It is normal for this kind of negotiation to not be able to go through the entire process at one time. Even if you visit with a clear purpose like Robin Li did, you will carefully check the contents of the contract again after the oral agreement.

Google is preparing to draft a sharing contract within one to two days, which is already very fast.

The overall intention has basically been achieved, and joining Google is a two-way street.

Meng Fanqi needs to use TensorFlow (TF), the deep learning framework to be developed by Google, but the current code implementation is too inconvenient.

Google's massive computing resources and later tensor computing unit clusters are also indispensable tools for Meng Fanqi to achieve various pioneering achievements in the future.

Thousands of computing units, whether they are Nvidia's GPU or Google's TPU, are always more comfortable in large factories.

If he builds a cluster by himself, not to mention the cost of hundreds of millions, it will be difficult for him to afford the losses just to support the continuous operation of these devices.

So at this point, Meng Fanqi must join Google. Google has also accepted the split method, but Google needs to carefully evaluate how to divide the split.

Now that the general direction of employment has been decided, the remaining time will naturally involve some bragging.

Don't underestimate this part, especially before the contract is officially signed. If the pie is spent well, the budget will be cut to pieces.

I really fooled you. With some value and emotion, it is very possible to win people over with a low budget.

"I've been at Google for almost fifteen years, and the company has seen the rise of the Internet, and now I think I'm leading the rise of artificial intelligence."

Now this is the time to discuss the past and future vision after talking about the business. Jeff would like to introduce some of his and Google's plans and blueprints for the future of AI.

"Helping computers recognize objects, understand language and speech, and even conduct conversations, things that seemed like a fantasy in the past, are now gradually becoming a reality."

"Taking the direction of computer vision as an example, in the past five to ten years, computers have rapidly developed the ability to see, and judging from your latest achievements, they have rapidly reached the level of human beings.

Level."

Technology in the AI ​​era is developing too fast, which is the core reason why Jeff is willing to spend a lot of money to recruit talents.

"Google now has many scenarios where it hopes to develop AI technology. We want to achieve mutual translation of more than 100 languages ​​so that people can communicate better; we want to intelligently analyze medical images and more accurately predict and diagnose diseases. All these applications

Among them, the most core things are actually two things, algorithms and computing power."

Jeff's summary is very concise. Modern AI is mainly based on the ancient algorithm of neural network. If you put aside computing power to talk about AI algorithms, it is completely a castle in the air.

"Google is determined to build the most powerful computing platform in the world, and we will definitely allow excellent and intelligent algorithms to maximize their value."

Meng Fanqi has no doubts about Jeff's determination, which was the reason why he initially chose Google.

"The meaning of computing power is relatively pure and easy to understand. But algorithms have too many meanings. In fact, I personally feel that the design of the network structure itself is not the key and core thing.

If you really want to change the world, you need a framework and platform that is simple and easy to use, easy to deploy, and optimized and accelerated in the data types of operations."

At this stage, the industry is paying great attention to the design of neural networks, specifically how to design which layer, and what operations are better.

During this period, the benefits of doing so are also huge. For example, last year’s Alex and this year’s Dream both experienced terrifying improvements.

However, in Meng Fanqi's view, the structure in the late AI era has not changed much. The most important thing is to work hard to achieve miracles. In addition, he knows which structure is better for which tasks, and the structural design is too simple for him.
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"When the competition comes to an end, large model training technology and massive high-quality resources are more critical."

Jeff and Hinton secretly exchanged glances, feeling strange.

Originally, it was Jeff who came to show this undergraduate student who was still exposed to research in school, Google's ambitions, what kind of multi-field blossoms, the largest computing platform and so on.

Why do I feel like this guy has such a clear understanding of the main pain points of the AI ​​industry? He doesn’t look like he’s conducting research in an ivory tower.

The academic community studies AI mainly to verify a certain conjecture and improve specific indicators.

Industrial AI is more pragmatic. How can it be implemented with fewer resources, how can it be built faster, and how can it be deployed on different devices?

The two sides often dislike each other. The academic community feels that the industry is just a wage earner who does dirty work and has no innovative breakthroughs. The industrial community feels that the academic community only writes papers and brags, and no one can use the things they produce.

Jeff and Hinton can be said to be representatives of industry and academia. Even when Jeff was studying, the graduation thesis he wrote was in the industrial direction, parallel training of large neural networks.

It was only 1990, and Jeff had already begun to study the most core technology in 2023, the training method of large models.

"I have to say, I thought you would be a more academic person who has continuously made breakthroughs in algorithms." Jeff's expression was filled with surprise, "I didn't expect your way of thinking about problems and the needs of our industry.

Very consistent."

Jeff has come into contact with many outstanding scholars, and even Hinton has the thinking inertia of the academic world. Therefore, within Google Brain, Hinton does not participate in any management and decision-making work, and is only responsible for academic research.

Perhaps this time, I am not just recruiting an outstanding algorithm researcher, he may also be able to give me a lot of help in the company's AI strategy.

Jeff had a vague premonition of this.

He was the technical backbone of Google for more than ten years and did not participate in many management projects. However, he strongly supported Ng's promotion of AI, so many things in this area were led by him.

As a leader, Jeff likes different perspectives and new things.

For example, although he also studied neural networks and AI in the 1990s, his work at Google was more focused on architecture, search, and advertising.

In fact, no AI knowledge has been updated.

Until 2011, when Ng was working on a project with Google, he suggested to Jeff that the situation was changing rapidly and Google should pay attention to AI technology.

Jeff quickly embraced this change, and it can even be said that he was naturally interested in this potential solution that he was not familiar with.

Once he understands the solution to the problem, he will lose interest.
Chapter completed!