The Lovie Awards were privileged to honour Yann LeCun with the 2016 Lovie Lifetime Achievement Award for his work as a pioneer in the creation of neural networks. LeCun's contributions to the science of machine learning, mobile robotics and computational neuroscience among other learned fields, is legendary. As a founder of Neural Nets, LeCun applied biological methods of perception to computer processors. Currently continuing his forward-thinking work as the Head of Artificial Intelligence at Facebook, LeCun is poised to see his genius bear inventive innovations to the social media platform that will affect a global user base. As he continues to inform and experiment with the future of mediums such as the incredibly fast-growing arena of machine learning, LeCun's work will only improve upon the already impressive technologies he has brought to fruition.

Google is announcing today that it's setting up a deep learning and artificial intelligence (AI) research unit in its office in Montreal and giving $3.37 million in grant money to deep learning luminary Yoshua Bengio and seven other people associated with the Montreal Institute for Learning Algorithms (MILA). Bengio himself has previously received backing from Google, and from other companies as well -- namely, IBM, Samsung, and Intel. But the new grant is "bigger than any of the other funding we've received from private companies up until now," he said during an interview with VentureBeat. Bengio will not be formally allying himself with Google proper, because he wants to stay independent. "That's who I am," he said, "that's the choice I made that fits with my values, and I don't need to get the millions, I'm fine. My salary is very good, and I care more about how what I can do could have a positive impact for science, humanity, and for training the next generation [of researchers]."

John Charles Thomas was born in Akron, Ohio and attended Ellet High School. He graduated from Case Western Reserve University majoring in psychology and minoring in mathematics and drama. He received a Ph.D. in experimental psychology from the University of Michigan. His dissertation compared human performance in a problem solving task to that of an early AI system called "The General Problem Solver." After graduate school, Dr. Thomas managed a research project on the psychology of aging at Harvard Medical School.

Are art and science of distinctly different cultures? The former often seems fixated on human experience, the latter on physical processes. In his most recent book, Reductionism in Art and Brain Science: Bridging the Two Cultures, published this year, the Nobel Prize-winning neuroscientist Eric Kandel argues that such a separation no longer exists. The best-known abstractionists, like Mark Rothko, Jackson Pollock, Dan Flavin, and Willem de Kooning, Kandel writes, effectively created "new rules for visual processing." Abstract art, says Kandel, is therefore the key to understanding both how art and science inform one another, and together, they might open up entirely new ways of seeing and imagining.

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions--about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results--may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. Information extraction--or automatically classifying data items stored as plain text--is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.

A product from Japan created quite the stir at Consumer Electronics Show in Las Vegas and CEATEC JAPAN in Tokyo this year. The "harmony" of clothing analysis, artificial intelligence (AI), and robotics blend together to produce a "fully automatic clothes folding machine." Japan technological alliance "seven dreamers laboratories' is the developer. The product details have been released in various places, so I won't get into that, but as the name says, "It's a robot that folds clothes. No further explanation is needed." The company announced a partnership with Panasonic (TSE:6752) and Daiwa House (TSE:1925) last year, and together established the joint venture Seven Dreamers Laundroid with plans to begin sales by reservation for their first machine "Laundroid 1" in March of 2017. The developer, Seven Dreamers, announced on November 14th the securement of 6 billion yen (around $60 million US) in funds from SBI Investment, in addition to Panasonic and Daiwa House. The shareholding ratios and payment date remain undisclosed. The concept began in 2005, and with the realization of "folding" from 2013, Laundroid was born. I heard from Seven Dreamers CEO Shin Sakane about the road it took to get here. I came today with the idea of asking straight out, "What happened to make robots fold the laundry?" Well, to be straight, "It's now possible to recognize clothes using artificial intelligence," is maybe the simplest answer I can give. Let's go through the process. How did the idea first come to you? Before that, first permit me to talk a little about what criteria the Seven Dreamers esteem. For us, there are three criterion for "Things that have not been realized yet but could change our lives, and also enrich them." The technological hurdles are high and our policy is to clear them. You've made something that sets high hurdles. Since first coming up with the idea, I was thinking about different markets to satisfy all the criteria. Looking around we see many products targeted at men. Starting now and into the future, 'women', 'the elderly', and'children' are the keywords that will become important. After thinking, the idea that maybe the answer lies within the home came to me and, while I don't usually talk with my wife about work, I casually mentioned it to her. What do you wish you had? She came back just as fast, "Of course, it has to be a machine that folds the laundry.

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings – if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.

One of the big trends we've seen over the last five years is automation. At the same time, we're also seeing more intelligence built into tools we already have, like phones and computers. Where do you see this process in five years? In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things. You will have more intelligent systems in the physical world, too -- not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world.

LAS VEGAS: Marvin is just a few inches tall, but he has a big mouth. "What do you think of that, human?" he sneers as he ties the scores 1-1."Is that the best you can do?" he jeers as he takes a 2-1 lead. "Oh no, IBM will fire me if I lose," he says, as the score is tied 2-2. The decisive move comes up, and Marvin is beaten. "My robot mind is officially blown," he intones, making a fizzing sound as the audience laughs and claps.

Michael Kagan: The ever-growing demand for higher performance drives technology innovations for HPC, which then spreads to other markets. We have witnessed several technology transitions over the years, such as the transition from SMP to clusters, or from single core to multi-core. We are now going through another technology transition, which some call Co-Design. There are many technology efforts to re-architect the data center from a CPU-centric architecture to a data-centric architecture in order to overcome the new performance bottlenecks. The new data centers will need to allow data operations and analysis everywhere in order to get insights in real time.