Latest news with #AssociationforComputingMachinery
South China Morning Post
04-04-2025
- Politics
- South China Morning Post
US diplomats in China face relationship ban, Xiaomi's fatal car crash: SCMP's 7 highlights
We have selected seven stories from this week's news across Hong Kong, mainland China, the wider Asia region and beyond that resonated with our readers and shed light on topical issues. If you would like to see more of our reporting, please consider subscribing Wang Xiaofeng, who was named a distinguished member by the Association for Computing Machinery in 2021, joined Indiana University Bloomington as an assistant professor in 2004 after receiving his PhD in computer engineering from Carnegie Mellon University. Photo: Handout Security raids on the homes of a noted Chinese-American cybersecurity researcher have reignited fears of racial profiling in Trump-led America, under what some are calling a de facto 'China Initiative 2.0'. The US government has imposed a sweeping ban prohibiting American diplomats, their families and security-cleared contractors in China from engaging in romantic or sexual relationships with Chinese citizens, Associated Press has reported. Zhang Yiming, the founder of ByteDance and China's richest man, has not obtained Singaporean citizenship, according to a statement from Douyin, the Chinese sister app of TikTok.
South China Morning Post
23-03-2025
- Science
- South China Morning Post
AI expert Guo-Jun Qi leaves US for China
Award-winning artificial intelligence (AI) researcher and entrepreneur Guo-Jun Qi has joined Westlake University in Hangzhou after a decade-long career in the United States Advertisement The 43-year-old Institute of Electrical and Electronics Engineers fellow and distinguished member of the Association for Computing Machinery is now a full-time faculty member at Westlake's School of Engineering, where he leads the Machine Perception and Learning (MAPLE) Lab, according to a university social media post. A former Microsoft researcher and chief AI scientist at Huawei Research USA, Qi now heads a team of 20 researchers to explore AI and deep learning for image, video and virtual environment generation. 'I was drawn to the free-spirited atmosphere at Westlake University and wanted to come back and pursue something I truly wanted to do,' the Chinese-born expert said in an interview with the university. Qi earned a bachelor's degree in automation from the University of Science and Technology of China (USTC) in 2005. He later obtained two PhDs – one from USTC in 2009 and another from the University of Illinois Urbana-Champaign in 2012. Advertisement During his doctoral studies, Qi won several prestigious awards, including the Microsoft Fellowship, IBM Fellowship and the award for best paper at the Association for Computing Machinery's International Conference on Multimedia – a leading event in the field.
New York Times
05-03-2025
- Science
- New York Times
Turing Award Goes to 2 Pioneers of Artificial Intelligence
In 1977, Andrew Barto, as a researcher at the University of Massachusetts, Amherst, began exploring a new theory that neurons behaved like hedonists. The basic idea was that the human brain was driven by billions of nerve cells that were each trying to maximize pleasure and minimize pain. A year later, he was joined by another young researcher, Richard Sutton. Together, they worked to explain human intelligence using this simple concept and applied it to artificial intelligence. The result was 'reinforcement learning,' a way for A.I. systems to learn from the digital equivalent of pleasure and pain. On Wednesday, the Association for Computing Machinery, the world's largest society of computing professionals, announced that Dr. Barto and Dr. Sutton had won this year's Turing Award for their work on reinforcement learning. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing. The two scientists will share the $1 million prize that comes with the award. Over the past decade, reinforcement learning has played a vital role in the rise of artificial intelligence, including breakthrough technologies such as Google's AlphaGo and OpenAI's ChatGPT. The techniques that powered these systems were rooted in the work of Dr. Barto and Dr. Sutton. 'They are the undisputed pioneers of reinforcement learning,' said Oren Etzioni, a professor emeritus of computer science at the University of Washington and founding chief executive of the Allen Institute for Artificial Intelligence. 'They generated the key ideas — and they wrote the book on the subject.' Their book, 'Reinforcement Learning: An Introduction,' which was published in 1998, remains the definitive exploration of an idea that many experts say is only beginning to realize its potential. Psychologists have long studied the ways that humans and animals learn from their experiences. In the 1940s, the pioneering British computer scientist Alan Turing suggested that machines could learn in much the same way. But it was Dr. Barto and Dr. Sutton who began exploring the mathematics of how this might work, building on a theory that A. Harry Klopf, a computer scientist working for the government, had proposed. Dr. Barto went on to build a lab at UMass Amherst dedicated to the idea, while Dr. Sutton founded a similar kind of lab at the University of Alberta in Canada. 'It is kind of an obvious idea when you're talking about humans and animals,' said Dr. Sutton, who is also a research scientist at Keen Technologies, an A.I. start-up, and a fellow at the Alberta Machine Intelligence Institute, one of Canada's three national A.I. labs. 'As we revived it, it was about machines.' This remained an academic pursuit until the arrival of AlphaGo in 2016. Most experts believed that another 10 years would pass before anyone built an A.I. system that could beat the world's best players at the game of Go. But during a match in Seoul, South Korea, AlphaGo beat Lee Sedol, the best Go player of the past decade. The trick was that the system had played millions of games against itself, learning by trial and error. It learned which moves brought success (pleasure) and which brought failure (pain). The Google team that built the system was led by David Silver, a researcher who had studied reinforcement learning under Dr. Sutton at the University of Alberta. Many experts still question whether reinforcement learning could work outside of games. Game winnings are determined by points, which makes it easy for machines to distinguish between success and failure. But reinforcement learning has also played an essential role in online chatbots. Leading up to the release of ChatGPT in the fall of 2022, OpenAI hired hundreds of people to use an early version and provide precise suggestions that could hone its skills. They showed the chatbot how to respond to particular questions, rated its responses and corrected its mistakes. By analyzing those suggestions, ChatGPT learned to be a better chatbot. Researchers call this 'reinforcement learning from human feedback,' or R.L.H.F. And it is one of the key reasons that today's chatbots respond in surprisingly lifelike ways. (The New York Times has sued OpenAI and its partner, Microsoft, for copyright infringement of news content related to A.I. systems. OpenAI and Microsoft have denied those claims.) More recently, companies like OpenAI and the Chinese start-up DeepSeek have developed a form of reinforcement learning that allows chatbots to learn from themselves — much as AlphaGo did. By working through various math problems, for instance, a chatbot can learn which methods lead to the right answer and which do not. If it repeats this process with an enormously large set of problems, the bot can learn to mimic the way humans reason — at least in some ways. The result is so-called reasoning systems like OpenAI's o1 or DeepSeek's R1. Dr. Barto and Dr. Sutton say these systems hint at the ways machines will learn in the future. Eventually, they say, robots imbued with A.I. will learn from trial and error in the real world, as humans and animals do. 'Learning to control a body through reinforcement learning — that is a very natural thing,' Dr. Barto said.
