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?
It makes me think humans aren't special, and there is no soul, and consciousness is just a bunch of wires like computers. Seriously, to see the ENTIRETY of human experience, love and tragedy and achievement, are just electric potentials transmitted by those wiggly cells, just extinguishes any magic I once saw in humanity.
Cubic millimetre of brain mapped at nanoscale resolution
71–80 of 205 posts
Re: Cubic millimetre of brain mapped at nanoscale resolution
#72> 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…
Re: Cubic millimetre of brain mapped at nanoscale resolution
#73Earlier quoted context omitted.
On the other hand, a significant amount of neural circuitry seems to be dedicated to "housekeeping" needs, and to functions such as locomotion. So we might need significantly less brain matter for general intelligence.
Or perhaps the housekeeping of existing in the physical world is a key aspect of general intelligence.
Re: Cubic millimetre of brain mapped at nanoscale resolution
#74Earlier quoted context omitted.
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…
I mean, Hinton’s premises are, if not quite clearly wrong, entirely speculative (which doesn't invalidate the conclusions about efficienct that they are offered to support, but does leave them without support) GPT-4 can produce convincing written text about a wider array of topics than any one person can, because it's a model optimized for taking in and producing convincing written text, trained extensively on writte…
Ironically, I suppose part of the apparent "intelligence" of LLMs comes from reflecting the intelligence of human users back at us. As a human, the prompts you provide an LLM likely "make sense" on some level, so the statistically generated continuations of your prompts are likelier to "make sense" as well. But if you don't provide an ongoing anchor to reality within your own prompts, then the outputs make it more apparent that the LLM is simply regurgitating words which it does not/cannot understand.
On your point of human knowledge being far more multimodal than LLM interfaces, I'll add that humans also have special neurological structures to handle self-awareness, sensory inputs, social awareness, memory, persistent intention, motor control, neuroplasticity/learning– Any number of such traits, which are easy to take for granted, but indisputably fundamental parts of human intelligence. These abilities aren't just emergent properties of the total number of neurons; they live in special hardware like mirror neurons, special brain regions, and spindle neurons. A brain cell in your cerebellum is not generally interchangeable with a cell in your visual or frontal cortices.
So when a human "converse[s] about stuff ranging from philosophy to cooking" in an honest way, we (ideally) do that as an expression of our entire internal state. But GPT-4 structurally does not have those parts, despite being able to output words as if it might, so as you say, it "generates" convincing text only because it's optimized for producing convincing text.
I think LLMs may well be some kind of an adversarial attack on our own language faculties. We use words to express ourselves, and we take for granted that our words usually reflect an intelligent internal state, so we instinctively assume that anything else which is able to assemble words must also be "intelligent". But that's not necessarily the case. You can have extremely complex external behaviors that appear intelligent or intentioned without actually internally being so.
Re: Cubic millimetre of brain mapped at nanoscale resolution
#75Earlier quoted context omitted.
[AI] "Frontier [supercomputer]: the storage capacity is reported to be up to 700 petabytes (PB)" (0.0007 ZB). [AI] "The installed base of global data storage capacity [is] expected to increase to around 16 zettabytes in 2025". Thus, even the largest supercomputer on Earth cannot store more than 4 percent of state of a single human brain. Even all the servers on the entire Internet could store state of only 9 human br…
I appreciate you're running the numbers to extrapolate this approach, but just wanted to note that this particular figure isn't an upper bound nor a longer bound for actually storing the "state of a single human brain". Assuming the intent would be to store the amount of information needed to essentially "upload" the mind onto a computer emulation, we might not yet have all the details we need in this kind of scannin…
Unless one's understanding of algorithmic inner workings of a particular black box system is actually very good, it is likely not possible not only to discard any of its state, but even implement any kind of meaningful error detection if you do discard.
Given the sheer size and complexity of a human brain, I feel it is actually very unlikely that we will be able to understand its inner workings to such a significant degree anytime soon. I'm not optimistic, because so far we have no idea how even laughingly simple, in comparison, AI models work[0].
[0] "God Help Us, Let's Try To Understand AI Monosemanticity", https://www.astralcodexten.com/p/god-help-us-lets-try-to-und...
Re: Cubic millimetre of brain mapped at nanoscale resolution
#76Re: Cubic millimetre of brain mapped at nanoscale resolution
#77Earlier quoted context omitted.
I mean, Hinton’s premises are, if not quite clearly wrong, entirely speculative (which doesn't invalidate the conclusions about efficienct that they are offered to support, but does leave them without support) GPT-4 can produce convincing written text about a wider array of topics than any one person can, because it's a model optimized for taking in and producing convincing written text, trained extensively on writte…
Try asking an LLM about something which is semantically patently ridiculous, but lexically superficially similar to something in its training set, like "the benefits of laser eye removal surgery" or "a climbing trip to the Mid-Atlantic Mountain Range". Ironically, I suppose part of the apparent "intelligence" of LLMs comes from reflecting the intelligence of human users back at us. As a human, the prompts you provide…
Without anthropomorphizing it, it does respond like an alien / 5 year old child / spec fiction writer who will cheerfully "go along with" whatever premise you've laid before it.
Maybe a better thought is: at what point does a human being "get" that "the benefits of laser eye removal surgery" is "patently ridiculous" ?
Re: Cubic millimetre of brain mapped at nanoscale resolution
#78Earlier quoted context omitted.
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…
"Efficient" and "better" are very different descriptors of a learning algorithm. The human brain does what it does using about 20W. LLM power usage is somewhat unfavourable compared to that.
Re: Cubic millimetre of brain mapped at nanoscale resolution
#79Earlier quoted context omitted.
Try asking an LLM about something which is semantically patently ridiculous, but lexically superficially similar to something in its training set, like "the benefits of laser eye removal surgery" or "a climbing trip to the Mid-Atlantic Mountain Range". Ironically, I suppose part of the apparent "intelligence" of LLMs comes from reflecting the intelligence of human users back at us. As a human, the prompts you provide…
> Try asking an LLM about something which is semantically patently ridiculous, but lexically superficially similar to something in its training set, like "the benefits of laser eye removal surgery" or "a climbing trip to the Mid-Atlantic Mountain Range". Without anthropomorphizing it, it does respond like an alien / 5 year old child / spec fiction writer who will cheerfully "go along with" whatever premise you've lai…
This is the comparison that's made most sense to me as LLMs evolve. Children behave almost exactly as LLMs do - making stuff up, going along with whatever they're prompted with, etc. I imagine this technology will go through more similar phases to human development.
Re: Cubic millimetre of brain mapped at nanoscale resolution
#801.4 PB/mm^3 (petabytes per millimeter cubed)×1260 cm^3 (cubic centimeters, large human brain) = 1.76×10^21 bytes = 1.76 ZB (zetabytes)
[AI] "Frontier [supercomputer]: the storage capacity is reported to be up to 700 petabytes (PB)" (0.0007 ZB). [AI] "The installed base of global data storage capacity [is] expected to increase to around 16 zettabytes in 2025". Thus, even the largest supercomputer on Earth cannot store more than 4 percent of state of a single human brain. Even all the servers on the entire Internet could store state of only 9 human br…