The data volumes expected from the Internet of Things (IoT) are certain to be large – too large, in fact, for even an army of trained analysts to turn into useful information in a reasonable amount of time. This is why every solution aimed at the IoT relies heavily on automation, simply to manage the flow of information between devices and to centralized storage and analytics systems. But even this is not likely to be enough. To fully leverage the IoT, it's becoming obvious that the enterprise will have to utilize new forms of artificial intelligence and machine learning to basically allow the environment to makes its own use of available data and tell human operators what needs to be done. Already, this is emerging on leading IoT platforms. Software developer C3 recently updated its IoT platform by pushing artificial intelligence to the edge where it can function on an application level for an improved user experience.

Authors: Lili Jiang, Shuo Chang, and Nikhil Dandekar In order to build a high-quality knowledge base, it's important that we ensure each unique question exists on Quora only once. Writers shouldn't have to write the same answer to multiple versions of the same question, and readers should be able to find a single canonical page with the question they're looking for. For example, we'd consider questions like "What are the best ways to lose weight?", "How can a person reduce weight?", and "What are effective weight loss plans?" to be duplicate questions because they all have the same intent. To prevent duplicate questions from existing on Quora, we've developed machine learning and natural language processing systems to automatically identify when questions with the same intent have been asked multiple times.

When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Coursera co-founder ANDREW NG, adjunct professor of computer science, says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is." Ng, who is chief scientist at Baidu Research, spoke to the Graduate School of Business community as part of a series presented by the Stanford MSx Program, which offers experienced leaders a one-year, full-time learning experience.

Tata Consultancy Services (BSE: 532540, NSE: TCS), a leading global IT services, consulting and business solutions organization, today unveiled its Global Trend Study titled, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies." Focused on the current and future impact of Artificial Intelligence (AI), the study polled 835 executives across 13 global industry sectors in four regions of the world, finding that 84% of companies see the use of AI as "essential" to competitiveness, with a further 50% seeing the technology as "transformative." Widespread AI adoption expected across job functions Exploring the views and actions of decision makers from global companies with average revenues of $20 billion, the study revealed AI is spreading across almost all areas of a company. The biggest adopters of AI today are, not surprisingly, IT departments, with two-thirds (67%) of survey respondents using AI to detect security intrusions, user issues and deliver automation. However, by 2020, almost a third (32%) of companies believe AI's greatest impact will be in sales, marketing or customer service, while one in five (20%) see AI's impact being largest in non-customer facing corporate functions, including finance, strategic planning, corporate development, and HR.

JUDY WOODRUFF: For decades, researchers have worked to create a better and more direct connection between a human brain and a computer to improve the lives of people who are paralyzed or have severe limb weakness from diseases like ALS. Those advances have been notable, but now the work is yielding groundbreaking results. CAT WISE: Dennis Degray is a 64-year-old quadriplegic who is writing a sentence on the computer screen in front of him using only his brain. A former volunteer firefighter, Degray had a bad fall 10 years ago which severed his spinal cord. As part of an early stage clinical research study led by Stanford University, Degray and two other volunteer participants with ALS had small sensors implanted in their brains in an area called the motor cortex, which controls movement.

Certain kinds of autonomous vehicles may not be safe, especially in an emergency situation, according to a new study published by the Lords Science and Technology Committee on Wednesday. With driverless technology, drivers may become over-reliant and complacent. However, with the development in the automotive technology over time, accidents by human error may be significantly reduced -- but they just might increase before they do. The committee also reported people may use driverless cars for shorter distances, as well, causing laziness and may prevent them from "getting exercise by walking." The UK Economic Opportunity split vehicles into levels from 0 to 5. Zero was fully controlled by an individual, and five was completely automated. According to peers on the committee, there was a "very dangerous" problem with vehicles reaching the middle of the scale, BBC News reported.

Artificial Intelligence (AI) is undisputedly going to make immense walks in the following 10 years. Cars will learn how to drive themselves, robots will perform surgeries, and you'll learn that the world isn't made of carbon but Silicon. AI machines notwithstanding, when discharged into a real world condition, can react unpredictably and in ways their makers most likely didn't expect, with hilarious and now and again offensive consequences. Here are the five times AI went miserably wrong. The Crime fighting Robot An alleged "crime fighting robot," made by Knightscope, crashed into a child in a Silicon Valley mall, injuring the 16-month-old boy.

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated a world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

A group of professors and researchers at the Technical University of Berlin, the University of Vienna, and ETH Zurich have recently been working on understanding deep neural networks (computer systems that are modelled after the human brain) in "a mathematically sound way", as Dr. Phillip Petersen refers to it. Although the official paper for this research, "Optimal Approximation with Sparse Deep Neural Networks," will not be published until next week, Professor Gitta Kutyniok graciously presented a preview of their work for the Center's Math & Data Seminar group this past Thursday. Neural networks, or artificial brains, represent functions in mathematics. For these researchers, the main goal is to uncover how well a deep neural network with sparse connectivity can approximate a function. Dr. Petersen likens the network to a tree -- deep neural networks are composed of multiple layers and are connected by edges. Those layers are made of nodes, or neurons where computation occurs, and are sparsely connected if they have few non-zero weights or edges.

What are the main differences between NLP for Chinese vs NLP for English? Linguistically speaking, Chinese is an isolating language different from English. There are no spaces between words in Chinese written texts, and Chinese grammatical relations are indicated by word order. These factors have multiplied the difficulty of Chinese disambiguation at lexical, syntactic and semantic levels, since modern linguistic concepts and principles are more suitable for English than for Chinese. Currently, most mainstream NLP methods are language-independent.