51–60 of 205 posts
> The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals. Wonder how they figured out which fragment to cut out.
I worry this might make the sample biased in some way.
> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adu…
Except you’d be missing the part that a neuron is not just a node with a number but a computational system itself.
The calculation is intentionally underestimating the neurons, and even with that the brain ends up having more parameters than the current largest models by orders of magnitude.
Yes the estimation is intentionally modelling the neurons simpler than they are likely to be. No, it is not “missing” anything.
Is there a name for the somewhat uncomfortable feeling caused by seeing something like this? I wish I could better describe it. I just somehow feel a bit strange being presented with microscopic images of brain matter. Is that normal?
Growing actual bio brains is just way easier. Its never going to happen in silicon.
Every machine will just have a cubic centimeter block of neuro meat embedded in it somewhere.
> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adu…
Quote:
"Large language models are made from massive neural networks with vast numbers of connections. But they are tiny compared with the brain. “Our brains have 100 trillion connections,” says Hinton. “Large language models have up to half a trillion, a trillion at most. Yet GPT-4 knows hundreds of times more than any one person does. So maybe it’s actually got a much better learning algorithm than us.”
GPT-4's connections at the density of this brain sample would occupy a volume of 5 cubic centimeters; that is, 1% of a human cortex. And yet GPT-4 is able to speak more or less fluently about 80 languages, translate, write code, imitate the writing styles of hundreds, maybe thousands of authors, converse about stuff ranging from philosophy to cooking, to science, to the law.
Another proof point that AGI is probably not possible. Growing actual bio brains is just way easier. Its never going to happen in silicon. Every machine will just have a cubic centimeter block of neuro meat embedded in it somewhere.
Earlier quoted context omitted.
My god. That is stunning. To think that’s one single millimeter of our brain and look at all those connections. Now I understand why crows can be so smart walnut sized brain be damned. What an amazing thing brains are. Possibly the most complex things in the universe. Is it complex enough to understand itself though? Is that logically even possible?
Crow/parrot brains are tiny but in terms of neuron count they are twice as dense as primate brains (including ours): https://www.sciencedirect.com/science/article/pii/S096098221... If someone did this experiment with a crow brain I imagine it would look “twice as complex” (whatever that might mean). 250 million years of evolution separates mammals from birds.
1.4 PB/mm^3 (petabytes per millimeter cubed)×1260 cm^3 (cubic centimeters, large human brain) = 1.76×10^21 bytes = 1.76 ZB (zetabytes)
Another proof point that AGI is probably not possible. Growing actual bio brains is just way easier. Its never going to happen in silicon. Every machine will just have a cubic centimeter block of neuro meat embedded in it somewhere.
> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adu…
Or you can subscribe to Geoffrey Hinton's view that artificial neural networks are actually much more efficient than real ones- more or less the opposite of what we've believed for decades- that is that artificial neurons were just a poor model of the real thing. Quote: "Large language models are made from massive neural networks with vast numbers of connections. But they are tiny compared with the brain. “Our brains…
Humans know a lot of things that are not revealed by inputs and outputs of written text (or imagery), and GPT-4 doesn't have any indication of this physical, performance-revealed knowledge, so even if we view what GPT-4 talks convincingly about as “knowledge”, trying to compare its knowledge in the domains it operates in with any human’s knowledge which is far more multimodal is... well, there's no good metric for it.