Artificial intelligence, machine learning, deep learning
In 1956, several computer scientists met at the Dartmouth conference to propose the concept of "artificial intelligence" and dreamed of using the computers that had just emerged to construct complex machines with the same essential characteristics as human intelligence. Since then, artificial intelligence has been haunting people's minds and slowly hatching in scientific research laboratories. In the following decades, artificial intelligence has been reversing at both poles, or it has been called the future of human civilization as a prophecy, or it has been thrown into the rubbish as a madman's wild imagination. Until 2012, these two kinds of sound still exist at the same time. After 2012, thanks to the increase in data volume, the increase in computing power, and the emergence of new machine learning algorithms (deep learning), artificial intelligence began to burst. According to the "Global AI Field Talent Report" recently released by LinkedIn, as of the first quarter of 2017, the number of technical talents in the global AI (artificial intelligence) field based on the LinkedIn platform exceeded 1.9 million, and only the domestic artificial intelligence talent gap reached more than 5 million. . The research area of ​​artificial intelligence is also expanding. Figure 1 shows the branches of artificial intelligence research, including expert systems, machine learning, evolutionary computation, fuzzy logic, computer vision, natural language processing, and recommendation systems. Figure 1 artificial intelligence research branch However, the current scientific research work is concentrated in this part of the weak artificial intelligence, and it is hopeful that a major breakthrough will be made in the near future. The artificial intelligence in movies is mostly depicting strong artificial intelligence, and this part is difficult to realize in the current real world. (Usually, artificial intelligence is divided into weak artificial intelligence and strong artificial intelligence. The former allows the machine to have the ability to observe and perceive, it can achieve a certain degree of understanding and reasoning, and strong artificial intelligence allows the machine to acquire the self-adaptive ability to solve some problems before. Encountered problems). Weak hopes for a breakthrough in artificial intelligence can be achieved, and where does "smart" come from? This is mainly due to a method of implementing artificial intelligence - machine learning. The most basic approach to machine learning is to use algorithms to parse data, learn from it, and then make decisions and predict events in the real world. Unlike traditional, hard-coded software programs that solve specific tasks, machine learning uses a lot of data to "train" and learn from data about how to accomplish tasks. To give a simple example, when we browse the online mall, product recommendation information often appears. This is based on your previous shopping history and long list of collections to identify which of these are products that you really are interested in and are willing to purchase. Such a decision model can help the mall provide advice to customers and encourage product consumption. Machine learning comes directly from the field of early artificial intelligence. Traditional algorithms include decision trees, clustering, Bayesian classification, support vector machines, EM, Adaboost, and so on. From the point of view of learning methods, machine learning algorithms can be divided into supervised learning (eg, classification problems), unsupervised learning (eg, clustering problems), semi-supervised learning, ensemble learning, deep learning, and reinforcement learning. The application of traditional machine learning algorithms in areas such as fingerprint recognition, Haar-based face detection, and object detection based on HoG features has basically reached the commercialization requirements or the commercialization level of specific scenes, but every step forward is extremely difficult until The emergence of deep learning algorithms. Deep learning is not originally an independent learning method. It also uses supervised and unsupervised learning methods to train deep neural networks. However, due to the rapid development of this field in recent years, some unique learning methods have been proposed (such as residual network), so more and more people regard it as a learning method. The initial deep learning is a learning process that uses deep neural networks to solve feature expressions. Deep neural network itself is not a completely new concept, but it can be roughly understood as a neural network structure that contains multiple hidden layers. In order to improve the training effect of deep neural networks, people make corresponding adjustments to the connection methods and activation functions of neurons. In fact, there were quite a few ideas that had existed in the early years. However, due to insufficient training data and backward computing power at the time, the final results were not satisfactory. Depth learning has accomplished various tasks, making it seem that all machine aid functions have become possible. Driverless cars, preventive health care, and even better movie recommendations are all in sight, or soon to be realized. Machine learning is a method of realizing artificial intelligence, and deep learning is a technology for realizing machine learning. We use the simplest method—concentric circles—to visually show the relationship among them. Figure 2 The relationship between the three
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