The U.S. may be trailing behind China in artificial intelligence (AI) research -- or at least in journal articles that mention "deep learning" or "deep neural network" -- according to the White House's National Artificial Intelligence Research and Development Strategic Plan. While the U.S. remains an early leader in deep learning research, China seems to be spending more time studying the technology and making influential contributions to the field than we are, according to White House's research, leading the U.S. in both the number of published deep learning studies and the number of studies cited by other researchers. The administration's plan proposes future research and development projects in the field of AI and was released in conjunction with Preparing for the Future of Artificial Intelligence, a report that overviews the current and future state of AI technology and its place in society. Both papers were shared in anticipation of the White House Frontiers Conference. Both countries, obviously, are devoting a lot of attention to AI and deep learning, in particular.

Alphabet has several productivity products aimed at least in part at enterprises, including Drive, Hangouts, and Docs. Many of the acquisitions were early (pre-2011) and helped these products come into being, including Urchin Software (acquired in 2005), which became Google Analytics, and Writely (acquired in 2006), which fed into documents. Virtual assistants like Google Now might be enhanced through the recent acquisitions of Timeful (artificial intelligence) and Emu (natural language processing), technologies that could help create smart scheduling features. Alphabet could also be trying to enter the mobile enterprise market with the acquisition of Divide, a company that allows employees to carry a single phone with a "work" mode and "personal" mode.

Data driven recruitment has a significant, positive impact on talent management strategies and business performance. As technology becomes more sophisticated, AI is playing an increasingly essential role in decisions made around hiring and is used by brands such as Facebook as an integral part of the screening and assessment of candidates. This article examines its ongoing effect on the jobs market and the ways in which HR can harness its advantages to better understand, improve and predict hiring needs and potential problems. AI is broadly defined as'machines which perform tasks which humans are capable of performing'. It has been traditionally been regarded as a threat to jobs, with the most drastic predictions suggesting that unemployment rates will reach 50% within 30 years, but perceptions and predictions are changing.

A team of researchers from Belgium think that they are close to extending the anticipated end of Moore's Law, and they didn't do it with a supercomputer. Using an artificial intelligence (AI) algorithm called reservoir computing, combined with another algorithm called backpropagation, the team developed a neuro-inspired analog computer that can train itself and improve at whatever task it's performing. Reservoir computing is a neural algorithm that mimics the brain's information processing abilities. Backpropagation, on the other hand, allows for the system to perform thousands of iterative calculations that reduce error, which lets the system improve its solution to a problem. "Our work shows that the backpropagation algorithm can, under certain conditions, be implemented using the same hardware used for the analog computing, which could enhance the performance of these hardware systems," Piotr Antonik explains.

Rather than leading to the violent downfall of humankind, artificial intelligence is helping people around the world do their jobs, including doctors who diagnose sepsis in patients and scientists who track endangered animals in the wild, experts said Thursday (Oct. Advancements in the field of artificial intelligence (AI) haven't always been met with enthusiasm. Famed astrophysicist Stephen Hawking warned on several occasions that a fully developed AI could destroy the human race, and Hollywood sci-fi movies are rife with fierce robots battling humans for control. But at yesterday's conference -- attended by the country's leading researchers, innovators, entrepreneurs and students -- scientists explained how newly developed AI is accelerating research and improving lives. Here is a look at five AI inventions that are already redefining technology.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Random forests are an excellent "out of the box" tool for machine learning with many of the same advantages that have made neural nets so popular. They are able to capture non-linear and non-monotonic functions, are invariant to the scale of input data, are robust to missing values, and do "automatic" feature extraction. Additionally, they have other benefits that neural nets do not. What follows is a look into how random forests work, how they may be usefully applied, and a discussion of some situations in which they may be preferable to neural networks. So how do random forests work?

But cars more fully integrated into the so-called internet of things -- everyday devices able both to send and receive data -- could become more of a seamless piece of the daily digital fabric of people's lives. Even now, Amazon's voice-activated home assistant, Alexa, can order up an Uber ride or find out how much gas is in a car's tank while the driver is still in the house. BMW announced this month that its Connected services would enable Alexa owners to lock the car doors and check car battery levels from the comfort of their sofas. Ford Motor plans to introduce Alexa integration into vehicles, including the Escape and Fusion, before the end of this year, said James A. Buczkowski, who oversees advanced engineering at Ford. "Your spouse could add things to the shopping list, which your car would alert you to," Mr. Buczkowski said.

We are launching the World's first Voice Activated Artificial Intelligence (A.I.) Assistant for #Saving & #Investing!

The artificial intelligence revolution has arrived. Companies are racing to deliver AI capabilities that make sense of the massive influx of data created in the last several years as a result of the cloud, mobile, social and IoT trends. But cracking the AI nut is difficult. The technical requirements, resources and expertise needed to deliver AI are enormous, and until recently it was only feasible for the largest companies. But as intelligence becomes the new currency in business, it is vital that these barriers be knocked down and that the power of AI reaches the hands of employees across organizations, in every industry and of all sizes.