Rebirth of the Internet Overlord

/*

I would like to use this chapter to respond to the reader named Lin Lie in the comment area.

I can't convince anyone. After all, the first version is similar to this chapter. The technical introduction and preview throughout the article make it boring to read.

Then I wrote too much, which is what happened in this chapter. Now some people say that I am a young writer, and some readers say that I am a prostitute. Some even say that I have never seen a woman, and half of my books are written about women.

What can I do?

I'm desperate too.

I can only write it down and pull out the buried things bit by bit.

This thing about stance, until the truth comes to light, will always be focused on the exposed part.

I am just a small boat.

*/

{Time: March 4, 2003}

{Location: Palo Alto, California}

Gaining 3 million user growth in 7 days, this kind of data means that [myspace] is definitely experiencing explosive growth. Of course, this has a lot to do with the celebrity resources accumulated by Jennifer [Celebrity Plan].

However, after the one-time delivery to [mysapce], the rest of the promotion work can only rely on users to slowly spread the word of mouth after experiencing the social attributes of [myspace]. As for Chen Shijun and Sachs,

, I can finally get away from the nightclub and really devote myself to the online promotion of [myspace].

As for how [myspace] and [Celebrity Plan] negotiate promotion commissions, this matter is left to Jennifer.

On the one hand, Jennifer is bound to be exposed to business negotiations, and Sachs and Chen Shijun will definitely consider giving themselves a gentle negotiation space. As a result, in any case, it is not what the [Red Man Project] needs to pay attention to now.

The explosive period of SNS has not yet arrived, and the [Celebrity Project] is still in the brewing period. The accumulation of celebrity resources during this period must be given to Jennifer to try. In this way, when the explosive period of SNS arrives in the future, the [Bing Ecosystem] can

Go on functioning normally without your own attention.

On the other hand, Ning Zimo also had to do important things.

There is already information about the person he wants Hoffman to help find.

…

Early in the morning on March 4, Ning Zimo arrived at the Lingying headquarters in Mountain View at the agreed time. As soon as he entered the door, he smiled at Hoffman and said, "I didn't expect you to find it so quickly."

"You said you asked me to help you find it at Stanford. Isn't it easy for me, a well-known graduate of Stanford, to be in such a familiar area?" Hoffman said with a smile.

The person Ning Zimo asked Hoffman to help find was related to the news coming from [bing] headquarters in Frankfurt, Germany.

Luqi's search engine team has made considerable progress. At present, the data crawler has been developed and has begun to crawl and collect data.

However, Lu Qi has never been very satisfied with the efficiency of the search algorithm, so the concerns in his email had to make Ning Zimo's hidden poaching plan surface in advance.

If Ning Zimo admired Beibao Company in his previous life, then he also admired Fengyu Company.

This company, which started as a search engine, adheres to the concept of "do no evil" and in its previous life, it once occupied a position within the "top 10 companies that changed the world" for several years.

So for Fengyu, Ning Zimo also studied its history like he studied Beibao Company.

At that time, Ning Zimo noticed a small company that many people had not paid much attention to - kaltix.

Although the name of Kaltix has not been exposed in the development history of Fengyu, it is three people from Kaltix. Three Stanford students, they developed Kaltix and used a set of proprietary algorithms to accelerate the basis of Fengyu’s pagerank algorithm.

calculations, and the search results are sorted based on individual interests rather than the consensus method developed by Fengyu.

This set of algorithms includes three technologies: quadratic extrapolation, blockrank and adaptive pagerank, which together form the basis of kaltix.

It is this set of technologies with the same name as the company that has increased the speed of Fengyu’s search engine by five times. In fact, according to the limited information on the Internet, Kaltix said that the technology provided by Kaltix is ​​nearly 1,000 times faster than the algorithm used by Fengyu in 2003.

.

These algorithms were mentioned in their graduation thesis at the 12th International World Wide Web Conference (2003) held in Budapest, Hungary on May 22, 2003.

Originally, Ning Zimo planned to contact them before May. But since Lu Qi exposed the shortcomings of [bing search] in advance, Ning Zimo could only advance the contact time.

"Actually, these guys happened to have registered our Lingying users. I first searched on the Lingying platform and found out that they were search engine experts, and then I contacted them instead."

Hoffman handed some pieces of paper to Ning Zimo, and Ning Zimo took it easily. It contained the information of the three people.

Hoffman said with a smile, "Now I am used to searching for talents from our Lingying database first. It happened that the three of them recently tried to put their own information on Lingying and did some search engine discussion topics.

That made me keep an eye out. When I went back to Stanford this time, I happened to ask about them again, so I made an appointment today."

At this moment, there was a knock on the door.

"People are here." Hoffman raised his eyebrows and said,

"That's a coincidence."

Ning Zimo stood up and walked to the door with Hoffman.

As soon as the door opened, there were three young people with green faces standing outside.

“Sep kamvar, taher and glen jeh?”

Hoffman accurately called the names of the three people and greeted them with smiles, which made the young people with green faces feel much relaxed.

"Boss, these three are the founders of Kaltix, sep kamvar, taher and glen jeh."

The name that Hoffman called him made Ning Zimo roll his eyes to the sky.

It's just that the people he wants to interview are a few young people, and he is not as "mature and stable" as Hoffman. So he deliberately used this method to aggravate his identity to make the three young people of Kaltix

The founder pays more attention to him.

"This is our CEO and chief product architect, Ning," Hoffman introduced to the three of them. "At the same time, he is the youngest COO of Beibao. He also led us Beibao to go public and sell."

Although he was not used to Hoffman calling him boss, Ning Zimo still smiled at him gratefully.

He walked over and shook hands with the three of them, and then led the three of them to sit casually on the sofa. "What would you like to drink?"

"Three cups of coffee, thank you."

"Okay, three cups of coffee. Boss, are you still the same?"

"Um."

Hoffman's behavior gave Ning Zimo the upper hand, but it also put some pressure on these young people. How could they have imagined that the oriental man in front of them, who was younger than them, was actually more powerful than their seniors?

weight.

So much so that after Hoffman went out, the three of them even acted a little reserved.

Ning Zimo could only chat casually at first. After Hoffman brought coffee, the scene became more cheerful.

Looking at the three of them, Ning Zimo patted Hoffman's arm and said with a smile, "Hoffman is just kidding you. In fact, we are now the co-founders of Lingying, and there is no superior-subordinate relationship.

We are of the same age, so there is no need to be too polite, so you can just call me Ning."

"Okay, Ning"

Ning Zimo lowered his posture, and Hoffman also smiled nonchalantly. The relaxed environment relaxed the atmosphere a lot, and the three of them visibly relaxed at this time.

The young man named Sepp spoke first, "Ning, Hoffman is our senior. He happened to find us a few days ago and said that you were interested in our project and ready to invest, but we didn't expect you to be so young."

"I didn't expect that the founders of Kaltix were three young people of similar age to me. I thought there were only a few geniuses like me, but today I saw three."

The similar compliments were very much like the "adult" way. Several people suddenly laughed tacitly, and the atmosphere became more harmonious.

Taking advantage of this excitement, Ning Zimo put down his coffee and said, "Since we are all young people, I will keep the story short. But when it comes to talking about it, it is a bit long. This is indeed quite contradictory."

Ning Zimo still likes to be funny on weekdays, but older people usually can't cooperate. However, in situations where there are more young people, Ning Zimo can't help but use jokes as a starting point.

The Kaltix trio smiled after hearing this, and motioned to Ning Zimo to continue. Then Ning Zimo, who picked up the coffee cup again, was like starting a conversation, and started talking with Mr. Richard Bean who had brought Lu Qi to see him.

Big flag.

"Last year, when I was confused, I had the good fortune to meet an old man I thought was called Richard Bing. He was a legendary old man. In his past years, he changed from an ordinary hop merchant to studying under Richard Shit.

Routh studied music and eventually switched to medical cardiology after the war.

... Mr. Richard Bing's story spans almost all the years of a whole century. After obtaining his permission, I wanted to start our product in his native Germany and name our search engine after him.

.I want to be able to make some achievements during his lifetime and let him see that I have not buried his trust in us."

Ning Zimo told the story of Mr. Richard Bean to the Kaltix trio in an embellished way, and he also quietly added some fictitious things.

After two years of experience, Ning Zimo has gradually learned how to tell stories. As long as it does not violate the principles, telling the ideals, future, and feelings for the future partners who join the team is almost an indispensable part.

.

Painting cakes sounds frivolous, but it can be of great use at certain moments. If this method can attract high-end talents, Ning Zimo doesn't mind being a bit shady.

And Ning Zimo decided to go all the way to the dark side of the road to abduct Dana!

“Wow, cool~~~~~”

The Kaltix trio listened intently when Ning Zimo unfolded the story of the legendary old man.

Whether it's his hops he admires,

Or the feud with teacher Richard Strauss,

And finally, his various breakthroughs in the field of heart disease,

These stories make the Kaltix trio shine with something called worship.

After a pause, Ning Zimo added, "This search engine is called bing. Now it has been developed and uses crawlers to crawl network information. But currently, our [bing] team has encountered problems with the search algorithm.

There was a problem. So much so that my epoch-making plan for big data in search engines encountered obstacles.”

"And these obstacles," Ning Zimo said solemnly, turning his fingers from himself to the three Kaltix people, "are the main reasons why I asked Hoffman to find you search experts."

"Big data epoch-making plan?" Sepp pondered in confusion. Finally, he raised his head and asked Ning Zimo, "It sounds like a very huge project."

"Yes, it is a very huge project. Because currently, for people who do not understand the value of search, search engines are just windows that bring people results. But to truly discuss the value of search engines, the best place to reflect its value is

It must be the epoch-making search engine era of big data.

Think about it, in the past, when we performed data analysis and statistics, we were only limited to the database, where we performed statistical analysis on the data tables. And limited by the amount of data and computing power, we could only perform statistics and analysis on the most important data.

.

Search engines have transcended this limitation and can become a large database that stores almost all accessible web pages in the world, the number of which may exceed one trillion, and all of which require tens of thousands of disks to store.

Although it seems that Fengyu is already doing this, Fengyu's plans for the future are not as clear as I imagined.

Because if we continue to develop further, I want Bing to be able to uniformly store and manage all kinds of text, pictures, videos and other things corresponding to technology, culture, knowledge, information, news, etc., to form a large database for the entire human race.

It records all the past data of human civilization and provides various supporting conditions for future development. Build it into a human Noah's Ark to benefit all mankind.

I can simply give a few stage-by-stage examples, such as a certain early stage of big data - the data warehouse era of big data applications.

Bing can break away from the concept of database to perform SQL operations and realize data statistics and analysis. In other words, people will get much more data storage and computing power at a cheaper price on Bing than before.

We can put running logs, application collection data, and database data together for calculation and analysis to obtain data results that were previously unobtainable, and the enterprise's data warehouse will also expand exponentially.

If you think about it, in the era of data warehouses, as long as there is data, statistical analysis must be carried out. If the data scale is relatively large, we will think of using big data technology. The development of technology also promotes the application of technology, which also provides a good foundation for the future.

Next, big data applications enter the era of data mining, laying the groundwork.

The data mining era of big data applications must be superior to the data warehouse era of big data applications. For example, merchants discovered through data a long time ago that people who buy diapers usually also buy beer, so they are smart

Merchants put these two products together to promote sales.

You can have various interpretations of the relationship between beer and diapers, but if it were not for data mining, you might not be able to think of a relationship between them without breaking your head.

In a business environment, it is not important how to interpret this relationship. What is important is that as long as there is a correlation between them, correlation analysis can be performed. The ultimate goal is to allow users to see the products they want to buy as much as possible.

In addition to the relationship between products and products, you can also use the relationship between people to recommend products. If two people buy many products that are similar or even the same, no matter how far apart they are, they must have something in common.

a relationship.

For example, they may have similar educational backgrounds, financial incomes, and hobbies. Based on this relationship, related recommendations can be made to let them see the products they are interested in.

In addition to product sales, data mining can also be used to mine interpersonal relationships. The six degrees of separation theory believes that two people in the world who do not know each other only need a few middlemen to connect them. The experimental results of this theory in the United States are

, you can contact two unknown Americans in just six steps.

In the future, like our [Lingying] or even [myspace], various social software will record our friend relationships. Through relationship graph mining, almost all interpersonal networks in the world can be mapped.

Modern life is almost inseparable from the Internet. Various applications collect data all the time. This data is constantly being analyzed and mined in the big data cluster in the background.

Of course, we can also give a high-level example and talk about the industry related to the legend Richard Bing - medical care.

For example, leukemia and lupus erythematosus, which are currently difficult for humans to conquer, can be gathered together by collecting data on the patient's living habits, growth environment, DNA, disease development and other information, and turn small special pathologies into large-scale cases that can be used for reference.

data.

Then, through continuous data mining, we can analyze the causes of these cases. Then scientific researchers will have more reference basis for these incurable diseases, turning the originally small possibility into a possibility that can be broken through with high probability.

Perhaps it is possible for people suffering from these conditions to be cured, or perhaps it is possible for embryos with potential genetic defects in their genes to avoid pain during the pregnancy process.

Whether these analyzes and mining brings us happiness or fear depends entirely on the efforts of big data practitioners. But it is certain that no matter what the final result is, this process will only accelerate and will not stop, and you and I can only invest in it.

But in any case, it is worth doing. Even in order to improve efficiency, we can hand over some tedious and regular work to artificial intelligence, which will make the big data era develop into the machine learning era of big data applications.

Like in the example just now, there is a pattern in the data, and this pattern is followed by all data. What happened in the past followed this pattern, and what will happen in the future will also follow this pattern. Once this pattern is found, what is happening now will

, we can make predictions according to this rule.

In the past, we were limited by data collection, storage, and computing power. We could only obtain a small part of the data through sampling, and could not obtain complete, global, and detailed rules. But in the future, with big data, we can collect all the data.

Historical data are collected, their patterns are statistically calculated, and then what is happening is predicted.

This is machine learning.

For example, let me give you another example: store all the chess data of human Go games in history, and record which moves can get a higher chance of winning for each board. After getting this statistical rule, you can use this rule to interact with people.

Play chess.

Each move is calculated where it will land to get a greater chance of winning, so we get a robot that can play chess. Maybe one day this robot will learn thousands of chess games in a few years and learn through commonalities and

The learning of local strategies, by analyzing the intentions of human moves, overwhelmingly defeated the top human chess players."

Regardless of the stunned four people around him, Ning Zimo took a sip of coffee to moisten his throat and continued:

"When I finish talking about these examples, I believe you have a longer-term view of the search engine in my mind. Yes, it is huge. It is more than just a window that can provide people with search results.

It is a window into the era of big data.

What bing has to do is to store all the information retained by human civilization from its birth to its development to this day, turning it into a huge database, allowing it to provide people from all walks of life with a large amount of data that can be verified, allowing human beings to

Make fewer mistakes and suffer less while traveling.

But maybe, that's just my wishful thinking. Because of the greed of human nature, we will have such advanced technology in time, but we still can't avoid so many problems.

But there is nothing wrong with technology. It all depends on the method we apply it and whether we practitioners can have a ruler to measure justice.

I can't do that much to measure justice, but in my lifetime, I just want to make technology go further and let the team around me contribute to human civilization.

As for what will happen when that great era arrives in the future, I believe that even if I get old, there will still be countless people of insight who can do more outstanding things than me.

Let mankind still walk the right path on the way forward."