Yesterday, Li Mu, an esteemed alumnus with a storied career in journalism, returned to his alma mater, Shanghai Jiao Tong University, for a lecture on LLM (Legal Linguistic Model) and his personal career journey. The event was attended by students and faculty, with the talk being transcribed from a video uploaded by Bilibili user @KoalaKlkl, to whom the university expressed gratitude.
In his opening remarks, Li humbly dismissed the title of a distinguished computer science alumnus, stating that it had been many years since he last visited. He mentioned that his reunion with his undergraduate mentor, Professor Li, led to the impromptu lecture. Initially, Li planned to delve into the intricacies of language models, but he decided to include personal anecdotes and reflections on the choices he made throughout his career, considering the diverse backgrounds of the audience.
The lecture was divided into two parts, with the first focusing on the current state and future prospects of language models. Li explained that language models can be broken down into three components: computational power, data, and algorithms. These elements work together to feed data into the model, which then learns to identify patterns and modify them to generate desired outputs. He used a metaphor, comparing machine learning to traditional Chinese medicine and deep learning to alchemy, highlighting the evolving nature of these technologies.
Li further elaborated on the importance of data, comparing it to the raw materials in alchemy. He emphasized the challenges of data acquisition, akin to the herculean efforts of fictional characters searching for rare ingredients. The significance of computational power, or fire, was also discussed, with advancements in hardware allowing for more efficient and powerful models. Algorithms, or the 丹方 (Dan Fang), represent the formula that binds these elements together, continuously improving and requiring meticulous attention to detail.
The current wave of language models, Li pointed out, differs from the previous era of deep learning. While earlier models were more specialized, akin to curing specific ailments, the latest advancements aim to imbue models with a soul, enabling them to solve a wider range of problems. This progression represents the ongoing evolution of technology.
In the second part of the lecture, Li forecasted the future developments in hardware, data, and algorithms. He began with the crucial role of bandwidth, as current model training often relies on distributed systems, where bandwidth is a major bottleneck. He cited the advancement from 400Gigabits to 800Gigabits in fiber-optic capacity and highlighted NVIDIA’s GB200 system, a testament to the industry’s efforts to address these limitations.
Li also discussed the trend towards more compact and interconnected hardware, utilizing water cooling technology to improve heat dissipation and increase computing density. This enables faster communication between chips, reducing latency and enhancing distributed training performance. He mentioned that NVIDIA’s GB200 system allows for better GPU communication, echoing the need for more closely integrated hardware.
In conclusion, Li Mu’s lecture at Shanghai Jiao Tong University provided a blend of technical insights and personal reflections, offering a unique perspective on the evolving landscape of language models and AI. His discussion on the future of hardware, data, and algorithms showcased the ongoing advancements that will shape the field and inspire the next generation of researchers and practitioners.
Note: The original text was written in Chinese, and this translation has been provided for English readers. Some nuances and cultural references may be lost in translation.
【source】https://www.jiqizhixin.com/articles/2024-08-26-5
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