Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959). Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

A recent Conversation piece pointed out that the British electricity mix in 2016 was the cleanest in 60 years, with record capacity from renewable energy, mainly from wind and solar power. But one problem with this great expansion in renewables is they are intermittent, meaning they depend on weather conditions such as the wind blowing or sun shining. Unlike conventional power, this means they can't necessarily meet surges in demand. National Grid, the UK grid operator, has several ways of ensuring supply can always meet demand. For shorter gaps in generation, it asks electricity suppliers to run their conventional power stations at below maximum potential output and ramp up as needed.

The superior performance of deep learning relative to other machine learning methodologies has been commented in several forums and magazines in recent times. I would like to post today on three reasons that, in my opinion, are the basis of this commented superiority. I am not the first to comment on this issue [1], and for sure I won't be the last. But I would like to extend the discussion by taking into account the practical reasons behind the success of deep learning. Hence if you are looking for its theoretical background you would do better to look for it in the deep learning literature, where the "Hamiltonian of the spin glass model [2]", the exploitation of compositional functions to cope with the curse of dimensionality [3], their capability to best represent the simplicity of physics-based functions [4], and the flattening of the data manifolds [5] have been proposed.

Doug Polk is a professional poker player who's won millions of dollars, mostly at heads-up Texas Hold Em No Limit. He was part of a team that recently beat an artificial intelligence bot programmed by MIT students. When they're not at the tables collecting cash, Polk and fellow poker pro Ryan Fee run Upswing Poker, a site that offers training to everyday players who want to improve. Since so much Wall Street trading is conducted by computer programs these days, I figured he might have some wisdom about going up against AI for the few remaining humans who discretionary trade. John Navin: Like many human, discretionary traders on Wall Street, you've gone up against an artificial intelligence bot.

The role of Artificial Intelligence (AI) as a major catalyst in the healthcare revolution is unquestionable. We are today experiencing the Fourth Industrial Revolution, and the proliferation of technologies that are fusing the physical, digital and biological worlds and thereby impacting global economies and industries is unparalleled. While we are seeing the pervasive influence of technology in our lifestyle, we are challenged by the burden of chronic disease on our healthcare system. In the United States chronic disease accounts for $3 of every $4 spent on healthcare or $7,900 for every American with chronic disease. Chronic disease is both predictable and preventable, and AI can play a pivotal role in addressing solutions that can provide personalized medicine, and interventions.

Nintendo has unveiled the Switch, the company's first games console since the disappointing Wii U. As its name suggests, the Nintendo Switch prides itself on versatility, with the surprisingly small handheld/console hybrid capable of being hooked up to a TV at home or being used in multiple modes on the go. Here's everything you need to know about the Nintendo Switch, ahead of its release. 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. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo.

With the Super Bowl upon us once again, millions of people will gather with friends and family around big-screen TVs--and monumental levels of finger food--to watch the Atlanta Falcons take on the New England Patriots. More than ever, tech this year is changing the way we enjoy the big game. From virtual reality to artificial intelligence to 8K cameras and smartphone selfies, here's a guide to tech built just for the big event.

Artificial Intelligence (AI) – the ability of machines to think for themselves – has been a topic long covered by Hollywood and one that conjures up concerning thoughts mostly driven by the likes of Skynet from the Terminator series, VIKI in iRobot, Ava from Ex Machina or Channel 4's biggest drama in 20 years, Humans. Yet some of the largest tech companies we've come to know and (mostly) love are gearing up to push our lives in this very direction. The full UK launch of Amazon Echo ( Alexa software) and the launch announcement of Google Home ( Assistant) puts products right in the centre of the so-called smart home – the first steps towards AI becoming an integral part of our lives. So what will this mean for users and businesses? And how will it change their technology and marketing needs in the shorter term?

To get everyone familiar with Arghon's many amazing features, we have decided to highlight one feature of our artificial intelligence each month. The feature for this month is Arghon Personalization: our AI's unique ability to learn and change for each individual user. Our AI learns a user's needs and adapts accordingly, proof of its superior standing in the AI market today. Arghon's ability to personalize to each user is arguably one of its best features.The more the AI gets familiar with a user's preferences, the more uniquely tailored it becomes. Each Arghon is different from any other AI in existence.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, which is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem.