Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity. There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you'll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name). To compare these approaches, you'll train a linear regression against a data set with known coefficients. Spark includes PySpark (supported by Cloudera), the Python API for Spark.

Google has made no secret of its A.I. ambitions, and on Thursday it announced the next step in its bold plans to realize them: a brand-new research group in Europe focused squarely on machine learning. Based in Google Research offices in Zurich, Switzerland, the new group will focus on three key areas of artificial intelligence: machine intelligence, machine perception, and natural language processing and understanding, according to a blog post by Emmanuel Mogenet, head of Google Research for Europe. It will research ways to improve machine-learning infrastructure and enable the technology for practical use, for instance. Researchers will also work closely with linguists to advance natural language understanding, Mogenet said. Zurich, meanwhile, is home to Google's largest engineering office outside the U.S. Researchers there developed the engine that powers Knowledge Graph as well as the conversation engine that powers the Google Assistant in its Allo messaging app. Google's presence in Europe hasn't been entirely smooth, however: It's facing ongoing scrutiny over antitrust concerns and tax issues.

Genpact (NYSE: G), a global leader in digitally-powered business process management and services, announces that it has entered into a definitive agreement to acquire PNMsoft, a Gartner Magic Quadrant-rated dynamic workflow, case management and work optimization solutions provider based around Tel Aviv, Israel. PNMsoft complements and easily integrates pre-existing systems of records that typically host manual process work, and will act as a core component in Genpact's digital portfolio whose roadmap comprises close to 100 digital solution components ("digital assets"). Terms of the transaction were not disclosed. Closing is subject to satisfaction of certain customary conditions and expected in the third quarter. The transaction is not expected to be material to current year financial performance.

In the daily edtech trenches, the forest is easily lost for the trees. Technological minutiae in the classroom carry such immense consequences that it can be hard to think beyond tomorrow's software update, nevermind next year's LMS rollout. In his opening keynote at the ISTE 2016 conference, noted physicist Dr. Michio Kaku showed educators the forest that he and others believe will encircle the classroom of the future. And oh, what a forest it might be. According to Dr. Kaku, talking wallpaper, data-reading toilets and other technologies that seem like miracles today are a mere fifty years away.

Today, Anki, which made its splashy debut a few years back with their little autonomous racing cars, has announced a new robot toy called Cozmo. Cozmo is (according to Anki) "one of the most sophisticated robots available today," which could be correct depending on your definitions of "one of the most sophisticated," and "available today." What does Cozmo do? "He is charming, a bit mischievous, and unpredictable. He recognizes and remembers you. He interacts with you, plays games, and gets to know you over time."

Is there a nice deep learning library/wrapper like Keras which allows use for Torch? Also is there an easy way to convert between Theano, Tensorflow, and torch backends with Keras code?

I wrote my first Python program in 1996, and my most recent a couple of weeks ago, so I can appreciate Python's advance to cover a very broad range of computing tasks. I don't program much anymore, but in my work over the years -- and yours too, if you do much coding -- data manipulation has always played an important role. You can't build and apply analytical models, manage transactions, craft a Web experience, or carry out any other significant task without investing time and attention to data acquisition, cleansing, and structuring. Python is ideal for those tasks, and then for model building and data analysis. Python is great for natural language processing (NLP), in particular, a special interest of mine, and for just about any data work that interests you, chances are.

Machine Learning has granted incredible power to humans. The power to run tasks in automated manner, the power to make our lives comfrotable, the power to improve things continuously by studying decisions at large sacle . And the power to create species who think better than humans. Read what Google's CEO Mr. Sundar Pichai had to say last week: 'Machine learning is a core, transformative way by which we're rethinking everything we're doing,' Pichai said. 'We're thoughtfully applying it across all our products, be it search, ads, YouTube, or Play.

June 27, Baltimore, MD - Youth Laboratories, the organizer of the first beauty contest judged by a panel of robots today announced the inclusion of AntiAgeist, an algorithm evaluating the difference between the chronological age of contest participants and the age predicted by a system of deep neural networks trained to predict human age. "We are very happy to have AntiAgeist on our jury of robot judges, since this is a rather novel idea of looking at beauty through the prism of how successfully the person is aging. We encourage teams from all over the world to submit algorithms and ideas on how machines can evaluate human beauty to the Beauty.AI contest. Best algorithms will get monetary prizes and will be promoted worldwide", said Anastasia Georgievskaya, general manager of Beauty.AI. Insilico Medicine specializes in drug discovery and biomarker development for a broad range of diseases with a mission to accelerate and improve lead generation and pre-clinical studies within biotechnology and pharmaceutical industries.