In a blog post, Coursera co-founder Daphne Koller announced she is leaving the company to join Alphabet subsidiary Calico. Koller founded Coursera with Andrew Ng back in 2012 after working together on artificial intelligence research at Stanford. The two supported the company's growth until Ng left to become chief scientist at Baidu's research arm. Today Coursera has grown to tech 20 million students material from 1,300 courses. Though both have now officially moved on to new challenges, Koller and Ng remain co-chair's of the Coursera Board of Directors.

Over the years, we have experienced dramatic changes in how we interact with computers. We went from flipping switches, to stacking punch cards, typing away on hard copy and now, manipulating objects on a desktop using touch screens. Each transition has provided us with a greater ease of interaction and a new metaphor (switches, direct commands, desktops) for thinking about that interaction. But, advances in speech recognition are completely changing the game. Siri, Cortana, Alexa and Google Now have given us a taste of what it means to directly communicate our goals and needs to the machine.

Is there a next phase for cloud computing? During the past few years, cloud computing has become a mainstream element of modern software solutions just as common as websites or databases. The cloud computing market is a race vastly dominated by four companies: Amazon, Microsoft, Google and IBM with a few other platforms with traction in specific regional markets such as AliCloud in China. In such a consolidated market, it's hard to imagine a technology being disruptive enough to alter the existing dynamics. Artificial intelligence (AI) is the type of technology with the potential to not only improve the existing cloud platform incumbents but also power a new generation of cloud computing technologies. The thesis of a new generation of cloud computing platforms might seem ludicrous at first but it also presents a very intriguing argument.

NASA's six foot, 300-pound humanoid robot is looking for a programmer. This week, NASA announced the start of the Space Robotics Challenge, a 1 million contest to program a virtual robot. Competing teams will be tasked with programming Robonaut 5, a humanoid robot, how to perform damage control after a Martian dust storm that has damaged a habitat. As part of this, they'll need to use the robot to complete three objectives: align a communications dish, repair a solar array, and fix a leak in the hab. Registration is open now, with the final round of competition held in June 2017. Robonaut also goes by the name Valkyrie at MIT, which we first met in 2013 as a DARPA challenge entrant.

Discussing the broad impact of AI on the enterprise, Jon points out that we are already seeing AI in action, if we know where to look. "Some say AI has been hidden away for a long time, but actually it hasn't. There are already AI applications in place in our normal daily activity. This year we have seen IBM Watson, Microsoft's Cortana and other personal assistant technology raise their marketing approach, yet Google Search and chatbots have been in use for some time. Part of what The AI Summit demonstrated was the extraordinary level of application and uptake that is already happening".

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Intel practically owns the business of selling chips for data center servers. IDC pegs its share of the market at 99%. But Intel doesn't have such a strong grip on the latest, and hottest, slice of the market: artificial intelligence. It faces stiff competition from graphics chip expert Nvidia, whose graphics cards are currently the most popular for powering deep learning neural networks that perform mainstay artificial intelligence tasks like image recognition, voice recognition and natural language processing. Hoping to push back against Nvidia's inroads, Intel announced on Wednesday a new server processor tailored for artificial intelligence, the third-generation Xeon Phi, code-named "Knights Mill."

What could possibly go wrong? Elon Musk's AI set to try and learn the art of human conversation ... Intel's secretive Knights Mill mega-chip will challenge GPUs for AI domination

What could possibly go wrong? Elon Musk's AI set to try and learn the art of human conversation ... Intel's secretive Knights Mill mega-chip will challenge GPUs for AI domination Intel's Knights Mill mega-chip to take on GPUs in AI This AI Can Tell if You're Depressed by Looking at Your Instagram Photos

There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships.