Despite the slightly sensationalist title, this piece explains the concepts of AI, machine learning and deep learning rather well.
Today, the programmable chips that Burger and Lu believed would transform the world—called field programmable gate arrays—are here. FPGAs already underpin Bing, and in the coming weeks, they will drive new search algorithms based on deep neural networks—artificial intelligence modeled on the structure of the human brain—executing this AI several orders of magnitude faster than ordinary chips could. As in, 23 milliseconds instead of four seconds of nothing on your screen. FPGAs also drive Azure, the company’s cloud computing service. And in the coming years, almost every new Microsoft server will include an FPGA. That’s millions of machines across the globe. “This gives us massive capacity and enormous flexibility, and the economics work,” Burger says. “This is now Microsoft’s standard, worldwide architecture.”
Paddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple “A.I. winters”? What’s the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?
A great presentation by Frank Chen.