The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes.Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
A robot equipped with artificial intelligence (AI) can excel at the Olympic sport of curling -- and even beat top-level human teams. Success requires precision and strategy, but the game is less complex than other real-world applications of robotics. That makes curling a useful test case for AI technologies, which often perform well in simulations but falter in real-world scenarios with changing conditions. Using a method called adaptive deep reinforcement learning, Seong-Whan Lee and his colleagues at Korea University in Seoul created an algorithm that learns through trial and error to adjust a robot's throws to account for changing conditions, such as the ice surface and the positions of stones. The team's robot, nicknamed Curly, needed a few test throws to calibrate itself to the curling rink where it was to compete.
Tiny robots that can transport individual neurons and connect them to form active neural circuits could help us study brain disorders such as Alzheimer's disease. The robots, which were developed by Hongsoo Choi at the Daegu Gyeongbuk Institute of Science and Technology in South Korea and his colleagues, are 300 micrometres long and 95 micrometre wide. They are made from a polymer coated with nickel and titanium and their movement can be controlled with external magnetic fields.
Artificial intelligence still needs to bridge the "sim-to-real" gap. Deep-learning techniques that are all the rage in AI log superlative performances in mastering cerebral games, including chess and Go, both of which can be played on a computer. But translating simulations to the physical world remains a bigger challenge. A robot named Curly that uses "deep reinforcement learning"--making improvements as it corrects its own errors--came out on top in three of four games against top-ranked human opponents from South Korean teams that included a women's team and a reserve squad for the national wheelchair team. One crucial finding was that the AI system demonstrated its ability to adapt to changing ice conditions.
Vibraimage is a digital system that quantifies a subject's mental and emotional state by analysing video footage of the movements of their head. Vibraimage is used by police, nuclear power station operators, airport security and psychiatrists in Russia, China, Japan and South Korea, and has been deployed at an Olympic Games, FIFA World Cup, and G7 Summit. Yet there is no reliable evidence that the technology is actually effective; indeed, many claims made about its effects seem unprovable. What exactly does vibraimage measure, and how has it acquired the power to penetrate the highest profile and most sensitive security infrastructure across Russia and Asia? I first trace the development of the emotion recognition industry, before examining attempts by vibraimage's developers and affiliates scientifically to legitimate the technology, concluding that the disciplining power and corporate value of vibraimage is generated through its very opacity, in contrast to increasing demands across the social sciences for transparency. I propose the term 'suspect AI' to describe the growing number of systems like vibraimage that algorithmically classify suspects / non-suspects, yet are themselves deeply suspect. Popularising this term may help resist such technologies' reductivist approaches to 'reading' -- and exerting authority over -- emotion, intentionality and agency.
GOWIN Semiconductor Corp., the world's fastest growing programmable logic company, will attend AI EXPO KOREA on September 23-25, 2020 to demonstrate GOWIN's latest GoAI solution at Hall D, COEX, SEOUL, KOREA. AI EXPO KOREA is the only Artificial Intelligence Expo to meet the future of artificial intelligence technology, new trends, new products, and the best chance to meet all of Artificial Intelligence that leads the 4th Industrial Revolution. Interested attendees can review the conference details at http://www.aiexpo.co.kr/main. "We choose AI EXPO Korea as the first tradeshow to introduce Gowin GoAI solution for the innovation and leading AI technology of Korea," said Stanley Tse, Sales Director (Asia) of Gowin Semiconductor Corp and General Manager of Gowin Semiconductor Hong Kong. "Korea is one of the earliest countries to deploy 5G infrastructure worldwide which enables Edge AI market; Gowin GoAI also enables Edge AI application by using Machine Learning Accelerator provided over 80x of performance improvement compared standalone MCU's; We already have customers in Korea on our GoAI Early Access Program and kick off project with our GW1NS-4C family."
Position Summary: The Samsung Computing Science Innovation Center is seeking a talented, highly motivated individual to join our Smart Systems team as a Research Data Science Engineer. The Computing Science Innovation Center is a key part of Samsung’s global R&D effort and aims to have direct impact on future Samsung products reaching hundreds of millions of users worldwide. The Smart Systems team is focused on advancing the state-of-the-art in system architectures, tools, protocols, and algorithms for smart homes. In this team, you will be responsible for collaborating with world-class researchers to produce proof-of-concept technology demonstrations on current and upcoming Samsung product platforms. Necessary Skills / Attributes Responsibilities: – Build software systems, prototypes, and applications that incorporate machine learning – Explore new ideas and conduct research collaboration with our existing Smart Systems team – Write well-structured and re-usable code in programming languages such as Java, Python, and C/C++ – Execute experiments to evaluate the performance and effectiveness of new technology – Obtain, clean, and manage experimental data sets, such as those acquired from Samsung devices – Be comfortable using machine learning platforms such as TensorFlow/Caffe – Maintain awareness of new prototyping tools, toolkits, libraries, and APIs as they emerge – Thrive in a fast-paced, innovative environment Qualifications: – BS/MS in Computer Science or equivalent – 3+ years of programming experience in Java or similar languages – Knowledge of tools used for machine learning and data analysis, such as MATLAB, R, and SQL – Hands-on experience in machine learning applications and models – Collaborative and inquisitive nature – Strong verbal and written communication skills Company Information Founded in October 1988, Samsung Research America (SRA) is a wholly-owned subsidiary of Samsung Electronics Co. Ltd, South Korea. SRA is headquartered in Silicon Valley, CA. with offices across the US. SRA is engaged in researching emerging technologies to create new businesses, and developing core technologies to enhance the competitiveness of Samsung's products. SRA offers a competitive compensation package for R&D achievements so that researchers can focus on developing cutting edge technology. The primary research areas of SRA include: advanced software, content/services, user experience as well as other new and emerging technologies. Samsung Research America (SRA) plays a pivotal role in developing the next generation of discovery in software, user experience and services for future products that can enrich your life. Our mission is to research and develop new technologies by partnering with the best and brightest and creating a collaborative environment between industry and academia. Headquartered in Silicon Valley, with locations in many technology centers in North America, SRA is driven to build a culture of innovation that rapidly translates research and new ideas into the unexpected.
A Seoul National University Master's student and developer has trained a face generating model to transfer normal face photographs into cartoon images in the distinctive style of Lee Mal-nyeon. The student (GitHub user name: bryandlee) used webcomics images by South Korean cartoonist Lee Mal-nyeon (이말년) as input data, building a dataset of malnyun cartoon faces then testing popular deep generative models on it. By combining a pretrained face generating model with special training techniques, they were able to train a generator at 256 256 resolution in just 10 hours on a single RTX 2080ti GPU, using only 500 manually annotated images. Since the cascade classifier for human faces provided in OpenCV-- a library of programming functions mainly aimed at real-time computer vision -- did not work well on the cartoon domain, the student manually annotated 500 input cartoon face images. The student incorporated FreezeD, a simple yet effective baseline for transfer learning of GANs proposed earlier this year by KAIST (Korea Advanced Institute of Science and Technology) and POSTECH ( Pohang University of Science and Technology) researchers to reduce the burden of heavy data and computational resources when training GANs. The developer tested the idea of freezing the early layers of the generator in transfer learning settings on the proposed FreezeG (freezing generator) and found that "it worked pretty well."
Sensors increasingly permeate our lives and generate a plethora of data, which has transformed the way we live in cities. Planners have been using data-science to improve our understanding of urban issues. While other domains have highlighted concerns with big data collection, aggregation, and analytical methods to understand different phenomena, urban planning has an additional aspiration: not only to understand, but to transform society through planning. Thus, on top of critically approaching data collection and analytical methods, for the emergent field of urban science to become a distinctively unique body of knowledge, it must examine the ontological and epistemological boundaries of the big data paradigm and how it affects urban decision-making processes and their short- and long-term consequences in cities. Data-driven approaches have transformed the way we analyze, design and make policy decisions in cities. This has been true during the COVID-19 pandemic, where countries have used self-reported information and tracing apps to map infected people. South Korea Corona Map, for example provides the addresses of all infected residents, and Singapore COVID19 maps each case and their social networks, to help other people identify if they had contact with an infected person, took the same flight or used the same urban facilities to be aware of their risk of contagion.