When American novelist David Foster Wallace delivered the commencement address at Kenyon College in 2005, he urged the graduating class to "exercise some control over how and what you think." If you don't at least try to regulate your thoughts and behaviors, Wallace cautioned, you will go through life "dead, unconscious, a slave to your head." Wallace himself long suffered with unwanted negative thoughts and crippling self-doubt--and took his own life three years after that speech. But can our mind become a "terrible master," as Wallace described? Kessler, the former commissioner of the U.S. Food and Drug Administration, has considered that question for the past two decades, studying how substances such as food, alcohol and tobacco can hijack our brain chemistry and compel us to act against our own best intentions--bingeing on brownies, booze or cigarettes.

It's all about trying to teach computers to make connections, similar to those humans make instinctively when growing up, in distinguishing objects. When it comes to video content, machine learning can help solve one of the growing issues in the industry. Barry Schwarz calls it'the paradox of choice' which he describes in his book and his excellent TED talk. Simply put, there has been an explosion of high quality video content production over the last decade. In 2014, Annalect reported that US consumers wanting to watch episodic TV had over 350 to choose from. Yet, consumers are less happy now than when they had fewer choices. It turns out that too many choices just make decisions harder. So, as an industry, we must come up with new ways of getting a better understanding of what each consumer wants to watch and create tools that will make discovery and recommendation more seamless and effective. In fact, machine learning could very well be the driver of a completely new set of content discovery and hyper-personalized services that will dramatically improve viewer satisfaction.

The word Artificial Intelligence is increasing. The latest example is Picturesqe, a tool for photographers that uses AI-powered automation to help pick out the best snaps and filter out the dross. Founders of Picturesqe, a machine-learning powered piece of software, are confident that it can select the good photos from your large stack. But unlike similar mobile Apps, Picturesqe is targeted specifically at professional photographers and semi-pros. Features of the application include smart grouping, which automatically groups similar photos based on visual content, intelligent zoom so that you can quickly compare the same spot on multiple shots, and aesthetic ranking.

Typography has its roots in the earliest days of machines, yet type design is deeply personal. Or even understand the basic qualifications of a typeface? It's not a ridiculous question; the past year has seen a huge influx of developers and artists experimenting with machine learning and computer vision, training artificial neural networks to do everything from caption New Yorker cartoons to describe what's happening in the intro to Star Trek. Erik Bernhardsson, a former Spotify engineer who now works at Better, recently published the results of one such experiment online. In a blog post, he explains that he--or more specifically, a script he wrote--collected some 50,000 fonts from around the web.

This year during its 26th annual 2016 New Director's Showcase at the Cannes Lions International Festival of Creativity, Saatchi & Saatchi injected a suprising entry into its lineup: an AI-created film. The agency challenged viewers by asking, "Can a film made by machines move you?" Along with the Saatchi's L.A.-based shop Team One and Zoic Labs, it put together a different kind of "film crew" comprising AI programs including IBM's Watson, Microsoft's Ms_Rinna, Affectiva facial recognition software, custom neural art technology and EEG data. Together, under the pseudonym of "Anni Mathison," they produced the film "Eclipse" (above), a striking, ethereal music video that looked like a combination of special effects, photography and live-action. But Saatchi didn't reveal this as the "artificially intelligent" entry until the very end.

At dawn on 17 March the inhabitants of Los Angeles were woken by a mild tremor. Less than three minutes later the Los Angeles Times website published an initial piece on the subject, at first sight a wire drafted in haste by a press agency: "A shallow magnitude 4.7 earthquake was reported Monday morning five miles [8km] from Westwood, California, according to the US Geological Survey. The temblor occurred at 6.25am Pacific time at a depth of 5.0 miles. According to the USGS, the epicentre was six miles from Beverly Hills, California, seven miles from Universal City, California, seven miles from Santa Monica, California, and 348 miles from Sacramento, California. In the past 10 days, there have been no earthquakes magnitude 3.0 and greater centred nearby. This information comes from the USGS Earthquake Notification Service and this post was created by an algorithm written by the author."

Relational logistic regression (RLR) is a representation of conditional probability in terms of weighted formulae for modelling multi-relational data. In this paper, we develop a learning algorithm for RLR models. Learning an RLR model from data consists of two steps: 1- learning the set of formulae to be used in the model (a.k.a. structure learning) and learning the weight of each formula (a.k.a. parameter learning). For structure learning, we deploy Schmidt and Murphy's hierarchical assumption: first we learn a model with simple formulae, then more complex formulae are added iteratively only if all their sub-formulae have proven effective in previous learned models. For parameter learning, we convert the problem into a non-relational learning problem and use an off-the-shelf logistic regression learning algorithm from Weka, an open-source machine learning tool, to learn the weights. We also indicate how hidden features about the individuals can be incorporated into RLR to boost the learning performance. We compare our learning algorithm to other structure and parameter learning algorithms in the literature, and compare the performance of RLR models to standard logistic regression and RDN-Boost on a modified version of the MovieLens data-set.

Is at Sea Without a News Anchor" [June 17]. Mary McNamara's view of network TV news hit the bull's eye and what she decries is, unfortunately, the result of the 24/7 news cycle and the out-of-control rampage of "get-it-now" satellite-delivered shout-casters. My list of the great ones would have included Chet Huntley and David Brinkley, both of whom brought dignity to the screen (much as Walter Cronkite did) as well as NBC News' original glass ceiling-buster, Pauline Frederick. Mary McNamara's column about a lack of a national news anchor that people can trust and respect has been true for years. That's the reason I abandoned TV news long ago and replaced it with NPR news. For me and many others, it is the only broadcast news that we pay attention to. Here I thought the malice America is facing has to do with a never-ending war in Afghanistan, high unemployment, etc., but no, the problem facing American is a lack of a good talking head. McNamara's piece bemoaning trusted anchors of ...

More than anything else, IBM's Watson supercomputer is probably best known for one thing: Appearing on Jeopardy!, the legendary TV game show, in 2011. With an internet connection and the ability the buzz in quicker than a human opponent could, Watson destroyed Jeopardy!'s longest-tenured champions, Ken Jennings and Brad Rutter, in devastating fashion throughout a week of games at the IBM campus. If you were to ask the average person if they know about IBM's supercomputer, there's no doubt that an affirmative answer would involve cleaning up on a gameshow. There are now more than 30 different Watson services as part of what IBM calls the Watson Developer Cloud, including a tool that discerns tone in writing. On top of that, there are consumer tools like Chef Watson, where the supercomputer helps generate recipes based on available ingredients.

Researchers have taught a computer to do a better-than-expected job of predicting what characters on TV shows will do, just by forcing the machine to study 600 hours' worth of YouTube videos. The researchers developed predictive-vision software that uses machine learning to anticipate what actions should follow a given set of video frames. They grabbed thousands of videos showing humans greeting each other, and fed those videos into the algorithm. To test how much the machine was learning about human behavior, the researchers presented the computer with single frames that showed meet-ups between characters on TV sitcoms it had never seen, including "The Big Bang Theory," "Desperate Housewives" and "The Office."