Comment by ants_everywhere
6 days ago
> We need to move past the humans vs ai discourse it's getting tired.
You want a moratorium on comparing AI to other form of intelligence because you think it's tired? If I'm understanding you correctly, that's one of the worst takes on AI I think I've ever seen. The whole point of AI is to create an intelligence modeled on humans and to compare it to humans.
Most people who talk about AI have no idea what the psychological baseline is for humans. As a result their understand is poorly informed.
In this particular case, they evaluated models that do not have SOTA context window sizes. I.e. they have small working memory. The AIs are behaving exactly like human test takers with working memory, attention, and impulsivity constraints [0].
Their conclusion -- that we need to defend against adversarial perturbations -- is obvious, I don't see anyone taking the opposite view, and I don't see how this really moves the needle. If you can MITM the chat there's a lot of harm you can do.
This isn't like some major new attack. Science.org covered it along with peacocks being lasers because it's it's lightweight fun stuff for their daily roundup. People like talking about cats on the internet.
[0] for example, this blog post https://statmedlearning.com/navigating-adhd-and-test-taking-...
>The whole point of AI is to create an intelligence modeled on humans and to compare it to humans.
According to who? Everyone who's anyone is trying to create highly autonomous systems that do useful work. That's completely unrelated to modeling them on humans or comparing them to humans.
But since these things are more like humans than computers, to build these autonomous systems you are going to have think in terms of full industrial engineering, not just software engineering: pretend you are dealing with a surprisingly bright and yet ever distracted employee who doesn't really care about their job and ensure that they are able to provide the structure you place them in value without danger to your process, instead of trying to pretend like the LLM is some kind of component which has any hope of ever having the kind of reliability of a piece of software. Organizations of humans can do amazing things, despite being extremely flawed beings, and figuring out how to use these LLMs to accomplish similar things is going to involve more of the skills of a manager than a developer.
Their output is in natural language, that's about the end of similarities with humans. They're token prediction algorithms, nothing more and nothing less. This can achieve some absolutely remarkable output, probably because our languages (both formal and linguistic) are absurdly redundant. But the next token being a word, instead of e.g. a ticker price, doesn't suddenly make them more like humans than computers.
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It's got an instant-messaging interface.
If it had an autocomplete interface, you wouldn't be claiming that. Yet it would still be the same model.
(Nobody's arguing that Google Autocomplete is more human than software - at least, I hope they're not).
By whoever coined the term Artificial Intelligence. It's right there in the name.
Backronym it to Advanced Inference and the argument goes away.
Go back and look at the history of AI, including current papers from the most advanced research teams.
Nearly every component is based on humans
- neural net
- long/short term memory
- attention
- reasoning
- activation function
- learning
- hallucination
- evolutionary algorithm
If you're just consuming an AI to build a React app then you don't have to care. If you are building an artificial intelligence then in practice everyone who's anyone is very deliberately modeling it on humans.
How far back do I have to look, and what definition do you use? Because I can start with theorem provers and chess engines of the 1950s.
Nothing in that list is based on humans, even remotely. Only neural networks were a vague form of biomimicry early on and currently have academic biomimicry approaches, of which all suck because they poorly map to available semiconductor manufacturing processes. Attention is misleadingly called that, reasoning is ill-defined, etc.
LLMs are trained on human-produced data, and ML in general shares many fundamentals and emergent phenomena with biological learning (a lot more than some people talking about "token predictors" realize). That's it. Producing artificial humans or imitating real ones was never the goal nor the point. We can split hairs all day long, but the point of AI as a field since 1950s is to produce systems that do something that is considered only doable by humans.
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Those terms sound similar to biological concepts but they’re very different.
Neural networks are not like brains. They don’t grow new neurons. A “neuron” in an artificial neural net is represented with a single floating point number. Sometimes even quantized down to a 4 bit int. Their degrees of freedom are highly limited compared to a brain. Most importantly, the brain does not do back propagation like an ANN does.
LSTMs have about as much to do with brain memory as RAM does.
Attention is a specific mathematical operation applied to matrices.
Activation functions are interesting because originally they were more biologically inspired and people used sigmoid. Now people tend to use simpler ones like ReLU or its leaky cousin. Turns out what’s important is creating nonlinearities.
Hallucinations in LLMs have to do with the fact that they’re statistical models not grounded in reality.
Evolutionary algorithms, I will give you that one although they’re way less common than backprop.
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You're anthropomorphizing terms of art.
Just because something is named after the name of a biological concept doesn't mean it has anything to do with the original thing the name was taken from.
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What your examples show is that humans like to repurpose existing words to refer to new things based on generalizations or vague analogies. Not much more than that.
What do you imagine the purpose of these models' development is if not to rival or exceed human capabilities?
> The whole point of AI is to create an intelligence modeled on humans and to compare it to humans.
This is like saying the whole point of aeronautics is to create machines that fly like birds and compare them to how birds fly. Birds might have been the inspiration at some point, but learned how to build flying machines that are not bird-like.
In AI, there *are* people trying to create human-like intelligence but the bulk of the field is basically "statistical analysis at scale". LLMs, for example, just predict the most likely next word given a sequence of words. Researchers in this area are trying to make this predictions more accurate, faster and less computationally- and data- intensive. They are not trying to make the workings of LLMs more human-like.
I mean the critique of this on the idea that the AI system itself gets physically tired - specifically the homoculus that we tricked into existence is tired - is funny to imagine.