Interesting Snippets

What does it mean that Google really is trying to build the Star Trek computer? I take it as a cue to stop thinking about Google as a “search engine.” That term conjures a staid image: a small box on a page in which you type keywords. A search engine has several key problems. First, most of the time it doesn’t give you an answer—it gives you links to an answer. Second, it doesn’t understand natural language; when you search, you’ve got to adopt the search engine’s curious, keyword-laden patois. Third, and perhaps most importantly, a search engine needs for you to ask it questions—it doesn’t pipe in with information when you need it, without your having to ask.
The Star Trek computer worked completely differently. It understood language and was conversational, it gave you answers instead of references to answers, and it anticipated your needs. “It was the perfect search engine,” Singhal said. “You could ask it a question and it would tell you exactly the right answer, one right answer—and sometimes it would tell you things you needed to know in advance, before you could ask it.

European buyers of the Ford Focus, a mid-sized car, can now leave it to drive itself and maintain a safe distance in steady traffic. The car can measure a parking space and steer itself into it. It reads road signs and admonishes the driver if he breaks the speed limit. Such gadgetry also increasingly makes decisions on the driver’s behalf and overrules him in an emergency, for instance, braking to avoid a crash.

unique EU-backed €1.5 million RoboLaw Project, Salvini is managing a team of roboticists, lawyers and philosophers (yes, philosophers) from a consortium of European universities, who are working hard to come up with proposals for the laws and regulations necessary to manage emerging robotics technologies in Europe

Arnold also sees virtual assistants as intellectual equalizers. A superb memory might cease to be an advantage as intelligent assistants are tasked with remembering names, dates and other details. Everyone will have the ability to see unusual but important connections between legal cases or patients’ symptoms, thanks to assistants that can identify relevant precedents or files.

IPsoft’s Eliza, a “virtual service-desk employee” that learns on the job and can reply to e-mail, answer phone calls and hold conversations, is being tested by several multinationals. At one American media giant she is answering 62,000 calls a month from the firm’s information-technology staff. She is able to solve two out of three of the problems without human help.

Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own. Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats.

We’re generally faster learners than our technology, as long as we are given something that can be easily approached and mastered. We’re more plastic and malleable – what we do changes our brains – so the ‘wily’ technology (and it’s designers) will sieze upon this and use it… All of which leaves me wondering whether we are working towards Artificial Empathy, rather than Artificial Intelligence in the things we are designing…

Frustrated that his (and fellow Googler Peter Norvig’s) Stanford artificial intelligence class only reached 200 students, they put up a website offering an online version. They got few takers. Then he mentioned the online course at a conference with 80 attendees and 80 people signed up. On a Friday, he sent an offer to the mailing list of a top AI association. On Saturday morning he had 3,000 sign-ups—by Monday morning, 14,000.

In the midst of this, there was a slight hitch, Mr. Thrun says. “I had forgotten to tell Stanford about it. There was my authority problem. Stanford said ‘If you give the same exams and the same certificate of completion [as Stanford does], then you are really messing with what certificates really are. People are going to go out with the certificates and ask for admission [at the university] and how do we even know who they really are?’ And I said: I. Don’t. Care.”

In the end, there were 160,000 people signed up, from every country in the world, he says, except North Korea. Rather than tape boring lectures, the professors asked students to solve problems and then the next course video would discuss solutions. Mr. Thrun broke the rules again. Twenty-three thousand people finished the course. Of his 200 Stanford students, 30 attended lectures and the other 170 took it online. The top 410 performers on exams were online students. The first Stanford student was No. 411.

Narrative Science’s algorithms built the article using pitch-by-pitch game data that parents entered into an iPhone app called GameChanger. Last year the software produced nearly 400,000 accounts of Little League games. This year that number is expected to top 1.5 million.

Narrative Science, a Chicago-based startup, has developed an innovative platform that writes reported articles in eerily humanlike cadence. Their early work focused on niche markets, clients with repetitive storylines and loads of numeric data—sports stories, say, or financial reports… One high-profile client, Forbes magazine, uses the platform to create what Forbes writer Lewis Dvorkin calls “computer-generated company earnings previews.” Each day, the platform sorts through recent stock data to profile a notably performing company. Another client is The Big Ten Network, which uses Narrative Science to create automatic sports recaps based on box scores and player data.

Zimmer reports that Ginsberg “conservatively guesses” that Dr. Fill can place in or near the tournament’s top 30. In simulations of 15 recent tournaments, Dr. Fill beat all its human competitors three times. Though the computer may not match humans in accuracy, it can whip them in speed, solving puzzles in 30 seconds and then editing its answers for another 90. Not even the top humans can complete the tournament-level puzzles in two minutes. The computer’s dictionary database currently contains 10 million entries.

Financial services is the “next big one for us,” said Manoj Saxena, the man responsible for finding Watson work. IBM is confident that with a little training, the quiz-show star that can read and understand 200 million pages in three seconds can make money for IBM by helping financial firms identify risks, rewards and customer wants mere human experts may overlook….
Watson the financial assistant will be delivered as a cloud-based service and earn a percentage of the additional revenue and cost savings it is able to help financial institutions realize.