Nude and private photos of celebrities including Emma Watson and Amanda Seyfried are circulating online, according to reports, leading to fears of a second major hacking attack. As with 2014's famous iCloud attack, the photos appear to have been stolen from people's phones and then traded online. In both cases, the photos appear to be old – suggesting that, like 2014's attack, the pictures have been circulating among collectors for some time. It is likely that the photos were stolen using fairly simple cyber attacks, of the kind that could hit anyone. While the recent spate of cyber attacks have become famous because of who they affected and the nature of the photos, the same techniques could be used to steal the most personal information from anyone.

Robotics is said to be the next technological revolution. Many seem to agree that robots will have a tremendous impact over the following years, and some are heavily betting on it. Companies are investing billions buying other companies, and public authorities are discussing legal frameworks to enable a coherent growth of robotics. Understanding where the field of robotics is heading is more than mere guesswork. While much public concern focuses on the potential societal issues that will arise with the advent of robots, in this article, we present a review of some of the most relevant milestones that happened in robotics over the last decades.

Max Aguilera-Hellweg is both a doctor and a photographer and has brought his unique sensibility to explore the point at which robots become more like humans. His new book of portraits of humanoids and androids displays the range created, from the geminoids designed to look and act like humans, to a scary robot created to understand how they learn.

Expanding on their Jetson TX1 and TK1 products for embedded computing, NVIDIA announced last week their Jetson TX2 platform--a hardware and software platform the size of a credit card designed to deliver AI computing at the edge. NVIDIA touts Jetson TX2 as delivering "unprecedented deep learning capabilities," and based on the form factor, it may be right as it paves the way for a number of cutting-edge uses--from highly intelligent factory robots and commercial drones, to cameras with AI for smart cities. NVIDIA has been running on all cylinders lately with datacenter machine learning, and I think this release, if it performs as promised, will solidify their place at the top of the machine learning class in certain classes of devices. NVIDIA announced the TX2 at an event I attended last week in San Francisco with many tier 1 vendors and startups with some interesting use cases. Jetson, by design, isn't targeted at every embedded device, it's for those non-mobile devices who need strong deep neural network performance at a given power draw. The TX2 is a significant step up from its predecessor.

The University of Manchester Data Science Institute has announced a call for abstracts for their'Advances in Data Science' Conference to be held on the 15th – 16th May. The conference is a two-day meeting to present recent developments in data science, with a focus on advanced analytics (machine learning, Bayesian statistics, scalable algorithms), privacy, visualisation, software and diverse applications. Speakers include leading data scientists from industry and academia. To be considered for a short oral presentation or poster please submit an abstract through the meeting's submission page. Abstracts should be prepared on one side of a4 (pdf format) and should describe new or recently published data science research.

The premise behind Google's Cloud Machine Learning Engine and TensorFlow technologies is to democratize access to machine learning tools and technologies. Additionally, these products are able to be implemented without the help of a PhD-educated data scientist. At the 2017 Google Cloud Next conference in San Francisco, a breakout session explained how a host of companies in various industries are using machine learning tools. Here are seven companies that implemented Google's machine learning tools to solve problems in their business. AXA is an international insurance firm.

I'm a huge fan of the original "Alien" franchise, largely because of Sigourney Weaver's performance and the crucial part technology plays in its lore. Not only does this tech allow humans to traverse immense distances to reach alien-infested worlds, but it provides them with androids: perfect robotic assistants so advanced that it's extremely difficult to distinguish them from human crew. In fact, they're superior to their human companions in many aspects, from their superhuman strength to their refined motor skills. As a part of promoting the upcoming sci-fi horror film "Alien: Covenant," (in theaters May 19), 21st Century Fox unit FOX, 1.34% Twentieth Century Fox released its branded short film "Meet Walter," starring Michael Fassbender. It introduces Walter, the latest synthetic android, with intelligence powered by AMD's AMD, -0.85% Ryzen and Radeon processors and manufactured by the film's fictional corporation, Weyland-Yutani.

Artificial intelligence (AI) will soon be part of nearly every company's technology stack (proprietary or not) and ingrained in every industry. It is therefore becoming increasingly difficult (and senseless) to draw the line between AI and non-AI. But while we still can, it is interesting to reflect on the state of Artificial Intelligence in Europe. Of them over 525 companies are VC funded. In 2016, 330 AI related companies got funded, an increase of 25%. Total European funding in 2016 exceeded €1.4 billion, an increase of 26%.

Build the Greatest Whole Number: Place Value Game Each player makes a copy (or print off the one provided) of the place value grid and places it in front of her on the table.

In recent articles I have looked at some of the terminology being used to describe high-level Artificial Intelligence concepts – specifically machine learning and deep learning. In this piece, I want to look at two other concepts which are vital to understanding how machines are becoming increasingly smarter and able to perform tasks which previously could only be done by humans. Supervised and unsupervised learning describe two ways in which machines - algorithms - can be set loose on a data set and expected to learn something useful from it. Today, supervised machine learning is by far the more common across a wide range of industry use cases. The fundamental difference is that with supervised learning, the output of your algorithm is already known – just like when a student is learning from an instructor.