Understanding the nature of artificial intelligence

Artificial intelligence (AI) has entered the vision of the general public, and we can see many AI-related products in our lives. Such as Siri, AI beauty, AI face change...

Although everyone listens a lot, most people don't understand AI, and there are even some misunderstandings. This article will not cover any technical details to help everyone understand the nature of artificial intelligence.

 

What is artificial intelligence?

Many people have some misconceptions about artificial intelligence:

  1. Robots in movies are typical examples of artificial intelligence
  2. Artificial intelligence seems to be omnipotent
  3. Artificial intelligence will threaten human survival in the future
  4. ……

The reason why there are many misunderstandings about artificial intelligence is mainly because everyone only sees the speech of some people, but does not understand the basic principles of AI. This article will help everyone understand the basic principles of AI. The nature of things is often not what everyone said So complicated.

We use traditional software and artificial intelligence for comparison, and it is easier to understand with a frame of reference.

 

Traditional software vs artificial intelligence


Traditional software

Traditional software is the basic logic of "if-then". Humans summarize some effective rules through their own experience, and then let the computer run these rules automatically. Traditional software can never cross the boundaries of human knowledge, because all rules are made by humans.

To put it simply: traditional software is "rule-based," requiring artificially set conditions and telling the computer what to do if it meets this condition.

This logic is very useful when dealing with simple problems, because the rules are clear and the results are predictable. The programmer is the god of software.

But in real life, it is full of a variety of complex problems. These problems are almost impossible to be solved by formulating rules. For example, the effect of face recognition through rules will be very poor.

Traditional software is rule-based logic

人工智能

Artificial intelligence has now developed many different branches, and the technical principles are also diverse. Here we only introduce the most popular deep learning today.

The technical principles of deep learning are completely different from the logic of traditional software:

The machine summarizes the laws from "specific" large amounts of data, summarizes some "specific knowledge", and then applies this "knowledge" to real-world scenarios to solve practical problems.

This is the essential logic of the development of artificial intelligence to this stage. The knowledge summarized by artificial intelligence is not like traditional software, which can be expressed intuitively and accurately. It is more like the knowledge learned by human beings. It is more abstract and difficult to express.

Artificial Intelligence Logic: Inducting Knowledge from Data

The above statement is still relatively abstract. Here are some aspects to help you thoroughly understand:

 

Artificial intelligence is a tool

The AI ​​is the same as the hammer, car, computer... we use, and its essence is a tool.

Tools must be used to be of value, and if they exist independently, there is no value, just like the hammer in the toolbox, no one waving it has no value.

Artificial intelligence is essentially a tool

The reason why the tool of artificial intelligence is spoken by the whole society is that it greatly expands the capabilities of traditional software. There were many things that computers couldn't do before, but now artificial intelligence can do it.

Thanks to Moore's Law, the power of computers has increased exponentially. As long as the computer can disengage, the productivity has been greatly improved, and artificial intelligence has allowed more links to catch the express train of Moore's Law, so this change Is extraordinary.

But no matter how it changes, traditional software and artificial intelligence are tools that exist to solve practical problems. This has not changed.

 

Artificial intelligence only solves specific problems

"Terminator" and "The Matrix"...Many movies have appeared against heavenly robots. This kind of movie gives everyone a feeling: artificial intelligence seems to be omnipotent.

The reality is that artificial intelligence is still at the stage of a single task.

Artificial intelligence currently can only handle a single task

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Single task mode.

Landline for phone calls, game consoles for games, MP3 for listening to music, navigation for driving...

Multitasking mode

This stage is similar to a smart phone. Many apps can be installed on one phone and do many things.

However, these capabilities are independent of each other. After booking a flight on the travel app, you need to set the alarm with the alarm clock app, and finally you need to call a taxi with the taxi app. Multi-tasking mode is just the superposition of a single task mode, which is far from human intelligence.

Integrate

You are playing Go with a friend, and you find that your friend ’s mood is very bad. You could have easily won, but you deliberately lost to the other side, and you still praise the other side because you do n’t want to make this friend more depressed Irritability.

In this small matter, you have used a variety of different skills: emotion recognition, Go skills, communication, psychology...

But the famous AlphaGo will never do this. No matter what the opponent's situation is, even if they lose the game, AlphaGo will win the game relentlessly, because it can't do anything except play Go!

Only when all the knowledge is formed into a network structure can it be integrated.For example, military knowledge can be used in business, and biology can be used in economics.

 

Know it, but do n’t know why

The current artificial intelligence is to summarize inductive knowledge from a large amount of data. This crude "induction method" has a big problem:

Don't care why

AI doesn't care why

Ponzi schemes take advantage of this!

  • It uses ultra-high returns to attract leeks and then turn money for everyone who gets up early to participate;
  • When bystanders found that all participants had actually made money, it was simply summarized as: historical experience shows that this is reliable.
  • So more and more people became jealous and joined until one day the crooks ran away.

When we use logic to deduce this thing, we can conclude that the scammer:

  • Such high returns are not in line with market rules
  • Don't lose money? I don't need to take high risks with high returns? Doesn't seem reasonable
  • Why does such a good thing fall on me? Doesn't seem right

Because the current artificial intelligence is based on "inductive logic", it also makes very low-level mistakes.

Labor can only make low-level mistakes

  • Left: The occlusion of a motorcycle makes AI mistake a monkey for humans.
  • Middle: The obscuration of the bicycle caused the AI ​​to mistake the monkey for a human, and the jungle background caused the AI ​​to mistake the bicycle handle for a bird.
  • Right: The guitar turns the monkey into a human, and the jungle turns the guitar into a bird

The image above shows the effect of a guitar on ps in a photo of a jungle monkey. This led the deep network to mistake monkeys for humans and mistake the guitar for birds, presumably because it believed that humans were more likely to carry guitar than monkeys, and birds were more likely to appear in the nearby jungle than guitars.

It is also because of inductive logic that it depends on a large amount of data. The more data there is, the more universal the generalized experience is.

 

The history of artificial intelligence

AI is not a brand new thing, he has been developing for decades! Below we introduce the most representative 3 development stages.

History of Artificial Intelligence

The above picture shows some milestones in the field of artificial intelligence from 1950 years to 2017 years. Summarized will be divided into 3 big stage:

First wave (non-intelligent dialogue robot)

20 century 50 era to 60 era

1950 10 month, Turing proposed the concept of artificial intelligence (AI), and proposedTuring testTo test AI.

The Turing test suggested that in a few years, people saw the "twilight" of the computer through the Turing test.

1966 year, the psychotherapy robot ELIZA was born

People of that era rated him very high, and some patients even liked to chat with robots. But his implementation logic is very simple, is a limited dialogue library, when the patient speaks a certain keyword, the robot responds to a specific word.

The first wave did not use any new technology, but used some techniques to make the computer look like a real person. The computer itself is not smart.

 

Second wave (speech recognition)

20 century 80 era to 90 era

In the second wave, speech recognition is one of the most representative breakthroughs. The core breakthrough was to abandon the idea of ​​the symbol school and changed it to a statistical idea to solve practical problems.

In the book "Artificial Intelligence", Kaifu Li introduced this process in detail, and he is also one of the important people involved.

The biggest breakthrough of the second wave was to change the way of thinking, to abandon the idea of ​​the symbol school, and to use statistical ideas to solve the problem.

 

The third wave (deep learning + big data)

21 century

The 2006 year is a watershed in the history of deep learning. In this year, Jeffrey Sinton published "A Fast Learning Algorithm for Deep Belief Networks". Other important deep learning academic articles were also released this year, and several major breakthroughs were made at the basic theoretical level.

The reason why the third wave will come is that the 2 conditions are mature:

After the years of 2000, the rapid development of the Internet industry has produced massive data. At the same time, the cost of data storage has also dropped rapidly. It makes the storage and analysis of massive data possible.

But by the full GPU acceleration tech The continuous maturity provides the necessary computing power to improve the usability of the algorithm and reduce the cost of computing power.

Deep learning is the mainstream technology today

Deep learning has developed a powerful ability after various conditions have matured. In speech recognition, image recognition,NLPThe field continues to set records. Make AI products truly available (for example, the error rate of speech recognition is only 6%, and the accuracy of face recognition is higher than that of humans.BERTThe stage of exceeding the human...) in the performance of the 11 item.

The third wave struck, mainly because of the big data and computing power conditions, so that deep learning can exert great power, and the performance of AI has surpassed human beings and can reach the stage of “availability”, not just scientific research.

The difference between the artificial intelligence 3 wave

  1. The first two craze was dominated by academic research, and the third craze was dominated by real business needs.
  2. The first two craze is mostly at the market level, while the third craze is at the business model level.
  3. The first two crazes were mostly in the academic world to persuade the government and investors to invest money. The third wave of enthusiasm was that investors actively invested in academic projects and entrepreneurial projects in hotspots.
  4. The first two booms raised questions more, and the third boom solved problems more.

To learn more about the history of AI, I recommend reading Kai-Fu Lee's人工智能", The content of the three waves above is excerpted from this book.

 

What can artificial intelligence not do?

3 levels of artificial intelligence

When exploring the boundaries of AI, we can first simply divide AI into 3 levels:

  1. Weak artificial intelligence
  2. Strong artificial intelligence
  3. Super artificial intelligence

3 levels of artificial intelligence: weak artificial intelligence, strong artificial intelligence, super artificial intelligence

Weak artificial intelligence

Weak artificial intelligence, also known as restricted-field artificial intelligence (Narrow AI) or applied artificial intelligence (Applied AI), refers to artificial intelligence that focuses on and can only solve problems in specific areas.

For example: AlphaGo, Siri, FaceID...

Strong artificial intelligence

Also known as Artificial Artificial Intelligence or Full Artificial Intelligence (Full AI), it refers to artificial intelligence that can do all the work of human beings.

Strong artificial intelligence has the following capabilities:

  • Reasoning when using uncertainties, using strategies, solving problems, and making decisions
  • The ability to express knowledge, including the ability to express common sense knowledge
  • Planning ability
  • Learning ability
  • Ability to communicate using natural language
  • Ability to integrate these capabilities to achieve a defined goal

Super artificial intelligence

Assuming that computer programs continue to evolve and are smarter than the world's smartest and most gifted humans, the resulting artificial intelligence system can be called super artificial intelligence.

Our current stage is weak artificial intelligence, strong artificial intelligence has not been realized (even far away), and super artificial intelligence is even invisible. So "specific areas" are still borders that AI cannot overcome.

 

What is the capability boundary of artificial intelligence?

If we go deeper and explain the boundaries of AI's capabilities from a theoretical level, we must move Master Turing out. Turing was thinking about three questions in the mid-30s:

  1. Are there any clear answers to all math problems in the world?
  2. If there is a clear answer, can I calculate the answer in a limited number of steps?
  3. For those mathematical problems that may be calculated in a finite number of steps, can there be an imaginary machine that allows him to keep moving, and finally, when the machine stops, the mathematical problem is solved?

Turing really designed a method that later generations called the Turing machine. Today's computers, including the new computers being designed around the world, are not beyond the scope of Turing machines in terms of their ability to solve problems.

(Everyone is a human being, how is the gap so big?)

Through the above 3 questions, Turing has drawn a line.This limit applies not only to today's AI, but also to future AI. .

Let's take a closer look at the boundaries:

Ability boundaries for artificial intelligence

  1. There are many problems in the world, only a small part of which is a mathematical problem.
  2. In mathematics, only a small part is solved.
  3. Among the solutions to the problem, only part of the ideal state of the Turing machine can be solved.
  4. In the latter part (the part that the Turing machine can solve), only part of it is solved by today's computers.
  5. The problem that AI can solve is just a part of the computer that can solve the problem.

Worried that artificial intelligence is too powerful? You think too much!

In some specific scenarios, AI can perform very well, but in most scenarios, AI is not useful.

 

Will artificial intelligence make you unemployed?

This question is the one that everyone cares about most, and it is also the one that has the greatest influence on each individual. So come up and talk about it separately.

First, the replacement of "partial human behavior" by artificial intelligence is an inevitable trend

Every new technology or invention will replace part of the labor force:

Time reporting-form

The work of pulling a rickshaw-car

Well digging work-drilling machine

……

It should be noted that technology replaces only certain jobs. The digging machine can only help you dig holes, but cannot help you determine where to dig.

The same is true of artificial intelligence, which is not aimed at certain occupations or certain people, but replaces some specific labor behaviors.

Second, there will be better new jobs as you lose your job.

The history of several technological revolutions tells us that although the emergence of new technologies has caused some people to lose their jobs, many new jobs will also be created. The jobs that are replaced are often inefficient, and the jobs that are created are often more efficient. Think about pulling a rickshaw, then think about driving a car.

When artificial intelligence frees up a portion of the workforce, it can do more valuable and interesting things.

do not be afraid! Using AI well is a super skill

Two points were mentioned above:

  1. The essence of artificial intelligence is a tool, and people need to use it
  2. Artificial intelligence replaces not people, but certain work links

So do n’t be afraid to replace yourself with artificial intelligence, you shouldActively learn AI, become the earliest person who can use AI, and become a person who can use AI well.

Think of people who would use computers and the Internet 20 years ago. They were very scarce in that era, so they earned the dividends of the Internet era. By the same token, the dividends of the intelligent age will belong to those who can use AI.

 

What jobs will be replaced by artificial intelligence?

Kaifu Li put forward a judgment basis:

If a job takes less than 5 seconds to make a decision, then there is a high probability that it will not be replaced by artificial intelligence.

4 job characteristics that are easily replaced by artificial intelligence

This work has four characteristics:

  1. Not much information is needed to make a decision
  2. The decision-making process is not complicated and the logic is simple
  3. Can be done on its own, without collaboration
  4. Repetitive work

Skills that are hard to replace by artificial intelligence

Scientists have identified three skills that are difficult to replace with artificial intelligence:

  1. Social intelligence (insight, negotiation skills, empathy...)
  2. Creativity (original power, artistic aesthetics...)
  3. Perception and operation ability (finger sensitivity, coordinated operation ability, ability to cope with complex environments...)

 

How to usher in the intelligent era?

Artificial intelligence will sweep the world like the industrial era. In this case, what we need to do is not to escape, but to embrace this change. Here are some specific suggestions for everyone:

  1. To understand the underlying logic and basic principles of the intelligent age, you don't need to learn to write code, but you need to know what might happen, what is impossible.
  2. Artificial intelligence will infiltrate all walks of life like computers in the future. You should try to understand artificial intelligence as much as possible, and learn how to use it to solve existing problems and become an early adopter of artificial intelligence.
  3. Make a career plan. Don't choose three non-professionals (no social, no creativity, no strong perception and operation skills)

 

Final Thoughts

The basic principle of artificial intelligence: Machines summarize laws from "specific" large amounts of data to form certain "specific knowledge", and then apply this "knowledge" to real-world scenarios to solve practical problems.

Based on this basic principle, there are three characteristics:

  1. Artificial intelligence is essentially a tool
  2. AI skills can only solve specific problems, not everything
  3. Artificial intelligence belongs to inductive logic and can tell you what it is, but cannot tell you why

 

So far, artificial intelligence has experienced 3 waves:

  1. 20s to 50s: non-intelligent dialogue robots
  2. 20s to 80s: speech recognition
  3. Early 21st Century: Deep Learning + Big Data

 

There are 3 levels of artificial intelligence:

  1. Weak artificial intelligence
  2. Strong artificial intelligence
  3. Super artificial intelligence

 

In terms of unemployment, artificial intelligence will indeed replace some human jobs, but at the same time, some new and more valuable jobs will appear. There are three skills that will not be easily replaced by artificial intelligence in the future:

  1. Social intelligence (insight, negotiation skills, empathy...)
  2. Creativity (original power, artistic aesthetics...)
  3. Perception and operation ability (finger sensitivity, coordinated operation ability, ability to cope with complex environments...)

 

`` Attach '' 2020 AI Development Trend

Let's review the important changes in artificial intelligence in 2019:

  1. Important progress has taken place in the NLP field, and pre-trained models such as BERT, GPT-2, XLNET have already played an important role in the product.
  2. The infrastructure is further improved: PyTorch is growing very fast, and TensorFlow is deeply integrated with Keras.
  3. GAN Rapid development, the emergence of popular products. DeepFake and ZAO let the masses experience GAN technology.
  4. It is also because of DeepFake that the social impact of artificial intelligence has been paid attention to by everyone, and AI-related laws are being improved globally.
  5. Auto-ML lowers the threshold of AI and makes the deployment of artificial intelligence very easy.

What are the development trends in 2020?

  1. The introduction of 5G will digitize more of the physical world, which will further promote the development and popularization of AI.
  2. The integration of the data science team and the business team will be closer.
  3. It is possible to see the development of multi-tasking AI models and move towards general artificial intelligence.
  4. Get rid of data dependence and get better models with less data.
  5. Achieve greater breakthroughs and development in the NLP field.
  6. Improve the interpretability of AI and solve the black box problem
  7. Social issues have intensified, and discussions on personal data security, privacy, and algorithmic bias have been increasing.

More important milestones in 2019 and development trends in 2020 can be found in the following two articles:

'Important developments of artificial intelligence, machine learning, and deep learning in 2019 and trends in 2020 (technical articles)"

'Important developments of artificial intelligence, machine learning, and deep learning in 2019 and trends in 2020 (research)"

 

Baidu Encyclopedia + Wikipedia

Baidu Encyclopedia version
Artificial Intelligence, abbreviated as AI in English. It is a new technical science that studies and develops theories, methods, techniques, and applications for simulating, extending, and extending human intelligence.
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Wikipedia version
In computer science, artificial intelligence, sometimes called machine intelligence, is the intelligence that machines display.
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