Category: Machine Intelligence

from the Biological-quantum-teleportation dept.
For those who have been seriously inspired or irritated by Roger Penroseís hypotheses on the possible basis of consciousness in quantum effects occurring inside neurons in the brain, a trio of researchers has published a speculative proposal that suggests that biological microtubules may act as quantum electrodynamic cavities and have the potential for quantum entanglement, teleportation and computation. The authors suggest that this mechanism may be responsible for how the brain works, or might at least provide biological building blocks for creating quantum computers. A preprint of their research paper is available online on the arXiv preprint server at http://www.arxiv.org/abs/quant-ph/0204021.

According to a press release (13 March 2002), researchers at Brown University in Providence, Rhode Island have demonstrated direct, real-time brain control of the movement of the cursor in a computer display. Their report appears in the March 14 issue of Nature.

Read more for details and links to additional coverage of the intriguing research.

from the Automated-engineering dept.
According to a press release (22 January 2002), a new method promises to change how companies create materials — using artificial intelligence and a technique that simultaneously tests thousands of formulations — dramatically speeding up the discovery process. The system, which combines hardware and software, was developed by Jochen Lauterbach, an associate professor of chemical engineering at Purdue University in West Lafayette, Indiana.

According to the press release, Lauterbach has developed an automated system that uses combinatorial chemistry, in which equipment systematically creates and tests thousands of chemical samples at the same time using thousands of tiny plastic beads coated with different catalysts. All of the beads, each bearing its own individual catalyst, are tested simultaneously. The system then uses infrared sensor technology to quickly screen each sample to evaluate its performance. A small percentage of the catalysts created are effective. Information is collected from both the best catalysts and the failed catalysts and fed into software that employs hybrid neural networks and genetic algorithms to mimics the logical and intuitive thought processes of chemists. Even though the majority of the catalysts created are not effective, the software uses the wealth of information gained from those failures to come up with entirely new catalysts.

from the basic-motivations dept.
An article in Technology Research News ("Software agents evolve purpose", by Kimberly Patch, 2 January 2002) describes work by researchers from the Keldysh Institute of Applied Mathematics at the Russian Academy of Science have shown that purposeful behavior can emerge naturally in a software simulation that has simple software beings, or agents, evolving over many generations. The researchers described these evolved behaviors as purposeful motivation. The researchers say the simulation showed that a system that uses motivations to control simple reflexes can emerge in an evolutionary process. Having motivation was an advantage likely to be passed on to subsequent generations of the agents, said Mikhail Burtsev, one of the researchers. "The population of agents with motivations had obvious selective advantages compared with the population of agents without motivations," he said.

The researchers began with a small population of simple, identical neural-net based agents that could move, eat (gain energy from the environment), and mate with other agents to reproduce. The agent population as a whole had one goal — survival. This goal required individuals to push toward two basic subgoals — to replenish energy, and to reproduce, said Burtsev. The agents evolved to seek out [food] and other agents. "The most important thing here is that we didn't force agents to follow these needs. The needs were prescribed explicitly by [the] environment, and only agents that had these two needs could successfully undergo selection pressure," said Burtsev.

The article also contains comments from another artificial life researcher, who expressed some skepticism at the interpretation that the agents had evolved motivated behavior; rather, he said, it may simply be the result of the neural net having better access to information about the environment and acting on it more effectively.

The Russian researchersí technical paper ("A Life Model of Evolutionary Emergence of Purposeful Adaptive Behavior") is available online at the Lawrence National Laboratory archive, as an Adobe Acrobat PDF file.