When Apple CEO Tim Cook introduced the iPhone X Tuesday he claimed it would "set the path for technology for the next decade." Some new features are superficial: a near-borderless OLED screen and the elimination of the traditional home button. Deep inside the phone, however, is an innovation likely to become standard in future smartphones, and crucial to the long-term dreams of Apple and its competitors. That feature is the "neural engine," part of the new A11 processor that Apple developed to power the iPhone X. The engine has circuits tuned to accelerate certain kinds of artificial-intelligence software, called artificial neural networks, that are good at processing images and speech.
Google is nothing if not ambitious about its machine learning plans. Around this time last year it unveiled its custom Tensor Processing Unit (TPU) hardware accelerator designed to run its TensorFlow machine learning framework at world-beating speeds. Now, the company is providing details of exactly how much juice a TPU can provide for machine learning, courtesy of a paper that delves into the technical aspects. The info shows how Google's approach will influence future development of machine learning powered by custom silicon. Machine learning generally happens in a few phases.
Startup Gyrfalcon is moving fast with a chip for inferencing on deep neural networks, but it faces an increasingly crowded market in AI silicon. A year after it got its first funding, the company is showing a working chip and claiming design wins in smartphones, security cameras and industrial automation equipment.
"I'd rather we spend more time addressing these very real issues than the sort of fantasies, or scenarios leading to evil killer robots," Mr. Ng said in a recent interview. Mr. Ng is the former chief scientist of Baidu Inc., where he started a 1,300-person division that helped create the Chinese tech conglomerate's AI-powered search engine, virtual assistant and other products. Before that, he co-founded Google Brain, Alphabet Inc.'s deep-learning research team. His work on neural networks helped lead to the creation of an AI system capable of identifying images, such as cats, by watching videos. In April 2017, he left Baidu to launch the Palo Alto, Calif.-based online education platform called deeplearning.ai.
It may sound like something out of a dystopian novel, but scientists are confident about a machine learning technology that can recognize and replicate human activities like seeing and thinking. Leading artificial intelligence experts are investigating ways to commercialize a rapidly emerging sub-field of research known as "deep learning." This month, a research team under renowned scientist Geoffrey E. Hinton's tutelage won a prize sponsored by Merck to design software to uncover molecules that are most likely to be good candidates for new drugs. The win was a particularly impressive feat given that the team entered at the last minute and was working with relatively small data-sets. Click here to read more about "how they did it."