Читать книгу Enterprise AI For Dummies - Zachary Jarvinen - Страница 73

For decades, artificial intelligence was the province of academics, scientists, and technicians with a highly specialized skill set. In the 1980s, some data scientists took the step from academe to commerce, applying AI to real-world problems and the development of expert systems. In the 1990s, commercial applications for AI expanded along with the Internet and the wealth of data it generated.

Even so, any business wanting to capitalize on the power of artificial intelligence had to commit a serious amount of capital, not only for rare and expensive data scientists, but also for major-league processing power and data storage.

More recently, full-powered AI solutions with simplified interfaces allow users to create and train models and produce reports and data visualization, reducing the need for a full team of dedicated data scientists.

In fact, Gartner predicted that workers using self-service analytics would output more analysis than professional data scientists. That’s good news for enterprises. And don’t worry about putting data scientists out of business. They are still in high demand. For the last three years, data scientist was the #1 ranked job in the U.S. on the career website Glassdoor.