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Building a Silicon Brain
prostoalex tips us to an article in MIT's Technology Review on a Stanford scientist's plan to replicate the processes inside the human brain with silicon. Quoting: "Kwabena Boahen, a neuroengineer at Stanford University, is planning the most ambitious neuromorphic project to date: creating a silicon model of the cortex. The first-generation design will be composed of a circuit board with 16 chips, each containing a 256-by-256 array of silicon neurons. Groups of neurons can be set to have different electrical properties, mimicking different types of cells in the cortex. Engineers can also program specific connections between the cells to model the architecture in different parts of the cortex."
A 2.0GHz dual-core CPU running 2^20 neurons in the net at 100Hz gets about 40 clock cycles per neuron per cycle...Somebody check my math please.
T
Laws are horrible moral guides, moral guides make even worse laws.
I would think a BOINC project might produce enough muscle to get a really big brain going. Imagine a BOINC cluster of...
;-)
If you mod me down, I shall become more powerful than you could possibly imagine.
I, like many other engineers, don't give a shit. We just want to solve problems to which there are no simple solutions and "AI" offers some approaches that work.
Leave the philosophy till after we have the science.
How we know is more important than what we know.
How can you build a software model of a process you don't understand? The best hope is to build a hardware approximation of a human brain and hope that, somehow, the same processes start occurring, quantum or otherwise. And if that doesn't work, then you'll have to do some real science.
Having been a fan of neuromorphic engineering for several years now(Note I'm not an active researcher but I pretend somedays :) ) one of the major advantages of neuromorphic functionality isn't necessarily it's ability to model biological systems but the fact that the devices are extremely low power. When modeling neurons in silicon(at least back in the day of Carver Mead's work and for cochlea and retina stuff and I'm doubting it's changed too bunch but I could be wrong) the transistors would run in sub threshhold mode(basically leakage currents so OFF) since the power curves modeled the expected neuro response curves. One of Boahen's stated goals(at least on his website when he was at Penn) was to reduce power consumption and improve processing power for problem solving via these techniques. His lab has been in Scientific America a couple times in the last few years for work in accurately modeling Neuronal spiking in hardware too. I have them but not at hand so I can't cite them at the moment but they were fun reads.
So in summary, it's more than just modeling the brain. It's about letting biology inspire us to make better and more efficient computing systems.
I don't care what you say, all I need is my Wumpabet soup.