Please invite your friends and colleagues to join the conversation and let me know what you think, and what we're missing. Just reply to this email, or email steve@axios.com. Let's dive right in with a question: 1 big idea: Could robots make us even more polarized? Sam Jayne / Axios Over the last decade or so, we've seen ordinarily apolitical topics polarize us into angry opposing mobs, among them vaccines, atmospheric gases and Russia. When there has been a super-strong view one way or another, it's been sucked into the hothouse and associated with an ideology.

The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). Every week, euRobotics will publish a video interview with a project, so that you can find out more about their activities. The goal of CYBERLEGs is to validate the technical and economic viability of the powered robotic ortho-prosthesis developed within the FP7-ICT-CYBERLEGs project. The aim is to enhance/restore the mobility of transfemoral amputees and to enable them to perform locomotion tasks such as ground-level walking, walking up and down slopes, climbing/descending stairs, standing up, sitting down and turning in scenarios of real life. Restored mobility will allow amputees to perform physical activity thus counteracting physical decline and improving the overall health status and quality of life.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen.

What do you do after garnering tens of millions of downloads and scores of clones of your AI-powered style transfer app? Why, keep innovating of course. Meet Sticky, the next app from the startup behind Prisma, which turns selfies into stylized and/or animated stickers for sharing to your social feeds. Sticky is launching today on iOS, with an Android version due in a week or two. While Prisma gained viral popularity last year, netting its Moscow-based makers around 70 million downloads in a matter of months, its core feature has been rapidly and widely copied -- including by social goliaths like Facebook.Read

As the demand for advanced security solutions grows, organizations are gradually realizing the need to go beyond perimeter based defences. They need to detect unknown threats, despite weak signals, in real-time. Only solution to this problem is to analyse user traffic, identify relevant patterns, and gain a clear insight on potential threats. Spotting this as an opportunity--as well as a challenge--Reveelium, the American subsidiary of a European-based cybersecurity provider, decided to develop a solution with advanced threat detection techniques. Rigorous R&D and collaboration with specialized labs allowed professionals at the company to launch in 2012 a behavioral analysis platform-- Reveelium AI.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

The frustration of trying to find a place to park could soon be over, thanks to a new app that promises to predict where spaces are available. Find and Pay uses big data, predictive analytics and machine learning to reduce the time it takes to find parking in congested urban areas. Easy Park, the Stockholm-based firm behind its creation, claims it could slash your search time for a spare spot in half. The smart software could be coming to a city near you in the near future. The frustration of trying to find a place to park could soon be over, thanks to a new app that promises to predict where spaces are available.

If you didn't think game day could get any better, get ready for robots. Two tech giants in the field of cognitive technology have recently announced plans to bring artificial intelligence to major international sports events. International Business Machines Corp. will partner with the Wimbledon tennis tournament, which starts July 3, while Intel Corp. will introduce a host of new technologies at the 2018 Olympic Games in South Korea. IBM will exercise a range of its AI abilities throughout the London-based tennis tournament. The company's signature AI platform, Watson, will help visitors track the most nail-biting matches and automatically tape video highlights of the event, IBM said in a statement.

One thing that makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network--activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems. Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job.

Machine learning- to put it mildly- is an incredibly broad and varied field, with multitudes of applications. Thus, writing a list entitled "10 Machine Learning Experts You Need to Know" proves challenging for a number of reasons. Firstly, I've restricted my ten picks to those currently working in the field- if I extended it to those living and passed, I never would have been able to identify only ten worthy of mention. Secondly, this list is in no way ranked- how would I decide which is more remarkable? Third, this is by no means an exhaustive list of people currently making significant contributions to the field of machine learning, or the wider world.