Despite more than 50 years of experimentation with artificial general intelligence (AGI), mankind still hasn't achieved the goal of creating a single machine that can perform all cognitive tasks as well as humans can. But what if there was a way to attain AGI-level artificial intelligence (AI) using today's technology? The fact is that artificial narrow intelligence (ANI) systems are already available that can approximate the capabilities of AGI by employing multiple cognitive engines in a parallel-processing architecture. And with the number of commercially-available cognitive engines expanding exponentially, this approach will enable ANI to rapidly overtake and even surpass human capabilities. Moreover, with the AI market set to expand by nearly a factor of 60 from 2016 to 2022, this method represents the best way for companies to capitalize on the industry's growth potential.

It's relatively easy to develop a drone that can fly on its own, but it's another matter developing one that can navigate the many obstacles of real life. That's where Microsoft thinks it can help. It just published an open source simulator, the Aerial Informatics and Robotics Platform, that helps designers test and train autonomous machines in realistic conditions without wrecking expensive prototypes. The tool has vehicles move through randomized environments filled with the minutiae you see on a typical street, such as power lines and trees -- if your drone can't dodge a tree branch, you'll find out quickly. You can see what the vehicle would see (including simulated sensor data), and the software ties into both existing robotic hardware platforms and machine learning systems to speed up development.

Around the time Raj Bhan injured himself, he decided to part ways with the data team at Netflix. He'd been training for a half marathon and plucked a regimen from the web. Unfortunately, he had grouped himself into the wrong bucket and hurt his leg. "That struck a chord," he says. "People can suffer adverse effects from being in the wrong program at the wrong time. Fitness programs really need to be personalized."

Pathology as it is practiced now is very subjective,

Applications such as spam filtering, economic forecasting, and Netflix recommendations are powered by algorithms that allow computers to learn from and make predictions based on vast collections of data. But even as machine learning has purged e-mail inboxes of Vi@gra, the technology has been slower to infiltrate chemistry. As described in this collection of stories, however, the gap is starting to fill. Some researchers are . . .

CBS station KFMB put in calls to the Navy and Air Force Monday night about the striking launch off the coast of Los Angeles, which was easily visible from the coast, but the military has said nothing about the launch. KFMB showed video of the apparent missile to former U.S. Ambassador to NATO Robert Ellsworth, who is also a former Deputy Secretary of Defense, to get his thoughts. It takes people\'s breath away,\" said Ellsworth, calling the projectile, \"a big missile\". Magnificent images were captured by the KCBS news helicopter in L.A. around sunset Monday evening. The location of the missile was about 35 miles out to sea, west of L.A. and north of Catalina Island.

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. According to Mr. Sachin Garg - Associate Director at MarketsandMarkets who tracks the global semiconductor market, the global artificial intelligence chipset market is expected to be worth USD 16.06 Billion by 2022, growing at a CAGR of 62.9% between 2016 and 2022.

NVIDIA (NASDAQ:NVDA), the leader in the graphics processing industry, has been growing like gangbusters. Its performance over the last several years has been nothing short of stunning, and investors will rightly want to know if this growth can continue. Below, we look at several key financial metrics to provide insight into the company's future potential. Revenue gains were the largest driver of results. In its recent earnings release, NVIDIA announced record quarterly revenue of $2.17 billion, which grew 55% over the prior-year quarter, as well as record full-year revenue of $6.91 billion, up 38% over the prior period.

When Worlds Collide, the 1951 Sci Fi film, painted a rather gloomy vision of the future of mankind on earth. However, the prospects for blended reality, in which the physical and digital worlds collide, couldn't be more promising. By fusing the virtual and real worlds, the possibilities are endless. By eliminating filters and limitations, blended reality enables expression at the speed of thought to improve experiences and inspire new technology. Using healthcare as an example, blended reality has significant implications for more personalized care, prevention and treatment by converging human biology, real-world context and technology advancements.

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We also present the results of a recent insideBIGDATA survey to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains. With all the quickly evolving nomenclature in the industry today, it's important to be able to differentiate between AI, machine learning and deep learning.