Intel has announced the intent to acquire Movidius– a startup that designs mobile based vision processor chips and software and development tools--for an undisclosed amount. In a bet to bolster its hold in computer vision and machine learning, Intel is increasingly determined to invest in Artificial Intelligence (AI) business and move beyond the PC-strategy. Combined with Intel's existing assets, Movidius could potentially aid Intel with low-power, high-performance System on a Chip (SoC) platforms for accelerating computer vision applications. The deal pans out well in line with Intel's RealSense vision and strategy and will possibly see growth after the integration of VPU (Vision Processing Unit) platform for on-device vision processing by Movidius. Josh Walden, Senior Vice President and General Manager, Technology Group, Intel believes, that RealSense depth-sensing cameras allowed devices to "see" the world in three dimensions.

Intel just announced a pending acquisition of Movidius, a chip manufacturer focusing on integrated system-on-chip solutions for machine learning and computer vision. It could be a key part towards building a standalone VR headset, and more. Cylance is an innovator, and they realise one thing most cybersecurity providers don't - most new malware is just a variant of everything currently on the scene. With that in mind, and with so many different combinations of the same thing, the question becomes: how do you manage this massive amount of data?

How much of our lives will be changed by artificial intelligence? According to a new report, by 2030, large urban cities will be transformed as self-driving cars, package-delivering robots and surveillance drones become a common phenomenon of the streets. Produced by an international panel of artificial intelligence experts, convened by Stanford University, the report takes an in-depth look at how artificial intelligence is already impacting society, and how it will continuously evolve over the next 14 years. "Until now, most of what is known about AI comes from science fiction books and movies," said Peter Stone, a computer science professor at the University of Texas who chaired the panel that produced the report. "This study provides a realistic foundation to discuss how AI technologies are likely to affect society."

William Santana Li imagines a future where robots will keep Americans safe. Communities, he dreams, will take security into their own hands by investing in wheeled machines that patrol streets, sidewalks and schools -- instantly alerting residents via a mobile app of intruders or criminal behavior. "What if we could crowd-source security?" said Li, co-founder and chief executive of a robotics company, Knightscope, that hopes to eventually do just that. His question is like many posed by Silicon Valley entrepreneurs seeking to modernize, privatize and monetize services once entrusted to the government -- and it's one that has intrigued venture capitalists who have pumped 14 million into his start-up. Already, Knightscope robots are edging into the private security industry, patrolling parking lots, a shopping center and corporate campuses in California.

It's partnering with safety supplier Autoliv on a joint venture that will create autonomous driving software (including driver assistance) not just for Volvo, but for any company looking to add hands-free features to their lineups. The two firms hope to start selling driver assistance tech by 2019, and full-fledged autonomy by 2021. The alliance will help Volvo, of course, since it'll both increase the amount of work on its own self-driving vehicles and give it a way to profit from competitors. However, it should also tackle one of the greatest challenges in the industry: making driverless technology accessible. Car makers that can't afford to design their own systems could soon buy it outright and focus their attention on building the cars themselves.

If you're fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes werebalanced, meaning there were approximately the same number of examples of each class. Instructors usually employ cleaned up datasets so as to concentrate on teaching specific algorithms or techniques without getting distracted by other issues. Usually you're shown examples like the figure below in two dimensions, with points representing examples and different colors (or shapes) of the points representing the class: The goal of a classification algorithm is to attempt to learn a separator (classifier) that can distinguish the two. But when you start looking at real, uncleaned data one of the first things you notice is that it's a lot noisier and imbalanced.

David Crook is a Microsoft Developer Evangelist formerly of Microsoft Consulting Services. He is focused on Data Science, Machine Learning and High Performance Computing. He is also known as the Mad Scientist on his team.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.

Sunil has created this guide to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world. Through this guide, he will enable you to work on machine learning problems and gain from experience. He is providing a high level understanding about various machine learning algorithms along with R & Python codes to run them.

There has even been controversy that some of the descriptions within these are too broad, with Gil Press asking of the inclusion of machine learning, 'Is [it] an "emerging technology" and is there a better term to describe what most of the hype is about nowadays in tech circles?' Instead, he argues, there should be'deep learning' or'artificial neural networks' used in its stead, given that machine learning is already a well established technology. Gil is certainly correct, with at least a relatively basic form of machine learning appearing in things like suggestion engines and programmatic advertising to some extent.