It's hard to believe, but one of the most important changes in the way people write in the last 50 years has been largely overlooked by historians of literature. The word processor--that is, any computer software or hardware used for writing, a nearly ubiquitous technology adopted by poets, novelists, graduate students, foreign correspondents, and CEOs--has never gotten its own literary history. Perhaps it was just too much under our noses--or, I suppose, in front of them. Now it finally has one. Five years ago, Matthew Kirschenbaum, an English professor at the University of Maryland, realized that no one seemed to know who wrote the first novel with the help of a word processor.

In June of 1975, the Office of the White House Press Secretary announced President Gerald R. Ford's picks for the National Medal of Science. One went to the Austrian-born mathematician and logician Kurt Gödel. Nicknamed Mr. Why by his parents, Gödel was known to a subset of his constituents as, simply, God. He received fan mail from all over the world, archiving it into files of "autograph requests," "inquiries from students and amateurs," "letters of appreciation," and "crank correspondence." A self-described "dunce fool of Mathematics" in West Bengal wrote seeking Gödel's "Guruship," and a svelte math teacher in California confessed that she'd taken the liberty of enlarging a photo of Gödel to make a poster for her classroom.

Imagine it's five minutes before a meeting. Your smartwatch, without prompting, sends you key points. While in the meeting, you take notes. Those notes are instantaneously absorbed by the system, then collated with relevant prior meetings, files and communications, in order to better prepare you for the next meeting. Born of the innovations of Big Data and possessed of a new net intelligence layer, self-learning software will have huge impacts on productivity across all departments of an enterprise.

Justine Cassell has taken her virtual assistant Sara on a road trip. They're in Tianjin, China, where Carnegie Mellon University's associate dean of technology strategy and impact traveled to offer a glimpse of tomorrow at this week's Annual Meeting of New Champions. Sara, for "socially aware robot assistant," has spent the past several days greeting hundreds of people coming to the event, hosted by the World Economic Forum, at a station showcasing the office of the future. A life-size face and torso on a big-screen TV, Sara served as the front end to the event app. That presentation might make you think of Max Headroom, the stuttering AI character from the 1980s show.

KPMG plans to use IBM's Watson cognitive computing technology for its professional services in Australia. The artificial intelligence deal with IBM includes a focus on audit and assurance services. IBM's Watson has been doing everything from diagnosing cancer and recommending treatment to analysing the Harry Potter books and running online university courses. "Already, data and analytics techniques are transforming audit by allowing analysis of much bigger populations of data than traditional sampling from which to draw conclusions," says Duncan McLennan, KPMG's national managing partner of audit. "Cognitive technology will allow us to build on these data and analytics advances. They will be a game changer in how the value of audit is perceived by the marketplace."

Justine Cassell has taken her virtual assistant Sara on a road trip. They're in Tianjin, China, where Carnegie Mellon University's associate dean of technology strategy and impact traveled to offer a glimpse of tomorrow at this week's Annual Meeting of New Champions. Sara, for "socially aware robot assistant," has spent the past several days greeting hundreds of people coming to the event, hosted by the World Economic Forum, at a station showcasing the office of the future. A life-size face and torso on a big-screen TV, Sara served as the front end to the event app. That presentation might make you think of Max Headroom, the stuttering AI character from the 1980s show.

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.

Machine learning is based on old artificial intelligence concepts. It was first defined in 1959 as the ability... This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners.

In the field of artificial intelligence, the phrase deep learning applies to software that improves its model of reality with experience. Consider, for example, a project developed at Google in 2012, in which a neural network running on 16,000 computer processors, browsing through 10 million YouTube videos, began on its own to identify and seek out one of the most popular YouTube genres: cat videos. The then director of that project, Andrew Ng, went on to become the founding chief scientist at Baidu Research, an innovation center run by the giant Web services company Baidu. The parent company owns the largest search engine in China, along with Chinese-language browsers, online encyclopedias, social networks, and other Web-based services. According to the company, Baidu responds to more than 6 billion search requests from more than 138 countries every day. Because search engines and advertising placement platforms (such as Baidu's Phoenix Nest) depend on artificial intelligence (AI) to satisfy vague or ambiguous requests, the company -- along with Google, Microsoft, and other providers of internet guidance -- has a natural interest in machine learning.

Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. "Perception and creativity are very intimately connected," Agüera y Arcas says. "Any creature, any being that is able to do perceptual acts is also able to create."