The next big trend in AI looks likely to be computers and robots that teach themselves through trial and error. Elon Musk and Sam Altman (of Y Combinator) caused a stir last December by luring several high-profile researchers to join OpenAI, a billion-dollar nonprofit dedicated to releasing cutting-edge artificial intelligence research for free. Today the nonprofit released the first fruits of its work, and it suggests that kind of learning will be important for the future of AI. The nonprofit has released a tool called OpenAI Gym for developing and comparing different so-called reinforcement learning algorithms, which provide a way for a machine to learn through positive and negative feedback. This week OpenAI also announced two new recruits, including Pieter Abbeel, an associate professor at Berkeley and a leading expert on applying reinforcement learning to robots. OpenAI Gym includes code and examples to help others get started with reinforcement learning.

A California start-up called View, which has raised a whopping 500 million from investors including Corning, General Electric and Khosla Ventures, is making high-tech windows that have the potential to bring to buildings what high-resolution touchscreens did for smartphones. View's windows eliminate glare, change hue, moderate internal temperature -- and at some point, could show entirely different views of the outside world -- via a process that uses a pane of glass sprayed with electrochromic material, which alters light transmission. The result is smart glass that increases energy efficiency and promises better worker productivity, via technology accessed through an app. "When you look at smart glass, the only smart surface we saw was on our phones," says Ben Bajarin, an analyst for Creative Strategies who follows the industry. "Now, we believe consumers are moving toward an age where smart glass can do almost anything -- for example, project images of the sun on your windows during a rainy day or viewing data on the window." While elements of the technology have been around on a smaller scale, such as car windows, View is the first company to commercially produce such glass at a large scale.

Stanford's five-foot "virtual diver" was originally built for studying coral reefs in the Red Sea where a delicate touch is necessary, but the depths go well beyond the range of meat-based divers. The "tail" section contains the merbot's onboard batteries, computers and array of eight thrusters, but it is the front half that looks distinctly humanoid with two eyes for stereoscopic vision and two nimble, articulated arms. Those arms are what make OceanOne ideal for fragile reef environments or priceless shipwrecks like La Lune, which sank off the coast of France over 350 years ago and hasn't been touched until now. Force sensors in each wrist transmit haptic feedback to the pilot, allowing them to feel the object's weight while staying high and dry on a dive ship. The robot's "brain" works with the tactile sensors to ensure the hands don't crush fragile objects, while the navigation system can automatically keep the body steady in turbulent seas.

Machine learning has come of age in public health reporting according to researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis. They have found that existing algorithms and open source machine learning tools were as good as, or better than, human reviewers in detecting cancer cases using data from free-text pathology reports. The computerized approach was also faster and less resource intensive in comparison to human counterparts. Every state in the United States requires cancer cases to be reported to statewide cancer registries for disease tracking, identification of at-risk populations, and recognition of unusual trends or clusters. Typically, however, busy health care providers submit cancer reports to equally busy public health departments months into the course of a patient's treatment rather than at the time of initial diagnosis.

SIOS Technology Corp., the industry's leading provider of software products that help IT ensure the performance, efficiency, and high availability protection of business critical applications, today announced it will co-host a live one-hour webinar featuring ActualTech Media Partner and VMware vExpert Scott D. Lowe who will show how using new machine learning based analytics tools can resolve application performance issues in virtualized environments.

There are many who say we can put controls in place that keep A.I. from becoming dangerous, but they've missed the fact that these technologies are already dangerous. Humans have already faced a strategic opponent that understands us better than we understand it, AlphaGo, which shocked the A.I. community by beating a human Go Master in 4 out of 5 games. The only thing we're now waiting to happen is for programmers to apply these technologies to disciplines that are more significant to our lives than board games. This will occur, and the raw power of these technologies will steadily advance, putting us at a greater and greater disadvantage. It seems we're destined to be out-matched, and not just on the Go board, but in countless strategic situations.

According to Sean Kilcarr (writing in Fleet Owner), many of the trends in logistics boil down to technological advancements. I agree with that view. And the good thing about it is that these technical changes allow evolutionary as well as disruptive innovation, and over time will move the entire field to greater efficiency and more effective performance for each customer, perhaps much faster than we think. Many of the changes are straightforward application of technology using industrial engineering techniques. They will, however, require a highly educated and technologically literate work force.

Eventually, the team devised a handwriting recognition tool that worked beautifully on modern Hebrew, and they decided to put it to the test on ancient inscriptions. All in all, their analysis revealed at least six different authors behind the 16 ostraca. Examining the contents of the text itself, the researchers concluded that these authors spanned the entire military chain of command. "The commander down to the lowest water master could all communicate in writing," Shaus said. "This was an extremely surprising result."

Big Data constantly hits that wall. Despite all the investment in infrastructure and people, Big Data just can't generate further value. This is happening because we're treating Big Data and the stuff it contains like algorithms and artificial intelligence (AI) just like we treat traditional information technology. From a high level, here is the wall. Brick 1 - Business knows business but doesn't understand AI.

In order to make self-driving cars viable, the automotive industry has recruited some of the best software developers, hardware engineers, and mobility analysts humanity has to offer. There's a new community working to push autonomous technology forward, but these researchers aren't human at all. Buried deep within the halls of MIT's Computer Science and Artificial Intelligence Lab (CSAIL) lies a small suburb called Duckietown, a mock-up municipality used to test and develop driverless technology. Populated entirely by rubber ducks riding on autonomous robo-taxis, Duckietown is the culmination of a graduate-level class that could prove invaluable to automakers in the future. "We believe a tool like this will help create a common platform and language for researchers to build on," said CSAIL postdoctoral associate Liam Paull, who co-leads the Duckietown course.