Earlier this month a self-driving shuttle in Las Vegas patiently waited as a delivery truck backed up, then backed up some more, then backed right into it. Inconveniently for the roboshuttle's developer Navya, this happened within hours of the shuttle's inauguration ceremony. The real problem is that the shuttle can't learn from the incident the way a human would: immediately and without forgetting how to do everything else in the process.
The demands of artificial intelligence on jobs means that AI training will become a "lifelong necessity," according to a report published Monday by the UK's House of Lords. The report, entitled "AI in the UK: ready, willing and able?" warns that workers will face continual training to maintain useful skills AI in the changing job markets. "As AI decreases demand for some jobs but creates demand for others, retraining will become a lifelong necessity and pilot initiatives, like the Government's National Retraining Scheme, could become a vital part of our economy," the report says. So while some jobs may disappear, new ones may become even more important. The key in both cases is staying on top of the latest advances and skills.
The Defense Advanced Research Projects Agency is in the process of spinning up a new research program to develop ways to teach machines to learn while they are operating -- and apply their knowledge to new situations "the way biological systems do." The agency is now accepting research proposals for the program's first funding opportunity via a Broad Agency Announcement, published last week. Dubbed the Lifelong Learning Machines program or L2M, DARPA plans through the four-year program to fund the development of "substantially more capable systems that are continually improving and updating from experience." Artificial intelligence systems today can't adapt to situations for which they were not already trained or programmed, as DARPA notes in its Broad Agency Announcement released last week. And so applying AI systems for military uses in areas like "supply chain, logistics and visual recognition" is difficult to do today, because many of those applications involve details that aren't defined in advance, according to DARPA.