Caltech Researchers Create the First Artificial Neural Network Out of DNA
California Institute of Technology (07/20/11) Marcus Woo
California Institute of Technology (CalTech) researchers have developed an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete information. The network, which consists of four artificial neurons made from 112 distinct strands of DNA, plays a mind-reading game in which it identifies a mystery scientist based on answering yes or no questions, such as whether the scientist is British. The network communicates its answers using fluorescent signals and was able to correctly identify the scientist in 100 percent of the 27 trials the researchers conducted. The DNA-based neural network can take an incomplete pattern and determine what it represents. The researchers say that biochemical systems with artificial intelligence could have applications in medicine, chemistry, and biological research. They based the network on a simple model of a neuron, known as a linear threshold function. "It has been an extremely productive model for exploring how the collective behavior of many simple computational elements can lead to brain-like behaviors, such as associative recall and pattern completion," says CalTech professor Erik Winfree.
Wednesday, July 20, 2011
Blog: Caltech Researchers Create the First Artificial Neural Network Out of DNA
Saturday, July 16, 2011
Blog: Internet's Memory Effects Quantified in Computer Study
Internet's Memory Effects Quantified in Computer Study
BBC News (07/16/11) Jason Palmer
Recent experiments have shown that computers and the Internet are changing the nature of human memory, as people presented with difficult questions began to think of computers. If the participants knew that the facts would be available on a computer later, they had poor recall of the answers but enhanced recall of where they were stored, according to the study, which described the Internet as serving as a transactive memory. Transactive memory "is an idea that there are external memory sources--really storage places that exist in other people," says Columbia University's Betsy Sparrow. The researchers used a modified Stroop test to study how people thought about difficult questions and whether they relied on computers for the answers. The researchers provided a stream of facts to participants, and half were told to file them away on a computer, and the other half were told the facts would be erased. Those who knew the information would not be available later performed significantly better than those who filed the information away. However, those who expected the information to be available were very good at remembering in which folder they had stored it.
Friday, July 15, 2011
Blog: Machines to Compare Notes Online?
Machines to Compare Notes Online?
AlphaGalileo (07/15/11)
Autonomous machines, networks, and robots should publish their own suggestions for upgrading the technology on the Internet, says the University of Southampton's Sandor Veres. Giving machines and systems a greater degree of self-control will be the best way to improve them in the future, but humans will be more likely to guide and trust them if their dialogue is transparent, Veres says. An autonomously operating technical system would have some modeling of a changing environment; learning of various skills in feedback interaction with the environment; symbolic recognition of events and actions to perform logic-based computation; the ability to explain reasons of own actions to humans; and efficient transfer of rules, goals, values, and skills from human users to the autonomous system. Veres says the natural language programming sEnglish system could be used to achieve the last three technical features. "The adoption of a 'publications for machines' approach can bring great practical benefits by making the business of building autonomous systems viable in some critical areas where a high degree of intelligence is needed and safety is paramount," Veres says.
Blog: Swarms of Locusts Use Social Networking to Communicate
Swarms of Locusts Use Social Networking to Communicate
Institute of Physics (07/15/11)
The swarming behavior of locusts is created by the same social networks that humans adopt, according to a study by researchers from the Max Planck Institute for Physics of Complex Systems and a U.S.-based scientist supported by the National Science Foundation. The researchers applied previous findings on opinion formation in social networks to an earlier study of 120 locust nymphs marching in a ring-shaped arena in the lab. Using a computer model that simulated the social network among locusts, the team found that the key component to reproducing the movements observed in the lab is the social interactions that occur when locusts, walking in one direction, convince others to follow them. Locusts create the equivalent of our human social networks, according to the researchers. "We concluded that the mechanism through which locusts agree on a direction to move together ... is the same we sometimes use to decide where to live or where to go out," says researcher Gerd Zschaler. "We let ourselves be convinced by those in our social network, often by those going in the opposite direction."
Tuesday, July 12, 2011
Blog: Computer Learns Language By Playing Games
Computer Learns Language By Playing Games
MIT News (07/12/11) Larry Hardesty
Massachusetts Institute of Technology professor Regina Marzilay has adapted a system she developed to generate scripts for installing software on a Windows computer based on postings from a Microsoft help site to learn to play the Civilization computer game. The goal of the project was to demonstrate that computer systems that learn the meanings of words through exploratory interaction with their environments have much potential and deserve further research. The system begins with no prior knowledge about the task or the language in which the instructions are written, making the initial behavior almost completely random. As the system takes various actions, different words appear on the screen. The system finds those words in the instructions and develops hypotheses about what those words mean, based on the surrounding text. The hypotheses that consistently lead to good results are referred back to more frequently, while the hypotheses that are proven unsuccessful are discarded. In the case of the computer game, the system won 72 percent more often than a version of the same system that did not use the written instructions, and 27 percent more frequently than an artificial intelligence-based system.
Blog: Cracking the Code of the Mind
Cracking the Code of the Mind
American Friends of Tel Aviv University (07/12/11)
Tel Aviv University researchers have developed a type of lab-on-a-chip platform that can show how neuronal networks communicate and work together. The researchers, led by doctoral student Mark Shein, applied mathematical and engineering techniques to connect neurons with electronics in order to understand how neuronal connections communicate. The tool could be used to test new drugs, advance artificial intelligence, and develop better artificial limbs, according to the researchers. The device enables researchers to see how neural circuits operate under different conditions and explore activity patterns of many neurons simultaneously. The researchers focused on studying how several groups of neurons communicate with each other, according to Shein. The researchers cultured different sized networks of neuronal circuits and found that neural networks have a hierarchical structure in which large networks are composed of smaller sub-networks.
Tuesday, July 5, 2011
Blog: A Futures Market for Computer Security
A Futures Market for Computer Security
Technology Review (07/05/11) Brian Krebs
A pilot prediction market that can forecast major information security incidents before they occur is under development by information security researchers from academia, industry, and the U.S. intelligence community for the purpose of supplying actionable data, says Greg Shannon with Carnegie Mellon University's Software Engineering Institute. "If you're Verizon, and you're trying to pre-position resources, you might want to have some visibility over the horizon about the projected prevalence of mobile malware," he says. "That's something they'd like to have an informed opinion about by leveraging the wisdom of the security community." Consensus Point CEO Linda Rebrovick says the project's objective is to draw a network of approximately 250 experts. Prediction markets have a substantial inherent bias--respondents to questions are not surveyed randomly—but there also is an incentive for respondents to respond only to those queries they feel confident in answering accurately. "People tend to speak up only when they're reasonably sure they know the answer," says Consensus Point chief scientist Robin Hanson. Even lukewarm responses to questions can be useful, notes Dan Geer, chief information security officer at the U.S. Central Intelligence Agency's In-Q-Tel venture capital branch.