Editor's note: NetEase smart channel launched the topic section "intelligent bacteria", focusing on the social hot spots triggered by artificial intelligence, launched every Wednesday and Friday. This article, Issue 21, discusses AlphaGo's upcoming challenge to Ke Jie's neural network technology and the practical challenges faced by artificial intelligence at the real business level. Reprint please contact us for authorization (public number Smartman163).
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In March of last year, AlphaGo, who would play Go, gave artificial intelligence a boost. What will be brought by the addition of AlphaGo Plus, which will prepare for Kochi in March this year? The author's title may be a bit crossed, but Ke Jie could not win AlphaGo even if he recently learned a new unique skill. Go’s ability to learn artificial intelligence is absolutely beyond humans by several orders of magnitude. With the development of time on the Go project, whether Ke Jie or who, humans can only be more and more abused. This is the irresistible "natural" law brought about by the "new evolution theory."
Can Ke Jie be so confident after the game?
However, for true artificial intelligence, AlphaGo can only be glimpsed, deep neural networks bring surprises for artificial intelligence, but not change. Companies such as Baidu's "stretching nerves" may not be helpful to their commercial success. With tens of thousands of GPUs, Wu Enda may bring some surprising artificial intelligence products to Baidu, but it is difficult to reproduce the profitability of the Putian Department of Printing. Artificial intelligence has only completed the transformation from fetus to infant, but it has to undergo countless hardships to survive.
However, the author is not trying to antagonize the "neuropathy" behavior of major companies currently facing artificial intelligence. Instead, it is an easy-to-follow analysis of the neural network technology that makes artificial intelligence stunning the world, and the practical problems faced by artificial intelligence at the real business level. Let everyone watch the Ke Jie and AlphaGo before the game to warm up, scientific and objective look at man-machine war.
Why is neural network fire?
Neural network This thing is not new, AlphaGo cattle X is just a branch of various artificial intelligence algorithms. Many algorithms in neural networks have existed for many years, and it is not surprising that they suddenly appear like magic. Recently it has begun to attract focus, mainly because of the rapid development of available computing power (CPU, GPU, AI-specific computing units), which makes mass matrix multiplication operations easier to test, verify, and iterate.
However, Deepmind's optimization of the neural network of the AI ​​game under the AI ​​is indeed amazing, and Deepmind's same technology can be applied to other fields. As a result, the people who do not know the truth really think that artificial intelligence will come to replace humans tomorrow. Major Internet companies also followed suit and immediately started an artificial intelligence business.
The author does not doubt the commonality of Deepmind neural network algorithms, but it is still difficult to solve the problem of obtaining training data in different fields. And in some areas of data complexity, perhaps only quantum computing can end training within the acceptable range of human life.
The neural network is more like the key to unlock the Turing puzzle
Neural networks have two major benefits. One is the use of relatively long pre-application training time in exchange for real-time processing in applications. Instead, the processing complexity of the training data is used to trade off the processing complexity of the preset logic. And AlphaGo made people believe that maybe neural networks are really the key to solving the complete Turing test.
In terms of macro desires, neural networks are expected to directly realize artificial thinking by simulating realistic biological thinking. An artificial neural network is an adaptive machine that models the human brain to complete a specific task or method of interest. It is a large scale parallel distributed processor composed of simple processing elements. Naturally has storage experience and features that make it available. Neural networks are similar to humans in two aspects: 1. The knowledge gained by neural networks is learned from the external environment; 2. The strength of the connected neurons, synaptic weights, is used to store acquired knowledge.
There are many problems in real life and cannot be solved with preset logic. When the problem is unpredictable, such as in handwriting recognition; or the problem of processing can easily change the requirements; the task of processing needs only a satisfactory solution instead of an exact solution. These conditions are not a problem for the fuzzy logic processing of the human brain. However, it is a problem for computer programs. The neural network can complete an optimal solution by having a large amount of empirical data that can be referenced, or the task itself can generate enough empirical data. It does not guarantee 100% correct task completion, but it can give an optimal solution to the experience like the human brain. From the perspective of mimicking human thinking, artificial intelligence may be the most versatile of all machine learning algorithms, and it is also the most likely approach to touchdown testing.
Neural network to solve everything? It is nerve
However, in many cases, neural networks are not the optimal solution for artificial intelligence. What issues are taken up by neural networks? That is neuroticism. Here is an example - basic digital identification.
Whether it is a neural network or a basic decision tree, or a vector machine, the most fundamental algorithm selection principle can still solve the problem. Many products are processed using popular recursive neural network algorithms, but if you can use simple preset logic, Bayesian algorithms and vector machines can achieve good results, there is no need to put the cart before the end. Deep neural network is likely to become the universal algorithm to solve the artificial intelligence problem in the future, but the large amount of data preparation and finishing that the neural network needs is also time-consuming and time-consuming. Compared with other machine learning algorithms, the neural network is more like using the large amount of data clean-up time and training time before the application for rapid calculation in use.
Comparing the performance of various algorithms, the neural network is not the optimal solution. Source: (artificial intelligence, Stuart-Russel)
Here are some commonly used machine learning algorithms, because they are too boring to do specific expansion. Based on the theoretical distribution of intervals: cluster analysis and pattern recognition, artificial neural networks, decision trees, perceptrons, support vector machines, ensemble learning AdaBoost, dimension reduction and metric learning, clustering, and Bayesian classifiers.
Constructive condition based on probability: regression analysis and statistical classification, Gaussian process regression, linear discriminant analysis, nearest neighbor method, radial basis function kernel.
Based on the probability map model: including Bayesian networks and Markov random fields
Approximate inference techniques: Markov chain, Monte Carlo method (AlphaGo also uses this algorithm), variational method.
Why do artificial intelligence scientists propose so many methods? In fact, the answer is very simple, there is no way to achieve optimal solution in any scene. The fact that neural networks are fired does not mean that artificial intelligence in the future will go completely in a single direction. Neural networks combined with other methods of assistance may be a more sensible choice.
Artificial Intelligence Realization Challenge
Wu Enda was recruited by Baidu in 2014 and Baidu started the age of artificial intelligence. You can say that Baidu has been imitating Google, but in artificial intelligence, Baidu has really dug up the core of Google's brain. Wu Enda published nearly 20 papers in Baidu's 3 years. Each of the results was either integrated into Baidu's existing product line or based on the results of a start-up company. From Wu Enda back to Baidu's first medical inquiry robot developed to the second phase of wake-up product of the recently launched speech recognition development platform, Baidu's progress in the field of artificial intelligence is obvious to all. Although there is no such thing as Deepmind, it has become an industry leader.
Nowadays, with AlphaGo's east wind, various artificial intelligence companies have mushroomed. Not only have all kinds of big data companies transformed into AI pioneers, even media companies relying on data mining today have claimed that they are an artificial intelligence company. Looking at the artificial intelligence from the outside is like a treasure island. Tens of thousands of people fought over the single-plank bridge to dig out only the treasures on the island. However, the biggest problem that Yokohama faces in all artificial intelligence companies is how to realize it. Returning to the actual level, there is neither gold nor silver on this island. It does not look like a decent piece of stone.
Take Baidu for example. Wu Enda is the authority of the field of picture recognition in the artificial intelligence, but Comrade Enda never had a successful case of commercializing products. When Stanford taught as a professor, he was supported by the school. Of course, Google's brain was kept by advertisers. There was never any pressure for liquidation. After coming to Baidu, of course, the pace of productization has accelerated, but it can only be the rhythm of being raised. For example, Baidu's inquiry robots, which were launched in 2015, are not a problem in promotion. The logic of relying on Baidu's inquiry and selling drugs should have a good prospect, but last year's Wei Zexi incident. Basically, Baidu completely crushed the logic of the realization of medical violence. As a result, the medical division became Baidu medical brains, and the inquiry robot products could only become tasteless. Of course, some of Baidu’s voice products still have a very good toB outlook on the technical level, but this prospect is not likely to become a moneyscape compared to Baidu’s mass and artificial intelligence.
Comrade Kai-fu said well that the core of AI's entrepreneurship is artificial intelligence scientists, but there must also be a group of people who understand and realize. This matter is very difficult in itself, because it is necessary to put together two groups of people who think differently and fight each day with different values. At present, at least the domestic style is like digging all kinds of famous professors, and some of them are basically not programmed. The paper code is implemented by the following students. It was difficult to realize the artificial intelligence. You also got a group of people who didn't go back to school to teach.
The arrival of artificial intelligence is irresistible, and the entry of capital will accelerate the process of landing artificial intelligence. However, if the wind blows, it is not so easy to land. The dawn of artificial intelligence is not far away, but the road ahead is long and the darkness is endless. How to break? Go back to knock code, do optimization, everything is still talking about the product.
Author's introduction: Liu Binhan, CEO of EditorsAI, an artificial intelligence startup company, ridicules the author. “In the field of artificial intelligence, I will write the most articles and write the articles that most understand artificial intelligenceâ€.
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