Large language model AIs might seem smart on a surface level but they struggle to actually understand the real world and model it accurately, a new study finds.
In other news, water is wet
“when we try to use a tape measure to hammer in nails it doesn’t really work, so tape measures are useless”
Cue tech obsessives trying to defend or deflect from LLMs etc and their problems in 5…
What exactly is there to defend or deflect, LLM’s are useful for some things and not for others, this is well known
Wow. How did you know?
😉
As such, it raises concerns that AI systems deployed in a real-world situation, say in a driverless car, could malfunction when presented with dynamic environments or tasks.
This is currently happening with driverless cars that use machine learning - so this goes beyond LLMs and is a general machine learning issue. Last time I checked, Waymo cars needed human intervention every six miles. These cars often times block each other, are confused by the simplest of obstacles, can’t reliably detect pedestrians, etc.
An important characteristic of a model is “stability.” Stability means that small changes in input produce small changes in output.
Stability is important for predictability. For instance, suppose you want to make a customer support portal. You add a bot hoping that it will guide the user to the desired workflow. You test the bot by asking it a bunch of variations of questions, probably with some RLHF. But then when it goes to production, people will start asking it variations of questions that you didn’t test (guaranteed). What you want ideally, is that it will map the variants to the best workflow that matches what the customer wants. Second best would be to say “I don’t know.” But what we have are bots who will just generate some crazy off-the-wall crap, and no way to prevent it.
We can’t even teach humans a coherent world model.
Because the world is full of contradictions
I would argue humans often have a world model that is too coherent. If you ask a flat earther about their beliefs they will always argue that the earth is flat and evidence to the contrary is manufactured or interpreted wrongly. That is a completely absurd world model, but perfectly coherent.
I think they mean WE struggle to understand these things have no understanding, probably because they are struggling with it also.
it guesses the next word, that is literally all it does, it’s not trying to build a model of reality to more accurately guess. It has no fidelity and anyone taking it seriously has themselves failed the turing test.
I am by no means an AI fanboy, and I extremely dislike the fact that it is in the hands of big tech, uses so much energy and is built on the work of people who are not being rewarded in any way. It is a new technology that is being forced and abused in the most capitalist way possible.
I do think however, that what you declare here as fact is not as certain as you make it out to be. Research indicates that machine learning models do in fact form some sort of model of understanding of their problem domain. For example this research. I am all for being critical of AI, but oversimplifying the issue might not work in our favour.
