Core Insights - The evolution of AI has transitioned from perceptual intelligence to generative intelligence, with future breakthroughs relying on active exploration and interaction with the real world [2] - The current natural language data may be exhausted by 2027-2028, while visual data, although abundant, is challenging to extract knowledge from [2][3] - The growth of computational power is outpacing the generation of data, leading to a mismatch in model data requirements [3] Group 1 - The origin of human intelligence is rooted in continuous interaction with the physical world, which has been a limitation for machine intelligence due to the finite supply of human knowledge [2] - Deep learning algorithms, such as CNN and ResNet, spurred the explosion of perceptual AI from 2011 to 2012, but these models are limited by their reliance on manually labeled data [2] - The introduction of the Transformer architecture in 2017-2018 allowed AI to extract knowledge from natural language, with models like GPT-3 processing text equivalent to a hundred thousand years of human creative output [2] Group 2 - The next stage of AI development requires overcoming the challenge of scarce active interaction data, as human learning is based on interaction with the physical world rather than solely on language or visual inputs [3] - The high cost of real-world interaction and the limitations of traditional solutions, such as simulators, contribute to the "Sim-to-Real Gap," where generated data may not accurately reflect reality [3] - The company has introduced the "KAIWU" world model, which aims to provide high-quality simulated data by considering time and spatial consistency, enhancing AI training capabilities [3]
商汤董事长兼CEO徐立：数据耗尽后，AI演进需与物理世界链接