"Banks, financial services, insurance companies as well as global enterprises in other data-intensive industries are using cognitive automation to solve operational problems," said Alex Lyashok, COO of WorkFusion. "Gartner's report highlights the opportunity to use smart machines to transform existing business processes. Our product helps operations teams apply smart automation in an easy, non-disruptive way. Not only does WorkFusion help deliver as much as a 60% reduction in common back-office processes, but customers are also able to improve customer experience by reducing cycle times and improving the quality of customer interactions." WorkFusion is the leading smart process automation solution for enterprise operations.

I just started on a project that is doing deep conv nets over a fairly large data set (73x144x1 images for now, once I have the basic pipeline setup up I want to train it on 146x288x10 "images", and I have a couple hundred thousand of them). I'm currently using TensorFlow on my 2011 MacBook Air to build out and test some of my models. This is cumbersome for a number of reasons: it hogs my laptop, its pretty slow, doesn't use the GPU, don't have enough disk space etc. My question for the community: What sort of hardware setup are you using to do your training?

I have two offers- MSc AI at University of Edinburgh and MSc Machine Learning UCL. I am an international candidate(non-EU) and while my leaning is more towards Edinburgh, I can't find any international candidate(non-EU) who finished the course and managed to land a good job in the field in UK. However, I saw many such cases from UCL, which makes me believe that UCL is safe choice. I am interested in application of machine learning to AI, especially computer vision and deep learning. Edit: I will be taking out a loan to support myself, and hence the interest in safe choice.

There are many posts on KDnuggets covering the explanation of key terms and concepts in the areas of Data Science, Machine Learning, Deep Learning, Big Data, etc. (see here, here, and here). In fact, it's one of the tasks that KDnuggets takes quite seriously: introducing and clarifying concepts in the minds of new and seasoned practitioners alike. In many of these posts, concepts and terminology are often expounded upon and fit into The Big Picture, sometimes miring down the key concept in exchange for defining some greater notion. This is the first in a series of such posts on KDnuggets which will offer concise explanations of a related set of terms (machine learning, in this case), specifically taking a no-frills approach for those looking to isolate and define. Not enough information provided in these definitions for you?

Golan Levin, an associate professor of art at Carnegie Mellon University, suggested. I was looking at a satellite image of the school's campus in Pittsburgh, embedded in the home page of Levin's latest online project, Terrapattern. "What you should immediately see are all the most tennis-court-ish patches of Allegheny County," he said. With gratifying speed, the right-hand side of my screen filled with dozens and dozens of tennis courts--solo or in pairs or in clusters of six, white on green, purple on green, green on red. A confusingly painted parking lot ended up in the mix, too.

The way you drive is surprisingly unique. And in an era when automobiles have become data-harvesting, multi-ton mobile computers, the data collected by your car--or one you rent or borrow--can probably identify you based on that driving style after as little as a few minutes behind the wheel. In a study they plan to present at the Privacy Enhancing Technology Symposium in Germany this July, a group of researchers from the University of Washington and the University of California at San Diego found that they could "fingerprint" drivers based only on data they collected from internal computer network of the vehicle their test subjects were driving, what's known as a car's CAN bus. In fact, they found that the data collected from a car's brake pedal alone could let them correctly distinguish the correct driver out of 15 individuals about nine times out of ten, after just 15 minutes of driving. With 90 minutes driving data or monitoring more car components, they could pick out the correct driver fully 100 percent of the time. "With very limited amounts of driving data we can enable very powerful and accurate inferences about the driver's identity," says Miro Enev, a former University of Washington researcher who worked on the study before taking a job as a machine-learning engineer at Belkin.

The best programmers turn complex code into intuitive tools that anyone can use. And those tools are easier than ever to master, requiring little more than a swipe or a tap. Interacting with code is so instinctive that even cats know how to do it. Now the challenge is figuring out how to make creating code as easy as using it. Osmo does that by turning abstract "building blocks" of computer programs into actual, real-world building blocks.

Humanity's got itself a problem. As Homo sapiens balloons as a species--to perhaps nearly 10 billion by 2050--the planet stubbornly stays the same size, meaning the same amount of land must support way, way more people. Add the volatility of global warming and consequent water shortages, and the human race is going to have some serious trouble feeding itself. Perhaps it's serendipitous, then, that the machines have finally arrived. Truly smart, truly impressive robots and machine learning algorithms that may help usher in a new Green Revolution to keep humans fed on an increasingly mercurial planet.

"Given an example of presence and absence of a specific concept, computers would use storage to memorize examples exactly as they appear from a shallow learning concept – classic memorization," Dreyer said. Humans adjust neural weighting to solve for current and future examples, so we don't have to see a future example to know what something is. If you show a computer a picture of a bunch of oranges and then show it an apple, the computer wouldn't know what the apple was because it hadn't seen it before. Humans exploit deep learning by classifying new objects, recognizing faces, and recognizing language, Dreyer said. We see examples every day of facial recognition technology (for example, used by Facebook and Twitter).

There is a difference between knowledge and understanding. Knowledge typically comes down to knowing facts while understanding is the application of knowledge to the mastery of systems. You can know a lot while understanding very little. Just as an example, IBM's Watson artificial intelligence system that defeated the TV Jeopardy champs a few years ago knew all there was to know about Jeopardy questions but didn't really understand anything. Ask Watson to apply to removing your appendix its knowledge of hundreds of medical questions and you'd be disappointed and probably dead.