Not long ago, in the "small table" artificial intelligence entrepreneurship class, Yu Xin CEO Zhu Mingjie shared the experience of artificial intelligence entrepreneurship. Yuxin is a provider of domestic financial big data risk control Solutions. At present, it has cooperated with more than a dozen institutions, including Minsheng Bank, Zhengtong, Xiaoying Finance, and Aiwujiwu. Zhu Mingjie is a Ph.D. student at Microsoft Research Asia. He has more than 10 years of experience in data mining and machine learning. I will listen to his views on artificial intelligence entrepreneurship.
First, the artificial intelligence of entrepreneurship has a very important principle, that is, must make money. Because the value of artificial intelligence products is determined by rational decision makers, if you can't receive money from the beginning, it is difficult to explain the commercial value of this product. When you do it later, it is difficult to go on. For example, many years ago someone did a humming search, and you can find a song and you can find the song. This product is very interesting, but it can't make money. It is hard to understand how to make money by this. But there are also some products that have no commercial value at the time. As time changes, it is possible to generate commercial value. For example, in the past, chatting robots could not make money; but now the company is facing huge business pressure and needs intelligent customer service. There is potential for redeveloping this product.
The second point is not to repeat the wheel. The meaning here is that the startup must have flexibility. Nowadays, many resources have already been prepared for you. You have to think about how to use these resources yourself. What many big companies don't or disdain to do is the opportunity for small companies to create value. Many people will worry, if big companies like Google, Microsoft, Ali, and Tencent do what I do, what should I do at this time? It depends on what you are doing. If you want to be a better platform than a big company, and a more bullish algorithm, this is unlikely. For example, Microsoft is doing artificial intelligence, and it can set up a research and development group of thousands of people, with the best scientists and product development teams, as well as extremely large data and hardware resources. These startups can't compare. But if you find a field that doesn't have a direct competitive relationship with a big company, it's possible to do it.
Therefore, when starting a business in the field of artificial intelligence, you must consider what you can do, and where is your advantage? If you are doing the research of the investor's money, or if you are behind closed doors, it is very dangerous.
The third point, don't do anything that is icing on the cake, be sure to focus on the place where you can get the most value, and focus on the most painful part of the user. Doing icing on the cake, not only has a limited return, but it is also difficult to make a breakthrough. Because artificial intelligence entrepreneurship has its own uniqueness, it needs a lot of data, so you must judge in advance whether you can get the valuable data you need, whether there is a suitable team, enough technical ability, and whether you really understand it. Customer needs and pain points. Second, the scene of realizing must be clear. Before starting a business, you should consider what problems it can solve, what value it produces, and how to make money in the end.
According to the above idea, Zhu Mingjie and their team decided to use artificial intelligence to do finance. The first is because finance has a lot of data and technology is needed to improve efficiency. The entry point for Yuxin's choice is to use artificial intelligence technology to solve the problem of risk decision-making. Nowadays, Internet consumer finance is developing rapidly, and the environment of the entire financial market is further popularized. The requirements for financial credit efficiency are getting higher and higher. But if you do this thing three or five years ago, it may not be a good time. Because the market environment at that time was still biased towards traditional finance, and more focused on making good customers. At that time, using artificial intelligence to make risk decisions was a icing on the cake, and it was difficult to have a single point of breakthrough. In today's financial environment, financial institutions provide new financial services, facing a new customer base, and every day there is a risk control challenge that needs to be solved. It is worthwhile to do this again.
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