Lifeboat Foundation News Blog: Information Science

Archive for the ‘information science’ category: Page 322

May 30, 2009

Create an AI on Your Computer

Posted by Seb in categories: complex systems, human trajectories, information science, neuroscience, robotics/AI, supercomputing

Singularity Hub

Create an AI on Your Computer

Written on May 28, 2009 – 11:48 am | by Aaron Saenz |

If many hands make light work, then maybe many computers can make an artificial brain. That’s the basic reasoning behind Intelligence Realm’s Artificial Intelligence project. By reverse engineering the brain through a simulation spread out over many different personal computers, Intelligence Realm hopes to create an AI from the ground-up, one neuron at a time. The first waves of simulation are already proving successful, with over 14,000 computers used and 740 billion neurons modeled. Singularity Hub managed to snag the project’s leader, Ovidiu Anghelidi, for an interview: see the full text at the end of this article.

The ultimate goal of Intelligence Realm is to create an AI or multiple AIs, and use these intelligences in scientific endeavors. By focusing on the human brain as a prototype, they can create an intelligence that solves problems and “thinks” like a human. This is akin to the work done at FACETS that Singularity Hub highlighted some weeks ago. The largest difference between Intelligence Realm and FACETS is that Intelligence Realm is relying on a purely simulated/software approach.

Which sort of makes Intelligence Realm similar to the Blue Brain Project that Singularity Hub also discussed. Both are computer simulations of neurons in the brain, but Blue Brain’s ultimate goal is to better understand neurological functions, while Intelligence Realm is seeking to eventually create an AI. In either case, to successfully simulate the brain in software alone, you need a lot of computing power. Blue Brain runs off a high-tech supercomputer, a resource that’s pretty much exclusive to that project. Even with that impressive commodity, Blue Brain is hitting the limit of what it can simulate. There’s too much to model for just one computer alone, no matter how powerful. Intelligence Realm is using a distributed computing solution. Where one computer cluster alone may fail, many working together may succeed. Which is why Intelligence Realm is looking for help.

The AI system project is actively recruiting, with more than 6700 volunteers answering the call. Each volunteer runs a small portion of the larger simulation on their computer(s) and then ships the results back to the main server. BOINC, the Berkeley built distributed computing software that makes it all possible, manages the flow of data back and forth. It’s the same software used for SETI’s distributed computing processing. Joining the project is pretty simple: you just download BOINC, some other data files, and you’re good to go. You can run the simulation as an application, or as part of your screen saver.

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Mar 12, 2009

Crowdsourced Women’s Health Books Released by CureTogether

Posted by Alexandra Carmichael in categories: biological, biotech/medical, information science, open access, open source

Over 300 Women Share Experiences, Treatments for Painful, Common Chronic Conditions

CureTogether, a Health 2.0 Startup based in Silicon Valley, has released the first crowdsourced books on vulvodynia and endometriosis: two common, poorly understood conditions causing daily pain for millions of women. Assembled from the input of 190 and 137 women living with these respective conditions, “Vulvodynia Heroes” and “Endometriosis Heroes” are the product of an ongoing online research study at http://www.curetogether.com.

“Patients came together and decided what symptoms and treatments they wanted to track. They went on to diligently gather detailed, quantitative data on their bodies and experiences,” said Alexandra Carmichael, co-Founder of CureTogether. “The hope of this book is to spread awareness, reach out to people in pain who may not have heard of endometriosis, and increase interest and funding for future research.”

“These heroes are pioneers not just in investigating their own condition, but in developing self-cure practices that others can follow.”, said Gary Wolf, Contributing Editor of Wired and Blogger at The Quantified Self. “Many other women who are suffering will find this very helpful and inspiring,” said Elizabeth Rummer, MSPT at the Pelvic Health and Rehabilitation Center in San Francisco. A patient with endometriosis added, “This is great. I am just starting to really appreciate what awesome power CureTogether can have.”

Endometriosis is a painful chronic condition that affects 5–10% of women, and vulvodyna affects up to 16% of women at some point in their lives. They are two of the most active condition communities at CureTogether, with information about symptoms, treatments, and causes added by over 300 women. The books are available at http://www.curetogether.com/VHeroes and http://www.curetogether.com/EHeroes.

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Tags: chronic pain, crowdsourced, CureTogether, endometriosis, quantified self, research, vulvodynia, wired, women's health

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Jul 5, 2008

Using Vaccines more Effectively to Stop Pandemics

Posted by Michael Graham Richard in categories: biological, biotech/medical, information science

If a pandemic strikes and hundreds of millions are at risk, we won’t have enough vaccines for everybody, at least not within the time window where vaccines would help. But a new strategy could help use the vaccines we have more effectively:

Researchers are now proposing a new strategy for targeting shots that could, at least in theory, stop a pandemic from spreading along the network of social interactions. Vaccinating selected people is essentially equivalent to cutting out nodes of the social network. As far as the pandemic is concerned, it’s as if those people no longer exist. The team’s idea is to single out people so that immunizing them breaks up the network into smaller parts of roughly equal sizes. Computer simulations show that this strategy could block a pandemic using 5 to 50 percent fewer doses than existing strategies, the researchers write in an upcoming Physical Review Letters.

So you break up the general social network into sub-networks, and then you target the most important nodes of these sub-networks and so on until you run out of vaccines. The challenge will be to get good information about social networks, something not quite as easy as mapping computer networks, but there is progress on that front.

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