One of the biggest misconceptions about Google's self-driving car right now is that you can't pull up Google Maps, pick a destination and tell the car to go there. That's because to learn new routes, the car has to be "trained" by a human driver at least once or twice first. But now a number of organizations, including Ford Motor Company, Stanford University and an investment firm run by Yahoo co-founder Jerry Yang, have invested 6.6 million into a company that promises to leapfrog that navigation issue by creating cheap, detailed maps that driverless cars will be able to read on the fly. And these maps will be created by regular drivers such as yourself, according to Civil Maps, the company behind the idea. In that respect, the concept is a bit like another Google-owned product, Waze.

Video Friday is your weekly selection of awesome robotics videos, collected by your melodramatic Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. I'm not entirely sure why being "targeted" by a bird robot like this would appeal to kids, but here you go: Fun fact: this works with real seagulls too. "This is a demonstration of Engineered Art's telepresence app on its Robothespian Humanoid robot. Although recorded multiple times to capture different camera angles, what you are seeing is a real conversation with Robothespian. It is being controlled and voiced remotely over the internet. There is no trickery here. Although we scripted some of the conversation to ensure it was repeatable, none of the content was pre-programmed. It was all created in real time using a headset and the telepresence app. It is an excellent way to ensure genuine human interaction, without being tripped up with it's complexities."

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Machine learning becomes more and more popular, and there are now many demonstrations available over the internet which help to demonstrate some ideas about algorithms in a more vivid way. There are certain arguments against having courses overloaded with interactive things since those frequently prevent students from diving into mathematics beyond machine learning, but generally I consider demonstrations helpful. ROC curve is fairly simple subject, but having a demo is nice way to demonstrate some important limit cases. I have prepared simple html demo for this. There are also other interesting demonstrations like t-SNE and RNNs, you're welcome to check Andrej's github page.

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks such as visual object and speech recognition. The key factor complicating such tasks is the presence of numerous nuisance variables, for instance, the unknown object position, orientation, and scale in object recognition or the unknown voice pronunciation, pitch, and speed in speech recognition. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks; they are constructed from many layers of alternating linear and nonlinear processing units and are trained using large-scale algorithms and massive amounts of training data. The recent success of deep learning systems is impressive – they now routinely yield pattern recognition systems with nearor super-human capabilities – but a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive.

What does a Spanish silver dollar have to do with Deep Learning? It's a question of standards and required precision.The widely used Spanish coin was introduced at the end of the 16th century as Spain exploited the vast riches of New World silver. It was denominated as 8 Reales. Because of its standard characteristics it served as a global currency. The American colonies in the 18th century suffered from a shortage of British coinage and used the Spanish dollar widely; it entered circulation through trade with the West Indies.

The first 2 of them may appear similar at first glance, but actually they make totally different statements. In the first one (Data Science Venn Diagram 2.0) for example, mathematics and statistics are a real subset of Data Science, whereas in the second one Data Science is a real subset of Mathematics, which is a completely different statement. Another question from the first one is, whether Data Science really covers all of computer science, mathematics and statistics etc. This does not sound reasonable. The third diagram tries to explain what exactly is meant by mathematics and statistics.

Dr George Beaton is a partner in beaton and a senior fellow in Melbourne Law School, Australia. His published works include NewLaw New Rules – A Conversation About the Future of the Legal Services Industry (2013) and Remaking Law Firms: Why & How (2016). You have been a pioneer in research into NewLaw, what place does technology have in NewLaw? Is it central to its development? Just 18 months ago when I wrote Fresh thinking on the evolving BigLaw–NewLaw taxonomy little mention was made of the role of technology in NewLaw or BigLaw business model firms.

Technology allowing a pre-programmed robot to shoot to kill, or a tank to fire at a target with no human involvement, might only be years away. And a new report from the UN warns of the dangers if terrorists got their hands on these kind of'killer robots'. The report, which was a result of a week-long meeting on such weapons, held in Geneva earlier this year, said swarms of autonomous weapons would be capable of carrying out attacks. As artificial intelligence advances, the possibility that machines could independently select and fire on targets is fast approaching. Fully autonomous weapons, also known as'killer robots,' are quickly moving from the realm of science fiction (like the plot of Terminator, pictured) toward reality Experts from dozens of countries gathered in Geneva earlier this year to consider the implications of'Lethal Autonomous Weapons Systems' (LAWS).

The U.S. National Science Foundation will spend more than US 400 million over the next seven years to fund next-generation wireless research in an effort to bring super-fast mobile service to the country. U.S. officials hope the investments, announced Friday, will speed up the county's move to next-generation 5G mobile service, potentially offering speeds of 10Gbps, and allow for a rapid expansion of the internet of things. The next-generation mobile services will enable self-driving cars, an "always on" IoT, smart cities, new virtual reality offerings, and video to aid police, firefighters, and emergency medical responders, said John Holdren assistant to President Barack Obama for science and technology. "Time and again, history has shown us that when we make sustained federal investments in fundamental academic research and in public-private partnerships ... we as a nation reap the benefits," Holdren said at an NSF event in Washinton, D.C., Friday. The NSF funding, part of a new White House Advanced Wireless Research Initiative, includes 50 million as part of a partnership with more than 20 mobile companies and trade groups to roll out advanced wireless testing sites in four U.S. cities.