This post was written by Ofir Press. Ofir is a graduate student at Tel-Aviv University's Deep Learning Lab. His main focus is on using deep learning for natural language processing. "Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

Smartphones are quickly evolving from pure information delivery tools into devices with advanced software capable of diagnosing and evaluating a number of health issues. Not only are they being used to expand the reach of mobile eye exams, but with the help of some new research and innovative artificial intelligence tools, they're stepping into the domain of behavioral health. Emotions analytics company Beyond Verbal has launched its Beyond mHealth Research platform in the hopes that it will eventually be able to identify physiological markers in the voice that point to different health-related issues. The project has been getting attention from many in the healthcare and tech communities, including Mark Zuckerberg. According to VentureBeat, the Facebook head has praised the platform for its interesting potential.

Robots are hailed for their intelligence and work efficiency, but excessive heating from prolonged hours of work often affects their performance. To address the heating problem faced by humanoid robots, Japanese researchers have devised an out-of-the-box solution. Using the analogy of sweating that happens in the human body as a result of continuous activity that cools the heated muscles, researchers at the University of Tokyo's JSK Lab presented a novel method at the IEEE/RSJ International Conference on Intelligent Robots and Systems held in South Korea. Their cooling solution addresses the heating problem of a musculoskeletal humanoid robot called Kengoro, which stands at 1.7 meters (5.6 feet) tall and weighs 56 kilograms (123.5 pounds). The Japanese researchers' cooling solution involves tinkering to make the robot "sweat" water straight out of its frame.

Earlier this week, the Obama administration discussed a new strategic plan aimed at fostering the development of AI-centered technologies in the United States. What's striking about it is, the Washington Post notes, although the United States was an early leader in deep-learning research (a subset of the overall branch of AI known as machine learning), China has effectively eclipsed it in terms of the number of papers published annually on the subject (Editor's note: the link could be paywalled; alternate source). From the report: The rate of increase is remarkably steep, reflecting how quickly China's research priorities have shifted. The quality of China's research is also striking. The chart narrows the research to include only those papers that were cited at least once by other researchers, an indication that the papers were influential in the field.

Everybody has had the experience of not recognising someone they know--changes in pose, illumination and expression all make the task tricky. So it's not surprising that computer vision systems have similar problems. Indeed, no computer vision system matches human performance despite years of work by computer scientists all over the world. That's not to say that face recognition systems are poor. The best systems can beat human performance in ideal conditions.

Humanity may still be years if not decades away from producing sentient artificial intelligence. But with the rise of machine-learning services in our smartphones and other devices, one type of narrow, specialized AI has become all the rage. And the research on this branch of AI is only accelerating. In fact, as more industries and policymakers awaken to the benefits of machine learning, two countries appear to be pulling away in the research race. The results will likely have significant implications for the future of AI.

Artificial intelligence, or "AI," is the current direction of technological innovation, but if the U.S. does not step up its game, another world power may lead the future. The number of journal articles mentioning "deep learning" or "deep neural networks" produced by China exceeds the number published about similar technological advances by the U.S. The same is true for cited publications, reveals a National Science and Technology Council (NSTC) report. The report's findings indicate that not only is China doing more AI research, but they are having a greater influence on this growing field. Chinese venture capitalists and tech giants like Baidu, a company commonly referred to as China's Google, is investing heavily in AI. For Baidu, major projects include driverless vehicles, search engine optimization, and improved speech recognition software.

As far back as 3,500 years ago ancient Egyptian doctors were performing invasive surgeries. Even though our tools and knowledge have improved drastically over time, until very recently surgery was still a manual task for human hands. When it came out about 15 years ago, Intuitive Surgical's da Vinci surgical robot was a major innovation. The da Vinci robot helps surgeons be more precise and dexterous and to remove natural hand tremors during surgery. In the years since da Vinci first came out, many other surgical robots have arrived.

Last weekend was supposed to be Roborace's big moment. The driverless car championship, which has been under development for about year, was slated to make its first public demonstration in Hong Kong during the season opener of Formula E, an all-electric urban racing series. It's what makes the successes so glorious, and the setbacks so frustrating. Roborace is documenting all of it--the good and the bad--in a docu-series that aims to capture the process of creating the world's first artificial intelligence, driverless electric racing series. The first episode was released Friday.

I'm intrigued by Toyota's Kirobo Mini, a companion robot that recognizes your facial expressions and engages you in casual conversation. As someone who's pro-tech, it's refreshing to see the Japanese car manufacturer step outside of its box to explore other avenues. I like adorable products and this bot is definitely that, but I'm not totally sold and I want to tell you why. The Kirobo Mini looks a lot like MegaMan, the iconic character from the popular video game franchise. It's an appealing design, especially to a regular gamer like me, but it's not the most advanced piece of tech I've ever seen.