Future super intelligent system

Chapter 51 Dynamic Feature Logic Theory
Everyone who had already started to get tired subconsciously lifted their spirits. In normal companies, there are two people who come to participate in this kind of proposal, a project leader and a technician.

Therefore, whether it is a project manager who wanders in the market all year round or a technician wearing a plaid shirt, they are naturally full of interest in what Liu Fan will say next.

"The reason why our face recognition technology can realize the application of the scene you saw just now is based on the logic theory of dynamic features we proposed."

Liu Fan started to explain, and some people started to record, some started to video, and some people started to take notes.

"The core of our current mainstream face recognition technology is features. Whether it is overall geometric features or local features, whether it is traditional algorithms or neural network simulations, the root of everything is to capture features and process them. But regardless of the subsequent How dynamic or high-dimensional the processing method is, the features we capture are static.

This makes it impossible to avoid being easily affected by the environment.So we have long been considering whether we can capture dynamic features instead of static features.Later, we realized that simply capturing dynamic features is still not enough. If big data can be used to mine dynamic feature logic, then no matter how the environment affects it, as long as a large enough dynamic feature logic model can be built, even if it relies on very vague Image data may also enable face recognition.

I simply give an example. "

Liu Fan said, and clicked the PPT to the next page, "First of all, everyone here must know a basic principle, that is, one leaf, one bodhi. There are no two identical leaves in this world, and there are no two leaves with the same growth process.

You can see the PPT. This is the first time we discovered the dynamic feature logic. After continuous mining of these 3000 million dynamic faces, the computer came up with an unexpected answer. When a person turns his eyes, the movement of his eyes is There is a functional relationship with the muscle changes in the position of the red spot under the cheek. This function is the same between different people. The only difference is in the coefficient.

The specific function involves commercial privacy, so I won’t show it here. I just make a simple analogy. The functional relationship between A’s eyeballs and cheeks is F(X)=y, and the functional relationship between B’s is F(X)=1.1y. The functional relationship is F(X)=1.2y, and so on, so when the video captures the variable X of a certain person's eyeball rotation, the corresponding function result is unique, and we currently have a dynamic function like this on the face 26 were found, and we're still working on it.

I believe everyone can understand that the advantages of dynamic functions are very obvious. For example, if a criminal covers his face, but as long as his eyes can be seen, as long as his eyes provide a variable X, then several and eye functions can be performed. Calculation of related functions, maybe he also covers his eyes, but the muscle changes in a certain part of the face also have a matching corresponding function, then."

"You let the algorithm deduce the algorithm!?" Liu Fan was mid-sentence when someone suddenly interrupted him.

And when the man in the black plaid shirt said this, the audience immediately became agitated.

They just listened to what Liu Fan said, but they haven't had time to realize what Liu Fan's algorithm would mean to the artificial intelligence industry if it really existed.

The so-called deep learning algorithm is the learning of ability. To give a slightly one-sided example, let the computer keep learning multiplication, and the multiplication calculation speed of the computer will gradually become faster and faster.

To use a more realistic example, many companies are now starting to develop robots. When you see robots interacting with people, you will feel that the era of artificial intelligence has really come.But in fact, in the process of a robot talking to a human, we simplify the process of deep learning. In fact, what to say is an optimized answer under a certain program setting.

For example, a girl told the robot that she was angry. The thinking mode of the current intelligent robot is like this. She said that she was angry. According to the principle that women are irrational when they are angry, the optimal solution is obtained at this time. , just apologize and send her a red envelope.

But how do real people think about things?Is she really angry?Why is she angry?So what should I do in such a situation?If it's not a big deal, then apologize, but if it's a matter of principle, you must not spoil her, because it may ruin her and yourself.

This is just an example. In real relationships, boys will have many more thoughts than this.

In fact, through such a comparison, everyone can easily understand the difference between human and artificial intelligence. One is to follow the set program to analyze and get the optimal solution, and the other is to make different instant reactions based on life experience and one's own emotions. .And this immediate reaction is theoretically completely chaotic and unpredictable.

We can find that people go through several processes when dealing with one thing: perception, analysis, and decision-making.

What artificial intelligence now does is input information, analyze information, and output optimal results.

Therefore, in terms of information processing, artificial intelligence looks similar to human beings, but once emotions and disordered things are involved, the difference between artificial intelligence and human beings will come out.

People will die for those they love, and today's artificial intelligence will never.

At this time, let’s look back at the algorithm implemented by Liu Fan. If the algorithm can deduce various functions based on the continuously input data, the behavior pattern at this time will simulate to a certain extent: people start from completely ignorant babies. As I continue to grow, I understand more and more about the world.

In fact, for artificial intelligence, the so-called algorithms, functions, and rules may be the rules of human survival.A person's life is also a process of constantly understanding life.

If we refine the process of human growth, is it possible that we are constantly discovering countless rules?It’s just that the brain has processed these problems and we haven’t noticed it ourselves.

So when the algorithm can discover the rules by itself, it is equivalent to opening another door for artificial intelligence.Although this direction is not necessarily completely correct, theoretically speaking, it is likely to be closer to the door of real artificial intelligence.

But this thing is easy to talk about, but it is too difficult to realize it. Just like Zhang Kaixiang has a strong mathematical foundation and corresponding conjectures, but he has no way to start.If Liu Fan didn't have the abnormal plug-in of the system, he might not be able to do it in his life if he wanted the algorithm to independently mine functions.

Of course, in fact, Liu Fan’s current algorithm is still far from the ideal state. On the one hand, there is still a lot of room for the accuracy of function derivation and mining capabilities, because it will be limited by the amount of data and data types. .For example, before this face recognition, Liu Fan's data association inverse calculation algorithm has not made any breakthroughs.

Although in the past, it is indeed possible to realize the use of unordered data as he imagined before, but the previous functions did not break through the traditional mathematical framework underlying the algorithm, which leaves the question of whether these functions are derived by the algorithm itself remains to be seen. Discussed.

However, this experiment on face recognition allowed Liu Fan to see a breakthrough, and the self-derivation of the algorithm seems to be really feasible.

Another problem is that Liu Fan's algorithm can only analyze now and cannot make decisions.

He tried many methods, but could not achieve decision-making ability.

He slowly realized that in order for the algorithm to have decision-making capabilities, it must break the existing underlying algorithm principles.

But this does not affect the shock caused by Liu Fan's current algorithm to the technical staff present today. Liu Fan and Zhang Kaixiang will have such conjectures. Naturally, others may also have such conjectures, but no one can overcome the technical difficulties. That's all, so when everyone realized what Liu Fan presented if it was true, it would be difficult for everyone to control their emotions.

And everyone's shock was exactly what Liu Fan expected. From the moment the GSCT was headed, Wood Dragon Technology was an army standing on the battlefield ready to attack the city, and there was no need to hold back its edge.
　I feel like I’m a bit of a nerd when I study artificial intelligence recently, but this industry is really interesting. Unfortunately, I’m too old to learn basic mathematics. It’s good to get in touch with this industry when I was in college.
　　
　
(End of this chapter)