Machine Learning is the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. It is the core of artificial intelligence, and it is the fundamental way to make computers intelligent. Its application spans all fields of artificial intelligence. Not long ago, The New York Times reported that Microsoft applied machine learning to new businesses. Lightspeed (Lightspeed Ventures) US Investment Director Jeremy Liew also introduced the example of “big data plus machine learning†to reshape the bank credit industry.
Applying machine learning technology, software and services are profitable points
In the next version of Office, Excel will be able to combine large amounts of data. For example, you can scan 12 million Twitter messages and generate a chart that tells you which Oscar nominee is being talked about most. The new version added in Outlook can evaluate users' email reading habits and determine which messages users want to read. Microsoft's machine learning software will be able to crawl corporate computer systems, just as Bing search engines crawl web pages and links on the Internet.
The explosion of data from sensors, connected devices, and cloud computing centers has created the big data industry. Computers need to find patterns and patterns from the massive amounts generated each day. In the long run, Microsoft hopes to apply more machine learning technology to its cloud computing platform, Azure, such as leasing data sources and algorithms to allow companies to design their own data forecasting engines. Microsoft can eventually charge for software services, not just software.
Microsoft has something that startups don't have: huge capital reserves – earnings at the end of the last quarter showed a $67 billion in cash and short-term investments – and the ability to invest in a big project for ten or even two Ten years. Gartner senior researcher David Smith said, "Microsoft has too many resources, Windows, Internet Explorer, Skype, Bing search, etc. They can do a lot of things. Data analysis will be their next big business."
Fragmentation association, "alternative credit" model appears
Some startups use massive data mining and algorithms to do some loan business.
Wonga is a startup that has just emerged as an alternative loan. Sonali De Rycker, an investment firm Accel, is a board member of Wonga. He said: "They use a lot of social media and other web tools, but these are absolutely unexpected. And this is their miracle."
The key to the problem is the algorithm, how it puts your zip code, the color of your car, how much your mortgage is, how it can relate you to these fragmented things. These are all key pieces of Wonga's data accumulation and organization of the fragmented information of its past customers. When Accel entered the Wonga board of directors in 2009, Wonga had 100,000 loan cases. The continuous integration and categorization of these 100,000 pieces of data has turned into an ever-increasing information network. Each of these lenders has between 6,000 and 8,000 pieces of data.
"You use a lot of data strings to make a story. We are willing to pay for this data because we need it. We can judge whether it is right or wrong by thousands of combinations."
With a large number of data sources and powerful big data analytics tools, it also means that it can borrow at a lower interest rate than other payday lenders. The basis of machine learning is non-performing loans, so the more times you fail, the more tuition you pay, and the more perfect your model will be.
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