What evidence can you provide to back up the statement of this "significant possibility"? Human brains use neural networks...
Modern ANN architectures are not actually capable of long-term learning in the same way animals are, even stodgy old dogs that don't learn new tricks. ANNs are not a plausible model for the brain, even if they emulate certain parts of the brain (the cerebellum, but not the cortex)
I will add that transformers are not capable of recursion, so it's impossible for them to realistically emulate a pigeon's brain. (you would need millions of layers that "unlink chains of thought" purely by exhaustion)
even if we bought this negative result as somehow “proving impossibility”, i’m not convinced plasticity is necessary for intelligence
huge respect for richard sutton though
More specifically: it is highly implausible that an AI system could learn to improve itself beyond human capability if it does not have long-term plasticity: how would it be able to reflect upon and extend its discoveries if it's not able to learn new things during its operation?
(That said, I agree plasticity is key to the most powerful systems. A human race with anterograde amnesia would have long ago gone extinct.)
If I'm a human tasked with editing video (which is the field my startup[0] is in) and a completely new video format comes in, I need the long term plasticity to learn how to use it so I can perform my work.
If a sufficiently intelligent version of our AI model is tasked with editing these videos, and a completely new video format comes in, it does not need to learn to handle it. Not if this model is smart enough to iterate a new model that can handle it.
The new skills and knowledge do not need to be encoded in "the self" when you are a bunch of bytes that can build your successor out of more bytes.
Or, in popular culture terms, the last 30 seconds of this Age of Ultron clip[1].
What do you think training (and fine-tuning) does?
No LLM currently adapts to the tasks its given with an iteration cycle shorter than on the order of months (assuming your conversations serve as future training data; otherwise not at all).
No current LLM can digest its "experiences", form hypotheses (at least outside of being queried), run thought experiments, then actual experiments, and then update based on the outcome.
Not because it's fundamentally impossible (it might or might not be), but because we practically haven't built anything even remotely approaching that type of architecture.
But there is no reason the company can't come up with a different paradigm.
But I suppose you could say we don't know 100% since we don't fully understand how the brain learns.
1. Either you are correct and the neural networks humans have are exactly the same or very similar to the programs in the LLMs. Then it will be relatively easy to verify this - just scale one LLN to the human brain neuron count and supposedly it will acquire consciousness and start rapidly learning and creating on its own without prompts.
2. Or what we call neural networks in the computer programs is radically different and or insufficient to create AI.
I'm leaning to the second option, just from the very high level and rudimentary reading about current projects. Can be wrong of course. But I have yet to see any paper that refutes option 2, so it means that it is still possible.
If you wanted to reduce it down, I would say there are two possibilities:
1. Our understanding of Neurel Nets is currently sufficient to recreate intelligence, consciousness, or what have you
2. We’re lacking some understanding critical to intelligence/conciousness.
Given that with a mediocre math education and a week you could pretty completely understand all of the math that goes into these neurel nets, I really hope there’s some understand we don’t yet have
MLPs and transformers are ultimately theoretically equivalent. That means there is an MLP that represent the any function a given transformer can. However, that MLP is hard to identify and train.
Also the transformer contains MLPs as well...
(I did AI and Psychology at degree level, I understand there are definitely also big differences too, like hormones and biological neurones being very async)
Transformers, while not exactly functions, don't have a feedback mechanism similar to e.g. the cortical algorithm or any other neuronal structure I'm aware of. In general, the ML field is less concerned with replicating neural mechanisms than following the objective gradient.
Numenta has attempted to implement a system to this effect (see the wiki page https://en.wikipedia.org/wiki/Hierarchical_temporal_memory) for quite some time with not particularly much success.
Personally I think the kinds of minds we create in silico will end up being very different, because the advantages and disadvantages of the medium are just very different; for example, having a much stronger central processor and much weaker distributed memory, along with specialized precise circuits in addition to probabilistic ones.
1. Humans have general intelligence. 2. Human brains use biological neurons. 3. Human biological neurons give rise to human general intelligence. 4. Artificial neural networks (ANNs) are similar to human brains. 5. Therefore an ANN could give rise to artificial general intelligence.
Many people are objecting to #4 here. However in writing this out, I think #3 is suspect as well: many animals who do not have general intelligence have biologically identical neurons, and although they have clear structural differences with humans, we don’t know how that leads to general intelligence.
We could also criticize #1 as well, since human brains are pretty bad at certain things like memorization or calculation. Therefore if we built an ANN with only human capabilities it should also have those weaknesses.
They don't, actually.