This group is a huge step forward, breaking down barriers for AI teams to share best practices, research ways to maximize societal benefits, and tackle ethical concerns, and make it easier for those in other fields to engage with everyone's work," said Mustafa Suleyman, Co-Founder and Head of Applied AI at DeepMind and Greg Corrado, Senior Research Scientist at Google in a statement.

Hackers demonstrated they can take over a Tesla from miles away if it connects to a malicious Wi-Fi hotspot. Dmitri Dolgov, a longtime veteran of Google's seven-year self-driving car effort, recently took over as technical lead, replacing Chris Urmson. SAN FRANCISCO -- Google's self-driving cars have hit another milestone on the road to the automotive future, notching two million miles on the autonomous-testing odometer. That mark, which the Alphabet-owned company announced Wednesday, was hit as other companies spent the summer dominating the self-driving headlines. Uber recently began picking up Pittsburgh passengers in its small fleet of driverless (though driver-monitored) vehicles, while Ford announced plans to sell transportation that lacked a steering wheel and pedals by 2021.

JuliaML (Machine Learning in Julia) is a community organization that was formed to brainstorm and design cohesive alternatives for data science. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages). Many of us have independently developed tools for machine learning before contributing. Some of my contributions to the current codebase in JuliaML are copied-from or inspired-by my work in OnlineAI. The recent initiatives in the Learn ecosystem (LearnBase, Losses, Transformations, Penalties, ObjectiveFunctions, and StochasticOptimization) were spawned during the 2016 JuliaCon hackathon at MIT. Many of us, including Josh Day, Alex Williams, and Christof Stocker (by Skype), stood in front of a giant blackboard and hashed out the general design. Our goal was to provide fast, reliable building blocks for machine learning researchers, and to unify the existing fragmented development efforts. Time to code! I'll walk you through some code to build, learn, and visualize a fully connected neural network for the MNIST dataset. The steps I'll cover are: Get the software (use Pkg.checkout on a package for the latest features):

Researchers at Cornell University have created a machine-learning model for generating an appropriate robot response based on evaluating human activities. For these researchers, the secret to creating a technological "glass ball" for robots to anticipate our actions is a conditional random field (CRF) model and Kinect real-time technology typically used in video games to trace motions. Capabilities such as these will open this technology up to a range of applications spanning from the restaurant industry to manufacturing lines. The idea of creating a model that allows robots to consistently and successfully respond to our actions stemmed from the notion that robots unable to anticipate and react to humans could be viewed as impractical in human-robot interactions. As machine hardware and software continues to advance, this model could be the next critical step for preparing robots to better integrate with natural human behavior. Previous research has been successful in enabling a robot to see human activities and label them, but have not found success in using that labeling system to anticipate the future.

Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive. This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

This article is by Featured Blogger Vivek Wadhwa from his LinkedIn page. We have been hearing predictions for decades of a takeover of the world by artificial intelligence. In 1957, Herbert A. Simon predicted that within 10 years a digital computer would be the world's chess champion. That didn't happen until 1996. And despite Marvin Minsky's 1970 prediction that "in from three to eight years we will have a machine with the general intelligence of an average human being," we still consider that a feat of science fiction.

If there were any doubts that IBM would put its cognitive computer Watson to work on the Internet of Things, it would be tough to argue now. IBM announced October 3 that it would not only put Watson to work on IoT, but would also ante up 200 million of a 3 billion total investment – the most IBM has ever spent in Europe – to open a new global headquarters in Munich for Watson's IoT business. The goal is for Watson to develop new IoT capabilities around Blockchain and security. Then there are eager IBM clients who are already driving outcomes by using Watson IoT technologies to draw insights from billions of sensors embedded in machines, cars, drones, ball bearings, pieces of equipment and even hospitals. IBM executives say there is escalating demand from customers who are looking to transform their operations using a combination of IoT and artificial intelligence technologies.

The only other active moving part of the robot is the body itself. The spherical induction motor (SIM) invented by Hollis, a research professor in Carnegie Mellon University's Robotics Institute, and Masaaki Kumagai, a professor of engineering at Tohoku Gakuin University in Tagajo, Japan, eliminates the mechanical drive systems that each used on previous ballbots. Because of this extreme mechanical simplicity, SIMbot requires less routine maintenance and is less likely to suffer mechanical failures. The new motor can move the ball in any direction using only electronic controls. These movements keep SIMbot's body balanced atop the ball.

Should we be worried about the future of artificial intelligence? Some of the biggest names in science and technology have recently made headlines by suggesting that yes, we should. Microsoft founder Bill Gates has joined this school of thought, saying he is "in the camp that is concerned about super intelligence," and predicting that in mere decades, AI could become "strong enough to be a concern." Gates' comments echo what several other forward thinkers have said about this topic. Elon Musk, founder of SpaceX, Telsa Motors, SolarCity, and PayPal has called AI our "biggest existential threat."

When I attend customer engagement and industry events, I inevitably field lots of questions that are close to the heart of a data scientist. Many executives are confused by the concepts of machine learning, deep learning, memory-based learning, and artificial intelligence. They wonder about the differences in these technologies, how everything fits together, and what they need to pay attention to. They wonder whether they need all of it or just some of it, and what they need to do to get started. And, yes, I hear people ask whether the ultimate goal is to replace humans with computers.