It emphasizes flexibility through the elegant use of object-oriented design patterns.

The Allen Institute for Artificial Intelligence has organized a 4 month contest in Kaggle on question answering. The aim is to create a system which can correctly answer the questions from the 8th grade science exams of US schools (biology, chemistry, physics etc.). DeepHack Lab organized a scientific school hackathon devoted to this contest in Moscow. Our team decided to use this opportunity to explore the deep learning techniques on question answering (although they seem to be far behind traditional systems). We tried to implement Dynamic memory networks described in a paper by A. Kumar et al.

It's not just for developers with a farm of servers and GPUs, though--hobbyists and modest users can be equally competitive because the Toolkit is flexible enough to run on a single laptop. Developers can also integrate into the Toolkit their own Python or C code.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.

Udacity is positioned perfectly to benefit from the rush on talent in a number of growing areas of interest among tech companies and startups. The online education platform has added 14 new hiring partners across its Artificial Intelligence Engineer, Self-Driving Car Engineer and Virtual Reality Developer Nanodegree programs, as well as in its Predictive Analytics Nanodegree, including standouts like Bosch, Harma, Slack, Intel, Amazon Alexa and Samsung. That brings the total number of hiring partners for Udacity to over 30, which means a lot of potential soft landings for graduates of its nanodegree programs. The nanodegree offered by Udacity is its own original form of accreditation, which is based on a truncated field of study that spans months, rather than years, and allows students to direct the pace of their own learning. It also all takes place online, so students can potentially learn from anywhere. For Udacity, hiring partners help prove the value of their program to potential students, as they're effectively votes of confidence made by exactly the kinds of companies where students are looking to get jobs.

When you throw an event hoping to draw 400 people but an audience of 29,000 shows up, do you think it's a good sign indicating you're onto something? That incredible interest is what happened in Beijing the week of 28 November 2016 at the International Summit on Machine Learning and Industry Application. The event's 20 speakers gathered from across industries and academia to offer their insights about machine learning trends and new directions. For the keynote address, Dinesh Nirmal, vice president, analytics development, at IBM, teamed up with Kent Ting, vice president, IBM Analytics Global Consulting Group, at IBM. Nirmal and Ting talked about the IBM focus on machine learning and the company's efforts to enable developers in China and elsewhere. Of particular interest to the audience was their demo of IBM Watson Machine Learning, a full-service IBM Bluemix platform offering.

Universities must not teach degree courses that are too specialised because of the risk that increasingly intelligent machines will put their graduates out of a job, the new vice-provost for research at Imperial College London has warned. Nick Jennings, who is a leading expert in artificial intelligence and was the UK government's chief scientific adviser for national security for the past six years, said that universities instead needed to give their students a deep understanding of a subject so that they can continue to learn in the future. His comments add to the debate around whether increasingly capable AI will replace white-collar jobs in the legal, financial analysis and accountancy sectors, and what universities need to do so their graduates still have an edge over machines. "I think you need to be clear where you're teaching skills versus core understanding," Professor Jennings told Times Higher Education. Universities cannot end up "churning out" graduates who have specialised in a skill that could be made obsolete "and can't understand much else", he warned.

Sebastian Thrun smiles a little awkwardly as he explains why he no longer believes in the educational revolution he sold to the world just a few years ago. The lean, balding robotics pioneer has been instrumental in convincing investors, governments, and colleges to splurge millions on the online college education platforms dubbed MOOCs, or massive online open courses, billed as opening up quality education to anyone on Earth (see "The Crisis in Higher Education"). Thrun, a Stanford professor, helped birth the frenzy when he put his introductory artificial intelligence course online in 2011, accidentally attracting 160,000 students. Amazed by the response, he took time out from Stanford and also from a side job working on autonomous cars and other research at Google to found Udacity, a company offering MOOCs in computing, math, and physics. It attracted $160 million in venture capital investment and teamed up with San Jose State University to offer courses valid for college credit. But within two years of Udacity's launch, Thrun began to question whether MOOCs could make much of a mark on the world in their current form.

This is computer-generated wordplay and an example of how the boundaries of artificial intelligence are shifting. If a computer can crack jokes, what other human activities could they start to replicate? What jobs could it take? Artificial intelligence has become an increasingly big issue for education - not least because many tech companies and publishers are circling around the huge commercial opportunities. But could students really get their answers from a robot rather than a teacher?

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.