this post was submitted on 12 Jun 2024
393 points (95.4% liked)
Technology
58306 readers
3180 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each another!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
Approved Bots
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I'm happy with the Oxford definition: "the ability to acquire and apply knowledge and skills".
LLMs don't have knowledge as they don't actually understand anything. They are algorithmic response generators that apply scores to tokens, and spit out the highest scoring token considering all previous tokens.
If asked to answer 10*5, they can't reason through the math. They can only recognize 10, * and 5 as tokens in the training data that is usually followed by the 50 token. Thus, 50 is the highest scoring token, and is the answer it will choose. Things get more interesting when you ask questions that aren't in the training data. If it has nothing more direct to copy from, it will regurgitate a sequence of tokens that sounds as close as possible to something in the training data: thus a hallucination.
The human could be described in very similar terms. People think we’re magic or something, but we to are just a weighted neural network assembling outputs based strictly on training data built from reinforcement. We are just for the moment much much better with massive models. Of course that is reductive but many seem to forget that brains suffer similarly when outside of training data.
That's a strong claim. Got an academic paper to back that up?
I'm slightly confused. Which part needs an academic paper? I've made three admittedly reductive claims.
First, I'm not trying to make some really clever statement. I'm just saying there is a perspective where describing the human brain can generally follow a similar description. Nevertheless, let's look at the only three assertions I make here. Given that the term neural network is given its namesake from the neurons that make up brains, I assume you don't take issue with this. The second point, I don't know if linking to scholarly research is helpful. Is it not well established that animals learn and use reward circuitry like the role of dopamine in neuromodulation? We also have... education, where we are fed information so that we retain it and can recount it down the road.
I guess maybe it is worth exploring the third, even though, I really wasn't intending to make a scholarly statement. Here is an article in Scientific American that gives the number of neural connections around 100 trillion. Now, how that equates directly to model parameters is absolutely unclear, but even if you take glial cells where the number can be as low as 40-130 billion according to The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting, that number is in the same order of magnitude of current models' parameters. So I guess, if your issue is that AI models are actually larger than the human brain's, I guess maybe there is something cogent. But given that there is likely at least a 1000:1 ratio of neural connections to neurons, I just don't think that is really fair at all.
So, first of all, thank you for the cogent attempt at responding. We may disagree, but I sincerely respect the effort you put into the comment.
The specific part that I thought seemed like a pretty big claim was that human brains are "simply" more complex neural networks and that the outputs are based strictly on training data.
While true, this is way too reductive to be a one to one comparison with LLMs. Humans have genetic instinct and body-mind connection that isn't cleanly mappable onto a neural network. For example, biologists are only just now scraping the surface of the link between the brain and the gut microbiome, which plays a much larger role on cognition than previously thought.
Another example where the brain = neural network model breaks down is the fact that the two hemispheres are much more separated than previously thought. So much so that some neuroscientists are saying that each person has, in effect, 2 different brains with 2 different personalities that communicate via the corpus callosum.
There's many more examples I could bring up, but my core point is that the analogy of neural network = brain is just that, a simplistic analogy, on the same level as thinking about gravity only as "the force that pushes you downwards".
To say that we fully understand the brain, to the point where we can even make a model of a mosquito's brain (220,000 neurons), I think is mistaken. I'm not saying we'll never understand the brain enough to attempt such a thing, I'm just saying that drawing a casual equivalence between mammalian brains and neural networks is woefully inadequate.
For what it’s worth, in spite of my poor choice of words and general ignorance on many topics, I agree with everything you said here, and find these fascinating topics. Especially that of our microbiome which I think by mass is larger than our brains; so who’s really doing the thinking around here?
Even the question of "who" is a fascinating deep dive in and of itself. Consciousness as an emergent property implies that your gut microbiome is part of the "who" doing the thinking in the first place :))