The Walt Disney Co. is kicking off the third session of its corporate accelerator this week, and revealed 9 new companies admitted to the program. A full list follows at the end of this post. The companies are developing everything from cinematic virtual reality and holographic content, to robots with human-like facial expressions. Because alumni of the Disney Accelerator have scored big partnerships with the media and entertainment juggernaut in the past, it is seen as one of the more desirable corporate accelerators out there. According to research by Future Asia Ventures, there are 131 active corporate accelerators worldwide today, with 13 new programs launching in the first half of 2016.

When playing with Cozmo, Anki's palm-sized artificial intelligence robot, it's easy to forgot all of the engineering and software running behind the scenes. Every action, from Cozmo's audible chirps of victory when it wins a game to its childlike mannerisms when it recognizes your face, conceals tens of thousands of lines of code. When the product launches this October, Anki hopes consumers won't think of its AI robot as undecipherable technology. Instead, the company wants people to wonder what's going on under the hood -- and, eventually, to alter it themselves. Hanns Tappeiner, Anki's co-founder and president, shows me the tools consumers can use to accomplish that feat.

Lauded as the world's first AI lawyer the story of "ROSS" began with a divorce When Jimoh Ovbiagele was 10 years old, his parents decided to separate. His mother started seeking out divorce lawyers, but was quickly halted by the astronomical hourly rates. "As a single mother with two very young kids, she couldn't pay for even a couple hours of this this divorce lawyer's time," says Ovbiagele. Years later, law seemed like a natural path for Ovbiagele – a way to help ensure others would not have to go through what his mother did--but while the University of Texas computer science major considered applying to law school in 2011, he was turned off by the amount of time he'd be expected to devote to research, rather than the practice, in the profession. The seed, however, had been planted.

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. 'In pricing, are we swapping specific risk for systematic risk?'[1] The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty.

This article is the first in a series of three. See also: Part 1, Part 2, Part 3. Is it possible for a computer program to write its own programs? Could human software developers be replaced one day by the very computers that they master? Just like the farmer, the assembly line worker, and the telephone operator, could software developers be next? While this kind of idea seems far-fetched, it may actually be closer than we think. This article describes an experiment to produce an AI program, capable of developing its own programs, using a genetic algorithm implementation with self-modifying and self-improving code.

According to a recent study, machine learning algorithms are expected to replace 25% of the jobs across the world, in the next 10 years. With the rapid growth of big data and availability of programming tools like Python and R –machine learning is gaining mainstream presence for data scientists. Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data. For instance, Netflix's recommendation algorithm learns more about the likes and dislikes of a viewer based on the shows every viewer watches. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.

Alight, so you have the NVIDIA CUDA Toolkit and cuDNN library installed on your GPU-enabled system. Let's get OpenCV installed with CUDA support as well. While OpenCV itself doesn't play a critical role in deep learning, it is used by other deep learning libraries such as Caffe, specifically in "utility" programs (such as building a dataset of images). Simply put, having OpenCV installed makes it easier to write code to facilitate the procedure of pre-processing images prior to feeding them into deep neural networks. Because of this, we should install OpenCV into the same environment as our deep learning libraries, to at the very least, make our lives easier.

In an effort to tap, and draft, the ever-evolving craft beer zeitgeist, a London-based microbrewery is using crowdsourcing and social media to tweak its recipes and give fans what they truly want. IntelligentX Brewing Co. offers "the world's first beer brewed by artificial intelligence" by using drinkers' opinions and an online feedback system to adjust recipes. "We've created a premium beer that uses complex machine learning algorithms to improve itself from your feedback," reads a statement on the company's website. "Because our A.I. is constantly reacting to user feedback, we can brew beer that matches what you want, more quickly than anyone else can." After sampling one of the company's four bottle-conditioned beers – golden, amber, pale, and black – fans are encouraged to share their thoughts via a Facebook chatbot.

Simply put: I'm a research scientist at MetaMind, acquired by Salesforce, where I poke datasets with the entropy stick and complain about deep neural networks converging too slowly. I graduated from Sydney University (BIT with First Class Honours & the University Medal) and Harvard University (Master of Science in Computational Science and Engineering). Before MetaMind, I was an engineer at the non-profit Common Crawl where I archived billions of web pages per month, the first employee at Grok Learning, data scientist at Freelancer.com, prodded Google App Engine at Google Sydney, and have been a tutor at the National Computer Science School for over half a decade.

Remember Cozmo, the cute little AI-powered robot that can recognize faces, become bored if you ignore it, or throw a tantrum when it loses a game? Turns out it's capable of far more than Anki, the San Francisco-based creators of the'bot, let on last week. On Monday, the startup announced the launch of a software development kit that will expose Cozmo's artificial intelligence smarts -- including facial recognition, positional tracking, and text-to-speech synthesis -- to any developer keen on tapping into them. Anki's motivations for doing so, explained Anki's president and co-founder Hanns Tappeiner at a showcase in New York City, are twofold. First, the company wants to distill Cozmo's deluge of mechanical parts, which include a gyroscope, accelerometer, cameras, a drop sensor, and four motorized joints, down to easy-to-understand functions that novice programmers can understand.