Daniela Hernandez of Wired reports, “Drawing on the work of a clever cadre of academic researchers, the biggest names in tech—including Google, Facebook, Microsoft, and Apple—are embracing a more powerful form of AI known as ‘deep learning,’ using it to improve everything from speech recognition and language translation to computer vision, the ability to identify images without human help. In this new AI order, the general assumption is that Google is out in front… But now, Microsoft’s research arm says it has achieved new records with a deep learning system it calls Adam, which will be publicly discussed for the first time during an academic summit this morning at the company’s Redmond, Washington headquarters.” Read more
Posts Tagged ‘deep learning’
Derrick Harris of GigaOM reports, “Researchers from the University of California, Irvine, have published a paper demonstrating the effectiveness of deep learning in helping discover exotic particles such as Higgs bosons and supersymmetric particles. The research, which was published in Nature Communications, found that modern approaches to deep neural networks might be significantly more accurate than the types of machine learning scientists traditionally use for particle discovery and might also save scientists a lot of work. To get a sense of how challenging particle discovery is, consider that a collider can produce 100 billion collisions per hour and only about 300 will produce a Higgs boson. Because the particles decay almost immediately, scientists can’t expressly identify them, but instead must analyze (and sometimes infer) the products of their decay.” Read more
Jordan Novet of Venture Beat recently wrote, “A startup called Ersatz Labs wants to help lots of companies intelligently answer lots of questions after reviewing lots of data, just as big tech companies like Google and Netflix do. Toward that end, today Ersatz is launching a cloud service for deep learning, as well as a hardware-software package to run inside companies’ existing facilities. While deep learning services are often geared toward specific uses, like text processing and image recognition, Ersatz makes deep learning available for any type of use.” Read more
New Startup Skymind Offers Support for Open Source Deep Learning
Derrick Harris of GigaOM reports, “A San Francisco-based startup called Skymind launched on Monday to offer support and services for deeplearning4j, an open source deep learning project it has created. It’s early to tell how much traction deep learning will gain among mainstream companies or even web companies, but the technology does hold a lot of promise. The existence of open source libraries backed by professional services could certainly help spur adoption – especially for a field of data analysis previously relegated to top universities and research labs at companies such as Google, Microsoft, Facebook and Baidu.” Read more
Derrick Harris of GigaOM reports, “Denver-based startup AlchemyAPI is keeping proactive in the world of artificial intelligence, launching on Monday night a new service that lets users perform computer vision tasks such as image-tagging and photo search via API. The product, called AlchemyVision, is the company’s first foray outside the natural-language processing space where it has focused since 2011. It also probably foreshadows a spate of computer vision services yet to come. AlchemyAPI first demonstrated its object recognition service in September but Turner said the company has done a lot of work in the meantime to get it ready for commercial use. Among the big differences is the sheer scale of the new system, which is running unsupervised across millions of online images and using context from the pages they’re housed on in order to determine what they are.” Read more
Derrick Harris of GigaOM recently wrote, “With all the money being spent on, and all the futuristic talk about about big data, machine learning, artificial intelligence and all things in between, it’s easy to forget that Microsoft and Google — two of the companies leading research in these technologies — still have large businesses in web search. So as cool and potentially life-altering as AI might be in fields such as medicine, we’ll probably continue to see the signs of things to come in search engines first. It’s big business and a great testing ground. Take, for example, Microsoft Bing’s new predictions feature that tries to predict the outcomes of popular fan-voting show such as The Voice, American Idol and Dancing with the Stars. Bing does this by analyzing a number of signals, including searches, Twitter and Facebook data, and, presumably the outcomes of previous episodes.” Read more
Martin Hack, CEO and co-founder of machine learning company Skytree, has a prediction to make: “In the next three to five years we will see a machine learning system in every Fortune 500 company.” In fact, he says, it’s already happening, and not just among the high-tech companies in that ranking but also among the “bread and butter” enterprises.
“They know they need advanced analytics to get ahead in the game or stay competitive,” Hack says. For that, he says, they need machine learning algorithms for analyzing their Big Data sets, and they need to be able to deploy them quickly and easily — even if those who will be doing the deployments are coming from at best a background of basic analytics and business intelligence.
“There just aren’t enough data scientists to go around,” he says. It’s very tough to fill those roles in most companies, he says, “so like it or not, we have to make it much, much easier for people to digest and use this.”
Tom Simonite of the MIT Technology Review reports, “Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time. New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera. That’s a significant advance over previous face-matching software, and it demonstrates the power of a new approach to artificial intelligence known as deep learning, which Facebook and its competitors have bet heavily on in the past year.” Read more
Will deep learning take us where we want to go? It’s one of the questions that Oxford University professor of Computational Linguistics Stephen Pulman will be delving into at this week’s Sentiment Analysis Symposium. There, he’ll be participating in a workshop session today on compositional sentiment analysis and giving a presentation tomorrow on bleeding-edge natural language processing.
“There is a lot of hype about deep learning, but it’s not a magic solution,” says Pulman. “I worry whenever there is hype about some technologies like this that it raises expectations to the point where people are bound to be disappointed.”
That’s not to imply, however, that important progress isn’t taking place when it comes to deep learning, which leverages machine learning methods based on learning representations with applications to everything from NLP to computer vision to speech recognition.
Google’s letting the cash flow. Fresh off its $3.2 billion acquisition of “conscious home” company Nest, which makes the Nest Learning Thermostat and Protect smoke and carbon monoxide detector, it’s spending some comparative pocket change — $400 million – on artificial intelligence startup DeepMind Technologies.
The news was first reported at re/code here, where one source describes DeepMind as “the last large independent company with a strong focus on artificial intelligence.” The London startup, funded by Founders Fund, was founded by Demis Hassabis, Shane Legg and Mustafa Suleyman, with the stated goal of combining machine learning techniques and neuroscience to build powerful general purpose learning algorithms.
Its web page notes that its first commercial applications are in simulations, e-commerce and games, and this posting for a part-time paid computer science internship from this past summer casts it as “a world-class machine learning research company that specializes in developing cutting edge algorithms to power massively disruptive new consumer products.”
NEXT PAGE >>