摘要：以大模型为代表的生成式人工智能将会颠覆式地改变人类的工作、沟通、学习和娱乐。所有行业都会被人工智能触及、改变、转型并提效。中国大模型与世界领先模型之间的差距已经缩小至短短几个月，受益于中国广阔的市场以及中国团队的世界顶尖工程能力和落地能力，中国 AI-Firs
分享我与《人民日报》英文客户端的深度对谈。在对谈中，我与记者交流了对人工智能热点问题的一些看法。
以大模型为代表的生成式人工智能将会颠覆式地改变人类的工作、沟通、学习和娱乐。所有行业都会被人工智能触及、改变、转型并提效。
中国大模型与世界领先模型之间的差距已经缩小至短短几个月，受益于中国广阔的市场以及中国团队的世界顶尖工程能力和落地能力，中国 AI-First 应用有望在今年崛起并跻身世界顶尖阵营。2025 年将会是 AI-First 应用爆发的元年，未来新的智算中心相当大的比例会专注于推理工作。
人际关系、信任、同理心和爱，是人类区别于人工智能的本质。人工智能会创造出许多全新的工作。而涉及人际沟通和服务导向的工作，将会是未来许多人可能会投身的领域。
以下是采访全文：
人民日报：您这两年的工作重心主要是在哪些方面？
李开复：过去两年，我在创新工场持续关注人工智能及其他高科技领域投资。此外，我还创办了AI 2.0大模型独角兽公司零一万物，该公司致力于以中国团队的创新力量，以“多快好省”的方式训出世界第一梯队性能的大语言模型，赋能千行百业，驱动实体经济的新增长范式。
人民日报：您作为AI行业的代表人物，在近期的公开场合说到了AI 2.0时代开启，您能跟我们详细解释一下什么是AI 2.0时代？为什么说AI 2.0时代开启？
李开复：我从事人工智能相关事业已有四十多年。多年来人工智能一直在努力模仿部分人类智能，但目前仅实现了人类大脑所具备的通用智能中的一小部分。
在过去两年间，我们发现计算机似乎有望具备与人类相同的通用智能。我所说的“通用”，是指计算机能够像大学生一样，全面理解人类知识的方方面面，并且能够在其他学科中迅速进行深入学习。
大约两年前，ChatGPT 首次展现出了这种通用智能。此后，在美国和中国涌现出许多具备此类能力的公司。
令人振奋的是，人工智能能够从人类所撰写的每一本书籍中汲取知识。不久的将来，人类制作的每一个视频以及说过的每一句话，都有可能成为人工智能的学习素材，进而打造出一个超级智能大脑。这个大脑所学习的数据量，将远远超出任何一个人在其一生中所能掌握的知识总量。
我们现在已经开始看到一些迹象，它不仅能进行概括、分析、撰写内容，还能进行推理和演绎，在不需要具体地被教导如何去做的情况下，它就可以解决非常复杂的数学和物理问题。
能够自主学习新事物并自我迭代，大语言模型所展现的这种能力，使我们看到了在未来十年内打造出AGI（通用人工智能）的希望，即创造出一种在总体智能水平上超越任何人类的智能体。
人民日报：众所周知，2024年“新质生产力”一度成为热词。AI作为新质生产力中的一员，您认为当下哪些AI应用在实现经济效益方面已经树立了很好的例子？给社会生产和生活带来了哪些影响？
李开复：我认为“新质生产力”这一概念非常有见地。它意味着，生产力的提升并非仅仅依赖于劳动力的增加，而是通过运用新颖独特且具有突破性的技术，从而实现价值的成倍乃至指数级增长。
人工智能不仅是这一概念的良好落地范例，甚至在我看来，它是迄今为止最佳的落地范例。因为人工智能的核心理念就是它能够像人类一样思考、推理、做决策、创造内容，并完善我们的决策，为我们提供反馈，帮助我们在任何可以想象的领域取得进步。
在传统行业中，律师可以借助人工智能完成大部分写作，其工作效率可提高五到十倍；会计师的工作效率也可以增加五到十倍，因为人工智能可以承担所有常规的数字计算工作，会计师只需指导人工智能该做什么；客服则有99%可以由机器处理，客户满意度更高，人类只需负责剩余的1%。
这种情况还适用于制造企业、房地产公司以及所有传统行业和服务行业。我们可以借助这些新人工智能训练机器人，进而大大降低生产商品的人力成本，这正是我们对新质生产力创造新价值的期待所在。更令人振奋的是，在其他被认为能够增强新质生产力的领域，借助人工智能的力量还能实现进一步的双重提升。
我认为，我们应当认识到并且接受人工智能是一种超级智能，一种规模庞大、类似人类大脑却又不同于人类大脑的存在。这意味着，人工智能可以成为每位知识工作者值得信赖的伙伴。它拥有比人类更大的记忆容量、更快的处理速度以及更全面的知识储备，但或许在人类直觉、特定经验以及人际交往等方面有所欠缺。
让人类专注于自己最擅长的事情，让人工智能发挥其优势，将直接使我们每个人的效率提升五到十倍。因此，我认为人工智能是发展新质生产力的最强大技术之一。
人民日报：我们已经看到AI这个学科已经在和不同的学科融合，比如神经和认知科学、心理学、艺术绘画等。展望未来，您觉得生成式AI的应用场景有哪些？换言之，AI可以跟哪些产业行业融合产生怎样的可能性？
李开复：我认为，当我们一般性地思考新技术的到来时就会发现，比如早期的个人电脑和互联网，或者移动设备和移动互联网，以及现在的生成式人工智能——通常当我们进入到新技术革命时，变革往往始于改变我们浏览或查看内容的方式；随后，是生产这些新内容的方式发生变化；接着，是搜索、组织和发现新内容的方式得到改进；再之后，是能够处理更丰富的内容形式，比如视频内容；最后，是进行交易和获取商业回报的方式发生变革。
对于生成式人工智能而言，也不例外。上面所说的是从任务的视角去看技术将如何演进。同时，我们也可以从人类需求的角度来思考。人类一直都有工作、沟通、学习和娱乐的需求，马斯洛人类需求理论仍然适应于AI 2.0时代。从这个角度再次回顾个人电脑和移动设备的发展，前两波技术浪潮都极大地改变了我们的工作、沟通、学习和娱乐方式，因此生成式人工智能也会带来同样的改变。
回到你的问题，答案其实是“所有行业”。你可以回想一下，过去我们是如何沟通的，最初是人与人面对面交流，然后是通过电话，接着是即时通讯、基于互联网的通话，随后是新的社交网络出现，现在我认为我们将看到人类与人工智能共同参与的全新沟通方式。
学习方式也是如此，最初是在教室里学习，然后出现了虚拟教师；工作方式同样不例外，以你的工作为例，它涉及确定采访主题、挑选采访对象、与对方沟通安排采访、准备问题、提问、获取答案、将答案转化为视频或报纸文章，就像我们现在正做的事。在未来，所有这些步骤都可以逐步实现自动化。因此，我认为所有行业都被人工智能技术触及、改变、转型并提效。
人民日报：国际上我们看到ChatGPT、Sora等生成式人工智能的不断问世，国内我们也有不少生成式人工智能模型，比如零一万物的Yi系列模型。在您看来，国内生成式人工智能产品与国际上ChatGPT、Sora这类是否有较大差距？若有，您认为有哪些差距？
李开复：是的，毫无疑问这些技术有一部分是美国人发明的，但中国人让它们变得更高效、更实用。我认为这将是根本的区别。
我在2018年写了一本书，名为《AI·未来》，我在书中谈到了移动互联网以及AI 1.0时代。这两个时代都出现了同样的情况，美国人发明了移动互联网，他们开发了最初的移动互联APP，但中国的移动互联网APP在易用性方面超过美国的APP；在AI 1.0时代，（中国）也出现了“AI 四小龙”，以及许多计算机视觉公司、深度学习公司、自动驾驶公司，这些公司可能在创新力上弱于美国公司，但是它们的落地执行力却超过了美国公司。
同样的情况延续到了生成式人工智能领域。显然，两年前ChatGPT问世时，中国可能落后了七年左右的时间。但在过去两年中，中国已经在快速学习并开发出了很多非常优质的大语言模型，模型性能非常接近美国顶尖模型，也许还比不上最好的模型，但已经相当接近。与此同时，中国模型的效率要高得多。
中国工程师确实找到了各种方法来降低成本，提出了新的算法，设计了新的模型结构，大大加速了模型训练进程的同时，使其能够在能力较差的芯片上运行，无论是国产还是非国产芯片都适配。训练速度更快，使用起来也就更快。这些中国模型所需的推理时间和推理成本，都比美国模型要小很多。
现在，DeepSeek和零一万物等中国团队与美国团队之间的技术差距从两年前的七年缩短到了现在短短几个月，这是巨大的进步。训练成本降低了十倍甚至更多，推理成本降低了大约三十倍，这些都是由中国公司取得的令人惊叹的进步，实际上这也让很多美国顶尖研究人员印象深刻、刮目相看。
但我认为最关键的还在后头——应用领域的全面突围。复盘过往的多次技术浪潮，应用层在价值链金字塔中创造了最大的经济价值。在技术领域的竞争中，中国已经具备世界顶尖工程能力和落地能力，明显超过美国的一个方向是构建APP，构建满足用户需求、创造经济价值的应用程序。
我们现在正处于这样一个阶段：无论是中国的还是美国的大语言模型，模型性能都非常优秀，而且成本很低，尤其是中国的大语言模型，成本更为低廉。这使得那些聪明的APP开发者可以将精力集中在如何构建人工智能APP上，而无需自身成为人工智能专家。我认为现在在中国，AI-First 应用百花齐放的土壤已经具备，那些在移动互联网时代就具备优秀APP开发能力的人，如今已经拥有了大展身手的舞台。
我期待2025年能成为中国 AI-First 应用真正崛起并跻身世界顶尖阵营的一年。
人民日报：我们看到国际上有许多对华“脱钩”的炒作或是论调，在您看来，如果对华“脱钩”会对AI发展造成什么样的冲击？您曾表示中国大模型公司要走出不同于OpenAI的第二条路，所谓的“第二条路”是什么？
李开复：我认为OpenAI所走的第一条道路是，每一年半就多投入十倍以上的资金，训练一个参数量非常大的模型，并持续这样做直到它能为人类所用。这被称为Scaling Law，但这条道路对中国来说是不可行的。我认为中国更适合的道路是实用主义，注重解决问题、提高效率并创造价值。
正如我之前所描述的第二条道路——中国的工程师们非常擅长找到巧妙的工程解决方案，并真正实现垂直深度整合，让研究员、工程师、芯片设计师共同合作，打造出非常高效的产品。
我认为，用一句话来描述第二条道路以及它为何取得了令美国研究人员都惊叹的惊人成果，那就是：“需求是创新之母”。（Necessity is the mother of innovation.）
“需求”是指，从现实情况来看，我们只有美国1/3至1/50的资源，而且我们无法获取最先进的芯片，所以我们有什么就用什么，但我们会尽力做到最好。我认为这正是中国公司和中国工程最强的地方。
需求是创新之母。过去，我曾经被在北京所遇到的中国研究员身上的勤奋、愿意投身艰苦工作的精神所打动，并一直铭记至今。那是在1990年，这也是我选择回到中国工作的原因之一。因为我认为，和具备这种职业道德的人一起，我们能够创造奇迹，而这正是当下生成式 AI 领域正在发生的事情。
人民日报：为满足人工智能产业发展的需要，全国各地都开始建设智算中心。与传统算力中心相比，您认为新一代的智算中心应该具备怎样的特点？
李开复：智算中心实际上承担着两项任务。一是帮助构建这些模型，通常被称为训练；二是帮助这些模型投入使用，这被称为推理。我认为这两项工作都很重要。
我乐观地认为未来会出现很多优秀的AI-First应用程序，考虑到中国庞大的用户数量以及我对人工智能大规模应用的乐观态度，我会更倾向于在推理而非训练上加大投入。
在过去，训练是智算中心被寄予厚望的主要使用方式，因为当时基于生成式人工智能的APP并不多。未来，我乐观地认为这类APP会越来越多。未来数据中心最大概率会被用于推理，因此我认为它们应该配备更多推理芯片，并且被合理地部署好，以便能够更高效地服务于全中国或至少部分区域内的所有人。
训练智算中心和推理智算中心是不同的。训练智算中心并不侧重于应对大规模用户使用场景，其核心在于集中大量数据并进行持续数月的模型训练。而推理智算中心则需要确保任何用户随时随地都能访问，响应速度非常重要，强大的网络连接也非常关键。当这些新的智算中心建成时，我认为应该有相当大的比例应该专注于推理工作。
人民日报：隐私和安全一直是人工智能领域的关注焦点，例如人工智能换脸技术所带来的风险。目前人工智能行业正在采取哪些措施来解决这些问题？
李开复：我认为人工智能会有不少风险和挑战，隐私只是其中之一。作为技术谨慎乐观派，我认为，但我们不应该过度放大这些问题，我相信新技术产生的问题终究可以被新技术解决。
面对这些新技术风险，我们将需要对应的技术解决方案，来抓获深度伪造者，鉴别被深度伪造的视频或图片。这些解决方案必须通过技术手段来开发。这些技术还可以更进一步被应用于其他场景，如辨别一些内容是否为原创内容。另一种机制是，在识别图像时放置一个不可移除的水印，这样你就可以知道图片是否被篡改过。这些都是需要进一步研究的技术。
但还有许多其他担忧，比如有人向语言模型询问“如何制作有害的毒品或武器”怎么办？我们如何防止有人提出这些问题，以及如何防止犯罪分子利用大语言模型来做坏事或制造虚假信息？我认为这些都是另外需要解决的问题。
制定法规是很有必要的，要明确使用这些技术从事非法有害行为的人将受到严厉惩罚，以此来阻止人们错误地使用这些技术。关键在于深思熟虑如何设置防护栏，如何通过明确且严厉的手段惩罚违法者来形成威慑。
此外，我个人更倾向于使用现有法律法规并将其扩展到人工智能领域，以非人工智能犯罪的惩罚方式为参考。复盘过往几次技术革命，新技术的传播和发展最终总是利大于弊，因此，限制新技术的广泛传播和发展并不是一个好主意。很多担忧虽然是真实的，但设置防护栏和法律法规，应该针对具体的非法行为，而不是一刀切地减缓技术的发展，因为那将会降低国家的竞争力。
人民日报：人工智能的渗透已成为不可逆转的趋势，人们在这个过程中可能会感到困扰或焦虑。在你看来，有哪些领域是人工智能无法取代人类的，你有什么建议可以帮助像我这样的人缓解对人工智能的焦虑？
李开复：焦虑是正常的，但人工智能的广泛传播和持续快速迭代也是无法阻挡的。首先，我们必须将消极的焦虑转化为积极的自我提升，而不是在焦虑之下无所作为，催生无助感。
未来有许多工作仍然会存在，就像我们看到汽车的出现取代了许多工作，但人类的工作总数并没有减少。计算机、移动手机，每一项发明都取代了一些工作，但新的工作也会随之而来。
那么哪些工作是比较安全的呢？首先，提升自己、使自己成为人工智能的老师，会是最好的工作。各行各业最顶尖的工作机会将依然存在，因为总需要有人为人工智能指明方向。
第二类比较安全的人，是那些能够洞察人类的优势所在，专注于发挥这些优势，且愿意与人工智能合作的人。人类有一些优势是人工智能所不具备的。其中一点是做真正的颠覆式创新，创造以前不存在的全新概念，因为人工智能是通过数据学习的。杰出的艺术家和研究员可以继续做出伟大的事业，这些成果可以被用于教导人工智能。然而，我也承认，这只是一个相对较小的群体。
还有一些可能更契合大众需求的工作选择。在我的几本AI书籍中都曾提到过的最重要的几点，就是人际关系、信任、同理心和爱。人工智能没有情感，它无法与人建立联系。所以我认为人们需要普遍关注这几项能力：理解他人的能力，获得信任的能力以及沟通和说服他人的能力。专注于所谓的软技能，即沟通、同理心、理解、建立联系和产生信任的能力，这些是人类独有的。
在医疗行业，未来的医生将更多地扮演富有同情心的护理者的角色，而人工智能则在后端负责确认最佳的药物组合。医生会问诊并梳理出问题所在，这些健康问题患者不会想告诉AI，但会告诉一个他认为值得信任的人。其他需要沟通、同理心、联系他人的职业也是如此。我认为，许多涉及人际沟通和服务导向的工作，将是未来许多人可能会投身的领域。
最后，我相信人工智能将创造许多新工作。今天，AI 已经创造了数千万个工作岗位，可能你没有意识到，它被称为人工智能数据标注。这个工作岗位可能不会永远持续存在，但是类似的新机会将被创造出来。
当移动互联网诞生时，现在回想起来，它也创造了很多新的工作岗位。线下零售店的店主、农民，现在都可以通过APP来对外销售自己的商品。随着科技的广泛应用，就业市场将发生巨大变化，所创造的工作种类将数不胜数。我们现在还不知道它们是什么，但我们可以耐心等待。我敢打赌，现在全球已经有数千万数据标注师，但是五年内，人工智能会创造十倍于此的新工作机会。
我深信，人类的智慧之光终将指引我们找到未来前行的路。
本文摘编翻译自《人民日报》英文客户端专访，原文如下：
Look forward to 2025 as the year where Chinese AI apps really rise up: Kai-Fu Lee
By Xu Zheqi, Cheng Weidan, Chen Lidan, Liang Peiyu and He Jiahao
Q: What have you been focusing on over the past two years?
Lee: For the last two years, I continued to make investments in AI and other fields on behalf of Sinovation Ventures. I also co-founded a company called 01.AI, which is really building large language models from China, and building models that work well for any language.
Q: As a trailblazer in the AI industry, you recently mentioned that the era of a new generation of AI has begun. Could you explain in detail what this era entails and why you believe it has started?
Lee: Yeah. I've been working on AI for over forty years. And for many years, AI has tried to emulate a little bit of human intelligence. But it only did one little sliver of the entire general brain that our brain has which we call intelligence.
In the last two years, we saw that it appears possible for computers to have that same general intelligence. And when I say general, I mean that it understands everything about human knowledge in a similar way to a college student. And then it can learn further and very quickly in any other discipline.
This general capability was made possible first by Chat-GPT about two years ago. And then both the US and China have seen more companies that have delivered such capabilities.
The excitement is that this capability for AI to learn from every book ever written. In the future soon, every video ever created, and everything ever spoken that it can create a super brain that learns from more data than any human can ever do in a human lifetime.
And we are now seeing glimpses where it can start to do not only generalization, analysis, writing content, but is able to do inference and make deductions, and solve very difficult mathematics problems and physics without ever having been taught to do so specifically.
This ability built on top of the general large language model with an ability to learn new things by itself and teach itself, gives us hope that we'll reach what's called AGI or artificial general intelligence which is overall smarter than any human being within the coming decade.
Q: "New quality productive forces" has been a buzzword in 2024 in China, with AI being a key player. Which AI applications do you think are good examples of achieving economic benefits? How have they impacted industries and people's lives?
Lee: The ideas of new quality productive forces I think are extremely insightful. It's the sense that productivity isn't just putting more labor, but rather using novel and new breakthrough technologies that can multiply or even exponentiate to see greater value being produced.
AI is not only a good example but I think by far, the best example of such technologies because the whole idea of AI is that it can do what humans do, think, reason, make decisions, create contents and refine our decisions, give us feedback, help us improve in any imaginable domain.
In traditional industries, a lawyer can be five or ten times more productive with AI doing much of the writing for the lawyer. An accountant can be five or ten times more productive, because AI does all the routine number crunching, leaving only the accountant to instruct AI what to do; and customer service can be handled 99 percent by machine with a higher level of customer satisfaction, with people only needed for one percent. This goes on, it goes to manufacturing companies, it goes to real estate companies and all traditional industries and service industries. We can have robots that are taught by these new AI, that can dramatically reduce human labor cost for producing goods, all of which lead to the expectations we have for this new quality value creation and productivity creation. And then the most exciting thing is in other areas that are largely viewed as new quality productivity enhancements, AI makes it a double enhancement.
I think the whole idea of thinking of and understanding that AI is all about another super smart, super big, human-like, but different from human brain. That means it's a partner that each of us as a knowledge worker can rely on, a partner who has a much larger memory, much faster processing, and much more complete knowledge. But maybe it lacks our intuition, maybe it lacks our particular kind of experience, maybe it lacks our human-to-human connections.
Keeping humans to do what humans do best and letting AI do what it does best leads directly to each of us, being able to be five or ten times more productive. So that I think is by far the most powerful technology that will lead to this new quality productivity gain.
Q: AI has already been integrated into various disciplines, such as neuroscience, cognitive science, psychology, and the arts. Looking ahead, what new application scenarios do you see for generative AI? What are the potential opportunities for AI to integrate with other industries?
Lee: I think if we think generally about the coming of a new technology, let's say earlier with PC and internet, or mobile and mobile internet, and now with generative AI. Usually when we enter this new technological era, it begins with how we change the way in which we browse or look at content. Then with how we produce this new content, then with how we search and organize and find new content. Then with how we deal with richer forms of content like video, then with transactions and making money.
With generative AI, it will be no exception. This is kind of at a task level, how things will improve. And also, we can think about it as human needs. Humans have always had a need to work, communicate and learn, and entertain ourselves. Again, with PC and mobile we've seen two waves in which these technology waves changed the way we communicate, work, learn and entertain ourselves, so we're going to see AI do the same.
The answer to your question really is everything.
If you think about how did we communicate in the old days, it was person-to-person, then it was through telephone, then it was through instant messaging, then it was through internet-based phone calls, then it's through new social networks. Now I think we're gonna see a brand new way of communicating with humans and AIs together. Also, learning used to be in the classroom, then you could have virtual teachers.
If you think about your job, it's about finding out what topics to have, picking out who to interview, talking to the person about arranging the interview, preparing the questions, asking the questions, getting the answer, turning the answer into a piece of video or a newspaper article, like we're doing right now. In the future, all of this can be one step at a time automated. So I think really there is no industry that will not be touched, changed and transformed and made efficient with AI technologies.
Q: Internationally, we've seen the rise of generative AI applications like Chat-GPT and Sora. In China, there are similar models such as Yi series models. From your perspective, is there a significant gap between domestic generative AI products and international ones? If so, what are the specific differences?
Lee: Yes, there's no doubt that Americans invented most of these technologies, but the Chinese made them more efficient, more usable. I think that will be the fundamental difference.
I wrote a book back in 2018 called AI Superpowers, where I talked about the mobile internet and also the AI 1.0 era. Both of which saw the same thing, that Americans invented the mobile internet. They made the first apps on mobile internet, but the Chinese mobile internet apps beat the American mobile internet apps in usability.
AI 1.0 also, there were "the four dragons," and many computer vision companies, deep learning companies, and autonomous vehicle companies that out-execute the American companies, although American companies generally out-invent the Chinese companies.
The same thing carries over. If we look at generative AI. Clearly one could also say that two years ago when Chat-GPT came out, China was probably easily seven years behind. What has happened in the last two years is that China has learned and developed all these large language models that are very very good, very close to American top models, maybe not quite as good as the best ones, but fairly close. Yet they are so much more efficient.
The Chinese engineers really found all the ways to reduce cost and come up with new algorithms, come up with new model structures, come up with faster training, make it work on lower capability chips whether domestic or not, and really made the training process much faster. When it's faster to train, it's faster to use. Using these models called inference time, compute is also a fraction of the American costs.
We are already seeing that Chinese technologies are around six months behind the US, starting two years ago, seven years behind, now six months behind, huge progress. Cheaper to train by a factor of ten or so or more. Cheaper to infer by a factor of 30 or so. These are amazing progress made by Chinese companies and it's actually made a lot of top American researchers really turn their heads and become very impressed.
But I think the best is yet to come, and the best, the single area where China clearly outshines the US in technologies is in building applications, applications that cater to users' needs and applications that create economic value.
And I think we're now at a stage where the LLMs (large language models) are very good, Chinese or American, and very cheap, in particular Chinese. All these smart application developers, who are not necessarily AI experts, can now turn their attention to how can they build an AI app. I think now hundreds of flowers can blossom in China, with all the people, who have the capability of developing great apps, who have done it in the mobile era. Now the stage is all set for them to enter.
And I look forward to 2025 as the year when Chinese AI apps really rise up and become among the best in the world.
Q: There has been rhetoric about decoupling from China on the international stage. In your opinion, what impact would decoupling have on the AI industry? You once mentioned that Chinese companies need to find a second path distinct from OpenAI. could you elaborate on what you mean by this second path?
Lee: I think the first path taken by OpenAI is every year and a half, spend 10 times more money, train a really big model, and keep going until it beats humans. That has been called the Scaling Law, which is a model not feasible for China.
I think the category that China is in is practical, get things done, make it efficient and make it valuable, so as I described earlier the second path.
The Chinese engineers are so good at finding clever engineering solutions, and doing vertical deep integration to let the researchers, the engineers, and the chip designers work together to make something very efficient.
I think the single sentence that describes the second approach and why it has led to a stunning result that even impressed the American researchers is the following sentence: Necessity is the mother of innovation.
The necessity is the reality that we have one-third to one-fiftieth as much resource, and we don't have access to the most advanced chips. So we have what we can, but let's make do with the best that we can do. This has been I think the strongest point of Chinese companies and Chinese engineering.
Necessities is the mother of innovation. That's why I really remember and I was very moved by the diligence, and willingness for hard work by the Chinese researchers I met in Beijing. This was in 1990, and that's one of the reasons I returned to China to work. Because I felt with people, with such work ethic we're going to make miracles happen, and that's exactly what has happened in generative AI today.
Q: To meet the needs of AI industry development, regional governments have been building and supporting intelligent computing centers. Compared to traditional ones, what characteristics should these new-generation AI computing centers possess?
Lee: The computing centers really perform two tasks. One is helping to make these models, which is usually called training. Secondly, helping these models to become used, which is called inference. And I think both of these are important workloads.
I think knowing how large the number of users are in China and how optimistic I am about the massive adoption of AI and how likely I think there will be so many great AI apps, I would be making a bigger bet on the inference than training.
In the past, training was the primary way that people wanted to see data centers used, because there weren't very many apps. Now that there are more and more apps, I'm optimistic about the future. I think the most likely way, these data centers will be used is through inference, so I think they should be populated with inference chips, and data centers that are well set up to service the people, in all of China, or at least regionally in a very efficient way.
The training data centers and inference data centers are different. A training data center isn't that concerned about massive connectivity, it's more concerned about getting all your data here and just keep training for two months. Inference is about anyone can access anywhere at any time, getting very fast. Response time is very important. Very strong networking is very important.
So, when these data centers are built, I think proportionately a very large number should focus on the inference workflow.
Q: Privacy and security have always been concerns regarding AI, such as risks posed by AI face-swapping technologies. what measures are the AI industry currently taking to address these issues?
Lee: I think privacy is not something that is the only issue with AI. AI actually has many issues, privacy is one of them.
I think those technologies will require technological solutions to catch the deepfake makers, to identify videos or pictures that are deepfaked, and those will have to be developed by technologies. Those technologies can be used even more in computing to find out if something is not original. Another mechanism would be, at the time of capture, placing an irremovable watermark. so that you know when the picture has been altered or not, These are new technologies that need to be invented.
But there are also many other worries, what about people who ask the language model how to make a harmful drug or weapon? How do we prevent those questions from being asked, and also how do you prevent criminals from using large language models to either do something terrible or to create misinformation. I think those are another set of issues that need to be addressed.
I think regulations are definitely needed that make it clear that people who use these technologies for illegal, harmful purposes, will be severely punished as a way to impede people from using these technologies in a wrong way.
What's important is to start thinking about the ways to protect a guardrail, the ways to create deterrence by significant and clear ways to punish offenders. Also, I think the laws and regulations should focus on the way that similar non-AI crimes are committed.
It is not a great idea to limit the proliferation of technologies. Because I think in the end, technology will do a lot of good. A lot of these concerns are real. Guardrails and regulations need to be put in place. They should be done on specific harmful, illegal acts, as opposed to generally slowing down the technologies because that will reduce the country's competitiveness.
Q: The penetration of AI at present has become an irreversible trend and people may be troubled or have anxieties in this process. In your opinion, are there any fields in which AI cannot replace humans, and what suggestions would you give to people like me to adjust AI anxiety?
Lee: Anxiety is normal, but AI’s proliferation and rapid continual improvement is unstoppable. First, we have to turn anxiety into proactive self-improvement, not turn anxiety into inaction and feeling of helplessness.
There are many jobs that will be around, just like we've seen automobiles remove a lot of jobs but total human jobs are not lacking in any means. Computers, mobile phones, every invention has replaced some jobs, but new ones will come around.
What are the types of jobs I think that are secure? First, people who are able to elevate themselves to be the masters of AI that would be the best job. The top jobs are gonna be around because someone needs to give AI direction. The second group would be those who can understand human strengths, focus on those strengths and work with AI. Some of the human strengths that AI doesn't have. I think one is the truly breakthrough innovation, inventing brand new concepts that didn't exist before. AI learns from data. Amazing artists and researchers can also continue to do great work, and in fact use it to teach AI, but I also acknowledge that's a small group of people.
What are some larger groups of people, types of things. I think one of the most important things which I've stated in all of my books is human connection, trust, empathy, and love. AI doesn't have emotion, AI doesn't connect with people. So I think people need to generally focus on their ability to understand other people, gain their trust and ability to communicate and convince other people. Focusing on so-called soft skills, the ability to communicate, empathize, understand, create connections, and render trust, these are uniquely human.
If we think about the medical profession, future physicians will be compassionate caregivers, but AI will be the back end that figures out what's the best drug combination. The physician has teased out what the issues are that maybe the patient wouldn't tell an AI, or wouldn't think to tell an AI, but would tell a trusting human.
And the list continues with all the other professions, that require communication, empathy, and connection. I think a lot of the service-oriented, human-to-human service-oriented jobs, I think will be what the large number of people might do.
And then lastly I'm sure AI will create a lot of jobs. Today AI has already created tens of millions of a category of a job, which you are probably not aware of, it's called labeling data for AI. That may not last forever, but similar opportunities will be created.
When mobile internet was created, now that we look back, it created new categories of jobs. People who have shops, the farmers who can now sell through top apps. The job market will change depending on the adoption of technology, and the types of jobs that are created will be numerous. We don't know what they are, but we can patiently wait. I bet in five years the number of new jobs created by AI will be ten times larger than the tens of millions of data laborers that exist throughout the world.
I have confidence that humans have the wisdom to figure out what to do.
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来源：新浪科技
