEO of Tenstorrent

Yoshua Bengio, expert in deep learning technology and professor at the University of Montreal, shared his latest research in a keynote presentation titled, “Towards a Safe AI Scientist System.” He introduced a safe AI machine learning algorithm that can prevent large language models (LLMs) from developing in directions not intended by developers.

Jim Keller, CEO of AI semiconductor startup Tenstorrent, introduced the open instruction set architecture (ISA) RISC-V during his session titled, “Own Your Silicon,” and emphasized that RISC-V will create new possibilities in next-generation AI through innovation in hardware structure design.

Overall, day 1 of Samsung AI Forum 2023 addressed two key topics: LLMs and the Transformation of AI for Industry, and Large-scale Computing for LLMs and Simulation. The topics covered AI and CE, respectively.

With SAIT serving as the company’s R&D hub and incubator for cutting-edge technologies, SAIT researchers shared their visions on how the future of semiconductor development and manufacturing will change by integrating AI into all areas of semiconductors, and explored the possibilities of future computing in semiconductor processing, including large simulation accelerated by machine learning.

Accolades Presented to Exceptional Researchers and Students

During the forum, Samsung also hosted a ceremony to announce the winners of the Samsung AI Researcher Award and the Samsung AI/CE Challenge. The intent of these accolades is to honor up-and-coming researchers, university students and graduates who are excelling domestically.

Samsung AI Researcher of the Year awards were presented to five AI researchers: Professor Connor Coley at Massachusetts Institute of Technology, Professor Jason Lee at Princeton University, Professor Emma Pierson at Cornell University, Professor Xiang Ren at University of Southern California, Professor Virginia Smith at Carnegie Mellon University.

Among the honorees Professor Lee is focusing on theoretical and applied research including deep learning, reinforcement learning and optimization. In particular, his work was highly praised for its contribution to the development of AI research around the world through the publication of several outstanding papers on optimization.

The honor of winning the Samsung AI/CE Challenge, with submissions from 1,481 students comprising 410 teams, went to 16 teams.

Ph.D. student Keondo Park from the Seoul National University Graduate School of Data Science, a member of the grand-prize winning team, said, “In the course of implementing our AI project directly, we were able to explore deeply about possible problems. The AI/CE challenge was a good opportunity to broaden our horizons on research.”

Furthermore, SAIT presented a poster of leading research papers as well as exhibitions of research projects in AI and CE. It also prepared networking programs for attendees to engage in the vital AI and CE ecosystems.

The official video for Samsung AI Forum 2023 will be available on the Samsung Electronics YouTube Channel (https://www.youtube.com/@Samsung) from November 16th.

Samsung AI Forum 2023, which is being hosted by the Samsung Advanced Institute of Technology (SAIT), will also focus on and highlight the direction of future research, with the presence of world-renowned AI scholars and industry experts. This seventh iteration of the Samsung AI Forum will be an in-person event, held under the theme of “large-scale AI for a better tomorrow.”

Kye Hyun Kyung, President and CEO of Samsung Electronics Device Solutions Division, will begin the forum with opening remarks, followed by keynotes from Yoshua Bengio, professor at the University of Montreal, and Jim Keller, CEO of AI semiconductor startup Tenstorrent.

Professor Satoshi Matsuoka of the Riken Institute of Computer Science in Japan, and Larry Zitnick, a research scientist from the Meta AI Research Lab will also be giving invited talks. In addition to these notable speakers, SAIT’s AI and CE research leaders — as well as leading academics from around the world — will share the status and vision of their research.

Samsung AI Forum 2023 will address two key topics: Large Language Models and Transformation of AI for Industry, and Large-scale Computing for Large Language Model and Simulation, which cover AI and CE, respectively.

During the forum, Samsung will also host a ceremony to announce the winners of the Samsung AI Researcher Award and the Samsung AI/CE Challenge. These accolades are intended to honor up-and-coming researchers, university students and graduates who are excelling domestically.

Furthermore, the forum will seek to vitalize the AI and CE research ecosystem by presenting posters of leading research papers and preparing networking programs for attendees.

“We believe AI- and CE-enhanced next generation semiconductor technology will play a pivotal role in improving the quality of life and SAIT has been working closely with academics and experts to seek Samsung’s new long-term growth drivers. We hope Samsung AI Forum will accelerate the expansion of the AI and CE research ecosystem around the world,” said Gyoyoung Jin, President of SAIT and Co-Chair of the Samsung AI Forum.

Registration will be available from October 12 on the Samsung AI Forum website, which allows attendees to pre-submit questions to the forum’s speakers.

Speakers representing various fields introduced newly developed AI algorithms, as well as innovative AI solutions that can benefit our lives in the future. Samsung Electronics livestreamed this year’s forum on its YouTube channel and offered participants – which included engineers, researchers and students in the field of AI – the opportunity to interact with experts during a Q&A session.

Read on for Samsung Newsroom’s recap of the presentations and key topics that took center stage during the two-day event.

Samsung AI Forum Day One

Developing AI That Addresses Common Problems

Hosted by the Samsung Advanced Institute of Technology (SAIT), Samsung’s R&D hub dedicated to cutting-edge future technologies, day one of the Samsung AI Forum began with opening remarks from Dr. Kinam Kim, Vice Chairman and CEO of Samsung Electronics. “Digital transformation has been accelerated in every industry, to which data science and machine learning are essential,” said Dr. Kim. “We at Samsung are open to discussing how to tackle important, common problems with researchers from all over the world, and we hope that the Samsung AI Forum can help facilitate that goal.”

This was followed by a keynote speech from Professor Yoshua Bengio of the University of Montreal, the co-chair of the Samsung AI Forum and a Samsung AI Professor. During his speech, Professor Bengio introduced a new machine learning tool called GFlowNets.

After explaining how the algorithms could be applied to the development of new drugs, he emphasized how “We find that [the model] converges to good solutions faster than other methods, and in addition, it finds a more diverse set of solutions. So this is very encouraging, and we are very excited about the potential applications in discovery in general.” After finishing his speech, the professor discussed ways to apply the algorithms during a Q&A session that featured scientists from around the world.

The keynote was followed by three technology sessions entitled “Scalable & Sustainable AI Computing”, “AI for Scientific Discovery” and “Trustworthy Computer Vision”. During these sessions, leading academics and startups spoke alongside some of Samsung’s top researchers.

Professors Kunle Olukotun of Stanford University, Gerbrand Ceder of the University of California – Berkeley and Antonio Torralba of the Massachusetts Institute of Technology shared key findings in their respective areas of AI research. Founders of startups based in Silicon Valley, including Andrew Feldman, CEO of Cerebras Systems, Bryce Meredig, CSO of Citrine Informatics and Daniel Bibireata, Vice President of Landing AI, presented insights on business models for various areas of AI research, as well as future business strategies. Representing Samsung were multiple leading researchers, including Changkyu Choi, Senior Vice President and Head of SAIT’s AI & SW Research Center, who introduced the company’s vision for AI and summarized the progress it has made through its research in the field.

The event also showcased rising talents and researchers in the field of AI. Samsung revealed this year’s five winners of the Samsung AI Researcher of the Year award, which was launched last year to recognize promising global AI researchers.

“I’m especially thankful to my students, whose work is really what’s being rewarded here,” said Professor Phillip Isola of the Massachusetts Institute of Technology, who received the award. “We’re trying to make progress to make AI systems that are closer to [reaching] human-like [and] animal-like abilities,” he added, describing natural intelligence.

“My research lies at the intersection of computer vision and machine learning, and my overall goal is to create vision systems that are reliable and accessible for everyone,” added Professor Judy Hoffman of the Georgia Institute of Technology.

For the Coexistence of Humans and AI

Day one of the forum closed with a panel discussion in which academics engaged in lively conversations and shared their insights. The panel’s moderator, Youngsang Choi, Vice President of SAIT, introduced topics related to each panelist’s area of expertise. After the discussion, participants were given free rein to ask the panelists questions.

One participant asked the panelists if they believed that it would be possible for AI algorithms to achieve human-level data efficiency in training, to which Professor Antonio Torralba said yes.

“When we think about the data that humans have, it’s not just visual data. They really sense the world through a lot of different mechanisms,” Professor Torralba explained. “Also, humans actually are not passive observers of the world. They are actually interacting with the world and performing all kinds of experiments. I think, in order to achieve [a human-like] level of efficiency, we need to incorporate all of these things and make them really like the main characters of the movie that AI is playing now.”

The panel discussion also offered an opportunity for students majoring in AI-related fields to share their concerns with the experts and receive advice. In the field of AI natural language processing (NLP), for example, the number of parameters is continuously increasing, which means that the costs required to train a model are too. Considering these circumstances, participants discussed which way academic research should be heading.

Professor Bengio concluded the panel discussion by offering some insightful advice to young AI researchers and students. “Don’t be afraid to go in directions that are very different from what has been established as state of the art,” said the professor. “Brain power is the thing that’s really behind innovation and [the] amazing progress that science brings us. So don’t be afraid to try things [and] don’t be afraid to question what has been apparently established for years or decades. That’s how we are all going to make progress.”

Samsung AI Forum Day Two

The Latest AI Research, All in One Place

Day two of the forum was hosted by Samsung Research, Samsung Electronics’ advanced R&D hub, which leads the development of future technologies for its Consumer Electronics and IT & Mobile Communications divisions. Dr. Sebastian Seung, President and Head of Samsung Research, emphasized that “AI is a technology that makes people’s lives better,” and offered an overview of the various AI-related projects that Samsung Research was engaged in, including those related to smartphone cameras, on-device AI, Open Source AI System Software, Machine Translation, and AI technologies for robots. “I’m really looking forward to today’s lectures by leading researchers in AI,” said Dr. Seung, heightening viewers’ expectations.

The day began with a keynote from Professor Leslie Valiant of Harvard University, who offered details on how to augment supervised learning with reasoning. “To make AI work, it takes several components,” Professor Valiant explained. “The first component is identifying which phenomenon or functionality you want to realize.”

Next came lectures delivered by academics who have been actively leading AI research. These include Professor Felix Heide of Princeton University, Research Scientist Been Kim of Google Brain and Professor Max Welling, a research chair in machine learning at the University of Amsterdam and a Distinguished Scientist at Microsoft Research.

AI’s Evolution Into a Tool for Gaining Insights

Day two’s panel discussion saw experts share their opinions on how AI technology will impact people’s lives in the future. The panel’s moderator, Dr. Daniel D. Lee, Executive Vice President and Head of Samsung Research’s Global AI Center, kicked off the discussion with a question.

“The first time AI came into presence, there was a lot of emphasis at that time on logical reasoning,” Dr. Lee explained. “But now, data-driven approaches such as deep neural networks are rising. And what we just heard from Leslie’s talk was [about] how we can actually use the logic [now] in combination with these more advanced neural network techniques. What would be the big advantage of doing that kind of return, in some sense, to logic with neural networks?”

“The idea that both learning and logic are important has been understood for a long time, [albeit separately],” Professor Valiant explained. “We are in a good position because I think the position of learning is now very much advanced. So, we have reason to be confident that there’s a lot of competence that we have as far as learning, and it’s a good basis on which to build logic.”

Researcher Efi Tsamoura of the Samsung AI Center in Cambridge added that “An increasing number of applications for many different areas, from computer vision to natural language processing, are taking advantage of background knowledge in order to build more robust and simpler models. Why is that? It’s because logic provides us with the ability to [complement] missing labels and to use the missing labels in order to train the model.” Tsamoura also pointed out that “An increasing number of researchers from different fields, mostly applied fields, are realizing the potential of logic.”

The discussion also touched on scientific discoveries that have been made with machine learning. “I think it’s worth noting that with the advent of 5G and with 6G coming down the pipe, communication networks have gone from being extremely complicated to super-extremely complicated… and I think the opportunities to optimize and manage the systems to make them even more efficient are vast. So I think there is a great chance to bring machine learning and AI tools to bear on the structure and operation of these communication networks to make them more efficient,” said Gregory Dudek, Head of the Samsung AI Center in Montreal. “We’ve had some very nice success in Montreal in adapting the tools that exist to these relatively new problems for that domain, and [have] actually significantly moved the needle to increase the performance of these systems.”

In order to commercialize machine learning for use in various areas, continuous simulations must be conducted. How then can the gap between simulation results and real-world phenomena be narrowed? Professor Welling shared his thoughts: “[Since simulations do not actually reflect all the complexities of the world,] I think probably the solution is some hybrid solution where you would simulate as much as you can, but you also identify where your system is uncertain about its predictions. And at that point, sort of in an active sense, you are then going to acquire data for that particular problem. So, active sensing might be an interesting solution.”

At the Lightning Talks session, employees from Samsung Research’s Global AI Centers presented some of their latest research including Adaptive Sharpness-Aware Minimization(ASAM), which is a deep learning optimizer developed by Samsung Research, and Named Entity Correction for Automatic Speech Recognition (ASR).

The thoughts and findings that were shared at the Samsung AI Forum indicate that a world in which AI is merged seamlessly with our daily lives may not be that far off. Full replays of both days of the Samsung AI Forum 2021, through which viewers can learn more about the current status of AI technology, its applications, and what the future may hold, can be viewed on the event’s official website and on Samsung Electronics’ YouTube channel.

Now in its fifth year, Samsung AI Forum serves as a platform that gathers leading experts to exchange the latest technology trends and research findings. The two-day event held on 1 and 2 November (KST) enabled participants to discuss applications of AI that will make a practical contribution to people’s daily lives. In this year’s AI Forum livestreamed on Samsung Electronics’ YouTube channel, there were opportunities for researchers and students in the AI field around the world to interact with world-renowned academics and experts through Q&A sessions.

Day 1 of Samsung AI Forum was hosted by Samsung Advanced Institute of Technology (SAIT), Samsung’s R&D hub dedicated to cutting-edge future technologies, under the theme, “AI Research for Tomorrow”.

Day 1 started with opening remarks from Dr. Kinam Kim, Vice Chairman & CEO of Device Solutions at Samsung Electronics, who spoke about the wide-reaching capability of AI to address pressing global issues.

“The advancement of AI is going beyond the electronics industry and expanding to various fields, such as basic science. We expect AI to provide solutions to social issues such as climate change and environmental pollution in the future, but there are still many challenges to tackle to make this possible,” said Dr. Kim.

He also shared his optimism that Samsung AI Forum will be a key venue for experts across the industry to start conversations and collaborate on AI as a means to help humanity on various fronts.

Shining a Spotlight on AI Leaders

Also on Day 1 of Samsung AI Forum, the company announced this year’s winners of the ‘Samsung AI Researcher of the Year’ awards. The awards were launched last year to discover rising AI researchers globally. The awards were presented by Dr. Gyoyoung Jin, President and Head of SAIT, who served as the co-chair for Samsung AI Forum.

This year’s awards went to Professor Diyi Yang (Georgia Tech), Professor Jacob Andreas (MIT), Professor Judy Hoffman (Georgia Tech), Professor Phillip Isola (MIT) and Professor Yarin Gal (Oxford).

“It’s an honor for me to receive the award presented by Samsung to young researchers in the AI field,” said Professor Phillip Isola of MIT. “I’ll put more effort to further develop the current AI system to realize AI that is close to natural intelligence,” he said. Professor Isola is one of the most prominent researchers in computer vision.

Expert Highlights: Keynote Speeches

The keynote on the first day of Samsung AI Forum was given by Professor Yoshua Bengio of University of Montreal, who also served as a co-chair of Samsung AI Forum and is a Samsung AI Professor. In his keynote, entitled GFlowNets for Scientific Discovery, Professor Bengio introduced AI algorithms used within scientific fields such as physics, chemistry and biology. He presented a new algorithm called GFlowNets, which is used to increase the prediction accuracy of experiment and test data.

The keynote lecture was followed by three technology sessions entitled Scalable & Sustainable AI Computing, AI for Scientific Discovery and Trustworthy Computer Vision. In these sessions, leading academics and startups spoke alongside some of Samsung’s top researchers.

Professor Kunle Olukotun of Stanford University in the U.S., who is the co-founder of a promising AI startup called SambaNova Systems, shared his insights on ultra-low power AI computing through an effective data flow architecture in his lecture, Accelerating AI with Dataflow Computing.

Professor Gerbrand Ceder of University of California – Berkeley, who is the founding director of the U.S. federal government-led Material Genome Initiative, which began ten years ago, gave his lecture on AI/Machine Learning in Material Research and the Laboratory of the Future. Professor Antonio Torralba of MIT in Massachusetts, U.S., gave his lecture, Learning to See.

From Samsung, multiple leading researchers, including Changkyu Choi, Senior Vice President and Head of SAIT’s AI & SW Research Center presented the progress and vision regarding Samsung’s research in the AI field. The speakers introduced various AI learning model developments and their applications, and suggested the memory-powered computing architecture, including engineering ultra-low power AI computing for processing AI models and big data.

In addition, founders of startups based in Silicon Valley, including Cerebras Systems shared their insights on business models for different AI research areas and future business strategies.

‘Samsung AI Forum 2021′ can be viewed again on Samsung Electronics’ YouTube channel, and Day 2 AI forum will be held on 2 November.

Stay tuned to Samsung Newsroom for more information on the Samsung AI Forum 2021.

To offer viewers a glimpse of the exciting topics that will be discussed at SAIF 2021, Samsung has released a pair of teaser videos previewing the two-day event’s distinguished speakers and sessions.

Those who are interested can register to participate through the Samsung AI Forum’s website up until the day of the event. Those who do so will be able to access SAIF’s schedule and submit questions for the experts before the event kicks off. In the meantime, check out the videos below for a preview of what SAIF 2021 has in store, and stay tuned to Samsung Newsroom for more updates.

This year’s AI Forum, the fourth of its kind, is being held over two days this November 2 and 3. The first day of the event, hosted by the Samsung Advanced Institute of Technology (SAIT), Samsung’s R&D hub dedicated to cutting-edge future technologies, is enabling participants to facilitate discussions around how to make the best use of AI technologies in a way that can benefit our daily lives in a rapidly changing world, particularly within the context of the unprecedented situations that have arisen recently due to the global pandemic.

AI Forum Day 1: The Past, Present and Future of AI

On November 2, Dr. Kinam Kim, Vice Chairman & CEO of Device Solutions at Samsung Electronics, commemorated the start of the first day of the AI Forum 2020 by delivering an opening speech that highlighted how AI technologies have shown remarkable progress over the years. He went on to note that, given these changes, many are expecting AI to address the issues brought on by the recent pandemic, but highlighted that since AI bases its models on massive amounts of real-life data and simulations, the task of modeling the current pandemic and other natural disasters with AI was a daunting one.

Dr. Kim went on to provide his own views on the ways in which AI technologies can move forward and be harnessed to have meaningful impact on real world problems, and also highlighted that Samsung Electronics, as a major provider of core technologies in the AI ecosystem, is proactively co-operating with global researchers to seek solutions to such real world problems. Dr. Kim ended his opening speech with the expectation that meaningful discussions on the present and future of AI technologies and their benefit for humanity were set to take place during this year’s Forum.

Recognizing Leading Talent in the Field

At this year’s AI Forum, Samsung introduced their inaugural Samsung AI Researcher of the Year awards with the view to identify prominent emerging researchers in the field from around the world and to support their research activities.

This year’s Samsung AI Research of the Year awards went to Professor Kyunghyun Cho of New York University, Professor Chelsea Finn of Stanford University, Professor Seth Flaxman of Imperial College London, Professor Jiajun Wu of Stanford University and Professor Cho-Jui Hsieh of UCLA.

Professor Kyunghyun Cho, a globally recognized researcher in natural language processing, has been publishing a consistent stream of acclaimed papers across the medicine, biology and optimization disciplines. “I am honored to have received a Samsung AI Researcher of the Year award and am committed to developing AI-focused research further down the road,” said Professor Cho of the recognition.

Expert Highlights: Keynote Speeches

Professor Yoshua Bengio, who served as this year’s co-chair and was selected as Samsung AI Professor of the Year, gave a presentation titled Towards Discovering Casual Representations. In his lecture, Professor Bengio explained that, up until now, conventional deep learning technologies have been relying on inference to recognize sensual information and learn from it, but AI technologies that are instead capable of learning the causality between hidden variables before drawing conclusions could be capable of making inferences just as humans do, and hence would be able to respond to unprogrammed situations. With visions of such a type of AI in mind, Professor Bengio shared the initial outcomes of his research and suggested how, based on this, AI technologies can make steps forward.

Professor Yann LeCun of New York University, a researcher who pioneered the Convolutional Neural Network widely applied to video recognition technologies, presented his latest model related to Self-Supervised Learning. Unlike supervised learning which returns a given answer to each given data set, self-supervised learning adopts a learning model consisting of autonomously creating questions within data and subsequently finding answers. Such a method has been applied to a massive linguistic model capable of generating sentences just as people do. Professor LeCun highlighted how self-supervised learning is similar to the way children experience and learn the world, and presented an energy-based model based on such a comparison.

Professor Chelsea Finn of Stanford University, a young researcher in the spotlight within the field of meta learning, gave a lecture titled From Few-Shot Adaptation to Uncovering Symmetries. In her lecture, Professor Finn introduced meta learning technologies in which AI, in spite of changes in data, can adapt swiftly to untrained data, and proceeded to share success stories of the application of these technologies in the areas of robotics and new drug candidate material design.

Professor Donhee Ham, Fellow at the Samsung Advanced Institute of Technology and Professor at Harvard University, delivered a presentation titled Reconstruction of the Brain. In his presentation, he highlighted that the current level of AI is based on the human brain but in fact works in a way different from how the brain functions, causing limitations to its capability. Professor Ham introduced cutting-edge neural science technologies that could mimic the structure and functionalities of the human brain circuit and create computer integrated circuits on their own.

Industry experts also took part in giving presentations. Dr. Tara Sainath of Google Research released the latest research outcomes of end-to-end models developed for speech recognition capable of enhancing the accuracy, efficiency and multi-lingual capability of voice assistant services widely available across smart devices.

Dr. Jennifer Wortman Vaughan of Microsoft Research gave a lecture titled Intelligibility Throughout the Machine Learning Life Cycle. She shared a human-centric machine learning concept, highlighting that, in order to develop a fair machine learning system capable of garnering the trust of people, people’s clear understanding of the system is required. Dr. Wortman Vaughan then introduced research outcomes that can objectively verify such a mechanism.

Since the Samsung AI Forum 2020 was held virtually this year, students and researchers alike in the AI research field from all over the world were able to engage in online discussions and exchanges. When tuning in to the Forum’s lectures on Samsung Electronics’ YouTube channel, attendees could ask questions to and receive answers from the distinguished speakers thanks to a real-time chat functionality.

Stay tuned to Samsung Newsroom for more information on the Samsung AI Forum 2020.

Since blue is known to be the most difficult color to implement out of the three primary QLED colors (red, blue and green), this achievement – coming in the wake of Samsung’s development of red QLED technology last November – once again proves Samsung’s excellence in the quantum dot technology sphere.

Blue Proves the Most Difficult of the Three Primary QLED Colors

Quantum dots (QDs) are semiconductor particles that measure a few nanometers in diameter (tens of thousands of times narrower than a single human hair). When illuminated, they re-emit light of a certain color depending on their size.

The blue QD, which has the largest band gap¹ among the three primary colors, rapidly oxidizes upon exposure to external light, resulting in a short lifespan and low luminous efficiency.² For this reason, up to now the industry had failed to develop even the technology required for blue quantum dot light-emitting diodes.

Overcoming Another Challenge by Developing Blue QLED Technology

But now, SAIT has successfully developed blue QLED technology, achieving industry-leading results such as 20.2% improved luminous efficiency, 88,900 nits of maximum luminance and 16,000 hours of QLED lifetime (measured at half-brightness for 100-nit luminance). These results were recorded in a study titled “Efficient and stable blue quantum dot light-emitting diode”, which was published by the journal Nature on October 14, 2020.

Eunjoo Jang, Samsung Fellow

“Samsung’s distinctive quantum dot technology has once again overcome the limitations of existing technology in the industry,” noted Dr. Eunjoo Jang, Samsung Fellow and corresponding author for the study. “I hope that this study goes on to help accelerate the commercialization of Quantum Dot light-emitting diodes (QLEDs).”

Quantum dots are made up of a basic structure of a core, a shell, and multiple ligands.³ In order to better stabilize the QD materials and secure durable photoresponse functionality, the researchers applied a structure with quantum dot double emitting layers and shorter ligands on the surface of the blue-light-emitting QDs while also improving current injection rates.

Taehyung Kim, Principal Researcher

Dr. Taehyung Kim, Principal Researcher and the first author of the study, said, “This research is meaningful in that we have not only established Quantum Dot light-emitting diode performance, but have also proven that the technology can deliver top-notch performance at the element level.”

(From left) Kwang-Hee Kim, Taehyung Kim, Eunjoo Jang, Sungwoo Kim, Seon-Myeong Choi from SAIT

¹ The difference of energy between the valence band of electrons and the conduction band.

² The ratio of the emitting luminous flux to the total input flux of source.

³ The core absorbs and re-emits light, while the shell layer surrounding the QD core improves lifespan and photoluminescence efficiency by preventing temperature/humidity-related damage. The branch-shaped ligands are located on the surface of the QD’s shell and help maintain inter-particle distance.

Day 1: AI Technologies for Changes in the Real World

On Day 1, which will be hosted by Samsung Advanced Institute of Technology (SAIT), Samsung’s R&D hub dedicated to cutting-edge future technologies, Dr. Kinam Kim, Vice Chairman & CEO of Device Solutions at Samsung Electronics will deliver opening remarks. Renowned AI experts will subsequently give presentations under the theme “AI Technologies for Changes in the Real World.”

This year, Dr. Inyup Kang, President of System LSI Business at Samsung Electronics will join the panel discussion with the presenters. Topics for in-depth discussions include: challenges that need to be overcome on a global level through AI technologies over the next decade; limitations that AI faces in tackling real-world issues such as a pandemic or climate change; and whether humans need human-level AI, among other topics.

Day 1 Sessions:

• “Towards Discovering Causal Representations” by Prof. Yoshua Bengio, the University of Montreal
• “Self-Supervised Learning” by Prof. Yann LeCun, New York University
• “Meta-Learning: From Few-Shot Adaptation to Uncovering Symmetries” by Prof. Chelsea Finn, Stanford University
• “Reconstruction of the Brain” by Prof. Donhee Ham, Fellow at the Samsung Advanced Institute of Technology, Professor at Harvard University
• “Intelligibility Throughout the Machine Learning Life Cycle” by Dr. Jennifer Wortman Vaughan, Microsoft Research
• “End-To-End Models for Speech Recognition” by Dr. Tara Sainath, Google Research

Professor Yoshua Bengio, the winner of the 2018 Turing Award, often referred to as “the Nobel Prize in computing,” is assuming co-chairmanship for the forum, and the newly established “Samsung AI Researcher of the Year” award will be presented at the event.

The awardee of the “Researcher of the Year” honor is selected among global AI researchers under the age of 35 through extensive evaluations and assessments made by AI experts at both Samsung Electronics and renowned academic institutions. On the first day of the forum, the award ceremony will be held to present the USD 30,000 prize and the awardee will give a presentation.

Additionally, Samsung has named Professor Yoshua Bengio as “Samsung AI Professor.” As the co-chair of the forum with Dr. Sungwoo Hwang, President and Head of SAIT, Professor Bengio will utilize his wide networks and expertise in the field of deep learning to broaden cooperation for expanding the boundaries of AI research at Samsung Electronics.

“We have an outstanding set of speakers and discussion topics which promise to shed light on both the limitations of current AI technologies, which raise both practical and theoretical questions, and research directions aimed at reaching human-level intelligence,” said Professor Yoshua Bengio.

Day 2: Human-Centered AI

Day 2 sessions will be hosted by Samsung Research, the advanced R&D hub of the company that leads the development of future technologies for Samsung Electronics’ SET (end-products) Business. Under the theme “Human-Centered AI,” Dr. Sebastian Seung, President and Head of Samsung Research, will deliver the keynote speech, and AI experts who have been actively engaging in AI research activities worldwide will share their insights.

Day 2 Sessions:

• “Natural Language Processing” by Prof. Christopher Manning, Stanford University
• “Vision” by Prof. Devi Parikh, the Georgia Institute of Technology
• “Human Robot Interaction” by Prof. Subbarao Kambhampati, Arizona State University
• “Robotics” by Prof. Daniel D. Lee, Cornell Tech, Executive Vice President at Samsung Research and Head of Samsung AI Center-New York

Professor Christopher Manning, a renowned expert in natural language processing (NLP), will speak on the current status and future of NLP technologies required for Human-Centered AI. He previously delivered the keynote speech at the first Samsung AI Forum in 2017 on the development of neural network-based natural language understanding technology. Samsung has been working with Professor Manning on Q&A and dialogue modeling and will continue to collaborate with him on the overall development of NLP technologies.

After the presentations, Sebastian Seung, a pioneer in AI research based on neuroscience, will preside over an in-depth panel discussion with the four speakers regarding the prospects and future direction of Human-Centered AI.

“We hope that Samsung AI Forum 2020 will contribute to enhanced understanding of AI technology developments and its applications that can bring positive impact to human lives,” said Seung. “Especially since this year’s forum will be held online, I hope that the event will be an opportunity for greater participation of those interested in AI technologies.”

The event will be open to pre-registered attendees. Registration is available through the Samsung Advanced Institute of Technology website and the Samsung Research website starting October 6.

Samsung Electronics is committed to leading advancements in the field of artificial intelligence (AI), with the hopes of ushering in a brighter future. To discuss what the future may hold for AI technology, and to address and overcome the technological challenges that researchers are currently facing, the company recently hosted its third annual Samsung AI Forum.

Held from November 4–5 in Seoul, this year’s forum featured renowned AI experts from around the world, who offered intriguing ideas for addressing some of the most pressing challenges facing AI research today.

Predicting the Next Big Trends in AI

(From left) Profs. Yoshua Bengio, Kyunghyun Cho, Noah Smith and Abhinav Gupta

Modern AI technology is not only capable of analyzing data with algorithms, it’s also making strides toward achieving human-like cognition. With increases in computing power and advances in deep learning, AI technology is attempting to analyze data on its own, and learning to identify the most appropriate response for a given situation or context. The application of big data in deep learning is accelerating this trend.

While recent advancements have proven promising, the speakers at this year’s AI forum agreed that certain technological challenges remain unaddressed. Prof. Kyunghyun Cho of New York University put the technology’s current status in simple terms. “Imagine a hypothetical AI agent equipped with the current technology,” said Prof. Cho. “It has barely opened its eyes so that it can see and detect objects; it has barely opened its ears to listen to people and hear what they are saying; it has barely opened its mouth to speak short utterances; it is barely learning to move its limbs. In other words, we have just taken a tiny step toward building a truly intelligent machine – or a set of algorithms to drive such an intelligent agent.”

Prof. Noah Smith of the University of Washington expanded on this point, noting that “We’ve seen a lot of progress through the use of increasingly ‘deep’ neural networks trained on ever-larger datasets.” Prof. Smith also identified preparing efficient algorithms, reducing system construction costs and improving data learning methods as points that will need to be addressed in order to take AI technology to the next level.

The speakers also offered their opinions on where AI advancements should focus next, spotlighting things like wireless network controls, increasing AI’s autonomy, expanding AI’s applications in chemical and biological research, and streamlining interactions between humans and AI.

As Prof. Abhinav Gupta of Carnegie Mellon University explained, “In the past few years, we have made significant advancements in AI, but most of these advancements have been in solving specific tasks where lots of data and supervision are available. On the other hand, humans can perform hundreds of thousands of tasks, often with little to no supervision or data for them. This is the next frontier in AI: developing general purpose smart and intelligent agents without access to lots of data and supervision.”

Going Beyond Deep Learning

The first day of the forum was organized by the Samsung Advanced Institute of Technology (SAIT), which was established under the philosophy of fostering ‘boundless research for breakthroughs.’ Keynote sessions saw distinguished experts deliver presentations on deep learning research methods that are driving AI innovation.

Dr. Kinam Kim, President & CEO of Device Solutions at Samsung Electronics, kicked off the event by discussing Samsung’s motivation for bringing these renowned AI experts together under the same roof. “AI technology is already impacting various aspects of our society,” said Dr. Kim. “Here at the Samsung AI Forum, alongside some of the greatest minds in the industry, we will discuss and suggest directions and strategies for AI development with the hope of making the world a better place.”

Dr. Kim then yielded the stage to the day’s first distinguished speaker, Prof. Yoshua Bengio of the University of Montreal, who presented a lecture entitled ‘Towards Compositional Understanding of the World by Deep Learning.’

“Humans are much better than current AI systems at generalizing out-of-distribution,” Prof. Bengio explained. “We propose that learning purely from text is not sufficient, and we need to strive for learning agents that build a model of the world, to which linguistic labels can be associated.”

“The focus of future deep learning methodology,” he continued, “will be how the agent perspective common in reinforcement learning can help deep learning discover better representations of knowledge.”

Next, Prof. Trevor Darrell of the University of California at Berkeley presented an engrossing lecture entitled ‘Adapting and Explaining Deep Learning for Autonomous Systems.’ Prof. Darrell’s presentation spotlighted limitations of deep learning technology when it comes to developing autonomous driving systems, and introduced approaches to help overcome those issues.

As Prof. Darrell explained, “The learning of layered or ‘deep’ representations has recently enabled low-cost sensors for autonomous vehicles and the efficient automated analysis of visual semantics in online media. But these models have typically required prohibitive amounts of training data, and thus may only work well in the environment they have been trained in.”

Prof. Darrell then suggested approaches for developing explainable deep learning models, including introspective approaches that visualize compositional structures in a deep network, as well as third-person approaches that can provide a natural language justification for the classification decision of a deep model.

Afterward, Prof. Kyunghyun Cho of New York University took to the stage to deliver a riveting presentation entitled ‘Three Flavors of Neural Sequence Generation.’

“Standard neural sequence generation methods,” Prof. Cho explained, “assume a pre-specified generation order, such as left-to-right generation. Despite its wild success in recent years, there’s a lingering question of whether this is necessary, and if there is any other way to generate such a sequence in an order automatically learned from data – without having to pre-specify it, or relying on external tools.” He went on to introduce three alternatives that could potentially be used in sequence modeling: parallel decoding, recursive set prediction, and insertion-based generation.

Day one of the Samsung AI Forum included a panel discussion featuring (from left) Profs. Simon Lacoste-Julien, Jia Deng, Yoshua Bengio, Jackie Cheung, Sanja Fidler and Kyunghyun Cho.

Day one’s keynote speeches were followed by a panel discussion, moderated by the University of Montreal’s Prof. Simon Lacoste-Julien, that discussed establishing data sets for deep learning models. Prof. Sanja Fidler of the University of Toronto proposed a new tool that enables more detailed labeling of image data, while Prof. Jackie Cheung of McGill University suggested an alternative to replace automatic text summarization systems that are based on news articles.

Prof. Jia Deng of Princeton University outlined a method for establishing a new recognition system that enables AI to analyze data more efficiently, and Prof. Lacoste-Julien discussed ways to enhance the learning efficiency of generative adversarial networks (GANs).

Developing AI with Human-like Intelligence

The second day of the forum was organized by Samsung Research, the advanced R&D hub that leads the development future technologies for Samsung Electronics’ SET(end-products) Business. Day two was headlined by experts from a variety of fields who discussed how they’ve been applying AI in their ongoing research and revealed more innovative ways to address the technology’s current limitations.

DJ Koh, President and CEO of IT & Mobile Communications Division at Samsung Electronics, set the stage for day two’s illuminating presentations by sharing his perspective on the importance of Samsung’s investment in AI. “In this hyper-connected world, where everything is connected through 5G, AI and IoT technology, the company that delivers the most innovative experience will become the global business leader,” said Koh. “I believe that Samsung will lead the way by spearheading 5G, AI and IoT innovation.”

The first keynote of the day was delivered by Prof. Noah Smith of the University of Washington. Prof. Smith, who is recognized as one of the world’s foremost experts in designing data-centered algorithms for the autonomous analysis of human languages, introduced rational recurrent neural networks (RNNs), and outlined a path toward more efficient deep learning models for language processing.

“Current deep learning models are not based on real language understanding,” Prof. Smith explained. “Therefore, it is hard to explain the reasoning behind their actions. Experiments have found that rational RNNs can perform competitively as language models and for various classification tasks, especially with smaller amounts of annotated data, while using fewer parameters and training faster.”

Next, Prof. Abhinav Gupta of Carnegie Mellon University suggested a new model for empowering vision and robot learning. Prof. Gupta demonstrated how this large-scale self-learning mechanism goes beyond the limitations of supervised learning¹, and discussed how to incorporate it into future AI agents.

The self-learning model introduced by Prof. Gupta is a methodology in which an AI system models the physical world through visual understanding, and gains an understanding of space and objects. The goal is to establish predictive models based on knowledge of physics, spatial perception and cause-and-effect relationships.

The ‘Invited Talk’ session that followed Prof. Gupta’s presentation discussed concrete methods for extending AI into more areas of our daily lives.

“It’s difficult for AI to make sense of the world using only the data that it’s been trained with, and when variables are involved, the data can produce a conclusion that’s completely different from what the developer intended,” said Prof. Vaishak Belle of Scotland’s University of Edinburgh.

Prof. Belle stressed the need for transparent and responsible AI development, and suggested that more efforts be directed toward 1) developing machine learning technology that’s accessible even to non-AI experts, 2) understanding biases in algorithms to ensure fair decision making, and 3) applying ethical principles to AI systems. The approaches he suggested were based on symbolic logic as it pertains to machine learning development.

Next, Prof. Joan Bruna of New York University introduced recent advancements in the development of deep learning models known as graph neural networks (GNNs). “A graph is an effective tool for integrating interactions involving users, devices and knowledge,” Prof. Bruna explained. “GNNs, which can represent graphs and learn and reason about relations are key for developing AI that’s capable of human-level intelligence.”

The sessions that followed were divided into two themes: ‘Vision & Image’ and ‘On-Device, IoT & Social.’ Both tracks featured fascinating presentations, delivered by a who’s who of AI experts, along with engaging discussions focused on AI technology and its applications.

Showcasing Samsung’s Latest AI Advancements

(Above) Dr. Sungwoo Hwang, Deputy President of the SAIT, offers a demonstration of Samsung’s on-device AI translation technology. (Below) Participants examine outstanding examples of AI research conducted by undergraduate and graduate students from across Korea.

Each Samsung AI Forum offers attendees an opportunity to examine Samsung’s latest advancements in the field of AI research. This year, the company used the forum as a stage to unveil on-device AI translation technology that provides users with fast, reliable service even without an internet connection.

The forum also served as a showcase for the next generation of AI experts. Posters set up outside of the lecture hall offered attendees a chance to examine the research and dissertations of students in undergraduate and graduate schools across Korea.

Samsung’s vision for AI technology is focused on creating a user-centric ecosystem of devices and services that enhance users’ lives in meaningful ways. In hosting this event, the company hopes to do more than simply showcase the latest advancements in AI research, but actively seek innovative solutions to some of the technology’s most pressing challenges.

¹ Supervised learning refers to a machine learning method that gathers meaningful information based only on learned data. Because rules can be created once a large amount of data has been collected, the larger the scale of the self-learning is, the more sophisticated the conclusion becomes.

Samsung Electronics last month announced its goal to strengthen its leadership in the global system semiconductor industry by 2030 through expanding its proprietary NPU technology development. The company recently delivered an update to this goal at the conference on Computer Vision and Pattern Recognition (CVPR), one of the top academic conferences in computer vision fields.

This update is the company’s development of its On-Device AI lightweight algorithm, introduced at CVPR with a paper titled “Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss”. On-Device AI technologies directly compute and process data from within the device itself. Over 4 times lighter and 8 times faster than existing algorithms, Samsung’s latest algorithm solution is dramatically improved from previous solutions and has been evaluated to be key to solving potential issues for low-power, high-speed computations.

Streamlining the Deep Learning Process

Samsung Advanced Institute of Technology (SAIT) has announced that they have successfully developed On-Device AI lightweight technology that performs computations 8 times faster than the existing 32-bit deep learning data for servers. By adjusting the data into groups of under 4 bits while maintaining accurate data recognition, this method of deep learning algorithm processing is simultaneously much faster and much more energy efficient than existing solutions.

Samsung’s new On-Device AI processing technology determines the intervals of the significant data that influence overall deep learning performance through ‘learning’. This ‘Quantization¹ Interval Learning (QIL)’ retains data accuracy by re-organizing the data to be presented in bits smaller than their existing size. SAIT ran experiments that successfully demonstrated how the quantization of an in-server deep learning algorithm in 32 bit intervals provided higher accuracy than other existing solutions when computed into levels of less than 4 bits.

When the data of a deep learning computation is presented in bit groups lower than 4 bits, computations of ‘and’ and ‘or’ are allowed, on top of the simpler arithmetic calculations of addition and multiplication. This means that the computation results using the QIL process can achieve the same results as existing processes can while using 1/40 to 1/120 fewer transistors².

As this system therefore requires less hardware and less electricity, it can be mounted directly in-device at the place where the data for an image or fingerprint sensor is being obtained, ahead of transmitting the processed data on to the necessary end points.

The Future of AI Processing and Deep Learning

This technology will help develop Samsung’s system semiconductor capacity as well as strengthening one of the core technologies of the AI era – On-Device AI processing. Differing from AI services that use cloud servers, On-Device AI technologies directly compute data all from within the device itself.

On-Device AI technology can reduce the cost of cloud construction for AI operations since it operates on its own and provides quick and stable performance for use cases such as virtual reality and autonomous driving. Furthermore, On-Device AI technology can save personal biometric information used for device authentication, such as fingerprint, iris and face scans, onto mobile devices safely.

“Ultimately, in the future we will live in a world where all devices and sensor-based technologies are powered by AI,” noted Chang-Kyu Choi, Vice President and head of Computer Vision Lab of SAIT. “Samsung’s On-Device AI technologies are lower-power, higher-speed solutions for deep learning that will pave the way to this future. They are set to expand the memory, processor and sensor market, as well as other next-generation system semiconductor markets.”

A core feature of On-Device AI technology is its ability to compute large amounts of data at a high speed without consuming excessive amounts of electricity. Samsung’s first solution to this end was the Exynos 9 (9820), introduced last year, which featured a proprietary Samsung NPU inside the mobile System on Chip (SoC). This product allows mobile devices to perform AI computations independent of any external cloud server.

Many companies are turning their attention to On-Device AI technology. Samsung Electronics plans to enhance and extend its AI technology leadership by applying this algorithm not only to mobile SoC, but also to memory and sensor solutions in the near future.

Four individuals who played key roles in developing Samsung’s On-Device AI Lightweight Algorithm. From Left to right; Jae-Joon Han, Chang-Young Son, Sang-Il Jung, Chang-Kyu Choi of Samsung Advanced Institute of Technology

1 Quantization is the process of decreasing the number of bits in data by binning the given data into sections of limited number levels, which can be represented in certain bit values and are regarded as having the same value per section

² Transistors are devices that control the flow of current or voltage in a semiconductor by acting as amplifiers or switches