Beauty contests are slightly computational to begin with. While the notion of beauty is of something ephemeral and unquantifiable, a beauty pageant asks that we categorize and rank it: determining rules that let us objectively measure an idea which must be, at its root, mysterious and subjective. No surprise, then, that here in 2016 we have just witnessed the first beauty contest judged by AI, as a jury of decidedly non-human bots picked out what they considered to be the best-looking people from a dataset of 6,000 entries. "New tools like machine learning let us analyze images in a way that was never available to us before," Anastasia Georgievskaya, co-founder and research scientist at Youth Laboratories, the company behind Beauty.AI, told Digital Trends. "Our goal was to investigate methods that would show new approaches to beauty evaluation."

Q-learning is a simple and powerful tool in solving dynamic problems where environments are unknown. It uses a balance of exploration and exploitation to find an optimal solution to the problem. In this paper, we propose using four basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent. Simulations show that the proposed affective agent requires lesser number of steps to find the optimal path. We found when affective agent finds the optimal path, the ratio between exploration to exploitation gradually decreases, indicating lower total step count in the long run

Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the \emph{first} time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is \emph{end-to-end} trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.

Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20% of hearing aid patients. This paper proposes a probabilistic modeling approach to the design of HA algorithms. The proposed method relies on a generative probabilistic model for the hearing loss problem and provides for automated inference of the corresponding (1) signal processing algorithm, (2) the fitting solution as well as a principled (3) performance evaluation metric. All three tasks are realized as message passing algorithms in a factor graph representation of the generative model, which in principle allows for fast implementation on hearing aid or mobile device hardware. The methods are theoretically worked out and simulated with a custom-built factor graph toolbox for a specific hearing loss model.

Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case basis; examples include robust linear regression, robust mixture models, and bursty topic models. In this paper we develop a general approach to robust Bayesian modeling. We show how to turn an existing Bayesian model into a robust model, and then develop a generic strategy for computing with it. We use our method to study robust variants of several models, including linear regression, Poisson regression, logistic regression, and probabilistic topic models. We discuss the connections between our methods and existing approaches, especially empirical Bayes and James-Stein estimation.

John McCarthy, an American computer scientist pioneer and inventor, was known as the father of Artificial Intelligence (AI) after playing a seminal role in defining the field devoted to the development of intelligent machines. The cognitive scientist coined the term in his 1955 proposal for the 1956 Dartmouth Conference, the first artificial intelligence conference. The objective was to explore ways to make a machine that could reason like a human, was capable of abstract thought, problem-solving and self-improvement. He believed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." It would prove a challenge that eluded him and which still eludes computer designers today.

Robots may not yet have the authority to influence their human masters, according to new research. The study stationed a cyborg guard beside a table of food marked with a'reserved' sign in a student common room. Researchers from New York-based Cornell University used a mObi robot manufactured by Bossa Nova in a simple test. While the robot is not designed to look particularly menacing or authoritative, it has cameras that enable it to'see' what people around it are doing. The behaviour of hundreds of students was captured by a hidden GoPro action camera, reports New Scientist. The results showed that a disappointing seven per cent snaffled reserved food from the table, despite the robot guard's presence.

A 12-year-old who read The Lord Of The Rings aged five has become the youngest Cornell University freshman in the Ivy School's history. Jeremy Shuler was home-schooled by his parents - both aerospace engineers from Grand Prairie, Texas - and started reading books in English and Korean aged two. To help get him into Cornell, Jeremy's parents moved to Ithaca, where his father, Andy Shuler, took up a post at Lockheed Martin Upstate New York. A 12-year-old who started studying calculus aged 6 has become the youngest Cornell University freshman in the Ivy School's history With his bowl-cut hair, cherubic face and frequent happy laughter, Jeremy is clearly still a child despite his advanced intelligence. He swung in his chair while his parents, who he calls Mommy and Daddy, recounted his early years during an interview at the engineering school where his grandfather is a professor, his father got his doctorate and Jeremy is now an undergrad.

NVIDIA Corporation (NASDAQ:NVDA) was a PC graphics chip company who has matured into a specialized platform company who targets primarily four markets (Gaming, Professional Visualization, Data Center and Automotive), continuing to provide cutting edge visual computing solutions. The company consists of two reporting operating segments, GPU and Tegra Processor. While GPU remains the core strength of NVDA, the company's significant and consistent investment in research and development has been the key driver of sales that have grown from just over 4B in FY14 to an estimated 7B in FY17. NVDA remains the market leader in gaming with key growth contributions coming from professional visualization, deep learning and automotive. In gaming, only one third of the active GeForce users have upgraded to Maxwell with Pascal starting to ship in FQ2, meaning there is substantial scope for future uptake during an upgrade cycle.

There's an old adage that goes, necessity is the mother of invention. This is certainly true in health care where market forces are spurring new innovations in biopharma. Recently, Deloitte published an analysis of some of the boldest breakthroughs likely to impact health care across the spectrum. In the Deloitte Center for Health Solutions' Top 10 health care innovations: Achieving more for less report, we surveyed leaders across the health care system on the innovations they believe will be transformative over the next decade. We define innovation as those activities or technologies that break performance constraints to attain a desired outcome in a way that genuinely pushes the envelope of change.