Robotics Companies Look to Near Future - Disruptions

The New York Times
Monday, August 13, 2012

Robotics Companies Look to Near Future - Disruptions

By NICK BILTON

MENLO PARK, Calif. - Millions of people watched a robot descend last week on Mars, about 154 million miles away, while it shared video, photos and status updates from its own Twitter account.

I had my own encounter with a robot last week. I had dinner with one - right here in Silicon Valley.

The dinner was at Willow Garage, a robotics company in Menlo Park, and was intended to introduce some reporters to the robots the company is building.

The main attraction was the PR2, which can pick things up, fold laundry, open doors and bring cups, plates and other small objects to people. The PR2 is pretty stunning to see in action. Its price, $400,000 for the fully functional version, is pretty stunning, too. And although it is impressive to watch, it is still easily baffled by the mundane.

At the dinner, one of the PR2s dropped a soda can on the floor and just stood there, befuddled. It couldn't figure out what had happened to the can. It was as if it had just performed a wonderful magic trick on itself.

It is hard to know how many robots are in use today because roboticists disagree on what a robot is. Must it have arms, or artificial intelligence, or facial recognition? The earliest definition of the word, which comes from the Czech word robota, means "forced labor," or "slave."

Robotics companies give various estimates for the numbers of robots in use. Whatever the numbers, people like Steve Cousins, Willow Garage's president and chief executive, think robots will become a lot more mainstream in the not-too-distant future.

For more, visit www.nytimes.com.

Factual’s Gil Elbaz Wants to Gather the Data Universe

Excerpt from an article in

The New York Times (Business)

Sunday, March 25, 2012

Factual’s Gil Elbaz Wants to Gather the Data Universe

By QUENTIN HARDY

BEVERLY HILLS, Calif. AT 7 years old, Gilad Elbaz wrote, “I want to be a rich mathematician and very smart.” That, he figured, would help him “discover things like time machines, robots and machines that can answer any question.”

In the 34 years since, Mr. Elbaz has accomplished big chunks of these goals. He has built Web-traversing software robots and answered some very big questions for Google, along the way becoming a millionaire several hundred times over.

His time-machine plans, however, have been ditched for something he finds more important: trying to identify every fact in the world, and to hold them all in a company he calls Factual.

“The world is one big data problem,” Mr. Elbaz says from his headquarters, a quiet office 14 floors above the Los Angeles Country Club. He is a slim, soft-spoken man who weaves in his chair when an idea excites him. “What if you could spot any error, as soon as you wrote it? Factual is definitely a new thing that will change business, and a valuable new tool for computing.”

In the booming world of Big Data, where once-unimaginably huge amounts of information are scoured for world-changing discoveries, Mr. Elbaz may be the most influential inventor and investor. Besides Factual, he has interests in 30 start-ups, including an incubator in San Francisco dedicated to Big Data. Factual’s headquarters, in a high-rise on the Avenue of the Stars, hosts seminars for a data community he hopes to foster in the Los Angeles area.

Mr. Elbaz also serves on the boards of the California Institute of Technology, his alma mater, and the X Prize Foundation, which offers cash prizes to teams that meet challenges in space flight, medicine and genomics. The company he sold to Google, Applied Semantics, is the basis of Google’s AdSense business, which brings Google close to $10 billion in revenue annually.

While valued for his investments and guidance, Mr. Elbaz remains relatively little-known. He is so self-effacing that he recently walked through a conference of 3,000 data scientists, recognized only by the staff members of one of his investments. He lives quietly with his wife, a former federal prosecutor, and his three children in a modest ranch house in West Hollywood. For fun, he plays basketball at a local sports club.

His mental and financial assets, he says, are like gifts he needs to deploy so the world works better.

“If all data was clear, a lot fewer people would subtract value from the world,” he says. “A lot more people would add value.”

Creating clear, reliable data could also make Factual a very big company.

“Gil is pretty far ahead of the rest of us, the one entrepreneur where it takes a few meetings before I really understand everything he is talking about,” says Ben Horowitz, a venture capitalist who backed Factual through his firm, Andreessen Horowitz. “Three years ago, he thought Factual was his biggest chance to change the world. Over time, the world has moved his way.”

How Machines Are Learning To Make Up their Own Minds

A Spanish research team is working on a significant breakthrough in artificial intelligence.

By Jason Deign
View All Contributing Writers

March 19 , 2012

Mud swirls through water as an undersea robot tries to find the source of a leak from a deep-sea oil well. But something goes wrong: a piece of metal, unseen in the murk, prevents the ‘bot from reaching the damaged area. Unable to complete its objective, the machine gives up.

For machines such as this, which have to operate without orders from their human controllers, dealing with unforeseen circumstances is a problem. The default design solution is to get them to return to base for reprogramming, which costs time and money.

But that is set to change thanks to a team from the Carlos III Universityof Madrid, which is working on how artificial intelligence (AI) can help machines select and prioritize between different tasks, and move on to the next thing on the list if they cannot complete the first.

Disqus: How could thinking machines help us in the future?

In the case of an undersea robot, that means it might be able to complete other useful tasks on site before resurfacing, says Ángel García Olaya, assistant professor at the university. And the process can have applications in other areas, ranging from logistics to tourism.

The team's research, which is critical to the creation of artificial entities recognized as being one of the top 10 technology trendsof the next decade, builds on 20 years of work that has been honed by NASAsince 2004 because of its value for the Spirit and Opportunity Mars rovers.

These machines follow programs that were originally devised before they travelled into space, and are now updated from earth via radio.

If a rover is unable to complete a task, its programming may allow it to come up with an alternative plan of action, but the plan would need to be verified by a human operator. NASA was keen to find out if it could improve upon this process.

The resulting branch of AI focuses creating a simplified plan of all actions, stripping out most of the variables.

The plan is nonsensical in a practical sense—it may assume you can be in two places at once, for example—but it provides a fair idea of the costs of each action, which can then be used to prioritize them.

It is also between three and seven times faster than looking at each action in detail, which makes it useful for situations where time is at a premium.

However, says García: "Until now this had only been done with plans that had achievable goals. We have added the capacity to use it for goal selection."

Besides helping underwater robots to come up with a ‘plan B', the process can help logistics systems to work out the best way to complete a complex schedule of deliveries. One of the team's early backers was a Spanish company with a large and complex distribution network.

The team's work has also attracted interest from IActive, a planning technology spinoff of Spain's University of Granada, which is interested in using AI to develop personalized city guidesthat take into account a user's interests and create an itinerary for them.

Last but not least, the European Space Agencyis keen to use the process to optimize the number of experiments its satellites can perform.

"We could have this up and running in experimental robots within a year," says García, "and in submarine robots between one and two years. In areas such as logistics, we could do it now if anyone wants to implement it."