In 1974, Roger Penrose, a British mathematician, created a revolutionary set of tiles that could be used to cover an infinite plane in a pattern that never repeats. In 1982, Daniel Shechtman, an Israeli crystallographer, discovered a metallic alloy whose atoms were organized unlike anything ever observed in materials science. Penrose garnered public renown on a scale rarely seen in mathematics. Shechtman won the Nobel Prize. Both scientists defied human intuition and changed our basic understanding of nature's design, revealing how infinite variation could emerge within a highly ordered environment. At the heart of their breakthroughs is "forbidden symmetry," so-called because it flies in the face of a deeply ingrained association between symmetry and repetition.

The neuroscientist was in the art gallery and there were many things to learn. So Eric Kandel excitedly guided me through the bright lobby of the Neue Galerie New York, a museum of fin de siècle Austrian and German art, located in a Beaux-Art mansion, across from Central Park. The Nobel laureate was dressed in a dark blue suit with white pinstripes and red bowtie. I was dressed, well, less elegantly. Since winning a Nobel Prize in Physiology or Medicine in 2000, for uncovering the electrochemical mechanisms of memory, Kandel had been thinking about art. In 2012 and 2016, respectively, he published The Age of Insight and Reductionism in Art and Brain Science, both of which could be called This Is Your Brain on Art. The Age of Insight detailed the rise of neuroscience out of the medical culture that surrounded Sigmund Freud, and focused on Gustav Klimt and his artistic disciples Oskar Kokoschka and Egon Schiele, whose paintings mirrored the age's brazen ideas about primal desires smoldering beneath conscious control. I'd invited Kandel to meet me at the Neue Galerie because it was the premier American home of original works by Klimt, Kokoschka, and Schiele. It was 2014 when we met and I had long been reading about neuroaesthetics, a newish school in neuroscience, and a foundation of The Age of Insight, where brain computation was enlisted to explain why and what in art turned us on. I was anxious to hear Kandel expound on how neuroscience could enrich art, as he had written, though I also came with a handful of doubts.

I started at Slate not too long ago, and in my first weeks in the magazine's New York office, I was faced with a problem that is familiar to anyone who's ever started a new office job. I was surrounded by dozens of new faces and struggled to connect them to names. Which man with a scruffy beard was the one in charge of podcasts again? Who was the friendly woman at my desk pod offering me some of her snacks? I found myself wishing, as I often do, that everyone was wearing a nametag.

Machine learning is an incredibly broad and diverse field, with a non-stop increase on research, along a multitude of applications. Thus writing a list enlisting the best machine learning researchers on the field proves challenging for a number of reasons. Please mind that this list encompasses researchers who are currently working on the field. Also, please mind that this list is by no means ranked. Everyone listed below has done extraordinary work to advance humanity's state of AI further.

This blog post provides highlights and a full written transcript from the panel, "Data Science Versus Engineering: Does It Really Have To Be This Way?" with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points. Recently, I had the opportunity to moderate the panel, "Data Science Versus Engineering: Does It Really Have To Be This Way?" with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. As Domino's Head of Content, it is my responsibility to ensure that our content provides a high degree of value, density, and analytical rigor that sparks respectful candid public discourse from multiple perspectives. Discourse that directly addresses challenges, including unsolved problems with high stakes. Discourse that is also anchored in the intention of helping accelerate data ...

What is the Future of Machine Learning in Healthcare?

Fisac is interested in ensuring that autonomous systems such as self-driving cars, delivery drones, and home robots can operate and learn in the world--while satisfying safety constraints. Towards this goal, Fisac discusses different examples of his work with unmanned aerial vehicles and talks about safe robot learning in general; including, the curse of dimensionality and how it impacts control problems (including how some systems can be decomposed into simpler control problems), how simulation can be leveraged before trying learning on a physical robot, safe sets, and how a robot can modify its behavior based on how confident it is that its model is correct. Below are two videos of work that was discussed during the interview. The top video is on a framework for learning-based control, and the bottom video discusses adjusting the robot's confidence about a human's actions based on how predictably the human is behaving. Jaime Fernández Fisac is a final-year Ph.D. candidate in Electrical Engineering and Computer Sciences at the University of California, Berkeley.

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.

Both the progress and the hype around cutting-edge machine learning techniques were on vivid display at the December 2018 NeurIPS Conference in Montreal, Quebec, says Will Knight, MIT Technology Review's senior editor for artificial intelligence. One big question hanging over the meeting, he says, was how to detect and reverse the sexism, racism, and other forms of bias that seep into machine-learning algorithms that train themselves using real-world data. Participants also previewed the coming generation of chips designed specifically to support deep learning--a field where US manufacturers face growing competition from China. Separately, Will looks to the most exciting AI trends for 2019, including the generative adversarial networks (GANs) being used to generate authentic-looking photos and videos. This episode is sponsored by PwC, a global consulting firm in 158 countries with more than 250,000 people. PwC transforms business outcomes and results, helping companies use digital and emerging tech to reimagine their business, from strategy and operations to tax and finance. In the second half of the show, Scott Likens, PwC's New Services and Emerging Tech Leader, shares details from a new PwC study on the main trends in artificial intelligence that business leaders need to know about in 2019. Business Lab is hosted by Elizabeth Bramson-Boudreau, the CEO and publisher of MIT Technology Review. The show is produced by Wade Roush, with editorial help from Mindy Blodgett. Will Knight: "China has never had a real chip industry. Making AI chips could change that." PwC 2019 AI Predictions: Six AI priorities you can't afford to ignore Elizabeth Bramson-Boudreau: From MIT Technology Review, I'm Elizabeth Bramson-Boudreau, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Intelligent nudging is part of a new toolkit to track, predict, and influence human behaviour. There are a vast array of nudge approaches and techniques, each with the intention to direct individuals to act or think in a certain way. Today, nudges can be aided by artificial intelligence, hence the term intelligent nudging. Adding AI can make decisions about nudges better informed and potentially more efficient and effective. Intelligent nudging has its foundations in nudge theory, which was brought to prominence by Nobel Prize winner Richard Thaler.