Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Need help preparing a romantic dinner for two? ARMAR will give you a hand.

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The hope that comes with this discipline is that including the experience into its tasks will eventually improve the learning. But this improvement needs to happen in such a way that the learning itself becomes automatic so that humans like ourselves don't need to interfere anymore is the ultimate goal. There are close ties between this discipline and Knowledge Discovery, Data Mining, Artificial Intelligence (AI) and Statistics. Typical applications can be classified into scientific knowledge discovery and more commercial ones, ranging from the "Robot Scientist" to anti-spam filtering and recommender systems. But above all, you will know this discipline because it's one of the topics that you need to master if you want to excel in data science. Today's scikit-learn tutorial will introduce you to the basics of Python machine learning: step-by-step, it will show you how to use Python and its libraries to explore your data with the help of matplotlib, work with the well-known algorithms KMeans and Support Vector Machines (SVM) to construct models, to fit the data to these models, to predict values and to validate the models that you have build. The first step to about anything in data science is loading in your data. This is also the starting point of this scikit-learn tutorial.

For most of its 60-year history, the capabilities of artificial intelligence (AI) has been limited to science fiction movies. But with funding increasing by a compound annual growth rate of more than 40 percent over the last five years, AI technology has begun moving out of fiction and into reality. Computers like IBM's Watson have become Jeopardy! The applications for AI are expanding rapidly and customer support is one field that will see significant change with the growth of AI. Artificial intelligence and customer support have already become partially acquainted.

As a coding instructor and curriculum designer I spend a lot of time thinking about where the tech industry is headed, to prepare my students for that new world. Here are several trends I think will dominate software development in 2017. Increase in client-server hybrid systems. In 2017 we will see more software systems that blend local and cloud computing in a variety of different proportions. In traditional web programming, a browser connects to a backend server, which in turn does all the actual processing.

Minje Kim, an assistant professor of intelligent systems engineering at the School of Informatics and Computing at IU Bloomington, has received a gift from Intel to pursue a method of lowering the power and computing cost of deep learning processes in artificial intelligence. Intel sought a portfolio of research projects focused on compelling new human-computer interaction advancements that have HCI on the precipice of a breakthrough. As smart devices have become more ubiquitous, advances in deep learning have allowed AI to reach a near-human level. Deep learning allows complicated intelligence jobs -- such as computer vision, near real-time language translation and music recognition to be performed quickly, but such computing comes at a cost. Because neural networks present each of the millions of parameters of a computation in up to 64-bit forms, the computations required are both sizeable and hungry for power.

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.