Professor Yang Qiang believes that DeepMind applies end-to-end deep learning to reinforcement learning, so that reinforcement learning can cope with big data, so it can completely knock down humans in Go. It does this through complete self-learning and self-cultivation. Self-correction, then iterate one by one. Yang Qiang also pointed out that the combination of search and learning is the development direction of artificial intelligence. We can't rely entirely on machines to automate self-learning. The drawback of machine learning is self-biasing, which still requires human intervention. In the future, migration learning will be the solution to this problem. Migration learning also allows artificial intelligence to get rid of the heavy reliance on big data, making artificial intelligence no longer just a "rich game."
“2016 Global Artificial Intelligence Technology Conference (GAITC) and the launching ceremony of the 60th anniversary of artificial intelligence†was held at the Beijing National Convention Center on April 22nd. Professor Yang Qiang was invited to deliver a keynote speech. His speech titled “Self-learning Labor intelligent". After the sponsorship of the Chinese Artificial Intelligence Society and Professor Yang Qiang, Xinzhiyuan brought the full-text record and ppt of Professor Yang Qiang's speech, which is full of dry goods and recommended collection.
First of all, everyone knows that on the 60th anniversary, the first thing we should remember is the Turing, the pioneer of artificial intelligence. Under the inspiration of his problems, we have such a grand event today and the rapid development of artificial intelligence today. His question "Can the machine think?" can be explained from different dimensions. First, human exploration of artificial intelligence can also be based on different interpretations of the [this] problem.
The first exploration should be said to be a logical level of exploration. [20th Century] In the 1960s, these pioneers of artificial intelligence considered using logic and search to study artificial intelligence, such as playing chess, reasoning, and path planning. They have a strong assumption that this assumption is somewhat intuitive: intelligence includes the intelligence that a computer may give, from the computational combination of computational physical symbols. As long as we can intelligently combine these physical symbols, humans can get [smart] from a series of zeros and ones. [But] After some achievements, [we] also found that this assumption is a bottleneck.
Later, some people focused on the study of artificial intelligence that can have learning functions, and different machine learning algorithms were developed. These include neural networks that everyone is familiar with.
Several milestones of artificial intelligence are familiar to us now. The first one is recognized as the milestone is dark blue. This game means a few things: one is to say that in the large-scale search state, the search in the possible state space actually The top is a permutation combination in the physical symbol space.
That is to say, some of the assumptions of the [60s] people in the 1960s were correct, and we can indeed get a lot of intelligence from this combination of search and physical symbols.
The next stage is that knowledge is power. This is a craze with the arrival of the Internet and big data. From the Internet, we will get a lot of data from different media. When we turn these data into knowledge, we can win like Such a TV contest in the human-machine battle.
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