Group 1 - The core viewpoint of the article emphasizes that AI is not just a flashy technology but is fundamentally restructuring products and user experiences [1] - The article highlights a live event featuring key figures from Plaud, Rokid, and Fuxi Technology, focusing on the underlying logic of AI hardware evolution and commercialization [2][4] - The discussion will cover how companies like Plaud and Rokid have managed to stand out in the AI hardware sector and the secrets behind the sustainable commercialization of AI hardware [4] Group 2 - The live event is scheduled for July 30, from 20:00 to 21:30, under the theme "Beyond Tools: The Underlying Logic and Breakthrough Path of AI Hardware Advancement" [2] - Key speakers include Mo Zihua, CEO of Plaud China, Duan Ran, CEO of Fuxi Technology, and Zhao Weiqi, Global Development Ecosystem Leader at Rokid [3] - Participants are encouraged to submit questions for the speakers, which will be addressed during the live session [5]

Computing Power Evolution - The industry has witnessed a dramatic growth in computing power over the past 5 decades, transitioning from early CPUs to GPUs and now specialized AI processors [4] - GPUs and accelerators have rapidly outpaced traditional CPUs in compute performance, initially driven by gaming [4] - Apple's M4 chip features a neural engine delivering 38 trillion operations per second, establishing it as the most efficient desktop SOC on the market [3] - NVIDIA's B200 delivers 20 quadrillion operations per second at low precision in AI data centers [3] Hardware and AI Development - The development of CUDA by Nvidia in 2006 enabled GPUs to handle more than just graphics, paving the way for deep learning breakthroughs [6] - The "hardware lottery" highlights that progress stems from available technology, not necessarily perfect solutions, as GPUs were adapted for neural networks [7] - As AI scales, general-purpose chips are becoming insufficient, necessitating a rethinking of the entire system [7] Efficiency and Optimization - Quantization is used to reduce the size of numbers in AI, enabling smaller, more power-efficient, and compact AI models [8][10] - Reducing the size of parameters allows for more data movement across the system per second, decreasing bottlenecks in memory and network interconnects [10][11] - Wafer Scale Engine 2 achieves similar compute performance to 200 A100 GPUs while using significantly less power (25kW vs 160kW) [12] Future Trends - Photonic computing, using light instead of electrons, promises faster data transfer, higher bandwidth, and lower energy use, which is key for AI [15] - Thermodynamic computing harnesses physical randomness for generative models, offering efficiency in creating images, audio, and molecules [16] - AI supercomputers, composed of thousands or millions of chips, are essential for breakthroughs, requiring fault tolerance and dynamic rerouting capabilities [17][20] Global Collaboration - Over a third of all US AI research involves international collaborators, highlighting the importance of global connectedness for progress [22] - The AI supply chain is complex, spanning multiple continents and involving intricate manufacturing processes [22]