Kodak's "Shirley Card" was given to photo processors to judge coloring in photos (Photo: the Nick DeWolf Foundation / Susan Etlinger) Is artificial intelligence bound to its makers' prejudices? At TEDxBerlin, data analyst Susan Etlinger turns to the past to investigate the future of AI. In the 1950s, photography in the U.S. was dominated by the Kodak company, and its staff's opinions of what is normal, Etlinger says. The company sent photo processors color-correction cards based on a single model named Shirley -- a white woman -- and Shirley became the poster woman for "normal" coloring in photos. "If Shirley looked good, the prints looked good," Etlinger says, "…and this was terrible for photographs of people of color."

Facebook has hidden a special new year easter egg in its website. The site is letting people celebrate New Year's Eve by watching fairly dismal fireworks light up their news feed. On New Year's Eve and New Year's Day, any time anyone writes the words "Happy New Year", they'll turn blue. Whenever anyone clicks on those words, a little firework animation will play. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Today's tech buzzword, Artificial Intelligence (AI), can be a difficult concept for many people to wrap their heads around. AI technology, which arguably holds vast potential to revolutionize the way the world works, is nonetheless often used to paint an apocalyptic view of the future. We now live in a world where talking computers and self-driving cars are no longer a thing of the future – but are we also at risk of developing computers so smart that they can replace humans? Influential tech figures such as Elon Musk and Stephen Hawking have cautioned the industry against diving head-on into AI – because the technology's implementation could do more harm than good if we aren't careful. While there's no arguing that some of the cautionary tales around AI hold merit, our current reality is far from the robot-run society we've seen in the movies.

And thanks to the newly released "Rogue One: A Star Wars Story", public sentiment towards droids is off the charts. It's unlikely the CNET team will stumble across anything as endearing new Star Wars droid K-2SO as it scours the halls in Las Vegas. Robots have primarily been used as marketing gimmicks or demonstration props at previous shows. That, says IHS analyst Dinesh Kithany, is set to change. "What we will see is more from the application point of view," Kithany said.

The following is the text of a talk I gave in San Francisco on December 1st, 2016. The audience was readers of my newsletter, Exponential View. You can sign up here. This is a long (7,500 word) transcript of the talk. You can scan it to see the slides and accompanying exhibits if that is easier. Or even read it in more than one sitting…. Exponential View has a purpose. In between all the emojis and all the spelling mistakes, this is what it's about: This is me on my first day at school back when I was in Zambia in sub-Saharan Africa. On the right is my friend Rehan, who I reconnected recently through Facebook. He is now known as Dr. Freeze and he does non-invasive body sculpting in Orange County. So I can get you a good rate. But I think it's important, this starting point is important. We often are inspired from where we come from and what the hell was I doing in Zambia? My dad was trained as economist and accountant, well he is retired now, but then he was an economist and was down in Zambia building the kind of institutions that we take for granted in countries like the U.S. and the U.K. to make the country function. Zambia had just got independence from the U.K. It needed a deeper civil service, it was having to build its legal system, create its system of distribution and so on. So I got an early exposure to the importance of economic institutions for making societies wealthier and making them work. While I was down in Zambia, which is a land-locked country and doesn't have great access to the sea and this is the 1970s, so we didn't have a vast range of toys.

Life has been busy for the stars of "Sherlock" since the series premiered in 2010, with Steven Moffat and Mark Gatiss applying new London style and contemporary quirks to Arthur Conan Doyle's famous consulting detective. Its fourth season -- there have been breaks -- begins Sunday on PBS' "Masterpiece: Mystery!" Martin Freeman, the series' Dr. John Watson, has gone from a guy you might have seen on the British version of "The Office" or in "The Hitchhiker's Guide to the Galaxy" to playing Bilbo Baggins in three "Hobbit" movies and the hapless Lester Nygaard in the first season of FX's "Fargo," and hosting "Saturday Night Live." Benedict Cumberbatch, its Sherlock (also in the "Hobbit" movies, as the voice of Smaug) has, among other things, played Khan in "Star Trek Into Darkness," the title role in "Doctor Strange," codebreaker Alan Turing in "The Imitation Game" and Richard III in BBC's "The Hollow Crown" Shakespeare cycle; sung "Comfortably Numb" with Pink Floyd's David Gilmour at the Royal Albert Hall; and has become something of an international, official hot guy. Conan Doyle wrote 60 Holmes stories, but the world has deemed that insufficient, and many other hands have filled out the tale. Holmes is a useful mix of specific qualities and scant details -- an attitude, occupation and method as much as a full-fleshed, full-fledged character, and so familiar that even some characters not called Sherlock Holmes, like Hugh Laurie's Dr. House on "House," are recognizably him.

Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of this problem is that submodular optimization algorithms require the calculation of pairwise benefits for all items in the dataset. This is infeasible for large problems, so recent work proposed to only calculate nearest neighbor benefits. One limitation is that several strong assumptions were invoked to obtain provable approximation guarantees. In this paper we establish that these extra assumptions are not necessary--solving the sparsified problem will be almost optimal under the standard assumptions of the problem. We then analyze a different method of sparsification that is a better model for methods such as Locality Sensitive Hashing to accelerate the nearest neighbor computations and extend the use of the problem to a broader family of similarities. We validate our approach by demonstrating that it rapidly generates interpretable summaries.

Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filtering algorithm's predictions over time. We investigate whether it is possible to identify items affected by these feedback loops. We state sufficient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender system's influence on the entire user-item rating matrix. We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user's rated item was recommended or an intrinsic preference. Our results indicate that it is possible to recover the ratings matrix of intrinsic user preferences using a single snapshot of the ratings matrix without any temporal information.

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model’s posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.