Engineered life
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(a): Volatile (short-term) memory property of two terminal Pt/WO3-x/Pt device before the forming process. Current change observed by applying sequence of positive voltage pulses at intervals of 40 s and widths of 0.5 s. Read voltage was 0.5 V. (b): Non-volatile (long-term) memory property in the device after forming process following application of sequence of positive and negative pulses with widths of 0.1 ms. Read voltage was 0.1 V. (c): Schematic illustration of the device structures before and after forming process.
On-demand synaptic electronics: Circuits that learn and forget
State of the Union “aisle hogs”: 2013 edition Which members of Congress will get up early to increase their chances of getting on national TV? VIDEO Jillian Rayfield Tuesday, Feb 12, 2013 1:45 PM UTC Politics Video , 2012 , State of the Union
Your next mayor: A computer
Researchers at the University of Cambridge have developed a simple mathematical model of the brain which provides a remarkably complete statistical account of the complex web of connections between various brain regions. Their findings have been published this week in the journal Proceedings of the National Academy of Sciences (PNAS) . The brain shares a similar pattern of connections with other complex networks such as social networks and the world wide web. However, until now, it was not known what rules were involved in the formation of the human brain network.
Wiring the brain
As computer scientists this year celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing, who set out the basis for digital computing in the 1930s to anticipate the electronic age, they still quest after a machine as adaptable and intelligent as the human brain. Now, computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing's work to its next logical step. She is translating her 1993 discovery of what she has dubbed "Super-Turing" computation into an adaptable computational system that learns and evolves, using input from the environment in a way much more like our brains do than classic Turing-type computers. She and her post-doctoral research colleague Jeremie Cabessa report on the advance in the current issue of Neural Computation . "This model is inspired by the brain," she says.
Computer scientists form mathematical formulation of the brain's neural networks
» Brian: a simple and flexible simulator for spiking neural networks
Brian: a simple and flexible simulator for spiking neural networks PDF version | Permalink Romain Brette and Dan Goodman 1 July 2009 New neural-simulation technology makes spiking neuron models more accessible to systems neuroscience and neuromorphic engineering. Neurons communicate with stereotypical electrical impulses called action potentials or spikes.
Dr. Peter Norvig is Director of Research at Google Inc. He is a Fellow of the Association for Computing Machinery and the American Association for Artificial Intelligence and co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field. Previously he was head of Computational Sciences at NASA and a faculty member at USC and Berkeley. In this talk he discusses, among other things, how non-parametric models can be applied to vision and language problems in data-rich environments.
Models and Theories
The Future of Moral Machines
The Stone is a forum for contemporary philosophers on issues both timely and timeless. A robot walks into a bar and says, “I’ll have a screwdriver.” A bad joke, indeed. But even less funny if the robot says “Give me what’s in your cash register.”
AI competitions
Consciousness Studies/The Philosophical Problem/Machine Consciousness
[ edit ] Elementary Information and Information Systems Theory When one physical thing interacts with another a change in "state" occurs. For instance, when a beam of white light, composed of a full spectrum of colours is reflected from a blue surface all colours except blue are absorbed and the light changes from white to blue.
Interesting thing at UAI 2011
Interesting thing at UAI 2011 I had a chance to attend UAI this year, where several papers interested me, including: Hoifung Poon and Pedro Domingos Sum-Product Networks: A New Deep Architecture . We’ve already discussed this one , but in a nutshell, they identify a large class of efficiently normalizable distributions and do learning with it.
Comparison of agent-based modeling software
In the last few years, the agent-based modeling (ABM) community has developed several practical agent based modeling toolkits that enable individuals to develop agent-based applications. More and more such toolkits are coming into existence, and each toolkit has a variety of characteristics. Several individuals have made attempts to compare toolkits to each other (see references).
A Taxonomy for Artificial Embryogeny
A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes.New Scientist TV: Born to be Viral: Pool-playing robot rivals humans
MacGregor Campbell, consultant Pool sharks, beware: a new robot will give you a run for your money. It's definitely not the fastest player but it completed 400 shots with an 80 per cent success rate. The robot, designed by Thomas Nierhoff , Omiros Kourakos and Sandra Hirche at the Technical University of Munich, Germany, has two arms that can move in seven different ways. Cameras mounted above the table track the position of the balls and cue, and feed this information to the robot's computers. It can then decide on the best move and calculate how the arms should be oriented to complete the stroke.Automated reasoning
Neurology
Natural language
Artificial Intelligence
machine learning
Artificial neural networks
Artifactual Intelligence--made things with minds
Emotion