Xi'an's Og

 “In the late 1980s, LeCun, then a researcher at AT&T Bell Labs, developed a powerful neural network that learned to recognise handwritten zip codes by training on thousands of examples. A parallel development soon unfolded at Harvard and Brown. In 1995, Zhu and a team of researchers there started developing probability-based methods that could learn to recognise patterns and textures (…) and even generate new examples of that pattern. These were not neural networks: members of the “Harvard-Brown school”, as Zhu called his team, cast vision as a problem of statistics and relied on methods such as “Bayesian inference” and “Markov random fields”. The two schools spoke different mathematical languages and had philosophical disagreements. But they shared an underlying logic – that data, rather than hand-coded instructions, could supply the infrastructure for machines to grasp the world and reproduce its patterns – that exists in today’s AI systems such as ChatGPT.” 

This entry was posted on October 2, 2025 at 12:25 am and is filed under Books, Statistics, Travel, University life with tags Bayesian inference, Beijing Institute for General Artificial Intelligence, Brown University, ChatGPT, China, China Initiative, Communist Party of China, computer vision, CPC, Cultural Revolution, emigration, Geoffrey Hinton, Harvard University, Harvard-Brown school, ImageNet, industrial spying, investigative journalism, large language models, LLM, neural network, Nobel Prize, Peking University, PRC, The Guardian, Thousand Talents Plan, TongTong 2.0, Trump administration, Tsinghua University, Turing Award, UCLA, US politics. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.