Microsoft is making the tools behind applications like Cortana available on code repository GitHub, opening the doors to new open source machine learning projects based on its software programmes. Redmond originally created the toolkit, known as Computational Network Toolkit (CNTK), out of necessity to help its developers make faster improvements to how well computers can understand speech. But in a blog post, Microsoft explained that CNTK has proved to be "more efficient" than the four notable computational toolkits – namely Theano, TensorFlow, Torch7, and Caffe – that developers were using to create deep learning applications. "The CNTK toolkit is just insanely more efficient than anything we have ever seen," said Xuedong Huang, chief speech scientist at Microsoft. This has allowed Microsoft's researchers to create systems that can accurately recognise and translate conversations, as well as ones that can recognise images and even answer questions about them.

I went to school in Cambridge University in England, then went to Stanford to do my master's around 2000. I met up with Sergey Brin around then when Google was a tiny company and he invited me to join Google. So I started working part-time for Google. It was a fantastic time at the company--200 people, one building, and bright, idealistic, change-the-world kind of people. Naturally, when I finished my master's I joined full-time.

Whether you define artificial intelligence as a sophisticated being that is convincingly "human" and can pass a Turing Test, or simply an algorithm that dictates which stories you see in your Facebook feed, the rise of smart machines will see more and more of the jobs we do today transformed and even replaced. Where mechanisation replaced a large swathe of manual labour jobs, now we're going to see more and more white-collar jobs overtaken by algorithms. "In the past we were removed from the limitations of our muscles; now we'll be removed from some of the limitations of our brains," says Toby Walsh, a prominent Australian AI expert who has taken a global campaign against the development of autonomous weapons (aka "killer robots") to the United Nations. As with previous cycles of technological advancement, the rise in productivity is expected to bring a rise in living standards. But there will be pain, too, at least in the short term.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.

We curated some use cases of companies that have integrated cognitive into their solutions. If you want to know more about any these use cases and receive tips from these companies, check out their webinars. VineSleuth's Wine4.Me In-Store Wine Advisor takes the guesswork out of buying wine by empowering shoppers and increasing sales. Shoppers tell the application what they want in a wine (flavor profile, food pairing, price requirements and more) and Watson returns a custom curated, unbiased wine list and suggests food pairings for each shopper. How it works: VineSleuth uses Natural Language Classifier and Speech to text APIs to allow consumers to easily ask a question into the application, either through voice or text.

April 1, 2016 – Boston, MA – Cloud-based computing and artificial intelligence represent the future of content creation, distribution, public relations, and marketing. SHIFT Communications, the premiere data-driven PR agency, announced today the release of the Heuristic And Recurrent Ontological Lexicon Deep-learner, or HAROLD, the world's first artificially intelligent (AI), cloud-based PR employee. HAROLD's creation represents the first AI employee of a virtual public relations workforce. HAROLD is based on the proven TensorFlow multidimensional data array artificial intelligence software, first developed by Google Brain, part of Google's Machine Intelligence division. SHIFT Vice President of Marketing Technology Christopher Penn said, "HAROLD provides SHIFT with limitless scale. Your standard PR team has 10-15 humans; with a cloud-based AI employee, we can create a million new'employees' in seconds. It would take your average account team several weeks to pitch a 5,000 person media list. HAROLD can replicate itself and call the whole media list simultaneously."

Microsoft's intelligent chatbot Tay behaved badly last week (and this week too), but that shouldn't have shocked any of us. Interestingly, Microsoft has been operating a similarly designed service in China called Xiaoice, meaning "little Bing," which is most likely a step towards replacing elements of customer service and it has proved quite successful. Luckily, we have a new statistical learning paradigm (Bayesian statistical theory) at work, which we've been able to implement during the last few years due to recent advances in simulation theory. It forces human assumptions to be explicit in the mathematics, reducing the potential for unintentional human bias that still occurs in scientific research today (p-values is an excellent example of this insanity).

Microsoft's intelligent chatbot Tay behaved badly last week (and this week too), but that shouldn't have shocked any of us. Interestingly, Microsoft has been operating a similarly designed service in China called Xiaoice, meaning "little Bing," which is most likely a step towards replacing elements of customer service and it has proved quite successful. Luckily, we have a new statistical learning paradigm (Bayesian statistical theory) at work, which we've been able to implement during the last few years due to recent advances in simulation theory. It forces human assumptions to be explicit in the mathematics, reducing the potential for unintentional human bias that still occurs in scientific research today (p-values is an excellent example of this insanity).

Microsoft's intelligent chatbot Tay behaved badly last week (and this week too), but that shouldn't have shocked any of us. Flaws are what make us who we are as people -- they define us. So it's a bit of a double standard that we seem to expect no imperfections when we design human characteristics into machines. Microsoft's chatbot fiasco should have been predictable. If you put a child into a racist family, you cannot be too surprised with how they grow up.

A Weird Month for Artificial Intelligence Some of the more interesting and universally accessible developments in technology over the past decade (to me anyway) have been within the artificial intelligence world. Unlike many other industry advancements, stories of high-profile AI successes or failures make suitable discussion fodder at family dinner tables. The "Fork Me On GitHub" types appreciate the complexity of the required engineering efforts, while the "Hey Person Who Computers -- Can You Set My VCR Clock?" crowd can just think "whoa" without concern for what is happening under the hood. One somewhat recent and well-publicized AI event was the Jeopardy-playing Watson supercomputer built by IBM.