Meet the algorithm that can learn “everything about anything”

Gigaom

The most recent advances in artificial intelligence research are pretty staggering, thanks in part to the abundance of data available on the web. We’ve covered how deep learning is helping create self-teaching and highly accurate systems for tasks such as sentiment analysis and facial recognition, but there are also models that can solve geometry and algebra problems, predict whether a stack of dishes is likely to fall over and (from the team behind Google’s word2vec) understand entire paragraphs of text.

(Hat tip to frequent commenter Oneasum for pointing out all these projects.)

One of the more interesting projects is a system called LEVAN, which is short for Learn EVerything about ANything and was created by a group of researchers out of the Allen Institute for Artificial Intelligence and the University of Washington. One of them, Carlos Guestrin, is also co-founder and CEO of a data…

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Urban Engines launches to use data to make city transportation better

Gigaom

Stanford computer science professor Balaji Prabhakar first became interested in how transportation systems move when, years ago, he got stuck in “the mother of all traffic jams” in India. Now, after two years in stealthy development, Prabhakar and his co-founder, former Google(s GOOG) exec Shiva Shivakumar, are launching a startup called Urban Engines that is using data, algorithms and behavioral economics to help make cities less congested and urban transportation operate more efficiently.

In an office in downtown San Francisco this week, six stories above the blaring horns of buses and cars running up and down Market Street, Shivakumar and Prabhakar showed me a screen of a train system that could be any big city in the world — Sao Paulo, San Francisco, Bangalore. Prabhakar clicked the play button and we watched a geometrical visualization of the flow of train commuters moving into stations, getting on trains and getting off at…

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Facebook shows off its deep learning skills with DeepFace

Gigaom

Facebook(s fb) has published a research paper explaining a project called DeepFace that’s almost as good at putting names to faces as humans are. In fact, it might be better. The company claims its system, which is built using deep neural network, or deep learning, techniques, performed with 97.25 percent accuracy on a dataset commonly used to measure the effectiveness of facial recognition systems.

MIT Technology Review first reported on the DeepFace paper, which Facebook researchers are presenting at the IEEE Conference on Computer Vision and Pattern Recognition in June.

Deep learning is currently an area of investment for a number of web companies ranging from Pinterest to Netflix(s nflx), although Facebook and Google(s goog) have probably made the biggest news with their high-profile hires and acquisitions. It’s such a hot field because deep learning techniques are proving very effective at recognizing objects within images and analyzing…

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Cloudera is rebuilding machine learning for Hadoop with Oryx

Gigaom

Hadoop software vendor Cloudera didn’t make a lot of waves when it bought a London-based startup called Myrrix last year, and it hasn’t made a lot of noise about the company’s machine learning technology since then. But the company’s technology and its founder, Sean Owen, could turn out to be very valuable assets.

Owen, whose official title is director of data science, now spends him time working on an open source machine learning project called Oryx. (It’s a species of African antelope; Cloudera also sells a product called Impala). Oryx is intended to help Hadoop users build machine learning models and then deploy them so they can be queried and serve results in real time, say as part of a spam filter or a recommendation engine. Ideally, Oryx will also suuport models that can update themselves as data streams in.

Owen calls it the difference between Hadoop’s traditional sweet spot…

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Próximos Cursos relacionados a Data Science

Data Analysis and Statistical Inference – Coursera

  • Início: 17 Fev 2014
  • Duração do curso: 10 semanas
  • Esforço estimado: 6h-8h por semana

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MOOCs by the numbers: Where are we now?

TED Blog

Whatever your opinion of them, you can’t deny that MOOCs have come a long way in the last few years. To help put the massive online courses into some perspective, Alex Cusack, a contributing writer at Moocs.com, a blog that covers news about MOOCs (edited by Zachary Davis, a producer for HarvardX, a spin-off of edX) shared this handy infographic.

Cusack, a consultant in educational technology, regularly works with corporations and universities looking to design online education programs. And he’s a MOOC alum himself; his own experience with the courses (he has variously started, completed and dropped out of classes offered by Coursera, edX, Udacity and Udemy) has informed his take on the topic. As he told me over the phone, he became drawn to MOOCs when he realized, “I could attend Stanford-level classes and get objective content at basically free or little cost.” As a business major at Azusa…

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