Why LLMs are not and probably will not lead to "AI" (an opion)
While the capabilities of Large Language Models are impressive, calling them "AI" remains contentious. Here's why some in the technical community, including Sam Altman, have our doubts:
Limited understanding and reasoning: LLMs excel at pattern recognition and statistical analysis, but they lack true understanding of the data they process. They can't reason logically, draw meaningful conclusions, or grasp the nuances of context and intent. This limits their ability to adapt to new situations and solve complex problems beyond the realm of data driven prediction.
Black box nature: LLMs are trained on massive datasets. This "black box" nature makes it challenging to explain their predictions, debug errors, or ensure unbiased outputs.
Lack of "general intelligence": LLMs currently lack the broad, transferable intelligence that characterizes humans. They excel at specific tasks within their training data, but struggle with novel situations or requiring different skills. An inability to generalize outside their training data restricts their claim to the title of "AI."
Focus on prediction over understanding: LLMs, for all their impressive feats, remain slaves to their training data. They excel at mimicking and recombining existing information, akin to a masterful DJ remixing familiar tracks. They remain powerful tools, like supercharged search engines and spell checkers, but calling them AI risks mistaking virtuosity for originality. LLMs are inherently statistical models, predicting outputs based on past observations, nothing more.
Overestimating progress: The rapid advancements in LLMs can lead to overoptimistic claims about their capabilities. Comparing them to intelligence is misleading, the underlying mechanisms and levels of understanding differ significantly.
40 comments
[ 3.3 ms ] story [ 99.8 ms ] threadIf your point is that LLMs won't lead to some kind of extreme AI capability that you're imagining or you've read about in science fiction books, you can just say that instead. But LLMs are already as AI as can be.
Data require processing will be transferred between these mixes of technologies.
I see them both being very very similar, possibly even the same thing. Are you saying a prediction only matters if it was done in a certain way, following a specific thought process?
I mentioned it countless times already, but it gets tiresome to repeat myself. Just ask your "AI", capable of "thought", a question (e.g., derive something given a set of axioms and definitions) from a non-widespread field (e.g., perspective geometry, Clifford algebra).
Then you might also arrive at the conclusion that it is a hyped up "Google search results mixer and matcher".
For me the biggest indicator that something is still missing, and a reason not mentioned here, is the lack of any killer app besides general chatbots and code completion, even more than a year out from their explosion onto the scene (at which point both of those use cases already existed). It was assumed that the LLMs would quickly change everything but it appears their seemingly-minor limitations are pretty fundamental for many use cases.
Can you come up with some examples that would demonstrate this on ChatGPT (GPT-4)? I'm really curious, as last time I tried that I was not able to come up with good examples.
These are some examples on GPT-4.
I feel like this has covered 100 times artificial intelligence doesn't necessarily mean superhuman intelligence.
People answer things wrong all the time. Showing an intelligence gets something wrong doesn't mean it's not an intelligence.
Ignorance is bliss.
Saying they "can't reason logically" is also purely subjective - you out the benchmark much higher than I do, given I've seen ChatGPT provide reasoning at a level many humans fail to reach on a regular basis. This does not mean it's good at it, but that humans reasoning ability is often rather poor.
I see nothing in this other than an argument from them not being good enough now coupled with unsupported implied speculation of what understanding and reasoning and intelligence implies that we have little evidence either for or against.
All in all this comes across as blind belief.
and you don't know for sure if it was really reasoning or memorization and stochastic parroting. From another hand there are many research results demonstrating that neural networks have limitation in learning even something like multiplying numbers with many digits.
And most humans struggle with multiplying numbers with even quite few digits.
most of the humans can learn the algorithm, and do many iterations reliably if you give pen and paper, current LLMs struggle with this.
So, if we define reasoning as learning and executing algorithm of complexity N, than humans can learn more complex algorithms for higher N.
Yet, still, I think you overestimate the average humans ability to reliably execute even algorithms that simple.
I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand. I could do it, but I'm not confident I wouldn't get sloppy and make mistakes without going back and reviewing my work.
I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs, or taking a grammar in an unspecified BNF variant abd generate grammatically valid sentences in it, or conversely validating a sentence against the grammar of a fictional language.
I think a whole lot of the arguments here assume a level of human reasoning well above average.
That's not to say current LLMs do not also have glaring blind spots, but so it seems to me does people dismissing their apparent reasoning out of hand because of assumptions about human abilities we mostly haven't tested.
but given enough time you can multiply any numbers, while LLM has fundamental inability to learn algorithm.
> I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs
this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim.
It can also use WolframAlpha. It’s not a pure LLM solution but it’s good enough to multiply numbers.
Have you tried it?
The irony of these objections is that people speak out of a "but we learned to do that at school" way of thinking, often on the basis of above average skills, rather than considering that while most people maybe could (I'm not convinced), most people won't do it step by step unless you really goad them into it and pay attention that they don't skip or gloss over steps.
The same with coding: it can produce code for tasks where something similar was in training dataset, but if you ask to do completely unknown problem - it will fail.
GPT4 at least knows the algorithms - many alternative version - and will follow them. What it won't do, just like most humans, is take the time to go over it and review each step for possible errors without further prompting and inducement.
Yes, humans could. Most humans don't, without outside pressure. With pressure applied and LLM will too.
Again, this is an example of how people overestimate how humans do in the same condition.
I would suggest you look up some studies on numeracy - sure, most people can learn if put through enough pressure, and good humans would do well against currebt LLMs, but average adult human numeracy is shockingly poor.
Consider if you would question people's ability to reason in general if you find a few examples of humans who do poorly at it.
It is fascinating when people compare LLMs this way against some platonic ideal of a human without considering that many of the failures of LLMs to stick to the prompt occurs in humans too. E.g. when you argue a human could do something with enough time, you leave out the effort you'd need to expend to convince said human to do so, and follow your instructions carefully enough.
Yes, there are still plenty of conditions where humans can beat LLMs given sufficient inducement, and even in many cases easily. But that is a very different subject.
> this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim
I'll happily do that if you do the same for your unsubstantiated claims first.
Otherwise you can simply actually try to ask ChatGPT about these things yourself, and then try to ask a random person about them.
unless it is fundamentally incapable.
Ask your "AI" something about a non-popular domain (e.g., Clifford algebra) or even ask it to derive something (e.g., the geometric product) given some axioms and definitions.
Mathematics is precise as it gets, yet it failed to derive the geometric product given a set of axioms (e.g., distributive property) and definitions. Ask it to derive a theorem from perspective geometry.
Or, try something else that is not so popular or widespread (something outside of math), I suspect, that it also does a poor job deriving things from principles (axioms, definitions, theorems).
If a hunk of metal looks like a car, sounds like a car, and drives like a car is it a car?
Also, hype is good for the stock market, so keep hyping it up? At the end, Elon Musk success seems to stem from hype and sensationalism. CNN (i.e., "breaking news" everywhere) has financial success due to sensationalism and hype, so hype is good, I guess?
and it still may be the interface by which we interact with a fully sentient machine.
BIG NOTE there is no universally agreed upon and useful definition for intelligence, so discussing intelligence is generally a bad time.