1. Strengthen the field of learning
Reinforcement learning is a method of learning through experiment and error, which is inspired by the process of human learning new skills. In the typical case of reinforcement learning, we ask participants to take action by observing the current situation to maximize the feedback results. Every time you perform an action, the experimenter will receive feedback from the environment, so it can judge whether the effect of this action is positive or negative.
2. Generate model fields
Through the collection of a large number of samples, the models generated by artificial intelligence have strong similarity. That is to say, if the training data is an image of a face, then the model obtained after training is also a synthetic image similar to a face.
Ian Goodfellow, a top expert in the field of artificial intelligence, put forward two new ideas for us: one is a generator, which is responsible for synthesizing the input data into new content; The other is a discriminator, which is responsible for judging whether the content generated by the generator is true or false. In this way, the generator must repeatedly learn the synthesized content until the discriminator cannot distinguish the authenticity of the generator content.
3. Store network fields
If the artificial intelligence system wants to adapt to various environments like human beings, it must constantly master new skills and learn to apply them. It is difficult for traditional neural networks to meet these requirements. For example, if a neural network is trained to solve task B after task A, then the network model is no longer suitable for task A. ..
At present, there are some network structures that can make the model have different degrees of memory ability. Long-term and short-term memory networks can process and predict time series; Progressive neural network can learn the lateral relationship between independent models, extract the same features and complete new tasks.
4. Data learning field
For a long time, deep learning mode needs a lot of training data to achieve the best effect. Without large-scale training data, the deep learning model will not achieve the best results. For example, when we use artificial intelligence systems to solve tasks that lack data, various problems will arise. There is a method called transfer learning, which is to transfer the trained model to a new task, so that the problem can be easily solved.
5. The field of simulation environment
If artificial intelligence system is to be applied to real life, then artificial intelligence must have the characteristics of applicability. Therefore, developing a digital environment that simulates the real physical world and behavior will provide us with an opportunity to test artificial intelligence. Training in these simulated environments can help us understand the learning principle of artificial intelligence system and how to improve the system, and also provide us with a model that can be applied to real environment.
6. The field of medical technology
At present, vertical image algorithm and natural language processing technology can basically meet the needs of the medical industry, and many technical service providers have appeared in the market, such as Suntech Cloud, which provides intelligent medical imaging technology, Zhiweixin Branch, which develops artificial intelligence cell recognition medical diagnosis system, Ruoshui Medical, which provides intelligent auxiliary diagnosis service platform, and statistical processing of medical data. Although intelligent medical care plays an important role in auxiliary diagnosis and treatment, disease prediction, medical image-assisted diagnosis, drug research and development, etc. Due to the lack of circulation of medical image data and electronic medical records between hospitals, the cooperation between enterprises and hospitals is opaque, which makes the contradiction between technology development and data supply.
7. In the field of education
Iflytek, school education and other enterprises have begun to explore the application of artificial intelligence in the field of education. Through image recognition, we can use machines to correct test papers, identify questions and answer questions. Pronunciation can be corrected and improved through speech recognition; Man-machine interaction can answer questions online. AI+ education can improve the distribution and cost of teachers in the education industry to a certain extent, and provide more efficient learning methods for teachers and students from the tool level, but it cannot have a more substantial impact on the educational content.
8. Logistics management field
Using intelligent search, reasoning planning, computer vision, intelligent robots and other technologies, the logistics industry has undergone automation transformation in the process of distribution, loading and unloading, transportation, warehousing and so on, and can basically achieve unmanned operation. For example, using big data for intelligent distribution planning of goods, optimizing logistics supply, demand matching, and logistics resource allocation.