Feature selection is one of the core topics in machine learning. In statistical science, it is called variable reduction or selection. Our scientist published a methodology to automate this process and efficiently handle la large number of features (called variables by statisticians). Here, we mention an article published by Isabelle Guyon and Andre Elisseeff in Journal of Machine Learning Research. While published in 2003, it is still one of the best ML papers on feature selection.

The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact with our environments. AI-driven smart services will sense one's surroundings, know one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel seamless. This quest to deliver AI driven smart services across all industries and business processes will usher the most significant shift in computing and business this decade and beyond. Organizations can expect AI driven smart services to impact future of work flows, IOT services, customer experience journeys, and block chain distributed ledgers. Success requires the establishment of AI outcomes (see Figure 1).

It seems like technical skills are all anyone cares about in the neverending war for talent. But with machines eliminating more and more technical tasks, soft skills will become the big hiring differentiator. Here are the skills yet to be claimed by our technological overlords that you should develop further. Machines may have found a formula for writing novels and songs in Orwell's "1984", but so far creativity is something that they are otherwise struggling with. In The Second Machine Age, Erik Brynjolfsson predicts that technology will amplify human creativity. Maybe the big opportunity for humans is to pair up with machines to take creative ideas and turn them into groundbreaking product execution.

And on Wednesday, scientists reported that the Smart Tissue Autonomous Robot could stitch together separate pieces of the bowel in pigs, the first time a surgical robot has completed a portion of an operation in living soft tissue without human guidance. The new paper, published in the journal Science Translational Medicine, amounts to what's known as a proof of concept -- a demonstration that the new advance appears to be possible. Outside experts described it as a key achievement in efforts to move toward autonomous robotic surgery, but noted the technology is years away from being used in operating rooms, or even in a clinical trial. "It's one step forward," said Dr. Dragan Golijanin, director of the Minimally Invasive Urology Institute at the Miriam Hospital, an affiliate of Brown University, who was not involved in the research. Surgeons use robots in operating theaters around the country, but as it stands, they guide them like a puppeteer directs a marionette, conducting every move and response.

In this post, we'll use a supervised machine learning technique called logistic regression to predict delayed flights. But before we proceed, I like to give condolences to the family of the the victims of the Germanwings tragedy.

Founders, designers and executives bet on AI, chatbots and VR. Last year, I wrote about the top web design trends to watch in 2016. My predictions received a lot of attention and questions about what's next not only in the web design but UX design world. I reached out to 10 UX design experts and asked them to share their opinion on what's going to be the biggest UX trend in 2017. Read on to find out why founders, designers and executives bet on AI, chatbots and VR. Noam Alloush is the founder of SITE123, a free website builder empowering people to make a website using ready-made styles and layouts.

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. You learned about backpropagation (and because of that, this course contains basically NO MATH), but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

Machine learning is increasingly becoming mainstream. It promises higher accuracy and better ROI, and has started to emerge as one of the more reliable analytical practices in recent times. It provides organizations with an edge over their competition. That said, building the right team is a tricky challenge. The traditional approach of building an in-house team for same is not only cumbersome, but also takes lot of time to scale.

Specifically designed in the context of big data in our research lab, the new and simple strong correlation synthetic metric proposed in this article should be used, whenever you want to check if there is a real association between two variables, especially in large-scale automated data science or machine learning projects. Use this new metric now, to avoid being accused of reckless data science and even being sued for wrongful analytic practice. In this paper, the traditional correlation is referred to as the weak correlation, as it captures only a small part of the association between two variables: weak correlation results in capturing spurious correlations and predictive modeling deficiencies, even with as few as 100 variables. In short, our strong correlation (with a value between 0 and 1) is high (say above 0.80) if not only the weak correlation is also high (in absolute value), but when the internal structures (auto-dependencies) of both variables X and Y that you want to compare, exhibit a similar pattern or correlogram. Yet this new metric is simple and involves just one parameter a (with a 0 corresponding to weak correlation, and a 1 being the recommended value for strong correlation).

TechCrunch Disrupt in London is on December 5-6. As well as speakers and panels, we'll be featuring some demos by some amazing tech companies. The first will be by Boston Dynamics. Yes, folks, delegates to Disrupt London will get to see one of those amazing BD robots up close and personal, almost literally in the flesh (if they had any flesh, that is). Marc Raibert, CTO and founder of Boston Dynamics will be demonstrating one of the amazing robots his team has created, but you'll have to come to find out which one… Raibert was a professor of electrical engineering and computer science at MIT and a member of the Artificial Intelligence Laboratory from 1986 through 1995.