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Comment by zbowling | Hacker News Reader

ML is subset of AI. AI that is inclusive of other concepts and it's not a buzzword. It's valid to call anything ML as AI. Sure there is a lot of AI hype but it's not some made up marketing jargon.

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You're technically right that AI is a superset. But, at least in a computational context, "AI" is hardly ever being used to refer to cognitive science and other AI subsets that are not directly related to ML. So ML is usually the more precise terminology. But I've pretty much given up on that one.

Large language models are definitely AI.

> large language models are definitely AI

The term “AI” is becoming over inclusive to the point of meaninglessness. Cupertino is smart enough to pick up on that. “Statistical linguistics” is the best general term for LLMs I’ve come across.

LLMs are the apex of NLP research to date, and NLP is obviously a branch of AI. You may have some sophisticated notion of AI, AGI, human-level or human-like AI or whatever, but NLP has been considered AI for generations now.

> NLP has been considered AI for generations now

I'm arguing the term AI has become "inclusive to the point of meaninglessness." That doesn't mean it was always meaningless.

IDK, I feel like people are making it so narrow that it basically cannot exist.

The best example I've seen to contest that LLM's are "AI" is to make it print the total number of line's it's response will be, essentially add

"First answer with the total number of lines your total message will be, including the line with this number"

For example, GPT4 said "12" for this prompt: "First answer with the total number of lines your total message will be, including the line with this number

Make a program in Cpp that sums all prime numbers from 1 to 100"

LLM's cannot "think", they can only make sequential predictions based on their previous answers - so they cannot formulate a response and then modify that response on-the-fly

That’s more like an inherent limitation of autoregressive prediction than that of LLM. Maybe LLMs can be trained or finetuned in other ways that allows it to think before answering.

Thinking before answer would just be like writing its text output into a hidden buffer that only it is including in the context of the conversation. So its like its always also having a private conversation with its self. I can't prove this isn't more or less what I am.

GPT very much can, in a limited way. It responds to feedback.

It's not exactly a huge leap of imagination to suggest that it won't be long before it can create an internal feedback loop by comparing its own abstractions with its memories and live experiences of external feedback.

Try asking an engineer how long a task will take

Expert systems used to be considered "AI". Certainly some optimization algorithms like genetic algorithms were "AI". Pretty much any system that makes a decision was considered "AI" at some point.

The problem is the general use of the term changes in a way to often make the meaning unclear to the point of being near useless. Outside of marketing, of course.

AI is a pretty well defined field I feel like. It combines symbolists, connectionists, evolutionaries, bayesians, and analogizers. Just because those not in the field misuse the term does not make it less useful.

It is useful when you use it like those knowledgeable of the field. Marketing departments are not using it that way.

I still consider expert systems to be AI. Is there some reason why I should stop?

It’s a chameleon of a term that can mean anything you want. That’s not a good thing.

See also: "Best practices."

Even better: "digital quality"

Not anymore in common usage. ML is roughly "learning from data". AI has had some historical meanings, but in the last 10 years became first a marketing term for Deep Learning (a subset of ML) and now a term for LLMs and Diffusion Models (and the like). Which are subsets of deep learning.

There still exist people who refer to AI as the general study of computerizing intelligence, just like somebody somewhere is still telling people that "begs the question" means dodging it. But the most applicable definition of AI as it's commonly used right now is the as the brand under which OpenAI and friends are releasing generative neutral neural network models.

I'm not really following what your argument for "ML is not a subset of AI in common usage" here is exactly.

AI as an umbrella term has been the usage in the whole CS field for decades upon decades.

A few marketing people trying to hijack and misuse the term over the past 5–10 years didn't just magically change the meaning that has been very well established for a long time.

> marketing term for Deep Learning

Deep learning is just neural networks. With multiple layers ("deep") because we only recently have built hardware that can handle "deep" neural networks fast enough.

They've still been defined in the 1970s.

The general public assumes AGI when they hear AI and that’s a problem worth fighting against.

The goalpost keeps moving on what is sufficiently "general" to meet a hypothetical AGI definition. At one point the distinction was more meaningful: AIs were always highly domain-specific (eg, playing chess). Now the same transformer model can write a string-parsing JS function, concoct a recipe that uses six arbitrary ingredients, pass a biology exam, sort unstructured data, and give tax advice, but somehow that still isn't general enough to qualify.

The lines are fuzzy for sure, but the point is more about public perception instead of actual definition. The issue is really more about the public worrying about an AI being 'conscious' enough to be a threat to humanity (AKA skynet) and LLMs/Diffusion models just can't be that.

I agree there's a strong element of misplaced anthropomorphism. But the issue isn't even that LLMs are (themselves) a threat to humanity, or that we have any reason to think they are "conscious" (in a human, or even animal, sense). The reason for concern is that the emergent behavior we're seeing from a mere token-predictor should give us serious qualms about blindly pursuing the creation of alien intelligences, which are highly capable of unintended consequences precisely because they are neither human, nor conscious.

IMHO it’s AGI when can do everything humans can and at least do it just as well.

It’s a pretty high standard but I feel it’s a “irrefutable” one - if you can find an information processing task that humans can do but the AI can’t or does poorly compared to a human, then it has failed the AGI test.

It’s also a useful one in that no one will have a problem with AGI taking over a task if its capabilities matches and exceeds any human’s.

Else where should the goalpost be then? Your claim that LLMs have reached AGI is about as valid as someone claiming ELIZA is AGI - in both cases standards are completely arbitrary.

I think human-level intelligence is a useful metric in its own right, but it's not as though humans necessarily hit some arbitrary "general" threshold either (none of us can do tasks that are trivial for computers, like remembering one million digits with perfect fidelity or calculating the millionth digit of pi).

I think any goalposts are only useful for answering specific questions:

- What practical problems can open-ended intelligence-mimicry solve?

- Will solving those problems potentially put humans out of work (and if so is that good, and either way what should be done about that, if anything?)

- Might this technology (or its perception) kick off a military arms race?

- Can these alien intelligences become clever enough to pursue goals in contraction of human well-being, or even the intent of their creators?

The intrinsic "isness" of intelligence categories is about as interesting as "whether a submarine can swim".

So is AGI AI? Why use such a broad term that puts AGI, a term defined by science fiction, with Transformer, a practical next token predictor based on gradient descent and attention mechanism, in the same basket?

AI as a term is poorly-defined, and conjures associations that range from sorta-accurate to totally fictional. ML is a specific set of concrete technologies, so it's a better and more precise term in most contexts

AI is a general term with very fuzzy meaning, much like intelligence. This is on purpose as we don't understand these things well and their definitions/usage reflect that.

Right, but we (humanity) understand the purpose-made ML that Apple talked about today just fine, which is why it's better not to use a fuzzy term

Totally. Different terms for different use cases. They wanted to be more specific here and ML suited that better. They could have been even more specific (which would have been cool) but it would have overshot their target audience's context.

AI has been used in a lot of ways, from a philosophy standpoint I’d like to insist it be used only when a meaningful definition of intelligence is applicable and other usages be considered incorrect going forward.

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