Andrew Ng said artificial neural networks are like a “cartoon brain”, which I think is pretty apt. It’s cool that having billions of parameters (cartoon “synapses”) can result in emergent behavior where a machine can “reason”. That was the original idea from as far back as the 1940s, and now we have the compute and have found a transformer architecture that can achieve the original vision. Very cool indeed. What’s not cool is the tech bros turning the tech into a large scale game of blackjack.
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tech bros turning the tech into a large scale game of blackjack
Agreed 100%
As a person who was bookish and deeply computer-focused from back when that meant only that you had no friends outside D&D -- before it also meant, "probably has a challenging career and makes good money".
That "tech" now prefixes the socializing extrovert bros who didn't spend their entire adolescence learning assembly language and soldering alone in their room... is a complete, 180-degree U-turn subversion from the original "techy nerd" meaning
Not blackjack. More like go fish with spoons instead of cards.
We also share half of our DNA with bananas. Therefore, we are also siblings of bananas.
Would you believe, the evidence is rather more compelling than that. Similarities in non-obvious failure modes, for example.
To reiterate the point, we also share that cognitive commonality with most birds and mammals too, so it doesn't really mean much.
Doesn't load for me. I work with ANNs solving problems and compare it with animals doing a similar thing for my PhD. You can see a lot of similarities, and can model some animal behavior after it. However, even with the presence of similarities you can't completely say it thinks "the same", but you can say it is functionally equivalent: If you have two functions that give the same output for the same inputs, then they are the same function for that domain, same goes for brains.
Just to be clear, LLMs have some similar thinking patterns with what we know about the neural activity of animal brains, but I wouldn't say it is the same, and saying they think "like humans" is very subjective.
One thing that is easier to notice is that they struggle with problems that require some form of recursive thinking, which is something a human can do. They can't simply go back to correct their beliefs, once they spit something out it stays there, so they have to commit to it.
They can’t simply go back to correct their beliefs
Backtracking is a common and integral part of the reasoning learned by what used to be called Large Reasoning Models but today is part of almost any modern model design, much like Mixture-of-Experts
They made it think like a human
they think like a human
"we're right all along"
So can we now make them do all our chores without costing million dollars nor mandatory surveillance? I'm tired of these "this tech change everything" while it doesn't even change baby diaper. It's nice that it help with some science breakthrough but i'm also tired of looking at another AI generated cat video thinking it's real.
You can replace all the 'reasoning' steps from an LLMs output with "please wait" and it will still supply the same final answer. We think but LLMs just throw up connected bits of their training. Which was stolen from human reasoning in the first place. They are an illusion of thought at best and a malfunctioning search engine most of the time.
replace all the ‘reasoning’ steps from an LLMs output with “please wait” and it will still supply the same final answer.
If you listened to the podcast (or were familiar with the literature) you'd know that's simply Not True.
Sometimes, by listening to bona fide experts, you might learn new things instead of already knowing everything.
Model output can look like human reasoning however they often ignore these intermediate steps and the output tokens are often filler designed to allow more context to load. There something there that's half way to reasoning because it's loading that related training data but it not a connected chain of thought as we do. I read this article about it a few weeks ago and it summarizes the current research.
https://www.quantamagazine.org/is-ai-reasoning-right-for-the-wrong-reasons-20260731/
look like human reasoning
You should really listen to the podcast. Not going to summarize it here, but it looks like human reasoning including semantic activation patterns, use of productions, and dozens of other things that cognitive psychologists have known about for decades but for which they have not been able to come up with a mechanism.
Thanks that sounds interesting, will have a listen. Mapping human cognition to what's likely represented with the stuff stored in neurons is very tricky but also quite intriguing.