The difference and connection of the three
Machine learning is a method to realize artificial intelligence, and deep learning is a technology to realize machine learning. We use the simplest method-concentric circles, to visually show the relationship between the three.
Figure 3 Schematic diagram of the relationship between the three
At present, the industry has a common sense of error that " deep learning may eventually eliminate all other machine learning algorithms ." This awareness is mainly due to the fact that the application of deep learning in computer vision and natural language processing far exceeds traditional machine learning methods, and the media has exaggerated reports on deep learning.
Deep learning, as the hottest machine learning method at present, does not mean that it is the end of machine learning. At least the following problems currently exist:
1. Deep learning models require a lot of training data to show magical effects, but in real life, small sample problems are often encountered. At this time, deep learning methods cannot be used, and traditional machine learning methods can handle them;
2. In some fields, the traditional simple machine learning method can be used to solve it well, and it is not necessary to use complex deep learning methods;
3. The idea of deep learning is inspired by the human brain, but it is by no means a simulation of the human brain. For example, after showing a three or four-year-old child a bicycle, he will see a bicycle with a completely different appearance again. In all likelihood, it can be judged that it is a bicycle. That is to say, the human learning process often does not require large-scale training data, and the current deep learning method is obviously not a simulation of the human brain.
When deep learning tycoon Yoshua Bengio answered a similar question on Quora, there was a paragraph that was particularly good. Here is a quote to answer the above question:
Science is NOT a battle, it is a collaboration. We all build on each other's ideas. Science is an act of love, not war. Love for the beauty in the world that surrounds us and love to share and build something together. That makes science a highly satisfying activity, emotionally speaking!
The general meaning of this passage is that science is not war but cooperation. The development of any discipline is never a road to black, but peers learn from each other, learn from each other, learn from each other, and complement each other, standing on the shoulders of giants. Forward. The same is true for machine learning research. Life and death is a cult, and openness and tolerance is the right way.
Combining with the development of machine learning since 2000, I am deeply impressed by Bengio's words. Entering the 21st century, looking at the development of machine learning, research hotspots can be simply summarized as manifold learning from 2000 to 2006, sparse learning from 2006 to 2011, and deep learning from 2012 to present. Which machine learning algorithm will become a hot spot in the future? Wu Enda, one of the three giants of deep learning, once said, “After deep learning, transfer learning will lead the next wave of machine learning technology”. But in the end, who is the next hot spot for machine learning?
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