Robots are rapidly becoming more and more lifelike and unrecognizable from humans. Eerily human-like robots built by China's University of Science and Technology stole the show over at the World Robot Conference in Beijing last week. You may have already seen Jia Jia, a super realistic robot capable of micro facial expressions and basic conversation with humans. Jia Jia can also recognize faces, identify your gender and age, and detect your facial expressions. But a newcomer robot also made a debut: the university unveiled a male robot that can accurately paint calligraphy.

There are two fields with seemingly endless career opportunities: healthcare and computer science. And when medical care intersects with technology, the possibilities are life changing. Machine learning introduces new insights to healthcare professionals through compiling big data to improve the way doctors and clinicians can diagnose, treat and even predict outcomes for their patients, according to Finale Doshi-Velez (pictured), assistant professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences. Doshi-Velez is on the front lines of educating and researching tangible ways to improve mental health through machine learning. She is working with students in several areas, but her focus is on machine learning for healthcare applications focused on dissecting the autism spectrum and helping to alleviate depression. Doshi-Velez spoke with Lisa Martin (@Luccazara), co-host of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the Stanford Global Women in Data Science (WiDS) Conference in Stanford, CA, about her work in these developing fields and how she is preparing the next generation by teaching students the emerging skills they will need in a new workplace.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.

Incuspaze, a Gurgaon-based co-working and incubation centre, will launch an incubation programme for startups in artificial intelligence, machine learning, IoT and big data, the company said in a statement on Monday. The 6,000 sq ft centre was launched by UK-based angel investor Sanjay Choudhary last week. Incuspaze will invest between Rs 1 -5 lakh in five to six startups in its batch, and will also offer parallel services such as marketing, accounting and legal. "We are building a global self-sustainable ecosystem for startups and entrepreneurs. We are not only providing a co-working space, but also investor support, mentoring, allied services, global partnerships and to ensure peak performance for all startup founders," said Choudhary in the statement. Choudhary said Incuspaze will enable startups to accelerate product/service development by implementing lean startup methodologies through calculated risk-taking.

Broad adoption of powerful cloud computing has unleashed innovation in artificial intelligence technologies, and 2017 is poised to be the year that AI and machine learning applications make their way into the hands of the general public. For those in IT and – more specifically – the managed services space, tools driven by AI are increasingly popping up in everything from customer service and security, to CRM and remote monitoring and management. Machine learning can have a particular impact for IT tech services firms, where increased efficiency can translate directly into more revenue falling to the bottom line. "There is an absolute revolution occurring in artificial intelligence," John Ball, general manager of Salesforce Einstein, told Bloomberg when that AI product launched in September. Machine learning, which represents one type of artificial intelligence, is joined at the hip with big data.

The first approach had its origins in Theano (another deep learning library Lab41 has previously used). In Theano (and Tensorflow) the user is responsible for everything. You define a graph of computation that is merely a thin layer on top of a matrix math library. In order to represent our regression in Tensorflow (or Theano) it's best to first mathematically represent our calculation:

Science-fiction visionaries have long promised us all kinds of futuristic transportation options, and while jetpacks and teleportation are still some ways off, the technologies are finally in place to make self-driving cars a reality. It's time for automakers to put the pedal to the metal as they compete with technology companies and other industry disruptors to put partially or fully autonomous vehicles on American roads. The auto industry has a head start: After decades of investments, today's vehicles offer many partially autonomous features like lane departure systems, adaptive cruise control, and emergency braking. Emerging technologies could enable even more vehicle-to-vehicle and vehicle-to-infrastructure connectivity, making the leap to fully driverless cars even smaller. In fact, executives from several leading automakers foresee advanced self-driving technology being available by 2021 or even sooner;1 some envision vehicles without steering wheels or pedals to be driven by advanced technology and sensors and not people.

A wearable AI that can predict whether the tone of the conversation is happy, sad or neutral can soon be realized to help fight social anxiety. The system will base its reading on the user's speech patterns and vital signs. Researchers from Computer Science and Artificial Intelligence Laboratory (CSAIL) of MIT and Insitute of Medical Engineering and Science (IMES) are a step closer in helping people with anxiety disorder or Asperger's condition. Tuka Alhanai, a graduate student and co-author of the study said that the wearable AI will be like a social coach in the user's pocket. Mohammad Ghassemi, together with Alhanai, will present the new technology in the Association for the Advancement of Artificial Intelligence (AAAI). The accuracy of the gadget is around 83 percent.

It's the stuff of many a sci-fi book or movie - could robots one day become smart enough to overthrow us? Well, a group of the world's most eminent artificial intelligence experts have worked together to try and make sure that doesn't happen. They've put together a set of 23 principles to guide future research into AI, which have since been endorsed by hundreds more professionals, including Stephen Hawking and SpaceX CEO Elon Musk. Called the Asilomar AI Principles (after the beach in California, where they were thought up), the guidelines cover research issues, ethics and values, and longer-term issues - everything from how scientists should work with governments to how lethal weapons should be handled. On that point: "An arms race in lethal autonomous weapons should be avoided," says principle 18.

The effective capacity of neural networks is large enough for a brute-force memorization of the entire data set.