On June 27, 2023, the Summer Davos Forum opened at the Meijiang Convention and Exhibition Center in Tianjin. The theme of this forum was "Cooperation in a Fragmented World." Chinese Premier Li Qiang and Klaus Schwab, the founder and executive chairman of the World Economic Forum, attended the opening ceremony and delivered speeches.
In the afternoon, Zhang Ya-Qin, a member of the Chinese Academy of Engineering, a chair professor at Tsinghua University, and the president of the Institute for Artificial Intelligence (AIR), attended the sub-forum "Generative AI: Friend or Foe" and delivered a speech. The following is the transcript of the dialogue.
Cathy Li: You are a industry veteran, with your experience with Vidua, Microsoft and now you're working at Tsinghua University. Can you tell us a bit more about, in particular the generative AI landscape in China?
Ya-Qin Zhang: It's quite interesting; we had a similar panel about 7 years ago at the winter Davos, and now we're here in China. The whole technology has completely transformed the industry, including in China. I'll talk about China a little bit more later, but I'd like to spend one minute summarizing my observations regarding ChatGPT and Stable-diffusion over the last couple of years.
ChatGPT is the first software that actually passed the Turing test. For a computer scientist this has been a major endeavor to develop something that can pass the Turing test.
This leads to AGI. It's not exactly AGI yet but it does provide them a pathway towards artificial general intelligence that is another goal that we've been trying to pursue.
More importantly, for industry, I consider GPT as an operating system for AI. Back in the PC days, we had Windows and Linux. In the mobile days, we had iOS and Android. So, this is the new operating system for the era of AI. It will completely reshape the whole ecosystem, whether it's the semiconductor or the application ecosystem. For example, Professor Wang just talked about education, which is actually a vertical model based on the large operating system. The data he used to train the exams is not the same data used to train the GPT, but it really works out because you can have an operating system that is a large language model, and then you're going to have a number of vertical models for different industries. They will have applications built on top of that. So, the industry world will be very different. All the apps and models will be rewritten and completely restructured.
All these years, China has been doing some terrific work in basic research, algorithms, and industry applications in every sector. And even though ChatGPT was not invented in China, there are almost a hundred companies that have emerged in the last six months or so in the generative AI space. Some of these companies are developing large models, while others are diving into generative AI for vertical models that can generate not only language but also images, videos, robotics, and even in the biological computing space. There are tremendous activities going on in China, and Professor Wang's company is one of them.
Cathy Li: I wanted to go back to you in your new capacity as a professor at Tsinghua University and also the dean of Institute for AI Industry Research. Can you elaborate how your research has integrated and incorporated genital AI and what are some of the significant outcomes so far that you're allowed to share.
Ya-Qin Zhang : I started this lab when I retired from Baidu about 3 years ago. We are obviously doing basic research, but a lot of our work involves applying that research to real-world problems. We use general AI for almost everything we do.One of our research focuses is on robotics and autonomous driving. Obviously, we need to collect a lot of data. We work with Baidu Apollo, which has hundreds of cars driving around in China, collecting a lot of data. We also have robots that collect data. However, the data we currently have is still very small compared to what we need. So, we use general AI to augment some of this data. Additionally, we use general AI for simulations because there's a dilemma. When you put a car on the street, you want to avoid accidents, but the goal of model training and algorithms is to minimize accidents, which means we don't have enough accident data. This is where stable-diffusion and the techniques we use come in handy. They allow us to generate long-tail cases, which have been extremely helpful. Furthermore, it enables us to establish end-to-end connectivity, from real-world scenarios to simulation and back to real-world scenarios. I call this "RSR," which stands for "real scenario to simulation and simulation back to real scenario."The second example is in biological computing, which is also one of our major efforts. We have built a GPT called BiomedGPT, similar to the education model, but focused on the biological and medical field. It doesn't have trillion parameters; rather, it has only 1.6B parameters. This model gathers data from various sources, including the protein structure, molecus structure in cells, genetic structure, literature, and patent data. The advantage of this model is that once you have it, you can easily generate downstream tasks, such as predicting and generating protein structures, performing molecular docking, and determining binding structures. We also have individuals working on multi-models, large models, and model-model interactions.Xudong just mentioned the ability to use a large model to train more models. In the future, when you attempt to accomplish a task, you can utilize a federation of different models, obtained from different companies and sources, including open-source and closed-source, as well as various verticle models. Additionally, we have people working on reinforcement learning. Moreover, we are deploying large models onto edge devices such as phones, robots, and IoT devices. However, I must note that this poses significant risks. When connecting the information world to the physical and biological world, there will be a plethora of safety issues and risks.
