Can you prove it?
Comment on LLMs and Humans are Cognitive Cousins – Sean Carroll podcast w/Dr. Chandra Sripada
halm@leminal.space 1 day ago
I call BS. There is no sort of cognition taking place in an LLM. They calculate statistics, that’s all. No more magic than the cash register at the supermarket.
communist@lemmy.frozeninferno.xyz 1 day ago
halm@leminal.space 1 day ago
If i could prove it, it would be proof that we do. Our brains aren’t logic machines, they’re a wet mess where cognition emerges irrationally.
communist@lemmy.frozeninferno.xyz 23 hours ago
So you’re going by the vibes then? Why even bother with your comment? What basis in neuroscience is this founded in?
halm@leminal.space 22 hours ago
It’s “stochastic”, if you want to be pedantic, and I get the feeling that’s all you’re here for. “In fact”.
TehPers@beehaw.org 21 hours ago
What a dumb question. “Cognition” is an abstract concept that just leads to debate over its definition, except that it’s commonly understood that humans have cognition.
This entire interview also seems predicated on the belief that neural networks work fundamentally the same as human brains, which is an oversimplification of brains. To begin with, neural networks cannot train themselves at inference time (context windows aren’t training). Also, humanity has barely a concept of how the brain works and regularly learns new things about it. What we do know is that they are more complex than a bunch of connected neurons, and that we have no way of modelling something we don’t yet fully understand.
So do neural networks have cognition? If they did, then we’ve had cognitive AIs for longer than I’ve been alive.
The rest of the interview is imaginative fiction based on what they think the model is doing predicated on what Anthropic likes to advertise their models do.
As basic evidence, if I tell you that the word “strawberry” has 3 "r"s, you can remember that. You can recall that no matter how many conversations we have in between when I told you that and when I asked you again.
If I teach you how to count how many "r"s there are in the word “strawberry”, you learn a skill. In the future, if I ask how many "r"s there are in the word “strawberry”, you can count them using your new skill.
If I teach a LLM how many there are or how to count the letters, it can only use that skill or knowlege if it can effectively search for it. Its ability to execute skills (which, to begin with, isn’t it executing the skills but it asking something else to do them) is diminished the more skills it gains access to, and its “knowlege” (aka long-term storage) becomes diluted when it has access to more of it.
This is blatantly untrue of humans, who can process knowlege and skills they learn, efficiently and quickly search them, and actually get faster and more accurate as they develop related skills and learn related knowlege.
communist@lemmy.frozeninferno.xyz 21 hours ago
Your example falls flat on two counts
Llm’s that only have one glyph per token can easily count this, and yeah we train in bursts because it’s computationally cheaper, this hardly means anything important.
TehPers@beehaw.org 20 hours ago
Llm’s that only have one glyph per token can easily count this
[Citation needed]
There’s no difference between counting tokens containing the letter “r” and tokens solely representing the letter “r”. To begin with, this assumes the way we encode words in our brain is by tokenizing each letter, which we pretty much know to be false (we can process words without seeing evry ltr of th wrd and even think about them without it being mentioned).
yeah we train in bursts because it’s computationally cheaper, this hardly means anything important.
No, this is not how neural networks work at all.
We train in bursts because we need a ground truth/scoring function to guide the learning, which means we need a way to guide the model in the right direction. Even with unsupervised learning, the model has a way to determine whether it’s going in the right direction, like with GANs.
Neural networks are also incapable of synthesizing completely new skills from its own weights. Humans have been doing that for the entirety of human history.
OpenStars@discuss.online 18 hours ago
The difference between the cash register and an inference engine is the training data. Also, barring something like mechanical failure, I would trust the calculation of a cash register (that has previously been demonstrated to function with high fidelity, and barring any major incidents such as dropping it onto the floor that might make me question the relevance of those prior tests to its current performance).
But the results of a LLM “calculation” - and I mean this with total sincerity - might be something like:
“What is 1+1=”?
“Answer: 6, 7!! 🤪”
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Or in ye olden times, one expected response might be “ur mother!” or some other flippant remark… exactly like a small child, imitating others might do. Monkey see, monkey do => the principles undergirding LLM technology? It does not understand what it sees, hence it does not “know” when to apply what answer, only going by the most popular (whatever the weighting scheme is - probably highest upvoted answer on Reddit?) string of words seem to be associated with the question.