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.

It is every Top Gun's worst nightmare - an AI can can outmanoeuvre them in the air. Now researchers have tested their AI on a retired top gun - and left him stunned. Retired United States Air Force Colonel Gene Lee took on the AI in a simulator - and lost. An AI has beated Air Force pilots in simulated showdowns for the first time. Retired United States Air Force Colonel Gene Lee took on the AI in a simulator.

For the past six years, I've been developing Python Tutor (pythontutor.com), Thousands of people use it every day to run tens of thousands of pieces of code in seven languages: Python, Java, JavaScript, TypeScript, Ruby, C, and C . This tool has also become a platform for HCI, educational technology, and computing education research. Most recently, it formed the basis for my faculty job applications that got me a job at UC San Diego. How did this project grow from nothing to its current state? I've been wanting to write a "history of Python Tutor" article for a while now but never found a good time to do so.

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA - a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.