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."

Soteria Intelligence is excited to announce the company's research and development focused on creating the next generation of social media threat intelligence solutions with the power to learn and become more effective over time. Instead of taking an approach that looks at combinations of keywords, conversations on a map or other social media analytics that produce a high number of recurring false positives, Soteria Intelligence is implementing machine learning to use false positives, vetted by analysts, as educational tools that continually improve system accuracy. The company is also training its system with proprietary data on past social media threats and, through the understanding of false positives and integration of historical data, combined with natural language processing, image recognition, sentiment analysis and other functionality, Soteria Intelligence is revolutionizing the way social media threats are identified and assessed. "As someone who has spent the past 10 years guiding large organizations throughout the world on complex social media initiatives while using social media analytics tools along the way, I understand where they shine and fall short. I also see what's needed to accurately identify threatening social media behavior on the horizon," stated Aaron Schoenberger, Founder and CEO of Soteria Intelligence.

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.

"How many robots can I control with this? That really is a robot army." Despite how it sounds, world domination isn't on the agenda. There are only two robots in the room, and right now they're more concerned with drawing neat triangles on pieces of paper, rather than enslaving humanity. The pair of Mirobots are demonstrating Project Bloks, Google's latest attempt to encourage children to learn programming skills.