Posted by ljmacphee on May 5, 2008 under artificial intelligence in the news | 
I ran across a story a while back on Engadget, Researchers create a nanobot-controlling brain, and realized I hadn’t looked to see where we are in nanotechnology in a long time.
Nano is a prefix representing one one-billionth of something, a nanobot is a robotic device less than 1 billionth of a meter in size. One billionth of a meter is enough length to hold about 7 hydrogen atoms. In actuality, nanotechnology refers to just about any technology that is ultra-tiny in size.
Early hopes were to develop nanobots that could be injected into humans to cure what ails us. So far that hasn’t happened. But some small progress is being made using nanomachines to kill cancer cells as medication delivery systems.
Some recent sci-fi books ( ‘Next’
, ‘Post Singular’) have brought up some of the scarier possibilities nanobots bring to us. What happens if the nanoparticles behave like swarms? What happens if those swarms turn deadly? Or how do we protect ourselves from self-replicating nanoparticles that turn out to be dangerous?
Most recently nanosilver has been in the news. Its value is in its antibacterial properties. The hope was it would be added to clothing, laundry detergent and in medical devices. Not too long after nano-silver stories hit the main news media nano-silver pollution stories hit the news. You may also remember a few Y2K nutcases who dyed themselves blue drinking silver. If nano-silver contaminates the water supply what happens? We just don’t know yet? Will we all become smurfs?
Like any new technology bringing great promise, nanotechnology also brings great dangers. Until we have some progress on usable nanotechnology for the masses the concerns are likely to dampen interest in nanotechnology and government funding. So here’s hoping for some useful nanobots in our near future.
More information:
Are nanobots on their way?
Howard Lovy’s NanoBot ( blog )
International Society of Nanoscale Science, Computation and Engineering
Nanularity
Nanosoccer debuts at RoboCup 2007
International Nanotechnology and Society Network
Nanotechnology portal
A Billionth of a Meter is a Big Deal
I, nanobot
See also:
Shape shifting robots escape Lost set
Living Neural Net Created
Posted by ljmacphee on May 1, 2008 under artificial intelligence in the news |
One of the wonderful things the internet has done is to bring to life the ‘Mechanical Turk’. Together we can all do small things and create something wonderful, like the internet. Google’s search engine works so well because we all contribute to it. Amazon works fantastically because of the book reviews users contribute.
Loren Carpenter did an experiment at Siggraph 91 that demonstrated how quickly and easily we can work together even with out communicating.
Probably the most unique event of SIGGRAPH ‘91 was Loren Carpenter’s Audience Participation piece presented during the Electronic Theater. Each person in the audience was given a wand with a red side and a green side. The colored retro-reflective material was scanned in by video cameras at the back of the auditorium, frame-grabbed and processed, and used to drive a video display that was projected on the big screen, all in real time. In its standby mode the system created a map of the auditorium, with enough resolution to show each seat, indicating whether the person in that seat was holding up the red or green (or neither) side of their wand. It was described as “being a pixel in a huge raster scan display”. Various games were played with this setup, from simple voting and “stadium flash card” type displays, to a round of massively parallel Pong. The Pong game was stunning because of how quickly the 5000 “autonomous agents” in the audience learned to cooperate and regulate their aggregate behavior. The way it worked was that each side of the auditorium controlled one of the Pong paddles, red moved the paddles up and green moved it down. In order to move the paddle to the correct position, just the right number of people had to signal with the appropriate color. Too many or too few and the paddle would overshoot or undershoot its mark. The final exercise was massively parallel control of a flight simulator. We crashed. [Phreak.org - archives ]
Before too much time goes by we will all have a smart computer with an internet connection in our shirt pocket everywhere we go. How much quicker will you be able to commute when the public’s telephones all communicate the quickest route home with out any interaction from the users?
The commute is already a perfect example of a human hive mind at work. On route 128 about Boston millions of cars travel an old cow path that is now a 2, 3 or 4 lane highway depending on your location. It curves and winds and there are on and off ramps every few feet it seems. Yet an amazingly large amount of drivers manage to get on, bounce between lanes, go forward, get off all while playing with the radio, phone, texting, reading and avoiding all the unskilled drivers. ( Because everyone besides ourselves is an unskilled driver. ) No central command directs each car. We manage it autonomously, with little contact between the drivers and very little thinking.
Capitalism is an excellent example of the human hive mind. When we need more of a thing, the price goes up and more people begin creating more things. No plan or central authority is needed. When the economy slows in one place and picks up in another, the right number of people move to level both locations at sustainable levels.
This is all going to begin to happen faster and much more effectively thanks not only to the internet but the fact the net will soon be portable in everyone’s pocket.
More information:
The Technodiva Speaks
The Year in Ideas; Smart Mobs
Rheingold: Smart Mobs
Real Time Traffic Routing from the Comfort of your Car
Books:
‘Out of Control’ Kevin Kelly
See also:
Human generated artificial intelligence
Posted by ljmacphee on April 28, 2008 under artificial intelligence in the news, computer vision |
New insight into how brains process visual information is a double edged sword. It will make for much better vision engines but with that will come the failure of our most popular human test at the moment — captcha.
Using a fly, whose brain is heavily coded for visual information, Nemenman and his colleagues were able to show information is passed along the spikes in the fly’s brain neurons.
. . .
Nemenman and his colleagues’ research is significant because it re-examines fundamental assumptions that became the basis of neuromimetic approaches to artificial intelligence, such as artificial neural networks. These assumptions have developed networks based on reacting to a number of impulses within a given time period rather than the precise timing of those impulses.
“This may be one of the main reasons why artificial neural networks do not perform anywhere comparable to a mammalian visual brain,” said Nemenman, who is a member of Los Alamos’ Computer, Computational and Statistical Sciences Division. “In fact, the National Science Foundation has recognized the importance of this distinction and has recently funded a project, led by Garrett Kenyon of the Laboratory’s Physics Division, to enable creation of large, next-generation neural networks.”
New understanding of neural function in the design of computers could assist in analyses of satellite images and facial-pattern recognition in high-security environments, and could help solve other national and global security problems. [ read more Language of a fly proves surprising ]
Papers:
PLoS: Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution
More information:
Is Captcha’s moment passing?
Posted by ljmacphee on April 24, 2008 under artificial intelligence in the news |
Totally cool and totally scary. This algorithm finds hidden social networks no matter how small. This may turn out to be an excellent resource against terrorist networks. Currently the algorithm has and is being used to detect genetic networks. The algorithm was inspired by stegography but can be applied to any network of information.
Human diseases and social networks would seem to have little in common. However, at the crux of these two lies a network, communities within the network, and farther even, substructures of the communities. In a recent paper in Physical Review E 77:016104 (2008), Weixiong Zhang, Ph.D., Washington University associate professor of computer science and engineering and of genetics, and his Ph.D. student, Jianhua Ruan, published an algorithm, a recipe of computer instructions, to automatically discover communities and their subtle structures in various networks.
Many complex systems can be represented as networks, Zhang said, including the genetic networks he studies, social networks and the Internet. The community structure of networks features a natural division of the network where the vertices in each subnetwork are highly involved with each other, though connected less strongly with the rest of the network. Communities are relatively independent of one another structurally, but it is thought that each community may correspond to a fundamental functional unit. A community in a genetic network usually contains genes with similar functions, just as a community on the World Wide Web often corresponds to web pages on similar topics.
All Zhang and Ruan need are data. Their algorithm is more scalable than existing algorithms and can detect communities at a finer scale and with a higher accuracy than similar algorithms. The impact of having such a computational biology tool is in genomics, where researchers may be better able to identify and understand communities of genes and their networks as well as how they cooperate in causing diseases, such as sepsis, virus infections, cancer and Alzheimer’s disease. [ read more Algorithm finds the network -- for genes or the internet ]
More information:
Jianhua Ruan’s Homepage
Weixiong Zhang’s Homepage ( links to several of his programs on homepage )
Nature paper describes technique for extracting hierarchical structure of networks
Papers:
In Search of the Biological Significance of Modular Structures in Protein Networks
Posted by ljmacphee on April 21, 2008 under artificial intelligence in the news |
Some UA Huntsville researchers who specialize in statistics are finding patterns in asymmetric threats to the US and US troops. While these attacks seem random some patterns are emerging.
While these patterns do not give specific information as to what will be attacked, when and how, it does give probabilities of likely targets, types of attacks and time frames. Greater resources can then be applied to those areas over that time frame.
But the question is; is there really a pattern? We humans can find patterns in almost any sufficiently large database of information. It is also well known any well published attack will attract copy cats.
If it turns out that there are patterns to these attacks what’s to stop the terrorists from using a random number generator to plan future attacks?
Since the Sept. 11 attacks we’ve had several lists of cities likely to be hit by terrorists. Not of which has been attacked. And we’ve had several predictions of types of terrorist attacks to come. So far all they have predicted is what else will need to be dismantled before you can board a plane.
If we hope to anticipate terrorism perhaps we need to look at something besides statistics and predictive markets?
More information:
Computer Models to Provide Better Intelligence for Army
RAND Tries to Model Risks of Terrorist Attacks
The statistics of fear
Eigenbehaviors
Prediction Markets are hot but here’s why they can be so wrong