Researchers at Oxford University are setting Google's artificial intelligence DeepMind loose on analyzing Hearthstone and Magic: The Gathering playing cards. According to Kotaku, the AI analyzes card data such as resource cost and damage, and turns it into code that a machine can read. Here's the abstract from the paper titled'Latent Predictor Networks for Code Generation': "Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. "Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification.

Artificial Intelligence is the discipline of thinking machines. It has been a field of growing interest since 1955 when John McCarthy first coined the term, defining it as "the science and engineering of making intelligent machines." The largest players in technology today, including Google, Facebook, Amazon, Microsoft and Apple, are all investing heavily in Artificial Intelligence. So are most of the technology focused Venture Capital firms. All indications are this hot field is about to accelerate dramatic capabilities into every corner of our economy.

Artificial intelligence systems based on neural networks have had quite a string of recent successes: One beat human masters at the game of Go, another made up beer reviews, and another made psychedelic art. But taking these supremely complex and power-hungry systems out into the real world and installing them in portable devices is no easy feat. This February, however, at the IEEE International Solid-State Circuits Conference in San Francisco, teams from MIT, Nvidia, and the Korea Advanced Institute of Science and Technology (KAIST) brought that goal closer. They showed off prototypes of low-power chips that are designed to run artificial neural networks that could, among other things, give smartphones a bit of a clue about what they are seeing and allow self-driving cars to predict pedestrians' movements. Until now, neural networks--learning systems that operate analogously to networks of connected brain cells--have been much too energy intensive to run on the mobile devices that would most benefit from artificial intelligence, like smartphones, small robots, and drones.

What are some recent advances in non-convex optimization research? Non-convex optimization is now ubiquitous in machine learning. While previously, the focus was on convex relaxation methods, now the emphasis is on being able to solve non-convex problems directly. It is not possible to find the global optimum of every non-convex problem due to NP-hardness barrier. An alternate approach is: when can it be solved efficiently (preferably in low order polynomial time).

Our CEO and founder, Dr. Dan Steinberg recently wrote about gradient boosting machines. Gradient boosting machines are a powerful machine learning technique, and have been deployed with great success over the years in Kaggle competitions. However, specifics of the construction and core ideas of gradient boosting machines can remain a bit murky. For more a more detailed look at the shapes and sizes of the trees formed in gradient boosting machines, read the discussion on Dr. Steinberg's blog:

Don't forget to check the FAQ. It has links to lots of good resources including Elements of Statistical Learning which is free online and one of the standard texts (Murphy and Bishop are the other two standard texts). Yaser Abu-Mostafa wrote a book called "Learning from Data" which I enjoyed quite a bit but wasn't listed in the FAQ. It doesn't cover many algorithms (just one or two I think) but does a good job providing an approachable introduction to theoretical aspects of ML that aren't always covered in the standard texts. Colah's blog isn't updated often but all the articles are fantastic You may also want to follow Yann LeCun on facebook. He posts stuff about ML on a fairly regular basis.

Yesterday, the 46-year-old Google veteran who oversees the company's search engine, Amit Singhal, announced his retirement. And in short order, Google revealed that Singhal's rather enormous shoes would be filled by a man named John Giannandrea. On one level, these are just two guys doing something new with their lives. But you can also view the pair as the ideal metaphor for a momentous shift in the way things work inside Google--and across the tech world as a whole. Giannandrea, you see, oversees Google's work in artificial intelligence.

A team of US researchers has built an energy-friendly chip that can perform powerful artificial intelligence (AI) tasks, enabling future mobile devices to implement "neural networks" modelled on the human brain. The team from Massachusetts Institute of Technology (MIT) developed a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU (Graphics Processing Unit) so it could enable mobile devices to run powerful AI algorithms locally rather than uploading data to the internet for processing. The GPU is a specialised circuit designed to accelerate the image output in a frame buffer intended for output to a display. Modern smartphones are equipped with advanced embedded chipsets that can do many different tasks depending on their programming.

As the Primary season has progressed, there's been no end of political pundits backpedaling and mea-culpa-ing over their previous inability to predict the rise of Donald Trump to become the frontrunner in the GOP. From Charles Krauthammer admitting that it was wrong to laugh at The Donald to innumerable others, both liberal and conservative, wishing they'd take Trump seriously, it seems like just about everyone in the Predictive Class will be dining on roast crow this Easter. But why did they get things so wrong? Was it because they assumed that he'd "crash and burn" like John Podhoretz? Was it because they assumed that he couldn't win because Republican voters hated him, as implied by Patrick Murray of Monmouth University when releasing early poll results in June of 2015?

So he and his business partner, Dan Roth, decided to create a program that would help lawyers manage electronic documents for litigation. Their idea led them to purchase an e-discovery application. By 2000, Leib and his partner launched their own creation, Discovery Cracker. "We saw a gap in the marketplace," Leib says. Lawyers need tools to keep up with it." Instead of wading through piles of paper, lawyers now deal with terabytes of data and hundreds of thousands of documents. E-discovery, legal research and document review are more sophisticated due to the abundance of data. So while working as chief strategy officer at kCura in Chicago, Leib saw a need again in the market. "For years, lawyers have been stuck with antiquated tools that focus primarily … on Boolean search. Better tools are needed to truly understand data." "What is the future of the industry?