There is a surge of interest and research into Artificial Intelligence (AI). AI is seen as the new technological revolution in the work place with machines tipped to replace most human jobs. There is significant improvement in the field of AI currently, than in the history of mankind, AI has been branded a failure or a hype in the past, but that notion does not seem to be the case anymore with the emergence of more sophisticated computer algorithms that have helped machines to pass the Turing Test – A test which determines whether or not a machine computer is capable of thinking like a human. Critics have branded the Turing test an emotionally unsatisfying test for intelligence but agreed that a machine passing the Turin test is an important milestone for AI. It is important to highlight that to get more conclusive results from tests of AI, the Turin test has had twists and variations from the original test by Alan Turing.

There is a massive amount of hype that surrounds AI. How much is fact, how much is fiction? This event is to decipher the status of AI technology currently by providing a group of AI experts to present and chat on the subject. The event will begin with two introduction presentations, one by Azeem Azhar on "Why the Boom Now?" and the second by Christoph Auer-Welsbach on IBM Watson and City.ai. Following, a panel discussion moderated by Tak Lo will ensue on "The Status of AI in Asia Today" with Sinuhe Arroyo, Jason Chiu, and Jeffrey Broer.

This is the conference, and here's my talk (will do Google hangout, just as with my recent talks in Bern, Strasbourg, etc): Through a series of examples, we consider problems with classical hypothesis testing, whether performed using classical p-values or confidence intervals, Bayes factors, or Bayesian inference using noninformative priors. We locate the problem not in the use of any particular statistical method but rather with larger problems of deterministic thinking and a misguided version of Popperianism in which the rejection of a straw-man null hypothesis is taken as confirmation of a preferred alternative. We suggest solutions involving multilevel modeling and informative Bayesian inference. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on Statistical Modeling, Causal Inference, and Social Science. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on All About Statistics.

Dubai: Dubai Municipality has launched robots to help save lives on public beaches in Dubai, a first such initiative in the Middle East. The robot, which can reach a speed of 35kmph or approximately 12 times the speed of a human lifeguard, works by using remote control technology, said Dubai Municipality. The robot is about 125cm tall and can cover a distance of more than 130km. The robots are designed to withstand the worst climatic conditions. They can be used in the event of high waves or heavy ocean currents that are difficult for the human lifeguard to conduct rescue operations.

Laughter is weird - and we do it a lot. One study found that people laugh seven times for every 10 minutes of conversation. We don't do it when we think we do. It's been found that if you ask people what makes them laugh they'll talk about jokes and humour, but we laugh most frequently when we are with other people - and hardly ever at jokes. It's a social emotion and we use it to make and maintain social bonds.

ABSTRACT We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences. Index Terms-- online learning, regression, Gaussian processes, kernel least-mean squares 1. INTRODUCTION Gaussian Process (GP) regression is a state-of-the-art Bayesian technique for nonlinear regression [1].

We propose a novel accelerated exact k-means algorithm, which performs better than the current state-of-the-art low-dimensional algorithm in 18 of 22 experiments, running up to 3 times faster. We also propose a general improvement of existing state-of-the-art accelerated exact k-means algorithms through better estimates of the distance bounds used to reduce the number of distance calculations, and get a speedup in 36 of 44 experiments, up to 1.8 times faster. We have conducted experiments with our own implementations of existing methods to ensure homogeneous evaluation of performance, and we show that our implementations perform as well or better than existing available implementations. Finally, we propose simplified variants of standard approaches and show that they are faster than their fully-fledged counterparts in 59 of 62 experiments.

US lawmakers mull long and short-term CRs, and'minibus' appropriations packages. Out of the shadows, the SCO now needs to justify its long-term existence to a new president. Defense dollars are the big issue as the'Big Four' lawmakers met to negotiate the 2017 defense poli… In a recent visit, Lockheed Martin's proposed move of the F-16 production line to India was a subjec… Directed energy may be ready in the future, but Kendall is tempering excitement. In a recent visit, Lockheed Martin's proposed move of the F-16 production line to India was a subjec… The next administration will grapple with the F-35's move to full-rate production and the first set… Is MEADS Back in Running for Poland's Missile Defense Competition? The United States is set to approve the sale of Mk-48 heavyweight torpedoes for Taiwan.

Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes.

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data--in this case, the insurance company's customer records--and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.