What is the experience of quarreling with Siri, an artificial intelligence service?

On March 9th, 216, Google's Deepmind Go program "AlphaGo" will face off against Li Shishi, a professional nine-stage player. In October last year, this program defeated Fan Hui, the second stage of China chess player career; It was the first time that Go AI defeated a professional player in a fair game. This achievement was published in the journal Nature in January this year, which also triggered an extremely heated discussion-and the most frequently asked question is, is AI finally going to occupy the world?

The problem that Siri pictures that can trick you by singing come from the Internet

is not alarmist. In a sense, AI has occupied it: from Apple's Siri, to the daily browsing search engine, to the online article recommendation and product recommendation system, all these are artificial intelligence-even if they are not in science fiction, our daily life is hard to separate from them.

but AlphaGo is different from these common AI. Their difference lies in the universality of learning methods and techniques.

Siri: a scripted assistant

Siri is an "intelligent assistant" who can understand our verbal commands, help us search online and find our contacts in the list. But its principle is simple: through voice recognition technology, the voice is transformed into the basic elements of the language, such as vowels, consonants and words, and then compared with the special commands built into the system. If the contrast is a practical problem, then execute the corresponding instructions; If there is an empty question, pick a paragraph from the relative paragraph library.

so its problem is obvious: if you order it to do commands that are not in the system, it will jump on the street. Although Siri is AI, it is a very limited AI: it can only solve pre-written problems.

In the face of the provocation of the big brother in Northeast China, Siri has made a fool of Deep Blue (or maybe just frightened him

No one can beat him in chess, but only in chess

In p>1997, the chess machine "Deep Blue" made by IBM defeated Kasparov, the world chess champion at that time. This is a landmark event in the history of artificial intelligence. However, although Deep Blue has defeated the world champion, it has the same shortcoming as Siri: it is too specialized.

As a program, Deep Blue's software is specially designed for chess. Its four criteria for evaluating the disk surface include force, position of chess pieces, safety of the king and layout rhythm-obviously, these indicators are completely dependent on the rules of chess itself, without any expansibility.

Kasparov versus Deep Blue

Even so, it still relies heavily on brute force. Deep blue hardware was the fastest chess machine of the year. Although there was a system to help screen, it still had to evaluate 2 billion possible situations every second. In order to meet this demand, IBM developed customized hardware for it.

As a result, it is more a chess machine than a chess program. Deep blue can only play chess, but can't learn go, even simple gobang. In contrast, Kasparov, as a human being, can learn Go, Gobang and painting. Deep blue technology, like a key specially designed for chess, has great limitations.

Self-driving car: Take a new direction

The principle of self-driving car can be simplified as the following steps:

First, it knows the surrounding environment through sensors, just like a driver uses his eyes to observe the surrounding situation;

Then we can get the route of the road through networking, just like we use navigation software when driving;

Then the computer program judges how pedestrians and cars will move nearby;

finally calculate your best route, and control the speed and direction of the car according to this route.

it is specific to the field of autonomous driving, but the basic idea is somewhat close to AlphaGo.

IBM Watson: Universal Intelligence

In p>211, IBM Watson's live-action answering program Jeopardy in the United States! Beat the human player in the world, and its technical concept is more like AlphaGo. Watson's decision-making consists of four steps: first, observing, collecting data from the environment, then making assumptions about the data, then evaluating these assumptions, and finally making a decision. However, there are some differences with AlphaGo. First, it is designed as a question-and-answer machine. Secondly, when training Watson, human experts need to participate-for example, the question about cancer requires scientists to eliminate outdated information and wrong information from a large number of books and papers, and feed the sorted data to the machine. But at least, it can handle many fields, which makes it have much stronger expansion possibilities than its peers: Watson has been used in the medical field now.

eh? A little cute ~

So, what is the technical idea of AlphaGo?

Deepmind created AlphaGo in an attempt to build a universal artificial intelligence through Reinforcement learning. Its concept contains two entities, one is artificial intelligence itself, and the other is its environment. There are two relationships between artificial intelligence and environment, one is to sense data through sensors, and the other is to influence the environment through specific actions. Because of the complexity of the environment, it can't get all the information, so it needs to repeat the cycle of perception-response constantly in order to get the maximum benefit in the environment. Most mammals, including humans, meet this set of rules.

before AlphaGo, they have used this idea to let AI play games. In 215, a paper published in Nature described how to make an algorithm play different Atari programs, including games such as "Space Invader" and "Playing Bricks". AI watches game videos like people, operates games like people, slowly learns from game white and becomes a game expert. AlphaGo is also based on the same principle, simulating the way people learn Go. It plays chess like people and slowly learns how to think like experts.

this technical concept requires original data, so it is more universal than those methods that need to input manually sorted data. In principle, AlphaGo learns to play Go, and Gobang is not a problem.

the reason why p>AlphaGo technology was first used in games is that games are much simpler than real problems, whether it is board games or computer games. Games are also likely to be the first field where similar technologies are put into practical use: after all, with the development of game technology, game developers gradually realize that good AI is as important as realistic images. Whether it is real-time strategy games, such as StarCraft or NPC in role-playing games, advanced artificial intelligence can not only become a strong opponent, but also become an excellent team partner.

However, its strongest point is, of course, adaptability and learning ability. Deepmind claims that this technical concept will soon be applied to the medical field, trying to solve the problem of personalized medical care. And this is definitely only the first step.

an AI

Do you know why there is no AI in this comparison list?

because no matter how powerful they are, they all serve you human beings.

obviously, however, this AI is here to hurt you.

hehe ~