My 1999

Hearing this, the big bosses who had been calm finally showed seriousness on their faces.

Those who can sit here are all smart people.

They can all hear the authenticity of big data applications from Xu Liang's words.

"However, big data and cloud computing are just the foundation. What really brings about industry change is artificial intelligence.

I believe many people have heard of the "Turing test".

Let a machine and a person sit behind the scenes, and let a referee communicate with the person and the machine behind the scenes at the same time. If the referee cannot judge whether the person he is communicating with is a person or a machine.

This means that the machine has the same intelligence as a person.

This is the full content of the famous "Turing test".

Computer scientists believe that if a computer achieves five things, it can be considered to have the kind of intelligence that Turing said.

First, speech recognition.

Second, machine translation.

Third, automatic summarization or writing of text.

Fourth, defeating the human chess champion.

Fifth, automatically answering questions.

Regarding how to achieve these five things, the academic community is divided into traditional artificial intelligence methods and other modern methods.

So what are traditional artificial intelligence methods?

Simply put, it is to first understand how humans generate intelligence, and then let the computer do it according to human thinking.

This method is also called the "bird flying school".

Just like humans observed the flight of birds and invented airplanes.

Observed the swimming of fish and invented submarines.

Invent through simulation.

But later, after years of research, it was proved that this method is very unrealistic.

Because machines are always machines and can never think like humans.

Scientists have to find another way.

In the 1970s, everyone began to try another development path for machine intelligence.

That is, using data-driven and supercomputing methods , to realize artificial intelligence.

This method is also called machine learning or knowledge discovery, which is what we have said before about the development of modern artificial intelligence.

The first person to make achievements in this regard was Fred Jarnik, a professor at Cornell University in the United States in 1972.

He is not an artificial intelligence expert, but an outstanding communication expert.

He believes that the human brain is an information source, and the process of encoding is from thinking to finding the right sentence and then speaking it out through pronunciation.

It is a problem of information transmission through a long channel from the medium (voice channel, air, etc.) to the ears of the listeners.

Finally, the listener understands it, which is a decoding process.

In other words, he believes that speech recognition of artificial intelligence is a typical communication problem.

It can be solved by solving the communication problem.

For this purpose, Jarnik used two mathematical models, namely the Markov model, to describe the information source and the channel respectively.

After finding the mathematical model, the next step is to use statistical methods to "train" the parameters of the model, which is machine learning today.

Through this method, the speech recognition rate of artificial intelligence has increased from about 70% in the past to 90%.

At the same time, the scale of speech recognition has increased from a few hundred words to more than 20,000 words, which can be called a revolutionary development.

The most important thing is that Jarnik's research has drawn a conclusion.

That is:

As the amount of data continues to increase, the system will become better and better.

Therefore, international artificial intelligence research is divided into two factions.

One faction is the bird-flying faction that imitates people, and the other faction is the data-driven faction.

The reason why the latter did not develop rapidly is mainly because data acquisition is very difficult.

First, there was no machine-readable data at the time.

Second, many different versions of literary pearls are scattered in different countries, and their translations are often not one-to-one.

Of course, there are many other reasons that I will not go into detail one by one.

However, this difficulty has been changed in the Internet era.

Its emergence allows research institutions to easily obtain machine-readable data around the world.

Moreover, the amount of data is still increasing several times or even dozens of times every year with the development of the Internet.

With the support of huge data, the error rate of speech recognition was reduced by half in the decade from 1994 to 2004.

The accuracy of machine translation has doubled.

20% of the contribution comes from the improvement of methods, and 80% comes from the increase in data volume.

Then there is the Global Machine Translation System Competition held in the United States in February this year.

Hongmeng and Google have achieved more than 50% BLEU scores through data-driven methods.

5% ahead of the famous top research institutions such as the University of Southern California and IBM Watson Laboratory that have been studying machine translation for decades.

In the past, it took 5 to 10 years to improve these five percentage points.

In the translation from Chinese to English, Hongmeng's score is 17% ahead of the third place, and Google, which also uses a data-driven method, is 15% ahead of the second place. This gap has exceeded the level of a generation.

Hongmeng and Google are both new companies that have been established for no more than ten years.

The foundation of artificial intelligence research and development is definitely not as deep as that of Southern California and Watson Laboratory.

But we have surpassed them.

Is the reason that we are better than them?

No.

So how did the gap come about?

It's simple.

As the two largest search companies in the world, Hongmeng Bing and Google both have huge search databases.

And we digitize all the pictures, books, and newspapers in the world every year.

This allows us to control the world's largest database.

Although the University of Southern California and IBM Watson Lab have more talents and a deeper research foundation than us,

they are far behind Bing and Google in terms of data volume.

So, they are behind.

The result of this competition has a huge impact on the field of artificial intelligence.

From the news we have received, most scientific research institutions in the world have abandoned the transmission "bird flying" method and switched to a data-driven method.

In other words, 2005 will be a watershed in the field of global artificial intelligence.

Starting this year, the bird flying school will be completely abandoned, and data-driven will become the only mainstream.

I believe that with the continuous accumulation of data, artificial intelligence will become more and more "intelligent" and "practical".

It will have a profound impact on all aspects of society. ”

Xu Liang, who was completely in his own rhythm, no longer needed a script.

At this moment, he completely let go of the identities of both parties.

He completely regarded the people in the audience as his audience.

And they were completely attracted by the content of Xu Liang's words.

"The future agriculture will completely get rid of the agricultural model of China that has consumed a lot of manpower and material resources and cultivated intensively for thousands of years.

It will be replaced by an intelligent agricultural factory.

In this factory, a large number of radio frequency chips are installed to collect all data such as temperature, humidity, soil fertility, etc., and collect them into the artificial intelligence brain.

Then the "intelligent brain" will inject water and fertilizer according to the needs of crops through drip irrigation.

Use 10%, or even less water and fertilizer, to grow twice or even more agricultural output.

In the past, we might need 20 farmers to plant 100 acres of land.

In the era of intelligent agriculture, only one person is needed to manage and maintain the "artificial intelligence brain" to manage thousands or even tens of thousands of acres of agricultural land.

Efficiency and output are improved by thousands of times.

If we can build more nuclear power plants, solar energy, wind energy and hydropower in the future, and bring down the price of energy.

Then we can make agriculture develop in a three-dimensional way.

Truly get rid of the restrictions of the natural environment on agriculture. ”

Xu Liang mentioned the ‘three-dimensional agriculture’. Before his rebirth, China established ‘three-dimensional agricultural factories’ in the western region with low energy prices because of the skyrocketing solar power generation.

However, even if the energy price falls, the investment is still relatively large.

So it can only be used to grow high-value cash crops.

There is no foundation for large-scale promotion.

So he didn’t plan to say more.

“In the future, the industry will help workers through intelligence and big data systems, and even replace workers, to achieve comprehensive intelligence in the manufacturing industry.

There will be more and more unmanned factories and unmanned assembly plants.

The price of industrial products will drop several times.

Now a mobile phone costs thousands of yuan.

In the future, mobile phones will not only have richer functions and more advanced performance, but you don’t even need to spend money. China Unicom and China Mobile will give them to you, because the income from phone and Internet fees far exceeds the value of a mobile phone. "

Looking at the doubtful eyes of the audience, Xu Liang did not explain much.

Time will tell everything.

"When big data and artificial intelligence enter all links of industrial manufacturing and sales, not only will the number of workers gradually decrease, but the entire manufacturing industry will be reshuffled.

Relying solely on the low-level competition of companies that reduce the number of workers will no longer have an advantage in manufacturing.

The future competition is the competition of the level of intelligence from design to sales.

In other words, China will be the last country to have and develop with the demographic dividend.

In ten or twenty years.

A large population will no longer be an advantage. "

After saying this decisively, Xu Liang continued.

"Intelligent medical care in the future.

No matter in which country, the biggest bottlenecks encountered in medical care are mainly reflected in several aspects.

First, the cost of medical care is getting higher and higher.

Now going to the hospital, any physical examination costs hundreds or thousands of yuan;

If you go to the hospital for medical treatment, a series of processes such as blood tests, urine tests, and MRIs will cost thousands or even tens of thousands of yuan.

For ordinary people, this is a very large expense.

So the situation of not being able to afford medical care will become more and more serious.

Second, the imbalance of medical resources.

The medical resources in first-tier cities far exceed those in third- and fourth-tier cities, and ordinary county towns are even more incomparable.

Until now, there are no tertiary hospitals in more than 1,000 cities and counties across the country.

Finally, and most importantly, many diseases cannot be cured.

For example, cancer, Parkinson's syndrome and Alzheimer's disease.

Although doctors and scientists around the world have worked hard for many years, and countries and research and development institutions around the world have invested a lot of money, the treatment of diseases such as cancer has always been slow over the past few years.

But we can use big data and artificial intelligence to solve the above problems.