5 ms·
> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/#__sec9title https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/#__sec9... Human have 86,000M and rats
by yonixw 6y ago
> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/#__sec9title https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484/#__sec9...
Human have 86,000M and rats have 200M cortex neurons (x430)
> https://chatbotslife.com/is-gpt-3-the-adam-of-natural-language-cf59656456f2 https://chatbotslife.com/is-gpt-3-the-adam-of-natural-langua...
"... the human brain has about 60 trillion parameters or about 300x more parameters than GPT-3"
We are approaching a rat brain processing power so consciousness should really be discussed ...
- auggierose 6y agoI would wait until you can discuss it with the AI.
- mjburgess 6y agoYes, but this is bullshit. An atom has a few dozen parameters. Computers do not model the atoms of the brain, of which there would then need to be 10^many parameters. This is only relevant because of the bullshit identification of really-existing neurones with a set of model paraterms that do not model them. It doesnt matter how many I have. I can have a model of my sofa with a trillion parameters, that doesn't mean my PC will be comfortable to sit on. For a system X to emulate a system Y, ie., participate causally in the relevant ways, it must be relevantly similar. Counting parameters here is meaningless.
- yonixw 6y agoWhat effects do the atom (or any underlying part) contribute to consciousness that parameters don't? If are you making a statement about a sensor precision, like eyesight or skin touch, it does affect consciousness but not negate it.
- mjburgess 6y agoMy point is that a system X perfectly emulates a system Y if all of its parts are identical, that is, if Y has all the basic atoms of X in the same arrangement. Anything short of that has to be saying that X is "like" Y in some relevant way. And here this has nothing to do with parameter count, complexity, or anything of this kind. Here "X" has to be behaviourally similar to "Y". Animal intelligence is essentially entirely concerned with managing animal bodies and their relationship to the environment; and secondarily, the environment. To do this requires a body (etc.). No mathematical model over a dataset can emulate animal intelligence, because a mathematical model has no behaviour. A mathematical model can describe an animal, but a description is not an emulation: the formula E=MC^2 isnt a nuclear explosion. A digital silicon machine instantiates a mathematical formula with an osciliating electric field. There is nothing about this electrical osciliation which behaves anything like animal intelligence.
- yonixw 6y ago> A digital silicon machine instantiates a mathematical formula with an oscillating electric field Exactly! That is exactly what is done by a neuron, the brain building block. So I hope you can see why I think an AI can be close enough to simulate the brain, as we still not know the exact border of intelligence (and thus we may be closer than we think).
- mjburgess 6y agoThat isn't "exactly what is done by a neurone". It has nothing to do with it.
- danielbarla 6y agoSure, but if the simulation is starting to walk like a duck and talk like a duck, this argument runs out of gas very quickly.
- mjburgess 6y agoThe argument motivates why this alleged duck isn't walking and talking like a duck. For sure, if it does, then it isn't relevant. The "duck" here is a person, and "walking and talking" are literal. Where is the machine that participates in my environment, with me, imagines/believes/advices/speculates/cares about what I am doing? (And so on). There is no such machine. The "quacking" we have isn't the quacking of a duck. It's a stupid buzzer that has been mashed too many times and a foolish audience who has never thought much about the sound of a duck.
- Isinlor 6y agoParameters in artificial networks and biological networks are indeed very loosely related. But we can bound computing power of any physical system if that system is doing computation that erases information due to the second law of thermodynamics. Erasure of useful information must increase entropy. The Landauer limit [0] suggests that a brain consuming 20W at 36C can carry out no more than ~1e22 irreversible operations per second (20W / (309.2K * Boltzmann constant * ln(2))). That bounds brains at ~1e21 FLOP/s (~10 bits erased per FLOP). The best supercomputer right now does ~4e17 FLOP/s. That's log2(1e21 / 4e17) ~11 doublings of computing power until supercomputers will be faster than thermodynamic maximum of FLOPS power of brains. Assuming we keep up the doubling time below 3 years, we will have supercomputers faster than a human brain in 3 * 11 ~ 33 years. [0] https://en.wikipedia.org/wiki/Landauer%27s_principle https://en.wikipedia.org/wiki/Landauer%27s_principle
- yonixw 6y agoI should add it is already been attempted in 2015: https://qz.com/481164/ibm-has-built-a-digital-rat-brain-that-could-power-tomorrows-smartphones/ https://qz.com/481164/ibm-has-built-a-digital-rat-brain-that...