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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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MIT to offer its first professional MOOC in big data

Gigaom

The Massachusetts Institute of Technology has been involved in online education since the early days, and now it’s taking it a step further. Yesterday, the college announced its first online, professional-leaning Massively Open Online Course (MOOC), entitled “Tackling the Challenge of Big Data.”

Led by a dozen faculty from the university’s Computer Science and Artificial Intelligence Laboratory (CSAIL) at the School of Engineering, the four-week course starts at the beginning of March and is directed specifically at technical professionals and executives — not academic-types. The course is the first in a new set of courses offered by the university called Online X, which offers professional classes through the edX platform.

One important thing, though: these classes may be open, but they don’t come cheap. Participating in the course will run users $495 — far from the free price tags of many MOOCs available. But it’s likely that extra cost…

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Synference thinks A/B testing can get a lot smarter with machine learning

Gigaom

Say a web publisher wants to find out which banner ad is most appealing to which audience, or which price point will make a certain user more likely to buy. Normally it would use multivariate A/B testing — the process of showing different versions of the same screen or screen elements to users and  gathering data on their reactions — but the process is lengthy and testing numerous variables like location, time of day, or browser used spreads the data thin.

Synference, an API and company that is launching today, aims to solve this.

The Ireland-based operation uses A/B testing, machine learning and basic user data garnered from IP addresses and user agent. As the API receives user feedback — did she click on a banner or not? — Synference detects patterns of user behavior and updates its statistical model accordingly. It also allows companies to exploit this information before…

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Maybe big data is the killer app for Google’s cloud

Gigaom

Google’s Compute Engine cloud doesn’t yet have a Hadoop offering of its own, but the platform is making a name for itself as a viable, if not ideal, place to run big data workloads. The latest validation came on Thursday when Qubole, the Hadoop-as-a-service startup from Hive creators Ashish Thusoo and Joydeep Sen Sarma, announced an option that users can choose to run on Compute Engine, which they claim provides better performance than Amazon Web Services.

Specifically, a company spokesperson told me via email, Qubole has seen 2-3x faster startup times for virtual servers using Compute Engine over Amazon EC2 and more reliable performance from Google Cloud Storage than from Amazon S3. We’ll also assume that AWS is the “CloudX” against which Qubole engineer Praveen Seluka benchmarked Compute Engine, some results of which he shared on the Google Cloud Platform blog. Qubole did launch as an AWS-based service…

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