Recent comments by machine learning experts have caused a stir, but debate over the novelty or architecture of deep learning might be best left in academia . As AI techniques make their way into developers’ hands, whether they catch on depends on whether they’re useful.
IBM has announced a long list of new Watson customers and startup partners, ranging from standby industries such as health care to new ones such as cybersecurity and nonprofits. Perhaps more importantly, the company also gave developers a handful of new Watson-powered APIs.
A text-analysis startup called Aylien has released an add-on for Googles spreadsheet application, which is actually pretty handy. It’s far from perfect at sentiment analysis as most services are, but it’s easy to use and does a good job extracting the stuff that matters.
Baidu says its 100-billion-neuron deep learning system will be complete within six months, powering a fast transition away from text as the dominant search input. Thanks to smartphones and its new Baidu Eye technology, the company expects voice and image search to dominate within five years.
A handful of new research projects from Google, IBM and the Allen Institute for AI highlight the ongoing quest to build computer systems capable of analyzing written language based on understanding concepts rather than just keywords.
Jeremy Howard, the former president and chief scientist of predictive-modeling platform Kaggle, is back with a new startup he thinks can revolutionize medical diagnostics using deep learning. There’s a lot of work to be done, but a lot of reason to be optimistic.
Netflix explained how it’s using data analysis to do more than recommend movies in a blog post this week. From optimizing bitrate to churning through user feedback, advanced algorithms are helping ensure that minimal issues affect the streaming experience.
Deep learning is all the rage among the tech scene right now, and that’s more a result of its utility than because it sounds cool. Some questioned the feasibility of the Secret Service’s requested “sarcasm detector,” but deep learning could help there, too.
Amazon Web Services, Gnip and two Australian research institutions have teamed up to track the emotions of tweets in near real-time and offer the data to the public via visualizations, downloadable tables and an API.
Researchers from Allen Institute for AI have built a computer system capable of teaching itself many facets of broad concepts by scouring and analyzing search engines using natural language processing and computer vision techniques.