Meta-learning is a relatively new direction in the field of artificial intelligence and is considered to be the key to realizing general artificial intelligence.
Why is he so important? How to quickly and easily understand the essence of meta-learning? This article will introduce you to the meta-learning in detail.
Why is meta-learning important?
The core of meta-learning isself-study ability. Then why do you need self-learning ability?
Machine learning: Simple machine learning is difficult to deal with complex problems;
Deep learning: Deep learning is better than machine learning in terms of complex problems, but it is impossible to deal with interactive problems or continuous problems, intelligently handling one-to-one mapping problems;
Reinforcement: Rely on massive training and require precise rewards. The cost is higher and more complicated.
Meta-learning: Self-learning ability to make full use of past experience to guide future tasks. Is considered to be realizedUniversal artificial intelligencekey.
What is meta-learning?
The idea of meta-learning is to learn the process of learning (training).
Meta-learning has several implementation methods, but the two methods of "learning the learning process" mentioned in this article are similar to the methods described above. In our training process, specifically, you can learn two things:
- The initial parameters of the neural network (blue in the figure);
- The parameters of the optimizer (pink ★).
I will introduce the combination of these two points, but each of the points here is also very interesting, and can be simplified, accelerated and some good theoretical results. Now, we have two parts to train:
- Using the term "model (M)" to refer to our previous neural network, we can now also understand it as a low-level network. Sometimes people also use "optimizee" or "learner" (learNer)" to call it. The weight of the model is indicated by ■ in the figure.
- Use "optimizer (O)" or "meta learner" to refer to the advanced model used to update the weight of the low-level network (that is, the above model). The weight of the optimizer is indicated by ★ in the figure.