Every few months it seems another study warns that a big slice of the workforce is about to lose their jobs because of artificial intelligence. Four years ago, an Oxford University study predicted 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative: A 2016 report by the Organization for Economic Cooperation and Development (OECD) said 9% of jobs in the 21 countries that make up its membership could be automated. And in January 2017, McKinsey's research arm estimated AI-driven job losses at 5%. My own firm released a survey recently of 835 large companies (with an average revenue of $20 billion) that predicts a net job loss of between 4% and 7% in key business functions by the year 2020 due to AI. Yet our research also found that, in the shorter term, these fears may be overblown.

From Siri to Google Translate, deep neural networks have enabled breakthroughs in machine understanding of natural language. Most of these models treat language as a flat sequence of words or characters, and use a kind of model called a recurrent neural network (RNN) to process this sequence. But many linguists think that language is best understood as a hierarchical tree of phrases, so a significant amount of research has gone into deep learning models known as recursive neural networks that take this structure into account. While these models are notoriously hard to implement and inefficient to run, a brand new deep learning framework called PyTorch makes these and other complex natural language processing models a lot easier. While recursive neural networks are a good demonstration of PyTorch's flexibility, it is also a fully-featured framework for all kinds of deep learning with particularly strong support for computer vision.

There is increasing interplay between the worlds of machine learning and high performance computing (HPC). This began with a shared hardware and software story since many supercomputing tricks of the trade play well into deep learning, but as we look to next generation machines, the bond keeps tightening. Many supercomputing sites are figuring out how to work deep learning into their existing workflows, either as a pre- or post-processing step, while some research areas might do away with traditional supercomputing simulations altogether eventually. While these massive machines were designed with simulations in mind, the strongest supers have architectures that parallel the unique requirements of training and inference workloads. One such system in the U.S. is the future Summit supercomputer coming to Oak Ridge National Lab later this year, but many of the other architectures that are especially sporting for machine learning are in China and Japan--and feature non-standard processing elements.

In this special guest feature, Michael Tso, CEO of Cloudian, discusses how AI is rapidly changing the business world, and for AI to deliver business value, the storage industry will play a key role – scale-out object storage with full S3 compatibility matches this role perfectly. Michael Tso holds 36 patents and has been a technology trailblazer for over 20 years. Michael co-founded Cloudian and Gemini Mobile Technologies, and built business and engineering operations in US, Japan, and China. For more than 10 years, Cloudian and Gemini Mobile have provided mission critical carrier grade infrastructure software which serve hundreds of millions of users. Now, Cloudian is trailblazing distributed object storage for cloud and enterprise storage use cases.

From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic "brain" can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities. Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious – and pessimistic – we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.

At the International Astronautical Congress last September, in Guadalajara, Mexico, Elon Musk convinced many die-hard space engineers he could get a fleet of private rockets filled with thousands of people to Mars. Musk's speech was long on orbits, flight plans, and fuel costs. But it was short on how any of those colonists would survive. In fact, the Mars journey would likely be a dead end. Bathed in radiation and with nothing growing on it, the Red Planet is basically a graveyard.

As of February 2017, there were 106 artificial intelligence startups in healthcare, according to a CB Insights report, and 70 of them launched last year. Here are 13 healthcare AI startups that have raised $25 million or more, listed along with their investors. Flatiron's platform connects community practices and cancer centers on a common technology infrastructure to address healthcare challenges with the goal of powering a national benchmarking and research network for cancer care. The company provides its platform to more than 265 community cancer clinics and three major academic research centers. Welltok's CafeWell Health Optimization Platform is designed to connect consumers with benefits, resources and rewards for personalized healthcare plans.

Progress in artificial intelligence has long been measured by its mastery of board games like chess, backgammon, and Go. Researchers are now working on poker and computer games such as Starcraft. Iyad Rahwan, a professor at MIT, respects those milestones but says the focus on beating humans in direct competition has led us to neglect other ways of measuring and advancing AI. He argues that as smart machines look set to become pervasive, more effort should be devoted to creating software that learns to coöperate with humans. "This is the next important problem, because AIs don't always have to replace us, they have to live with us," says Rahwan.

Embedded with Iraqi special forces, Ben C. Solomon was on the front lines in Mosul when a threat came from above: An ISIS drone dropping a grenade.

Video Friday is your weekly selection of awesome robotics videos, collected by your 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. There will be more on this at ICRA next month, and we're hoping for a live demo. OpenAI has created "the world's first Spam-detecting AI trained entirely in simulation and deployed on a physical robot."