The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Japan's most famous novelist is set to return to his alma mater and teach -100 years after his death. Soseki Natsume is being recreated as an android by Nishogakusha University Graduate School, and will be programmed to read material out loud and give lectures. Created in a sitting posture, the robot will be 130 centimeters high and built using 3D scans of a death mask and vintage photos. Soseki Natsume is being recreated into an android by Nishogakusha University Graduate School, which will be programmed to read out material and give lectures. Students from Nishogakusha University Graduate School plan to explore Nastume's life and gather information regarding his physical appearance and size for a closely accurate robot, which they have been granted access to a large college of photos and works by the major newspaper Asahi Shumbun – a former employer of Nastume, reports RocketNews24.

After a recent study that suggested automated essay graders are as effective as their human counterparts in judging essay exams, "roboreaders" are receiving a new wave of publicity surrounding their possible inclusion in assessments and classrooms. But while developers of the technology are happy to have the attention, they insist the high profile has more to do with timing of policy changes such as the push to common standards than with any dramatic evolution in the essay-grading tools themselves. "What's changed is the claims people are willing to make about it. "I think, over time, a mixture of technologies will make this really good not only for scoring essays," but also for other assignments, said Mr. Cohen, the director of AIR's assessment program. "But we really need to be clear about the limits of the applications we are using today so we can get there." The study, underwritten by the Menlo Park, Calif.-based William and Flora Hewlett Foundation, is driven by the push to improve assessments related to the shift to the Common Core State Standards in English/language arts and math, and is based on the examination of essays written specifically for assessments.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Thousands of openings in artificial intelligence and machine learning posted on job boards are going unfilled. In fact, though AI is one of the fastest-growing areas for high-tech professionals, according to a recent Kiplinger report, there are too few qualified engineers. "Supply is far lower than demand," says Boris Babenko, a machine vision engineer at Orbital Insight, a company in Palo Alto, Calif., that uses AI to make sense of data gathered from satellite images. "That's true of all software engineering, but AI is a niche on top of that." The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt.

Matt Krisiloff is in a small, glass-walled conference room off the lobby of Y Combinator's office in San Francisco's South of Market neighborhood, shouting distance from some of the country's wealthiest startups, many of which Y Combinator has nurtured and helped fund. Krisiloff, who manages the operations of the tech incubator's program for very early-stage companies, is explaining why it is committed to investing an amount said to be in the tens of millions of dollars in a venture that is guaranteed never to make a penny. It's the simplest business model conceivable: hand thousands of dollars over to individuals in return for nothing, no strings attached. Krisiloff insists he and his Y Combinator colleagues can't wait to get started giving away the money. "This could be really transformative," he says. "It may help change how humans, society, and technology all operate together in the future."

What happens when machines are advanced enough to take our cognitive heavy lifting? It's a question at the heart of hundreds of clickbait articles with titles like, "Will robots take your job?" The answer is typically either utopian (we'll all be living like the Jetsons, as machines take care of our every need) or depressingly bleak (The birth of Skynet?). The truth, as so often happens, is somewhere in the middle. The fourth industrial revolution will have an impact on labour in the short term, partly offset by the creation of new jobs that emphasise creativity and emotional intelligence.

Matt Krisiloff is in a small, glass-walled conference room off the lobby of Y Combinator's office in San Francisco's South of Market neighborhood, shouting distance from some of the country's wealthiest startups, many of which Y Combinator has nurtured and helped fund. Krisiloff, who manages the operations of the tech incubator's program for very early-stage companies, is explaining why it is committed to investing an amount said to be in the tens of millions of dollars in a venture that is guaranteed never to make a penny. It's the simplest business model conceivable: hand thousands of dollars over to individuals in return for nothing, no strings attached. Krisiloff insists he and his Y Combinator colleagues can't wait to get started giving away the money. "This could be really transformative," he says. "It may help change how humans, society, and technology all operate together in the future."

In July, we talked about whether the change in law should be characterized as "Disruption, Eruption or Interruption?" This week, we drill down into one likely source of change, IBM's Watson. Lawyers have been thinking for a while about whether artificial intelligence would ever start to displace or complement lawyers. Richard Susskind, the leading legal futurist/technologist, did his work in this area starting in the mid-1980s. In the August issue of the ABA Journal, one of the commenters to an article about LegalZoom feared: "Once we have fully artificial intelligence enhanced programs like LegalZoom, there will be no need for lawyers, aside from the highly specialized and expensive large-law-firm variety."

The phrase "life's work" is one that seems to be fading into obscurity with each passing year. The ideal of seeing a finish line and giving your all to get across it sometimes seems romanticized -- until you meet someone like Fil Alleva. "I started working in the speech area in 1977," explains the affable Partner Group Engineering Manager. Alleva was planning to be a chemical engineer in his undergraduate years at Carnegie Mellon University in the late '70s when he befriended Professor Raj Reddy in an introductory programming class. Soon after, he found himself taking a 2-an-hour job programming computers, if only because it was a better gig than working in the school cafeteria.