In the decades since the concept of “artificial intelligence” was put forward, it did not affect people’s life in such a fast and in-depth way as today. It did not put wings on it until the birth of massive data in various forms and dimensions and the improvement of computer computing power.
I believe that many science fiction fans like me hold a feeling of expectation and fear for the development of artificial intelligence. They not only hope to have a close housekeeper like javis, but also worry that the end of AI development will be the end of mankind like mechanical public enemy and aochuang. One day two weeks ago, a consultant of partner company said during a chat that he had received a sales call. After listening to it for a while, he realized that the opposite side was customer service robot rather than real person sales. Therefore, while thinking deeply and fearfully, it also aroused my unprecedented interest and wanted to have a deeper understanding of the industry. In the following, I just list some current AI applications and small thinking points on how to consider job opportunities. Only personal views are allowed. You are also welcome to add suggestions.
In recent years, the flow of capital has not only aroused the enthusiasm of the AI industry, but also brought the spring of algorithm and data technology talents. Many enterprises offer hundreds of thousands of annual salaries for fresh graduates of master’s and doctor’s degrees, and some even reach millions. As a result, just like the once extremely scarce front-end clients, economic factors are attracting more and more computer, mathematics related and even unrelated talents to enter the work field of algorithms. The influx of talents is certainly a good thing for the development of the industry. While bringing technological progress, it is bound to return to a more balanced salary rationality. In the long run, AI is indeed an industry representing the future.
At present, AI technologies that are widely used in the market are machine learning, natural language processing, computer vision and speech recognition. In each direction, highly representative products and well-known companies have been produced. Robots have little contact with the field of intelligent medicine and have not been understood for the time being.
Machine learning is the core of artificial intelligence. My simple understanding is to study how to make computers learn knowledge or skills like humans. It is widely used and often combined with computer vision, natural language processing and other technologies, such as search technology, personalized recommendation, fraud information detection of financial institutions, travel route prediction in LBS and so on. Almost all well-known Internet and AI companies in the market have machine learning algorithm technicians, which is in great demand.
Natural language processing is an important direction in the field of AI. It is a technology to study how to talk with computers through natural language. Perhaps the computer does not understand the internal logic of human processing text, but it does not prevent it from processing text in a certain way. NLP is mainly used in retrieval, intelligent dialogue system, machine translation and so on.
The goal of computer vision is to let computers observe and understand the world through vision like humans. It is also a relatively good AI direction at present. It has been almost applied to all AI entrepreneurial directions, such as face recognition, security, medical image analysis, character recognition, map search, VR / AR, unmanned driving and so on. Many well-known companies with rapid business development have been born in this field, such as face + +, Shangtang, Hanwang, Meitu and Yitu. Some foreign enterprise research institutes such as Microsoft, Samsung, Canon and Fujitsu also have a good accumulation in the field of computer vision. Baidu, Sogou and Ali have a lot of practical experience in searching for pictures.
Speech recognition is a technology that allows the machine to convert the speech signal into the corresponding text or command through the process of recognition and understanding. When it comes to speech recognition, Siri may emerge in many people’s minds. Speech recognition is widely used in smart home, education and other fields. At present, the very popular smart speakers, educational robots and other products in China are good landing projects. The well-known companies in this field mainly include Baidu, iFLYTEK, yunzhisheng, go out and ask, sibichi, etc. some companies also have certain advantages in some vertical fields.
As a human resources and headhunting practitioner for many years, in addition to thanking the cooperation opportunities brought by the needs of a large number of technical posts, I must also understand and analyze my candidates. In this process, I have to pay attention to how to implement AI technology and the company’s profitability. In the cold winter of capital and more rational investment, these are especially full of challenges for the survival and development of enterprises. From a layman’s point of view, let’s share some points I will pay attention to when thinking. I hope it can be helpful. If you need advice, you can also leave me a message.
1,My understanding of algorithm application is to solve practical problems through mathematical ideas and mathematical tools. Therefore, when analyzing the development prospect of a company, I still need to peel off the wrapped AI coat to see its business essence, and see what kind of problems it solves and what kind of business it is.
2,AI is based on big data, so please also know whether the company has enough reliable data sources to support when selecting a job opportunity.
3,AI is an industry with a high level of knowledge. The strength of the founder and core team needs to be paid close attention to.
4,Whether the company’s products and technologies have a landing scene or need several years to cultivate and develop the market (such as driverless), please match it in combination with your own acceptance.
5,Algorithm engineers are often jokingly referred to as the “outsourcing and participation” Xia, mainly because in addition to the core research department, the daily work of many algorithm practitioners is mainly to grasp data, label and adjust parameters, so please understand more before entering the industry and reduce psychological expectations appropriately.