In 1956, several computer scientists gathered at the Dartmouth conference to propose the concept of "artificial intelligence", dreaming of using a computer that had just emerged to construct a complex machine with the same essential characteristics as human intelligence. Since then, artificial intelligence has been lingering in people's minds and slowly hatching in research laboratories. In the decades that followed, artificial intelligence has been reversed at the poles, or as a prophecy of the dazzling future of human civilization, or thrown into the garbage by the madness of being a technological madman. Until 2012, these two sounds still exist at the same time.
After 2012, artificial intelligence began to break out due to the increase in data volume, the increase in computing power, and the emergence of new machine learning algorithms (deep learning). According to the "Global AI Talent Report" released by LinkedIn recently, as of the first quarter of 2017, the number of technical personnel 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 field of artificial intelligence research is also expanding. Figure 1 shows the various branches of artificial intelligence research, including expert systems, machine learning, evolutionary computation, fuzzy logic, computer vision, natural language processing, recommendation systems, and so on.
Figure 1 Artificial Intelligence Research Branch
However, the current research work is focused on the weak artificial intelligence, and it is hopeful that major breakthroughs will be made in the near future. Most of the artificial intelligence in the film is depicting strong artificial intelligence, and this part is difficult to realize in the current real world. (The artificial intelligence is usually divided into weak artificial intelligence and strong artificial intelligence. The former allows the machine to have the ability to observe and perceive, and can achieve a certain degree of understanding and reasoning, while strong artificial intelligence allows the machine to acquire adaptive capabilities, and solve some problems before. Have encountered problems).
Weak artificial intelligence is hopeful to make a breakthrough, how is it achieved, and where does "intelligence" come from? This is mainly due to a method of implementing artificial intelligence - machine learning.
Machine learning: a way to implement artificial intelligenceThe most basic approach to machine learning is to use algorithms to parse data, learn from it, and then make decisions and predictions about events in the real world. Unlike traditional software programs that solve specific tasks and hard code, machine learning uses a large amount of data to "train" and learn how to accomplish tasks from data through various algorithms.
As a simple example, when we browse the online store, information about product recommendations often appears. This is the mall based on your previous shopping records and lengthy collection list, identify which of these are products that you are really interested in and willing to buy. Such a decision model can help the mall to provide advice to customers and encourage product consumption.
Machine learning comes directly from the early days of artificial intelligence. Traditional algorithms include decision trees, clustering, Bayesian classification, support vector machines, EM, Adaboost, and so on. From the perspective of learning methods, machine learning algorithms can be divided into supervised learning (such as classification problems), unsupervised learning (such as clustering problems), semi-supervised learning, integrated learning, deep learning and reinforcement learning.
The traditional machine learning algorithms in the fields of fingerprint recognition, Haar-based face detection, HoG-based object detection and other fields have basically met the requirements of commercialization or the commercialization of specific scenes, but each step is extremely difficult until The emergence of deep learning algorithms.
Deep learning: a technology for machine learningDeep learning is not an independent learning method. It itself 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 the 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 representation. The deep neural network itself is not a completely new concept and can be roughly understood as a neural network structure containing multiple hidden layers. In order to improve the training effect of deep neural networks, people have made corresponding adjustments to the connection methods and activation functions of neurons. In fact, many ideas have been there in the early years, but due to insufficient training data and backward computing power, the final result is not satisfactory.
Deep learning has ruthlessly fulfilled various tasks, making it seem that all machine auxiliary functions are possible. Driverless cars, preventive health care, and even better movie recommendations are all in sight or nearing.
The difference and connection between the threeMachine learning is a way to realize artificial intelligence. 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 2 is a schematic diagram of the relationship between the three
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