Lin Hui's ultimate goal is to say goodbye to some of the rules shaped by the West in the current world.

When Lin Hui grows up to the point where he does not need to rely on the rules of the West to stand up, even when the West has to rely on the rules set by Lin Hui to stand up.

Then Lin Hui can say goodbye to the current Western rules.

This indictment of "reasonable" interpretations that are not based on logic is not just an indictment of certain systems that are prone to injustice.

This kind of accusation against "reasonableness" ultimately boils down to this:

——The battle for “right to speak”.

Having the right to speak is equivalent to being able to explain rationality yourself.

In this way, having absolute right to speak is equivalent to having authority.

And with absolute authority, it can even sometimes ignore the factual objective laws.

Including but not limited to logical rationality.

The scenario is roughly: "Whatever is reasonable or unreasonable, lz is reasonable. If lz says it is reasonable, then it is reasonable."

What is the use of authority other than this kind of willfulness?

Of course it is useful to have such voice and authority.

This will increase the speed of forest ash transport to the fullest.

Moreover, possessing absolute authority can also directly maximize the benefits of the information brought by Lin Hui from his previous life.

After all, absolute right to speak means absolute monopoly.

Once he has absolute authority, even if Lin Hui has never thought about pursuing a market monopoly, he can still achieve similar effects to a monopoly.

Take the “labeled data” that Lin Hui just thought of.

Lin Hui has a large amount of annotated data in his past life information.

Labeled data involving machine learning can be exchanged for money.

Annotated data in natural language processing can also be used to exchange money.

But is labeling data the fastest way to exchange money?

of course not.

When it comes to natural language processing, what really earns the most money is narrow knowledge.

Narrow knowledge is also a source of knowledge in natural language processing.

There are three main categories of knowledge sources used in natural language processing projects:

——Narrow sense knowledge, algorithms and data.

As the old saying goes, there is a golden house in a book.

Although it is not necessarily a golden house in the real sense.

But it is well known that knowledge can be exchanged for money.

Since knowledge can be exchanged for money.

Then the means of source of knowledge can naturally be used to exchange for money.

Even the source of knowledge is likely to make money faster than the knowledge itself.

Algorithms and data as a source of knowledge for natural language processing can be exchanged for money.

Narrow knowledge, which is also the source of natural language processing knowledge, can naturally be exchanged for money.

It is basically known to the world that algorithms can exchange money.

Even if you didn't know that you can exchange money before, if you pay attention to the key points that Lin Hui was busy with for a long time, you will be able to understand it.

Previously, what concerned Lin Hui most of the time was basically the generative summary algorithm.

If you don't have any profit, you can't afford it early. Without enough profit to drive Lin Hui, naturally he won't be ready to do anything about this matter.

It is basically not difficult to understand that algorithms can exchange money.

Algorithms often directly affect the efficiency of some algorithm-driven products.

And efficiency is real money.

Algorithms that can directly affect efficiency can naturally be easily exchanged for generous rewards.

Understanding the algorithm can exchange money.

In fact, it is not difficult to understand why data can be exchanged for money.

After all, data is the cornerstone of many machine learning algorithms.

The emergence of machine learning algorithms often relies on labeled data.

And for a long time, machine learning algorithms have not only relied on labeled data.

And it relies on a large amount of labeled data.

When the amount of labeled data is small, it is often not enough to train a machine learning algorithm with excellent performance.

From this perspective, it is not difficult to understand why data can be exchanged for money.

In many cases, data can even be understood as a kind of implicit knowledge.

The process of data annotation is actually the process of structuring and labeling scattered discrete data.

Beyond algorithms and data, what is the so-called narrow knowledge?

Narrow sense knowledge generally refers to explicit knowledge defined manually through rules or dictionaries.

Narrow sense knowledge mainly includes three types:

——That is, language knowledge, common sense knowledge and world knowledge.

Among them, language knowledge refers to the definition or description of the morphology, syntax or semantics of language.

Its main feature is the definition of synonym sets. Each synonym set consists of words with the same meaning.

Common sense knowledge refers to the basic knowledge that people obtain based on common experience.

World knowledge includes entities, entity attributes, relationships between entities, etc.

Maybe some people don't understand?

Why can this kind of knowledge be exchanged for money?

Aren't these things obvious?

Although this knowledge is essentially explicit knowledge that people can understand.

But explicit knowledge that is obvious to people.

It is not equally obvious to the machine.

This type of knowledge often needs to be processed through regularization or lexiconization so that the knowledge can be easily understood by the machine.

This knowledge that is easily understood by machines is called narrow knowledge, also known as expert knowledge.

Although now the main training model is to seek algorithms or even the data itself.

But knowledge in the narrow sense is quite marketable.

Anyway, judging from previous exchanges with Eve Carly.

It’s already 2014, and Silicon Valley still has to cooperate with universities such as Harvard and Oxford to develop machine learning.

The reason why these people rely on Harvard and Oxford is not only to rely on these universities to label data.

The main reason should be to count on the blessing of these universities in terms of narrow knowledge.

It is easy for these people to understand this.

After all, when it first came to model data in natural language processing, people used narrow-sense knowledge for training instead of relying on data and algorithms.

In Lin Hui's impression, even in the previous life, before the rapid rise of the Internet, the only way people could train natural language processing models was to use narrow-sense knowledge.

Lin Hui possesses quite a lot of narrow-sense knowledge, and the level should be much higher than what is currently used in the Western world.

Judging from the tens of millions of dollars spent every year in Silicon Valley to acquire narrow knowledge.

If some of the narrow-sense knowledge in Lin Hui's hands could be monetized, it would be more convenient than using annotated data to monetize it.

But this is only theoretically easy to realize.

Lin Hui does not have the absolute right to speak and the authority that comes with it.

How can Lin Hui tell potential audiences that the narrow knowledge materials he possesses are superior to the general materials currently used in Silicon Valley?

In fact, the potential buyer Lin Hui was very clear about it.

You must know that even in the next few years, there will not be particularly many buyers who are interested in a large amount of narrow knowledge and are not short of money.

Lin Hui estimates that the buyers who may be interested in large-scale narrow knowledge bases in this time and space are none other than super giants such as Microsoft and Google.

But even if he knew about these potential buyers, Lin Hui would not be able to take the initiative to find them.
Chapter completed!