He also said that the real smart hardware is to connect the hardware to the network, you can use the sensor to collect a large amount of data, there is an intelligent system (deep learning) in the cloud through big data, you can generate intelligent judgments, and then feedback this judgment result To the hardware, this can produce a true smart closed loop.
The following is part of the interview record:
Moderator: Now that artificial intelligence is too hot, let's talk about whether artificial intelligence is now an outlet or a bubble.
Hongwei Zhou: Many people have asked me this question. Artificial intelligence is a tune or a bubble. I think it is because precisely because it is an outlet and represents the direction of the future, everyone believes in what Ray always said. When the typhoon comes, pigs can fly in the sky, so now all the pigs are on the artificial intelligence gate. In time, the pigs flying on this estuary naturally brought bubbles. Therefore, sometimes the bubble is not necessarily a bad thing because it can allow a large number of intelligent people, large amounts of investment, large amounts of funds, and a large number of companies to invest in a period of time.
Of course, in the end, many people will not necessarily get reasonable results, but it will accelerate the promotion of this industry. Therefore, both the mobile phone and the Internet in the past experienced this kind of tuyere and bubble process. But I think there are a few misunderstandings that I would like to correct with everyone. Because some time ago I also went to the United States to study artificial intelligence experts, professors, and scholars to make some exchanges. Because we see too much of science fiction movies in the United States, we have to mention artificial intelligence that reminds us of Terminator 2 humanoid robots or variants. King Kong, so many people will say after the artificial intelligence comes out, the machine will not replace human beings, and it will evolve very quickly. Finally, humans will all be destroyed. In the future, our security company will have something to do. We must become a machine or a person. Is it a machine or a person? This is worrying people. Frankly speaking, this artificial intelligence strictly speaking did not achieve a great breakthrough in the entire basic principle. The original neural network and machine learning algorithms are equal to this algorithm when the current Internet brings powerful computing power. It is a very good place to use, but it still seems to be applicable in some areas. In some areas it is not practical.
Therefore, today's so-called artificial intelligence is not omnipotent, nor does it produce the ability to truly think about itself as humans, and it is even less likely to be as conscious as human beings. It is impossible for human consciousness to be generated by human consciousness, and it is impossible for human beings to become aware of it. Therefore, it is impossible to have emotion and become the last machine monster to replace human beings. This is certainly not possible. Actual machine learning algorithm is applied in what kind of field? They call training with big data. There is an algorithm that trains a pattern. You give him similar data. It can give you a certain conclusion. For example, if we want to teach the machine to recognize the cat, give him 1 million pictures of all kinds of cats. After training, take a picture of the new cat. It can recognize this as a cat. This is big data.
The algorithm that may have been invented ten years ago suddenly finds use today for two reasons. First, various cloud computing, computer CPU capabilities, and image processor enhancements have made the past algorithm, if there are many neural networks. At a deeper level, this algorithm is very time consuming, but it is very fast. The second reason is that since we have the Internet, we can collect real big data. In the past, where did you get a picture of a million cats? With the combination of these two factors, deep learning has made rapid progress in these areas of image recognition. However, in some areas, such as human-machine dialogues, you will find machines that we can train to transfer from speech to speech. Into the text, as long as there is a correct comparison between the voice and the text can be trained, this is what the HKUST News is doing. But you have to understand and understand the meaning of each sentence, because there are too many knowledge in each sentence, too much knowledge, such as we will go downstairs to eat for a while, this downstairs means what, what does meal mean? Because you have this knowledge, I can understand you when I speak, but the machine translates my sentence into words, but it may not be able to understand what I mean.
Moderator: Your downstairs refers to the 360 ​​downstairs and not downstairs?
Zhou Hongyi: Yes, I just give an example to illustrate that the deep learning algorithm is not a panacea. Again, for example, if I just take a picture of a cat and let it recognize the dog it will not recognize it. To teach a child, if you show him a cat, the next time he sees it, the cat can basically recognize it. This is where the machine and the person are not the same. I use this example to say that artificial intelligence is now able to achieve rapid and effective results in the field of big data training through today’s big data plus powerful computing power. This is what we should seize. In return, if some people blindly AI like science fiction exaggerated, blowing out of God, everything can become a bubble. So, you asked me if I was a tug or a bubble, and I think it depends on how to do it.
I feel that many people must have an objective and rational understanding of artificial intelligence. This is my first point of view. Secondly, today's point of view is to abstractly engage in artificial intelligence. I don't think the significance is great, because artificial intelligence must find a business and it can be a good combination of areas, because in this area you know what to collect The data can only be trained on the machine learning network and the deep learning network.
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