Russia is carrying out a sustained campaign of cyber attacks targeting democracy and critical infrastructure in the West, UK Defence Secretary Sir Michael Fallon has warned. Moscow was "weaponising misinformation" in a bid to expand its influence and destabilise Western governments and weaken Nato, he said. Vladimir Putin had chosen to become a "strategic competitor" of the West. Sir Michael said it was vital alliance members strengthened cyber defences. His speech, at the University of St Andrews, comes as Theresa May is to use an informal summit in Malta to press EU Nato members to boost defence spending.

With Sunday's big game around the corner, you may be wondering how to outsmart your co-workers to win the office pool. You could stick with the Las Vegas line, which has New England favored to win by three. You could go with the theory that says that Atlanta lost its chance when it opted for red uniforms, as teams wearing white, like New England on Sunday, have won 11 of the last 12 championships. Or you could bet on Swish Analytics' artificial intelligence predictions to give you an edge. If you hate Tom Brady, you'll like what it says for Sunday's game.

Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. You don't have to limit yourself to a binary classifier of course; CNNs can easily scale to thousands of different classes, as seen in the well-known ImageNet dataset of 1000 classes, used to benchmark computer vision algorithm performance. In the past couple of years, these cutting edge techniques have started to become available to the broader software development community. Industrial strength packages such as Tensorflow have given us the same building blocks that Google uses to write deep learning applications for embedded/mobile devices to scalable clusters in the cloud -- Without having to handcode the GPU matrix operations, partial derivative gradients, and stochastic optimizers that make efficient applications possible.

We've talked before about the rapid advances being made in the area of speech recognition. It's only a matter of time before companies like Doppler Labs augment our hearing such that we're able to experience real-time language translation. You'll soon be able to call him on that but only if you feel with 100% certainty that the translation is accurate. Why wouldn't language translation be accurate you ask? The simple answer here is one word.

Learning to learn by gradient descent by gradient descent, Andrychowicz et al., NIPS 2016 One of the things that strikes me when I read these NIPS papers is just how short some of them are – between the introduction and the evaluation sections you might find only one or two pages! A general form is to start out with a basic mathematical model of the problem domain, expressed in terms of functions. Selected functions are then learned, by reaching into the machine learning toolbox and combining existing building blocks in potentially novel ways. When looked at this way, we could really call machine learning'function learning'. Thinking in terms of functions like this is a bridge back to the familiar (for me at least).

Dear colleagues: Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks. As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI. The report also makes recommendations for specific further actions by Federal agencies and other actors. A companion document lays out a strategic plan for Federally-funded research and development in AI. Additionally, in the coming months, the Administration will release a follow-on report exploring in greater depth the effect of AI-driven automation on jobs and the economy.

Clarifai, a startup that creates visual recognition software, has hired four members of Twitter's machine learning team, Cortex, as well as an engineer formerly with Google Brain, the search giant's research group focused on artificial intelligence. Founded in 2013 by computer science PhD Matthew Zeiler after he did an internship with the Google research team, Clarifai licenses customizable software that can automatically organize and filter images. The startup's clients include BuzzFeed, travel site Trivago and consumer-packaged goods giant Unilever. The 40-employee startup (including new hires) has raised $41 million, including $30 million in a Series B round led by Menlo Ventures. The round included contributions from Union Square Ventures, Lux Capital and Qualcomm Ventures.

AI has been a hot topic for years. Artificial Intelligence simply means the theory and development of computer systems able to do work that usually requires human intelligence. Now Artifical Intelligence has achieved a milestone of defeating humans in poker for the first time. The AI namely Libratus which is developed by Carnegie Mellon University with $1.7 million worth of chips against the popular and professional poker players in the world. AI managed to beat them in a 20-day marathon poker tournament that was held in Philadelphia on Tuesday.

Marking a major step forward for artificial intelligence (AI), Libratus, an AI developed by Carnegie Mellon University (CMU), has resoundingly beaten four of the best heads-up no-limit Texas hold'em poker players in the world in a marathon, 20-day competition. After 20 days and a collective 120,000 hands played, Libratus closed out the competition Monday leading the pros by a collective $1,766,250 in chips. "I'm just impressed with the quality of poker Libratus plays," pro player Jason Les, a specialist in heads-up no-limit Texas hold'em like the other three players, said at a press conference yesterday morning. "They made algorithms that play this game better than us. We make a living trying to find vulnerabilities in strategies. That's what we do every day when we play heads-up no-limit. We tried everything we could and it was just too strong."

After reading this article does this affect the debate regarding praxeology and econometrics? RW response: Praxeology, as Murray Rothbard put it, "rests on the fundamental axiom that individual human beings act, that is, on the primordial fact that individuals engage in conscious actions toward chosen goals." Artificial intelligence did nothing to stop the poker players referenced from acting, that is, choosing goals. All AI did in the referenced example is make quicker and better calculations than the poker players, in a given fixed environment. This is an example for all practical purposes of a jacked-up hand calculator.