2023年6月27日，夏季達(dá)沃斯論壇在天津梅江會展中心開幕，本屆論壇的主題是：“企業(yè)家精神：世界經(jīng)濟(jì)驅(qū)動力”。國務(wù)院***李強(qiáng)、世界經(jīng)濟(jì)論壇創(chuàng)始人兼執(zhí)行主席Klaus Schwab出席開幕式并致辭。

當(dāng)天下午，中國工程院院士，清華大學(xué)講席教授、智能產(chǎn)業(yè)研究院（AIR）院長張亞勤出席了Generative AI: Friend or Foe（生成式人工智能：友或敵）分論壇并發(fā)言。一同出席的還有IBM公司董事長兼總經(jīng)理陳旭東，斯洛文尼亞數(shù)字化轉(zhuǎn)型部部長Emilija Stojmenova Duh，香港科技大學(xué)電子及計(jì)算機(jī)工程系講席教授馮雁，可之科技創(chuàng)始人王冠，本次分論壇由世界經(jīng)濟(jì)論壇AI、數(shù)據(jù)和元宇宙業(yè)務(wù)負(fù)責(zé)人李琪主持。

張亞勤院士首先分享了他對ChatGPT、Stable-diffusion等生成式人工智能的發(fā)展的三個(gè)觀察：

• ChatGPT是第一個(gè)通過圖靈測試的軟件，這對于計(jì)算機(jī)科學(xué)家來說是一個(gè)重大的成就；

• 生成式AI為實(shí)現(xiàn)人工通用智能（AGI）提供了一條途徑，雖然還不完全是AGI，但已經(jīng)顯示出了可能性；

• 大模型是人工智能的操作系統(tǒng)，就像PC時(shí)代的Windows和Linux，移動 時(shí)代的iOS和Android一樣，它將完全重塑整個(gè)生態(tài)系統(tǒng)，無論是底層芯片還是應(yīng)用層面。

他表示，如今整個(gè)技術(shù)已經(jīng)完全改變了行業(yè)，包括中國。中國在基礎(chǔ)研究、算法、行業(yè)應(yīng)用等方面都做了很好的工作，盡管ChatGPT不是在中國發(fā)明的，但在過去的六個(gè)月左右，有近百家新興的生成式人工智能公司涌現(xiàn)，不論初創(chuàng)公司還是大公司，充分競爭的市場才是好市場，充分競爭的公司才是好公司。大模型時(shí)代才剛剛開始，42公里的馬拉松我們剛跑到5公里，算力、數(shù)據(jù)不夠都不成問題。中國在PC時(shí)代落后于美國，但在移動互聯(lián)時(shí)代領(lǐng)先于美國（數(shù)字支付、微信、短視頻），AI時(shí)代要給創(chuàng)業(yè)者、科研人員、企業(yè)更多信心。

隨后張?jiān)菏恐亟榻B了清華大學(xué)智能產(chǎn)業(yè)研究院（AIR）。AIR是一個(gè)面向第四次工業(yè)革命的一個(gè)國際化、智能化、產(chǎn)業(yè)化的研究機(jī)構(gòu)。在產(chǎn)業(yè)方面，我們希望和產(chǎn)業(yè)合作來解決真正的問題、實(shí)際的問題。同時(shí)，作為清華大學(xué)的研究機(jī)構(gòu)，我們肩負(fù)著人才培養(yǎng)的職責(zé)和使命，目標(biāo)是培養(yǎng)未來的CTO、未來的架構(gòu)師。AIR的科研方向包括三個(gè)，也是人工智能在未來五年十年具有巨大影響力的三個(gè)方向。第一個(gè)是機(jī)器人和無人駕駛，又稱為智慧交通；第二是智慧物聯(lián)，特別是面向雙碳的綠色計(jì)算、小模型部署到端等；第三是智慧醫(yī)療，包括藥物研發(fā)等。以機(jī)器人和自動駕駛研究為例，這方面的研究需要海量的數(shù)據(jù)，盡管我們與百度Apollo合作，同時(shí)也有自己的機(jī)器人，但收集的真實(shí)數(shù)據(jù)遠(yuǎn)遠(yuǎn)不夠，所以我們提出了Real2Sim2Real 現(xiàn)實(shí)-仿真-現(xiàn)實(shí) （RSR）的概念，用仿真技術(shù)來增強(qiáng)數(shù)據(jù)，模擬駕駛長尾現(xiàn)象，實(shí)現(xiàn)真實(shí)場景和仿真場景的雙向連接。

在生物計(jì)算研究方面，AIR最近開源了一個(gè)輕量級的模型BioMedGPT-1.6B，用大數(shù)據(jù)、模型結(jié)合規(guī)則、知識體系、知識圖譜把知識和數(shù)據(jù)相結(jié)合，里面有文獻(xiàn)信息、專利信息、蛋白質(zhì)基因、細(xì)胞等這些數(shù)據(jù)，同時(shí)AIR也有已經(jīng)做好的知識圖譜，集成訓(xùn)練出基礎(chǔ)模型，經(jīng)過一些監(jiān)督微調(diào)訓(xùn)練，就可以做各類下游的任務(wù)，包括蛋白質(zhì)結(jié)構(gòu)解析、分子對接、靶點(diǎn)生成等。此外，AIR還有很多其他的科研工作，包括多模態(tài)大模型，模型間的交互，強(qiáng)化學(xué)習(xí)，邊緣部署等，目標(biāo)是通過模型輕量化和系統(tǒng)底層優(yōu)化等手段，支撐模型在邊緣端的高效運(yùn)行。

分論壇上，其他嘉賓也從不同角度分享了對生成式人工智能的看法和見解。IBM公司董事長兼總經(jīng)理陳旭東著重介紹了IBM在生成式人工智能方面的技術(shù)創(chuàng)新和商業(yè)應(yīng)用，以及公司如何幫助企業(yè)實(shí)現(xiàn)自身AI的發(fā)展和數(shù)據(jù)安全管理。斯洛文尼亞數(shù)字化轉(zhuǎn)型部部長Emilija Stojmenova Duh闡述了斯洛文尼亞政府在推動數(shù)字化轉(zhuǎn)型和支持生成式人工智能發(fā)展方面的政策和舉措，如將AI引入學(xué)校教育、提升公務(wù)員和公民的數(shù)字化能力、開辟與公民溝通的新渠道等。她也指出了AI可能帶來的偏見問題，呼吁消除人工智能帶來的偏見。香港科技大學(xué)電子及計(jì)算機(jī)工程系講席教授馮雁深耕對話型AI領(lǐng)域研究近30年，她驚嘆于如今大模型的智能涌現(xiàn)，同時(shí)也呼吁大家關(guān)注AI治理，并建議應(yīng)該探尋如何與機(jī)器更好地合作，而非對立。可之科技創(chuàng)始人王冠則重點(diǎn)探討了AI大模型在教育領(lǐng)域的應(yīng)用，致力于提升優(yōu)質(zhì)教育的規(guī)模化，降低甚至消除教育資源獲取的不平等。

分論壇最后，嘉賓們與現(xiàn)場觀眾還就生成式人工智能的機(jī)遇與挑戰(zhàn)，以及未來的發(fā)展趨勢和合作方向進(jìn)行了熱烈的討論和互動。生成式人工智能是人工智能領(lǐng)域的一個(gè)重要方向，也是全球經(jīng)濟(jì)社會發(fā)展的一個(gè)重要驅(qū)動力。嘉賓們表示，希望繼續(xù)加強(qiáng)跨國界、跨領(lǐng)域、跨學(xué)科的交流和合作，共同推動生成式人工智能的科學(xué)研究和實(shí)際應(yīng)用，為解決全球性問題和提升人類福祉做出貢獻(xiàn)。

以下為張亞勤院士對話原文：

Cathy Li: You are a industry veteran, with your experience with Vidua, Microsoft and now you're working atTsinghua 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.

1. 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.

2. 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.

3. 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 verticlemodels. 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.