#machine-learning algorithms

FedEx Introduces Same-Day Delivery Robot

Imported from ttnews.com

BusinessTechnologyAutonomousMarch 1, 2019 2:30 PM, EST FedEx Introduces Same-Day Delivery Robot

FedEx Corp. unveiled an autonomous delivery device designed to help retailers make same-day and last-mile deliveries to customers, showing off the robot on “The Jimmy Fallon Show” on Feb. 26.

The next…

Imported from ttnews.com

BusinessTechnologyAutonomousMarch 1, 2019 12:30 PM, EST AI in Trucking

The terms artificial intelligence and automation pepper discussions about the transportation industry’s future, often closely followed by mentions of autonomous vehicles. But while self-driving trucks are still in their…

#Google Expanding #YouTube #Video #Moderato Team to Improve #UX -- #Xanjero

Google states it will expand its human YouTube moderator team by up to 10,000 employees to monitor video uploads, thereby improving UX…

Google might well be the world’s largest search engine. And, it might also lead the way in web technology. But, its subsidiary YouTube continues to suffer from negative public perception. 

Google Expanding YouTube Moderator Team to Improve User Experience

In…

#AI Powered #Google #Jobs #Search Feature Launches Right on the #SERP -- #Xanjero

The new AI-powered Google jobs search feature, previously spotted in limited testing, makes its way to the main search engine results page…

Back in May, certain queries returned job search results in what appeared a limited test. Now, search giant Google officially introduces an artificial intelligence-powered job search feature.

AI-Powered Google Jobs Search Feature Rolls Out to the SERP

Google…

With 2.5 million daily users, the Tor network is the world’s most popular system for protecting Internet users’ anonymity. For more than a decade, people living under repressive regimes have used Tor to conceal their Web-browsing habits from electronic surveillance, and websites hosting content that’s been deemed subversive have used it to hide the locations of their servers.

Researchers at MIT…

By John Markoff, NY Times, December 28, 2013

PALO ALTO, Calif.--Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.

The first commercial version of the new kind of computer chip is scheduled to be released in 2014. Not only can it automate tasks that now require painstaking programming--for example, moving a robot's arm smoothly and efficiently--but it can also sidestep and even tolerate errors, potentially making the term "computer crash" obsolete.

The new computing approach, already in use by some large technology companies, is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information. It allows computers to absorb new information while carrying out a task, and adjust what they do based on the changing signals.

In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control. That can hold enormous consequences for tasks like facial and speech recognition, navigation and planning, which are still in elementary stages and rely heavily on human programming.

Designers say the computing style can clear the way for robots that can safely walk and drive in the physical world, though a thinking or conscious computer, a staple of science fiction, is still far off on the digital horizon.

"We're moving from engineering computing systems to something that has many of the characteristics of biological computing," said Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centers devoted to developing these new kinds of computer circuits.

Conventional computers are limited by what they have been programmed to do. Computer vision systems, for example, only "recognize" objects that can be identified by the statistics-oriented algorithms programmed into them. An algorithm is like a recipe, a set of step-by-step instructions to perform a calculation.

But last year, Google researchers were able to get a machine-learning algorithm, known as a neural network, to perform an identification task without supervision. The network scanned a database of 10 million images, and in doing so trained itself to recognize cats.

In June, the company said it had used those neural network techniques to develop a new search service to help customers find specific photos more accurately.

The new approach, used in both hardware and software, is being driven by the explosion of scientific knowledge about the brain. Kwabena Boahen, a computer scientist who leads Stanford's Brains in Silicon research program, said that is also its limitation, as scientists are far from fully understanding how brains function.

"We have no clue," he said. "I'm an engineer, and I build things. There are these highfalutin theories, but give me one that will let me build something."

One great advantage of the new approach is its ability to tolerate glitches. Traditional computers are precise, but they cannot work around the failure of even a single transistor. With the biological designs, the algorithms are ever changing, allowing the system to continuously adapt and work around failures to complete tasks.

Traditional computers are also remarkably energy inefficient, especially when compared to actual brains, which the new neurons are built to mimic.

I.B.M. announced last year that it had built a supercomputer simulation of the brain that encompassed roughly 10 billion neurons--more than 10 percent of a human brain. It ran about 1,500 times more slowly than an actual brain. Further, it required several megawatts of power, compared with just 20 watts of power used by the biological brain.