Robots may take four million British jobs in the private sector within the next decade, some business leaders believe. Those surveyed for by YouGov for the Royal Society of Arts said 15 per cent of all jobs were under threat. The most vulnerable fields are finance and accounting, transportation and distribution, manufacturing and marketing and public relations, the survey found. But the research was not all doom and gloom, noting that technological advance creates new jobs, partly because increased productivity reduces prices freeing up consumers to spend money elsewhere in the economy. The RSA added that AI and robotics will mostly automate individual tasks rather than replace whole jobs.

Amazon is planning to release a pair of Alexa-enabled smartglasses as the latest addition to its range of voice-controlled devices, according to reports. Unlike most previous smartglasses, such as the ill-fated Google Glass experiment and Snapchat's Spectacles, the Amazon glasses won't feature a camera in any form, bypassing the privacy concerns that have plagued the form-factor in the past. Instead, they will focus on providing a link to Alexa, Amazon's voice-controlled personal assistant, through a bone-conduction audio system, which transmits sounds into the wearer's head by vibrating their skull, rather than through headphones inserted in their ear. According to a report by the Financial Times, the glasses could be revealed at a product launch event expected to be held soon alongside a home security camera, designed to tie in with its Echo Show video screen. Other reports have suggested the company will shortly release a new version of the Fire TV, its streaming media set-top box, with an Echo-style speaker system built-in.

Keeping up with technology trends can be exhausting and confusing. Many times, the terms used to describe technology investing opportunities aren't always defined and can leave investors with more questions than answers. So let's take a quick look at how NVIDIA Corporation (NASDAQ:NVDA), a graphics process maker with a leadership position in these spaces, defines each of them -- and what the company's potential is in these businesses. Artificial intelligence (AI) is sometimes thought of as the intelligence we see from robots in movies or television shows. That level of AI isn't possible yet, and instead, tech companies that are working on artificial intelligence right now are usually doing what's called "narrow AI."

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Online learning portal Udacity launched its first 36-week "nanodegree" course for self-driving car engineering last year. There's a new, introductory course available now as well, focused on bringing students with minimal programming into the larger program. Even better, Udacity has partnered with Lyft (which has self-driving plans of its own) to provide scholarships to the intro course in order to increase diversity to the program. Lyft says that people "from all backgrounds and perspectives" should have the opportunity to contribute to the future of transportation in the form of self-driving cars. "Diversity is crucial for creating solutions that serve everyone, and ridesharing is for everyone," the company writes on its website.

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks. Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases.

Description: Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples!

In the past, the terms business analyst and data scientist have sometimes been used interchangeably, and indeed, in a small company, the lines between the two sorts of jobs may blur. But as more and more companies look to big data for business insights, they are shifting from relying on business analysts to predict what the future of a business might look like, and moving towards using data scientists and machine learning to interpret data and predict trends. What's the difference, you might ask? While the end result of these two jobs is often similar, a business analyst and a data scientist use different tools to get there. In general, data scientists have much greater technical expertise, especially in computer programming, systems engineering, and statistics.

This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opinion dynamics model based on the DeGroot model, with a general function to describe the functional dependence of each individual's susceptibility on her own opinion, and show that this general model is analogous to the Friedkin-Johnsen model, which assumes a constant susceptibility for each individual. We then consider two specific functions in which the individual's susceptibility depends on the \emph{polarity} of her opinion, and provide motivating social examples. First, we consider stubborn positives, who have reduced susceptibility if their opinions are at one end of the interval and increased susceptibility if their opinions are at the opposite end. A court jury is used as a motivating example. Second, we consider stubborn neutrals, who have reduced susceptibility when their opinions are in the middle of the spectrum, and our motivating examples are social networks discussing established social norms or institutionalized behavior. For each specific susceptibility model, we establish the initial and graph topology conditions in which consensus is reached, and develop necessary and sufficient conditions on the initial conditions for the final consensus value to be at either extreme of the opinion interval. Simulations are provided to show the effects of the susceptibility function when compared to the DeGroot model.