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"The growing power of computer vision is a crucial first step for the next generation of computing, robotic and artificial intelligence systems."

"We're going to see artificial intelligence do more and more, and as this happens costs will go down, outcomes will improve, and our lives will get better. Soon countless pieces of AI will be working on our behalf, often in the background. They'll help us in areas ranging from trivial to substantive to life changing. Trivial uses of AI include recognizing our friends' faces in photos and recommending products. More substantive ones include automatically driving cars on the road, guiding robots in warehouses, and better matching jobs and job seekers. But these remarkable advances pale against the life-changing potential of artificial intelligence."

passage about robot merging

the term nanotechnology was coined back in the 80s by K. Eric Drexler. I'm sure at the time it wasn't much more than a science fiction dream, but now it is a very real technology. "When K. Eric Drexler (right) popularized the word 'nanotechnology' in the 1980's, he was talking about building machines on the scale of molecules, a few nanometers wide-motors, robot arms, and even whole computers, far smaller than a cell. Drexler spent the next ten years describing and analyzing these incredible devices, and responding to accusations of science fiction. Meanwhile, mundane technology was developing the ability to build simple structures on a molecular scale. As nanotechnology became an accepted concept, the meaning of the word shifted to encompass the simpler kinds of nanometer-scale technology. The U.S. National Nanotechnology Initiative was created to fund this kind of nanotech: their definition includes anything smaller than 100 nanometers with novel properties."

"A basic definition: Nanotechnology is the engineering of functional systems at the molecular scale. This covers both current work and concepts that are more advanced. In its original sense, 'nanotechnology' refers to the projected ability to construct items from the bottom up, using techniques and tools being developed today to make complete, high performance products." This is the definition of what nanotechnology is-building something from almost nothing.

"How has the approach toward nanotechnology changed as we've learned more about nature?"

i don't believe this will work in the long run "The Google driverless car is a project by Google that involves developing technology for autonomous cars. The software powering Google's cars is called Google Chauffeur.[2] Lettering on the side of each car identifies it as a "self-driving car." The project is currently being led by Google engineer Sebastian Thrun, director of the Stanford Artificial Intelligence Laboratory and co-inventor of Google Street View. Thrun's team at Stanford created the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge and its US$2 million prize from the United States Department of Defense.[3] The team developing the system consisted of 15 engineers working for Google, including Chris Urmson, Mike Montemerlo, and Anthony Levandowski who had worked on the DARPA Grand and Urban Challenges.[4]"

The Google driverless car is a project by Google that involves developing technology for autonomous cars

In any realistic learning application, the entire instance space will be so large that any learning algorithm can expect to see only a small fraction of it during training. From this small fraction, a hypothesis must be formed that classifies all the unseen instances. If the learning algorithm performs well then most of these unseen instances should be classified correctly. However, if no restric- tions are placed on the hypothesis space and no "preference criterion" 1124] is supplied for comparing competing hypotheses, then all possible classifications of the unseen instances are equally possible and no inductive method can do better on average than random guessing [261. Hence all learning algorithms employ some mechanism whereby the space of hypotheses is restricted or whereby some hypotheses are preferred a priori over others. This is known as inductive bias I hope to use this source to learn more about how artificial intelligence learns, as I have read in other places that the kind that learns from the "bottom up" learns by making mistakes and learning from them. AI, if to be truly intelligent, is probably going to have to learn the way we did; by experience and example. In Kaku's book, he mentions the differences between two artificially intelligent robots that he "met". One, STAIR, had a limited database and was programmed to do what it did. Another, LAGR, piloted itself through a park, bumping into miscellaneous objects and learning their locations so that on the next pass, it would not hit them. I hope to learn more about that kind of logic by reading this article, as I think it is important to have a better understanding of exactly how artificial intelligence learns.

"Top-down and bottom-up are both strategies of information processing and knowledge ordering, used in a variety of fields including software, humanistic and scientific theories (see systemics), and management and organization. In practice, they can be seen as a style of thinking and teaching." This article gives information about the two ways to approach artificial intelligence: top-down, which is teaching the robot all of the rules of intelligence from the beginning, and bottom-up, which is making the computer learn like a human instead of teaching it everything. This source is reliable because it cites where it got its information.

Mr. Obama's 2010 budget proposal for NASA asks for $18 billion over five years for fueling spacecraft in orbit, new types of engines to accelerate spacecraft through space and robotic factories that could churn soil on the Moon - and eventually Mars - into rocket fuel.

"In the future rush to get to work, the day's tasks will be checked using a personal robotic butler, the misplaced car keys will be located by entering the word "keys" into a cellphone and getting a call back saying "bedroom." I find this site useful because it gives another insight on what future computers will be like. It gives great examples of what the technology will be like. For example; our phone will be able to command the dishwasher and the laundry machine while we do other activities. This site is reliable because the concept of this site correlates with many other sources about the future of the computer.

It's a little out there but hey..

LAGR

ASIMO

"elepresence refers to a set of technologies which allow a person to feel as if they were present, to give the appearance of being present, or to have an effect, via telerobotics, at a place other than their true location."