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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."
that's great, but will it run linux?
now is the winter of our discotheque
This is hardly something new. Intel had a chip a number of years ago, called ETANN that was a pure-analog neural network implementation. Another cool aspect of this chip was that the weight values were stored in EEPROM-like cells (but analog) so the training of the chip would not be erased if it lost power.
But the whole technology of neural networks almost pre-dates the Von Neumann architecture. Early analog neural networks were constructed in the late 40's.
Not only are these simulations nothing new but they are in every-day products. One of the most common examples is the misfire detection mechanism in Ford vehicle engine controllers. Misfire detection in spark ignition engines is based on so many variables that neural networks often perform better than hard-coded logic (although not always, just like the wetware counterparts, they can be "temperamental").
There are several other real-world neural network applications (autofocusing of cameras for example).
Ahh the hidden magic of embedded systems...
A soul...
For those interested in this field, may i suggest a book, Naturally Intelligent Systems? It's slightly older, but it explains a wide gamut of neural networks without a single equation, and manages to be funny and engaging at the same time. it is one of the three books that changed my life (by it's content and ideas alone - i'm not otherwise into AI). highly recommended: Naturally Intelligent Systems on amazon
CS majors know the time/space tradeoff, but they never get taught the 3rd, crucial, tradeoff of the set: comprehension!
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.
Read "How Brains Think" by William H. Calvin; he's a neurologist and the book goes into lots of detail about how brains think (dur), how they evolved, and the possibility of AI.
He's an expert in the field and you can feel his bitter dislike of "quantum consciousness" proponents through his writing. He writes that it's just saying "we don't know how X works, and we don't know how Y works, but if we say that Y depends upon X then we have one problem instead of two".
Consciousness is built on the interactions of neurons. We understand how neurons work at interact at a low level (from studying the ~50 neuron brains of snails etc), and we understand on a large level which regions of the brain do what, but we don't understand the "middle ground".
It's as if we understand the transistor, and logic gates, and we can recognize which part of a chip is the ALU and which is the cache, but we can't recognize an adder circuit or microinstruction translator for what it is.
Quantum physics is certainly involved in the action of transistors but it doesn't explain how they combine to process data.
(On a similar note some I saw, in a documentary, one crackpot explain away "spontaneous human combustion" with an unknown quantum particle.)
// MD_Update(&m,buf,j);
void SuckAtNipple();
void CryForAttention();
void Shit();
I think Shit() has a return type...
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