Sir David Baulcombe is one of the world's top scientists whose work identified small RNAs, and he's a nice person as well. He will be a Keynote Speaker at the upcoming UK Plant Sciences Federation meeting in Dundee, Scotland, April 2013, which is sure to be a stimulating meeting http://www.plantsci2013.org.uk/programme/
DARPA's Physical Intelligence program represents a potential major advance in artificial intelligence research, as the “physical intelligence” device would not require computer programming or the use of human controllers to provide directions, as with traditional robots. Instead, the device operates via nano-scale interconnected wires that send signals through synthetic synapses, just like the human brain. Such a system is capable of remembering information, meaning that robots might be able to act like humans in the foreseeable future.
Compared to traditional artificial intelligence systems that rely on conventional computer programming, this one “looks and ‘thinks’ like a human brain,” said James K. Gimzewski, professor of chemistry at the University of California, Los Angeles.
Gimsewski is a member of the team that has been working under sponsorship of the Defense Advanced Research Projects Agency (DARPA) on a program called Physical Intelligence.
The stated objective of the program is: "The analysis domain is to develop analytical tools to support the development of human-engineered physically intelligent systems and to understand physical intelligence in the natural world".
The increasingly ambiguous divide between man and machine just got blurred that much more with Stanford's recent announcement: scientists have successfully created the first truly biological transistor made entirely out of genetic material.
We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.
So today we’re launching the Quantum Artificial Intelligence Lab. NASA’s Ames Research Center will host the lab, which will house a quantum computer from D-Wave Systems, and the USRA (Universities Space Research Association) will invite researchers from around the world to share time on it. Our goal: to study how quantum computing might advance machine learning.
Machine learning is highly difficult. It’s what mathematicians call an “NP-hard” problem. That’s because building a good model is really a creative act. As an analogy, consider what it takes to architect a house. You’re balancing lots of constraints -- budget, usage requirements, space limitations, etc. -- but still trying to create the most beautiful house you can. A creative architect will find a great solution. Mathematically speaking the architect is solving an optimization problem and creativity can be thought of as the ability to come up with a good solution given an objective and constraints.
Classical computers aren’t well suited to these types of creative problems. Solving such problems can be imagined as trying to find the lowest point on a surface covered in hills and valleys. Classical computing might use what’s called “gradient descent”: start at a random spot on the surface, look around for a lower spot to walk down to, and repeat until you can’t walk downhill anymore. But all too often that gets you stuck in a “local minimum” -- a valley that isn’t the very lowest point on the surface.
That’s where quantum computing comes in. It lets you cheat a little, giving you some chance to “tunnel” through a ridge to see if there’s a lower valley hidden beyond it. This gives you a much better shot at finding the true lowest point -- the optimal solution.
We’ve already developed some quantum machine learning algorithms. One produces very compact, efficient recognizers -- very useful when you’re short on power, as on a mobile device. Another can handle highly polluted training data, where a high percentage of the examples are mislabeled, as they often are in the real world. And we’ve learned some useful principles: e.g., you get the best results not with pure quantum computing, but by mixing quantum and classical computing.
Can we move these ideas from theory to practice, building real solutions on quantum hardware? Answering this question is what the Quantum Artificial Intelligence Lab is for. We hope it helps researchers construct more efficient and more accurate models for everything from speech recognition, to web search, to protein folding. We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics. We’re excited to get started with NASA Ames, D-Wave, the USRA, and scientists from around the world.
There is a fundamental chasm in our understanding of ourselves, the universe, and everything. To solve this, Sir Martin takes us on a mind-boggling journey through multiple universes to post-biological life. On the way we learn of the disturbing possibility that we could be the product of someone elses experiment.
By: Mike Wall Published: 04/18/2013 02:55 PM EDT on SPACE.com NASA's Kepler space telescope has discovered three exoplanets that may be capable of supporting life, and one of them is perhaps the most Earth-like alien world spotted to date,...
String theory is one of the more popular candidates to combine quantum mechanics and relativity into a grand unified theory. But it had remained completely untestable until recent experiments at the Large Hadron Collider.