Crossover: 2014

Chapter 240 Volume is Impossible to Volume

Lin Hui made a generative summary algorithm before, which invisibly mobilized the busyness of many scientific research institutions in the world. Doesn't this explain this situation?
The busyness of one person makes a bunch of people have to be busier and pay more at the same time.

What is this phenomenon called in the words of the previous life?

Yes, that's right, it is "volume".

Past life computer industry volume is the norm.

Even after being reborn, Lin Hui unconsciously brought over some habits from his previous life.

Although it was just an unintentional move, it is still a reborn person.

The reborn is not the king of scrolls, should he take the lead and lie flat?
Sooner or later, Lin Hui will kill all potential hostile forces in this time and space.

……

At 26 o'clock in the morning on the 3th, BJ time, Lin Hui was busy.

There are also many people chasing Lin Hui's footsteps behind Lin Hui.

Of course, although there are many people chasing after him.

But there are many who get lost, and there are also many who feel desperate when they see the gap.

It's exactly 11 a.m. in California.

Many researchers in the NOTEXIST laboratory on the campus of Stanford University in California are still busy.

But not everyone is busy.

Fishing also exists.

Looking at the time on the watch, it was almost noon.

Dr. Eclair Kilkaga was ready to leave work when the time came.

Cooking is the most important thing.

It is impossible to roll, and it is completely unnecessary.

The key is to not roll at all.

Since the birth of the generative text summarization algorithm made by LIN HUI.

Dr. Eclair Kilkaga and his team have recently been following up on the technology with the team of Professor Jules of the Department of Mathematics at Princeton University.

At the beginning, the whole team was full of ambition.

But soon the mood faded.

The reason why I was so ambitious at the beginning was because Dr. Eclair Kilkaga and his team discovered the direction of long-term and short-term neural networks.

Dr. Eclair Kilkaga and his team once thought that this direction was the right research direction.

Compared with ordinary recurrent neural network, this kind of neural network is insensitive to the length of the gap in the text when it is applied.

Long Short-Term Memory Neural Networks are a class of neural networks that perform well on longer sequences.

It is precisely because the characteristics of the long-term short-term memory neural network are consistent with some of the characteristics shown in the practical application of the generative summary algorithm made by LIN HUI.

At that time, Dr. Eclair Kilkaga and his team thought they had found the right direction.

After the recent algorithm tracking research on LIN HUI.

Dr. Eclair Kilkaga and his team found that while their guess was right, they weren't quite right.

Even though LIN HUI made a generative text summary, it uses a long-term short-term memory neural network.

But it is absolutely impossible to apply the most basic long-term short-term memory neural network very superficially.

The high probability of the application of LIN HUI in the algorithm is a more special neural network that relies on the long-short-term memory neural network and has been modified to a certain extent.

Although it is not clear what type of modification is applied to LIN HUI.

But it must have been embellished.

As for what to modify, it's hard to say.

What one person hides may not be found by 1 people.

Similar reasoning, although LIN HUI may have only been modified a little bit.

What exactly is this change is impossible for Dr. Eclair Kilkaga and his team to figure out in a short time.

The only thing that makes sense is that LIN HUI uses a very clever application of the LSTM neural network.

Very helpless.

With the deepening of research, some progress has been made.

But the problem is that what is gained is not only progress, but also a better understanding of the opponent.

The true meaning of technology has not been discovered too much.

Instead, fully realize the strength of LIN HUI technology.

Dr. Eclair Kilkaga and his team seem to understand what it means to:
——The closer you get to the opponent's strength, the more you understand the opponent's strength.

This is how Dr. Eclair Kilkaga and his team felt when facing LIN HUI.

While it may be possible to catch up, the techniques involved in generative text summarization are not pure techniques.

Behind this is the market.

Technical issues can afford to wait, but can commercial issues afford it?

It's almost time for the opponent to tap the market potential.

What's the use of re-entering?
The most critical thing is that Dr. Eclair Kilkaga and his team are now cooperating with the Department of Mathematics of Princeton University.

Cooperating with these people often has a counterproductive effect on the progress of the project.

Some of the supposedly applied problems these math nerds always try to solve problems mathematically.

The embarrassing situation of this kind of cooperation often reminds Dr. Eclair Kilkaga of the dilemma of the Mythical Man-Month.

In the case of various stumbling blocks.

He felt that it was impossible to expect to surpass Lin Hui's generative text summarization algorithm in a short period of time.

Dr. Eclair Kilkaga and his team decided to report to the Google Marketing Department to follow up on the generative summarization algorithm.

And give suggestions from the perspective of marketization.

To be honest, as a technician, I will give advice from a market perspective.

Seems like a downer.

But Dr. Eclair Kilkaga had no good idea about it.

If it is something purely theoretical, maybe we can play some word games on the report.

But algorithms like generative text summarization are closely related to applications.

It can be measured by real text processing capabilities.

There's simply no room for tricks on this.

Dr. Eclair Kilkaga recently learned a word called tang, ping from a game called RR
Perhaps this word is more suitable for his state of mind at this time.

Fortunately, after Dr. Eclair Kilkaga submitted the report to the marketing department earlier, the marketing department agreed to seek the algorithmic authorization of Linhui's generative text summarization almost without thinking.

This reaction surprised even Dr. Eclair Kilkaga.

He promised so quickly, and he and his team didn't have to fight for so long.

It was only later that Dr. Eclair Kilkaja learned that it was Lin Hui who made a generative text summarization, a derivative technology about Chinese that was recognized by the Chinese state.

In order to avoid encountering some technical protections from the administrative level.

Google, which seeks to bring a broad market for generative text summarization but has been unable to follow up in terms of technology, seems to be unable to wait any longer.

Of course, it seems that there is no "delay". In fact, Google's actions are not slow.

But I don't know why the various actions of LIN HUI seem to press the fast forward button.

(End of this chapter)