Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

From left, Dr Michael Tompsett (UK), Professor Eric Fossum (USA) and Professor Nobukazu Teranishi (Japan) are announced as the winners of the 2017 Queen Elizabeth Prize for Engineering at Carlton House Terrace on Wednesday, Feb. 1, 2017 in London. Taking a selfie is one of the easiest and quickest things you can do on your smartphone. But as with any landmark invention, it took decades and plenty of graft to develop the camera technology that lives in your pocket. A trio of engineers behind the invention of the image-sensing technology found in billions of smartphones, camera phones, PCs and hospital scanning technology, won the £1 million ($1.3 million) Queen Elizabeth Prize for engineering on Wednesday, and spoke about where the image-sensor technology they developed should go in the future. "I feel gobsmacked and very thankful to the Queen Elizabeth prize for this honor," said one of the engineers, Eric Fossum.

Artificial Lawyer recently caught up with Michael Kholodenko, co-founder of Seattle, US-based start-up, Binadox, which makes use of Natural Language Processing (NLP) to provide a compliance tool for software licences that can prevent costly legal violations. At first you may think this does not sound that world-changing, but sometimes it is the very precise uses of AI technology that end up creating a very successful and useful product for lawyers and legal departments. This would appear to be doubly so, as Bindox points out, because getting your software licences in a mess, or just allowing staff to click'Yes – Accept Terms' can open a Pandora's box of legal problems. Bindadox seeks to meet this problem head on in an automated way. Fundamentally the software seeks to answer two questions: what happens when an employee downloads new software or subscribes to a cloud service (SaaS) and clicks on the little'Yes' box that agrees to the licence terms, with all the legal issues that suddenly connects to?

Mel Silverman walked over to a whiteboard and picked up a marker, listing all the academic disciplines that the band of renegade scientists asking him for money represented. Assembled there 12 years ago at Vancouver's Metropolitan Hotel was a group of about 15 people, ranging from computer scientists to biologists to experimental engineers. What united them was their interest in a concept that was, at the time, generally perceived as the domain of the lunatic fringe. They believed it was possible to teach a machine to learn the same way a child does, through artificial neural networks that mimic the function of the human brain. In the process of teaching a machine to learn like a human, they figured there was likely a lot to discover about how humans learn as well.

Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

I work in computational quantum condensed-matter physics: the study of matter, materials, and artificial quantum systems. Complex problems are our thing. Researchers in our field are working on hyper-powerful batteries, perfectly efficient power transmission, and ultra-strong materials--all important stuff to making the future a better place. To create these concepts, condensed-matter physics deals with the most complex concept in nature: the quantum wavefunction of a many-particle system. Think of the most complex thing you know, and this blows it out of the water: A computer that models the electron wavefunction of a nanometer-size chunk of dust would require a hard drive containing more magnetic bits than there are atoms in the universe.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

When I think about what makes a fun VR experience, I constantly come back to the work of Tyler Hurd: lead artist on a batch of experiences that can best be described as smile machines. His beat-heavy bits basically force participants to dance like they just don't care (a trick aided by the fact that eye-covering VR headsets allow you to pretend that you're dancing like nobody is watching). When I first came across his work at the Tribeca Film Festival, where his VR video the Future Islands song Old Friend made a well-received appearance, I spent a fair chunk of time simply watching people glow with joy as they danced with abandon. This January at the Sundance Film Festival, I witnessed a similar stream of smiles with his newest experience, Chocolate, which he produced with Viacom Next. Towards the end of Sundance, I caught up with Tyler to talk about his new experience, and what the secret is to making VR that people seem to really, really like.

Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati stands as he works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017.

The call for proposals is open for the O'Reilly Artificial Intelligence Conference NY 2017 now through January 18, 2017. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode I spoke with Vikash Mansinghka, research scientist at MIT, where he leads the Probabilistic Computing Project, and co-founder of Empirical Systems. I've long wanted to introduce listeners to recent developments in probabilistic programming, and I found the perfect guide in Mansinghka.