Crossover: 2014

Chapter 261 New Track & New Partners

Wouldn't that be a sell?
Selling is tantamount to a direct loss of initiative.

After all, according to the buyer's thinking logic is:
Touting is tantamount to the seller not being confident about the product.

That is, the commodity lacks authority.

The lack of authority over some commodities is tantamount to a death sentence.

Why should buyers pay for content that lacks authority?
Maybe this is just Lin Hui's conjecture, but Lin Hui feels that this kind of thing is a high probability event.

Not authoritative, even if it is something of a higher level.

It is also very blue to exchange for money.

With absolute authority, things are different.

Many times it becomes a seller's market.

Buyers come asking for that.

Just like the algorithm team in natural language processing described by Eve Carly, the rise and fall of the algorithm team continues.

But universities like Harvard and Oxford never have to worry about not having an algorithm team to cooperate with them.

After all, to some extent, these top universities are almost equivalent to the authority in the narrow sense of knowledge, especially in the language knowledge that some NLP development cannot do without.

In this case, don't say that these colleges and universities don't have to worry about food.

Even many algorithm teams have to look at other people's faces.

Be authoritative.

The level is not so high and it is easy to attract money.

It is not even necessary for these colleges and universities to produce knowledge in person.

Sometimes it is even possible to directly label "common knowledge".

Yes, that's right, knowledge labeling.

This is the real meaning of lying down and making money.

And it's the kind that lies in the atmosphere.

It's beautiful, but far away.

But Lin Hui doesn't need to be too discouraged.

Because it involves the pursuit of the right to speak, Lin Hui is not alone.

Walking with Lin Hui is not someone.

It is an ancient oriental country with a long history of 5000 years.

Lin Hui believes that through continuous seeking, the ultimate pursuit involving the right to speak will be realized one day.

The ideal is beautiful, but the road is tortuous.

Now that there is no right to speak, it is difficult to rely on some narrow knowledge to exchange money.

Unless it is to find some universities with the same level as Harvard and Oxford and ask them to endorse Lin Hui.

But wouldn't this be making money by looking at other people's faces?
Uh... the most important thing is that in this way, [-]% of it is likely to be someone else's.

Lin Hui would not do this kind of thing of making wedding dresses for others.

It is impossible to lie in the atmosphere without enough right to speak.

It seems that it is better to honestly make money through data labeling or other realistic means.

Even if the data labeling is used properly for Lin Hui, it is a huge asset.

Gold dust was discovered in the Sacramento River during the westward expansion of the United States in the late eighteenth century.

Under the influence of courage and greed, workers, farmers, sailors and missionaries came to pan for gold one after another.

This is the famous "gold rush".

However, in this vigorous westward movement, there are not many people who really made a lot of money by panning for gold.

On the contrary, the water sellers who found another way during the gold rush process made a lot of money.

The field of "data labeling" is to some extent the "water seller" in the rapid rise of artificial intelligence in the previous life.

Why do you say that?
In the previous life when artificial intelligence was rapidly rising.

Most overseas technology companies almost all focus on the pursuit of more advanced algorithms, platform framework construction, and commercialization.

"Data annotation" as a field is neither magnificent nor unique.

Even though data annotation plays a very important role in machine learning, especially supervised learning.

However, the field of data labeling still makes many overseas technology companies dismissive.

Even many overseas giants and some overseas companies specializing in artificial intelligence in previous lives are dismissive of data labeling.

Or it's not disdain, it's just selective ignorance.

After all, in the eyes of many overseas technology companies, data labeling is a thankless manual work.

And investors because they don't know much about data labeling.

There is also often little attention paid to the field of data labeling.

On the contrary, those technology companies with technology as the core or technology as the core on the PPT are more likely to stand out and be favored by investors.

However, in the previous life, the hustle and bustle of artificial intelligence is no longer in the limelight.

After taking off the gorgeous coat and looking at the various companies in the artificial intelligence industry.

You will find that those overseas companies that used to pursue advanced algorithms, commercialization, and platform framework construction with great fanfare did not necessarily make much money.

(To put it bluntly, most of them are losing money, and it is the kind of loss that burns money
For example, deepmind, a banner of artificial intelligence in the previous life, has basically been burning money since it was acquired by Google)

On the contrary, some small overseas companies engaged in data labeling that were not very popular at the beginning made a lot of money.

There are even some unicorn companies with a valuation of around $70 billion.

Although things like valuation generally have a lot of moisture.

But as an artificial intelligence-related company, the valuation of 70 billion US dollars is almost the same.

After all, deepmind, which has always been called the vane of artificial intelligence in previous lives, was only valued at less than one billion US dollars when it was acquired by Google.

In this case, Lin Hui feels that it is not an exaggeration to regard data labeling as a new track in the development of artificial intelligence.

……

By the way, why do all the companies mentioned above refer to overseas companies? Even the so-called "small companies that are not very popular" refer to some overseas companies?

It is no wonder that Lin Hui will distinguish domestic Internet companies separately.

Due to some tedious reasons, domestic Internet companies are basically flowers in the greenhouse.

But most of the domestic Internet is really not very up-to-date except for a few that are relatively capable.

Many times when looking at issues from an international perspective, some domestic Internet companies are found to be strange.

Even always give people a kind of inexplicable feeling.

Or to put it in terms of high emotional intelligence, domestic Internet companies generally have several versions of understanding ahead of the earth's online.

In many cases, the domestic Internet will take on different forms according to different periods.

Sometimes domestic Internet companies behave like real estate companies, sometimes like media companies, sometimes like car companies, and sometimes like cx companies.

Only it doesn't look like a technology company.

Many times, Lin Hui simply ignores the ghosts and ghosts of domestic Internet companies.

If you really want to start a business, you should compete with international giants such as IBM and Microsoft.

It is really not challenging to compete in the small fish ponds of the domestic Internet.

……

Specific to data labeling.

In the previous world, domestic data labeling seemed to have always been a mess.

Because there is no threshold for data labeling, at least it seems that there is no threshold.

A college student can basically do ordinary data labeling in less than a day of training.

Such an industry is naturally very powerful.

How many rolls are there?

Lin Hui remembered that in his previous life, he was still studying when he first came into contact with data labeling.

At that time, it was even a crowdsourcing task.

You can easily earn 50-70 by marking in almost an hour.

Pay day/end, very nice part time job.

Lin Hui remembered that during college, he was short of money for a while and was too embarrassed to ask his family for it.

After half a month of data labeling, I unexpectedly saved some money.

On the eve of Lin Hui's crossing, the data labeling of the same intensity can basically only cost about ten yuan an hour.

It would be good if the salary can be settled monthly (some even in March), and there is also a tax deduction.

What Rebs said was true, pigs could fly when they stood on the wind.

In many cases, even if you can't fly, you can catch up with the bonus period, and you can still get some meat.

Standing on the tuyere, pigs can indeed fly.

But what about when the pigs fly?

Can it land smoothly?

The fact is that many pigs that once flew up, when the bonus period is over, they just drop chicken feathers, no, pig feathers.

The truth is that as long as you have anything to do with the Internet.

Don't care about the level, in short, it won't work.

But when it comes to data labeling, this is really too much.

In the case of rising wages in various industries on the Internet, the data indicates that the wages of employees in this industry have directly shrunk by one-fifth.

It can be said to be appalling.

In the case of such crazy volumes in the field of data labeling in the previous life.

In many cases, even bad money drives out good money.

Wait until the big manufacturers with core data realize the importance of data labeling and prepare to end.

Only to find that there was not even a place to stand.

Even with core data.

For data labeling, many times it can only be outsourced.

Many data labeling platforms, such as Ferry Public Test, Goudong Weigong, Ali Crowdsourcing, Goose Factory Souhuo, etc., are basically such products.

It's just outrageous.

But this incident also reminded Lin Hui from the side.

If Lin Hui can really make a name for himself in the data labeling.

It doesn't make sense to have no strength in areas such as data interpretation and data visualization.

Then the tentacles of the forest ash can easily reach other places.

Leaving aside these for the time being, they are just words that form the control of data labeling.

Also very awesome.

This almost means that in the future, Linhui may completely block the possibility of many enterprises entering artificial intelligence at the data level.

At the very least, if many companies want to get a share of the artificial intelligence field, it depends on Lin Hui's face.

Uh, why does it sound more and more like a villain?

But it doesn't matter, most of the time Lin Hui is willing to be kind to others.

After all, being kind to others is a virtue, but blindly being silly and sweet in the turbulent Internet environment has a price to pay.

You don't need to lift the table, but you must have the strength to lift the table.

But these are things for the future.

Although I suddenly realized the economic value of the super-large-scale text data annotation contained in the past life information and the unique status of the annotation data in the era of artificial intelligence.

Lin Hui didn't show much abnormality on his expression.

After all, no matter how magnificent the things that come to mind are.

In actual implementation, it can only be done step by step.

It is very difficult to reach the designated position in one step.

For example, Lin Hui thought of labeling some text data in exchange for money.

Similar to sales, it is still difficult to use data annotation to realize large-scale realization.

Where should I find buyers who can consume hundreds of thousands of texts, millions of texts, or even larger texts at one time?

In fact, Lin Hui is a potential buyer who knows about ultra-large-scale text annotations.

But similar to knowledge in the narrow sense, even if he knew potential buyers, Lin Hui would not be able to sell them.

It is easy to be passive if you are too active.

It seems like the best course of action is to use a broker, a middleman.

Implicitly disclose the news that Lin Hui has a large amount of data annotation information in his hands and intends to realize it to feasible buyers, and then make connections from them.

But where to find such a middleman?

Lin Hui doesn't have an ideal answer to this question.

Could it be that it depends on Eve Carly?

Looking at Eve Carly who just asked a question and looked curious.

Lin Hui felt that it would be difficult for a pure person like Eve Carly to be competent for this kind of work.

Lin Hui even felt guilty for having such an idea.

Lin Hui should indeed feel a little guilty.

Because his thinking just now seemed to have left Eve Carly in the cold for a while.

But Lin Hui will not let Eve Carly wait for nothing.

Lin Hui believed that his next conversation with Eve Carly was destined to be a profound exchange.

It will make Eve Carly reap the rewards.

The facts are just as Lin Hui expected.

It was indeed a profound conversation.

Lin Hui has gained a lot from this conversation.

The reason why I say this is a fruitful conversation.

It is because I got two very good news from Eve Carly and Lin Hui:
One of them is IBM's recent decision to spend huge sums of money to build a new, more efficient and smarter text summarization tool.

Then IBM is also a potential customer of Lin Hui's previous summary algorithm.

After all, it involves text summarization.

In many cases, not having a powerful algorithm is basically equivalent to saying goodbye to "efficient".

As for the "intelligence" IBM is looking for.

The algorithm that Lin Hui came up with is completely competent.

After all, they are also technicians who came through time after the artificial intelligence of later generations ravaged.

It is embarrassing to show people the algorithm without some smart tags.

Although objectively speaking, in fact, Lin Hui's previous algorithm also has a lot of artificial mental retardation.

But how?

As long as the peers will set off, a bicycle can also become a motorcycle.

In many cases, you may not need to be very strong.

As long as your opponent is good enough, you are the best one.

Anyway, as far as the current era is concerned, the algorithm that Lin Hui worked on before is in terms of the intelligence of the algorithm.

If the intelligence of Lin Hui's algorithm in terms of text processing is second, it will not be the first.

Lin Hui still has this confidence.

In short, the text processing algorithm previously carried by Lin Hui is in line with IBM's requirements in terms of efficiency and intelligence.

Perhaps it is also because Lin Hui thinks that the algorithm that Lin Hui created before is more in line with IBM's requirements.

It was Eve Carly who informed Lin Hui of the news.

Lin Hui does not reject IBM as a potential partner.

First of all, IBM is definitely not short of money.

But money is not the point of the question.

Money is very important for Lin Hui's future career.

But compared to money, some resources that are difficult to buy directly with money are also very attractive to Lin Ash at this time.

(End of this chapter)