General Electric said on Tuesday it has acquired two tech startups to build its artificial intelligence capability, a move that helps it compete with IBM's Watson product. GE ge said the acquisitions of Bit Stew Systems and Wise.io will expand its Predix platform for industrial internet applications, which connects big machines such as power plants and aircraft engines to databases and analytical software. Terms of the deals weren't disclosed. Berkley, California-based Wise.io has advanced machine learning technology that GE sees "as really well-built for the industrial world," Bill Ruh, chief executive officer of GE Digital, GE's software arm, said in an interview. A branch of artificial intelligence, machine learning allows computers to adapt to new data without new programming.

BARCELONA Pioneering computer mouse maker Logitech isn't ready to walk away from the personal computer business just yet, even though its chief executive spends all his time these days thinking how to expand into faster-growing accessory markets.

Researchers from the US have developed an artificial intelligence (AI) system that surfs the internet, extracts information from the available plain text and organizes it for quantitative analysis in very less time. Recently at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head. Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. In their new paper, the MIT researchers trained their system on scanty data -- because in the scenario they're investigating, that's usually all that's available. But then they find the limited information an easy problem to solve.

Google has published numerous experiments with its cloud AI technologies, but'Quick, Draw' is perhaps the most fun one yet. Using the same technology that interprets written symbols in Google Translate, the game attempts to guess what you are drawing. When you start, you are prompted to draw a specific thing, and the game continues making guesses until it wins or time runs out. At the end of six rounds, it provides you with information about all your drawings, including other guesses and what other users provided. It learns from every drawing, so theoretically, it should become smarter over time.

Get yourself ready for the bot invasion. Powered by artificial intelligence and fueled with big data, bots are gearing up to serve people the data, insights, and services they demand. Anybody who's interacted with Apple's Siri, Amazon's Alexa or Microsoft's Cortana is familiar with the question-and-answer capabilities of bot technology, which uses natural language processing and machine learning to answer questions or provide information via email, social media, messaging platforms, and mobile apps. But the bots you've seen up to this point will pale in comparison to the bots of the future. Consider that, by 2020, the average person will have more conversation with bots than with their spouse, according to Gartner.

At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s. Since then, we have overcome this issue. In this post, I explore the "hardness" in optimizing neural networks and see what the theory has to say. In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.

It seems like we've perennially been on the edge of major breakthroughs in AI (artificial intelligence), virtual reality, personal robots, and other such cool tech for the past two decades. The first set of science fiction imaginings came true rather rapidly -- think trans-continental air travel, space stations, even drone warfare -- but it appears that the emergence of next-gen tech wizardry has stalled. But while we still can't chat with the on-board computer on our personal spaceship, artificial intelligence is far more pervasive in our daily lives today than most of us realize. As anyone who has trained their mobile phone assistant can attest, years of painstaking research and investment in artificial intelligence technologies is starting to yield impressive results. Siri can predict our commute patterns, Microsoft Cortana warns us of bad weather, and the Google Assistant diligently sets calendar reminders with the impassive demeanor of an English butler of yore.

What is A.I. or artificial intelligence but the'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London. But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.

A year ago, I wrote that I had seen the future. I had attended the IBM Insights conference in Las Vegas, an entire conference devoted to the Internet of Things, analytics, cognitive marketing, and related topics. And if the concept of Moore's Law (that technology doubles every year, or every 18 months or two years, depending on what you read) holds true, then a lot has happened since then. While last year's future is now a history lesson, what is currently happening in the World of Watson is moving at warp speed! This past week more than 17,000 people attended IBM's World of Watson, the new name of the conference devoted to cognitive technology. Fifteen "influencers" were invited to attend IBM's first-ever Cognitive College that included product demonstrations and lectures about how Watson's cognitive abilities are impacting marketing and commerce.

Pixar founder George Lucas may have given the world the word "Droid" before he sold the company to Apple's Steve Jobs, but Apple CEO Tim Cook makes no secret that he wants his company to play its part in making intelligence in machines a reality. Cook talked up Apple's efforts in augmented reality just last week. This week he's spilling at least some of the beans on his company's plans for Artificial Intelligence, though his vision seems more connected to machine intelligence and pattern recognition than the evolution of a smart bot like C3PO, at least right now. "A.I. is horizontal in nature, running across all products," he told Nikkei. He said it is used "in ways that most people don't even think about."