My 1999

After all, the scale of his companies and the industries they span now have surpassed Delong.

And compared to the Tang brothers, he is only twenty-six years old.

It’s the age when ‘you can’t do anything without talking a lot’.

When others see him for the first time, they can't help but become suspicious.

Can such a young person really integrate the huge Delong Group?

Is he really at the helm of Delong Assets, Hongmeng, Hanhua, and Master Kong at such a young age?

Once he goes astray like Old Tang, the impact of the collapse of the Xu Group will be ten times or a hundred times that of Delong.

There is no reason for the chief assistant not to worry.

However, this inherent impression based on age is a habit formed over thousands of years, and Xu Liang alone cannot change it.

He doesn't want to change either.

It would be great if the enemy looked down upon him because of this.

While Xu Liang continued to read the speech materials he had prepared, a staff member came over.

"Mr. Xu, the time is up, please come with me."

Xu Liang nodded.

After briefly sorting out the information, he strode out the door.

Under the leadership of the staff, we came to a hall.

After the other party made a gesture to invite him in, Xu Liang stopped, took a deep breath, and strode in.

‘Wow’!

Loud applause broke out.

This warm applause also dispelled a trace of nervousness in Xu Liang's heart.

Standing on the rostrum, he glanced downward.

Sixty or seventy people were seated on rows of sofas, with the head of state and chief assistant sitting at the front, surrounded by members of the elders.

Xu Liang saw the figure of his old father-in-law behind them.

From now on, except for a very few, the rest are basically unknown.

He looked down at the laptop in front of him.

The PPT inside has been moved to the first page.

Open the speech you brought in and place it on the table.

After calming down his emotions, Hong said.

"I'm honored to be invited by the cabinet to give this speech. I'm a little nervous to face so many big names for the first time. If there's something wrong with what I say next, I hope you seniors can take it for granted that I'm still a young boy. Please forgive me."

"Xiao Xu, please feel free to speak boldly.

Don't be burdened, just think of us as your employees. "The head of state smiled.

"With your words, I feel relieved."

After saying a polite word, Xu Liang stopped wasting time.

“The title of today’s speech is: The impact of big data, cloud computing and artificial intelligence on the future!

First, let’s be clear, what is data?

In many people's minds, numbers are data, or must be made up of numbers.

In fact, this is not the case. Data is much bigger than numbers.

Any content on the Internet, such as text, pictures, and videos, is data.

All files in the hospital, including medical images, are also data;

Various design drawings in companies and factories are also data;

The texts and illustrations on the unearthed cultural relics, and even their dimensions and materials, are also data.

Even our human activities themselves can be regarded as a special kind of data.

Data in various fields around the world continues to expand outward, and another feature is gradually forming, that is, a lot of data begins to intersect.

Data in various dimensions gradually evolved from points and lines into a network.

In other words, the correlation between data has been greatly enhanced. In this context, big data has emerged. "

After a pause, Xu Liang adjusted the PPT.

“So how to use data and big data?

It can be roughly divided into the following processes.

Obtain data → analyze data → build model → predict the unknown.

Let's take a simple example.

Now we want to know the age distribution of a movie theater’s audience for marketing purposes.

Suppose we divide the audience into four groups: under 15 years old, 16~25 years old, 26~40 years old and 41 years old and above.

A simple way to find out the proportion of each group is to go to the cinema door and ask the age of those watching the movie.

For example, we learned through the survey that there are about 343 people under 15 years old, 459 people between 16 and 25 years old, 386 people between 26 and 40 years old, and 490 people at 41 years old and above.

Based on this data, we can roughly draw the following conclusions:

Audiences aged 15 and under account for about 20%, and those aged 16 to 25 account for more than a quarter, but less than 30%;

Slightly less than a quarter of the audience is between the ages of 26 and 40, and the audience aged 41 and above is the largest, accounting for about 30%.

But if we just sample 10 people on a weekend night, we'll find out.

There were three spectators aged 15 and under, five spectators aged 16-25, and two spectators aged 26-40.

We obviously cannot conclude that 80% of the audience is under the age of 25, while middle-aged people aged 41 and above never come to the cinema.

But I think everyone will admit that when the statistical sample is insufficient, the results obtained will deviate greatly from the actual results.

Therefore, the more accurate statistical results you want, the greater the amount of statistical data required.

In the above example, the total number of samples counted is 1678 people.

But if we must say, ‘The audience aged 41 and above is 29.2%’, or ‘The audience aged 15 and under must exceed 20%’.

If it is so certain, everyone may challenge this conclusion.

Because statistics are random and have errors.

Such accurate conclusions cannot be drawn from data of only a few thousand people.

In addition to requiring sufficient data, statistics also require that the sampled data must be representative.

Sometimes, it is not necessarily accurate if the data is large enough and the same class is passed.

A very simple example is that a love movie and a war movie have different audiences.

So if we only collect the audience of the love movie in the month of its release, it is not generally representative.

So how to avoid this situation and get accurate conclusions?

The 19th century Russian mathematician Chebyshev gave his conclusion to this problem, namely the Chebyshev inequality.

P(|X - E(X)|≥ε)≤ Var(X)/ε^2.

The meaning of this formula is that when the number of samples is large enough, the error between a random variable and its mathematical expectation can be arbitrarily small.

Apply Chebyshev's inequality to the problem of understanding the age distribution of cinema audiences.

The random variable is: the observed proportion of audiences of each age group.

The mathematical expectation is: the proportion of different age groups among all moviegoers in real situations.

When we bring in the sample data, we can roughly draw the following conclusions.

Audiences under 15 years old account for 20%, 16-25 years old account for 27%, 26-40 years old account for 24%, and over 40 years old account for 29%, with an error of less than 5%.

But if we want to improve the accuracy of the audiences in the four age groups to one decimal place, then we need about 10 times the data, that is, about 20,000 samples.

If we magnify this problem.

We want to know the age distribution of the audience of a movie in the world, and we must be specific to the number of people in a more detailed age group.

For example, 18-20 years old, 21-24 years old, etc.

Or more specific regions.

China, Japan, South Korea, etc.

In a larger and more detailed range, in order to obtain more accurate results, the amount of data we need will increase thousands of times.

When we get super data.

Ordinary computers are already difficult to complete the calculation.

And even if it can be completed, it takes a lot of time.

Time is money, which is obviously unacceptable in business.

Therefore.

In order to get the results as quickly as possible, we need one or several supercomputers to calculate.

But the cost of using supercomputers is very expensive.

Companies that want to know the age of cinema audiences are obviously unwilling to spend so much on this issue.

So what should we do? "

Xu Liang operated the computer.

Three huge regular characters appeared on the projection screen behind him.

Cloud computing.

"Cloud computing, 'cloud' is the Internet, and 'computing' is the literal meaning.

The current cloud computing is a distributed computing, which means that the huge data computing and processing program is decomposed into countless small programs through the network "cloud".

Then, through a system composed of multiple servers, these small programs are processed and analyzed, and the results are returned to the user.

The whole calculation process only takes a few seconds.

In other words, cloud computing turns a problem that originally required the use of supercomputers and took several days or even more than ten days to calculate.

It becomes accurate data that can be obtained in just a few seconds and costs tens of thousands of yuan, or at most hundreds of thousands of yuan.

It greatly reduces expenses, improves efficiency, and obtains more accurate results.

Maybe some people think that counting the age distribution of movie audiences is not of much value.

But what if a catering company counts the age distribution of the audience of beverage products?

As long as accurate data is available, catering companies can develop more targeted advertisements and services for people of different age groups, thereby increasing their sales.

This has been applied by Master Kong in actual operations.

According to Pangu's big data survey, Master Kong found that the largest audience for its "Jianlibao" sports drink products is young people between the ages of 15 and 25.

Among them, males accounted for 41% and females accounted for 59%.

Then, data was obtained through multiple channels such as offline sampling surveys and online questionnaires, and data-driven methods were used to calculate the stars that this group of people are interested in, the types of TV series they like, and other data.

Combining these intersecting big data, detailed advertising plans and publicity channels were formulated.

In just one quarter, Master Kong's sales increased by 22% and net profit increased by 14.8%.

The same method can be applied to all consumer goods fields such as automobiles, catering, and entertainment.

There is no doubt that this will form a huge business change.

The original extensive advertising and publicity methods will become more detailed and targeted.

The original products with uniform tastes will be developed into products that are more in line with local characteristics based on the tastes and consumption habits of consumer groups in various provinces across the country.

Consumers will become the real subject.

It is safe to say that all consumer product companies that refuse big data will not survive for long. ”