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Comment by t-writescode | Hacker News Reader

I’m a bit frustrated. AI can do a looot of things; but I think as we continue to muddy the waters between LLMs and more traditional machine learning like Monte Carlo, Genetic Algoriths, Expert Systems and other Statistics magic tricks, we’re too aggressively conflating established and morally neutral activities in ML with the concerns that people have about LLMs and Stable Diffusion.

Though I also imagine that that is the point.

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It is a problem because people will talk about what AI can do implying that an LLM can do that thing, making it seem like a pure LLM can do almost anything. On the other hand people will say AI will never be able to do X because an LLM can’t do that thing well natively. AI has become too vague of a term to be useful.

We're relearning that intelligence is spikey, and that different things that we consider 'intelligent' can have vastly different capabilities.

We're learning that people are way too lax with where they apply the term "intelligent". LLMs aren't remotely intelligent, but people are trying to ride the hype train and call them intelligence.

this is just false.

by any meaningful measure of intelligence. the latest models are much smarter than the bulk of the population.

how would you define intelligence?

“Intelligence is the ability to learn.”

That is a meaningful measure of intelligence that every LLM completely fails at.

> LLMs aren't remotely intelligent

Maybe I'm just significantly and unrepresentatively unlucky, but Claude is significantly more intelligent than the average human around me on most any metric I can think of.

Very much indeed. The term itself is not properly defined, strictly speaking.

I wish I could wave a magic wand and just make the word "AI" go away. It has no actual meaning. It could mean anything from your opponent in Mario Kart to Stable Diffusion.

I disagree. AI is doing exactly what it was predicted it would in science fiction.

The computer can now literally talk to you in natural language and then perfectly produce sophisticated actions in response to completely arbitrary and unstructured input. It trivially passes the Turing test. By any definition prior to the year 2023 we are living with Artificial General Intelligence and it’s here now.

So where are the androids? If it's AGI, why is it used as a tool, waiting to be prompted or executed by humans? Where is Skynet? Military applications still rely on human operators.

Robotics is advancing a bit slower, but is making progress as well.

Yes, but unlike a lot of science fiction, robots, LLMs and other AI remain tools for human use. Augmented Intelligence would have been the more accurate word for real world AI.

You realize llms as a field is barely 5 years old? Give it at least another 5.

I doubt LLMs will give us full embodied intelligence that science fiction androids have. Maybe some other approach. But I suspect for the forseable future LLMs, robots and other AI methods will remain tools, not independent agents like Star Trek Data or Skynet.

VLAs are new LLMs. Give them 5 years to develop. But even good old LLMs are still improving every six months.

Yeah. We'll be arguing "is it really AGI" for many more years. Meanwhile, everyone interesting is going to have moved on from that question, choosing to spend time on "who cares if it's AGI, can it do $foo", for whatever value of $foo is interesting to them. Whether the machine is folding clothes or folding proteins, AGI isn't well defined other than "I'll know it when I see it", so whether or not it's AGI, the question is what job is the machine capable of and is it cheaper than a human? A humanoid robot that can work a warehouse is not putting anyone out of a job if it costs a billion dollars, and neither is a digital AI employee that costs the company a billion dollars either.

Current LLMs don't pass the turing test.

Remember, the interrogator is allowed to be hostile, so they would obviously employ all known prompt injections and typical LLM 'gotchas' to figure out who the AI is.

"AI" == "what (through tech) can replace a professional"

It may seem similarly vague, but it does in fact open interesting, productive, and necessary questions. A "computer" was a professional crunching numbers - "replaced", "easily" because of the deterministic procedural nature of said work, but what about the technical effort to arrive there, and what about the less "mechanical" jobs? When do "processes" become "intelligence"?

Some of us had studied AI originally to study the mind - "how do we formalize thought". It's the interdisciplinary, transversal nature of the area.

Also maybe compare that with that large and important intersection between CS and Economics - the "science of optimization" and its implementation in efficient IT systems. The effort in terms of that different discipline may not be evident, yet lots of engineering is "optimizing" and the generalization of those solutions we call Economics (see the book Algorithms to live by).

So: the term "Artificial Intelligence" may not be important as CS solutions to practical problems are built (you just focus on the better solution), but there is relevance to the "side disciplince" of AI, and from that perspective that is the cone, the scope anyway. "How would an intelligent solver approach the problem".

> "AI" == "what (through tech) can replace a professional"

But as you point out, we used to have human calculators. So is a simple desk calculator a form of "AI"? If so, what type of software isn't AI?

> is a simple desk calculator a form of "AI"

If what it does is "taking care of the carry", it represents a pretty minimal requirement for intelligence - it does replace a professional that could do it, but that professional does not have to apply too much proficiency and cleverness to do its job. It is improper AI.

> what type of software isn't AI

That which would not correspond to the job of an intelligent entity. Maybe blitting bitmaps around a screen?

As I tried to convey, it is more of a matter of perspective: the area of "implementing ways to solve problems as an intelligent entity would". It is a discipline that intersects others - engineering, logic, brain science, philosophy, epistemology, maybe again economics (as "the science of optimality and efficiency" - as an intelligent solver would do)... Consider it a special discipline that spans many other realms.

> Maybe blitting bitmaps around a screen?

Okay, that makes sense. Even so:

> If what it does is "taking care of the carry", it represents a pretty minimal requirement for intelligence - it does replace a professional that could do it, but that professional does not have to apply too much proficiency and cleverness to do its job. It is improper AI.

I think you're underselling how much mental work is required to solve complex arithmetic. Yes, it's simple for a computer, but (1) even basic computers are extremely complex in absolute terms, and (2) even the most complex computing tasks could be considered simple once you break them down far enough—for example, a large language model is "just" fancy matrix multiplication.

So I feel like there's a "sufficiently advanced technology is indistinguishable from magic" element here. Something becomes AI once it seems sufficiently advanced. But then time passes and it doesn't feel that advanced anymore.

I understand that human language doesn't always have a super precise definition, and I'm not trying to be pedantic. I think the term "artificial intelligence" is under-specified to the point of having virtually no meaning. To the extent that it is useful—obviously, a lot of people are using it conversation, so something is getting communicated—it's because it's possible to infer from context what someone is referring to (ie "the student used AI to write her essay" is clearly referring to an LLM, not Eliza).

We'd all be better off if we used words that describe what we're actually talking about.

But I am not sure the point is clear. There are different levels - and in fact, matrix multiplication is "easy", matrix multiplication of what (which architecture) is not.

Defining a procedure for arithmetic is easy. Implementing it in silicon is not. To carry on the procedure for the former has low relevance to intelligence. To carry on the job of the latter does have high relevance to intelligence. If the latter is performed by a professional it is intelligence. If it is performed by an algorithm it is artificial intelligence. "Automating finding out good ways to implement ALUs" is AI; the ALUs running are not.

So, studying AI, asking ourselves which new "devices" (abstract sense) we can find so that our algorithms have aspects of cleverness, is productive as it simply and plainly pushes, invests in the production of that class of algorithms.

Surely there is a continuity between "sort" and "genetic alg." - but the direction counts, it is in that direction that we strived to produce them producers.

So, it's very much not about the complexity of the product («sufficiently advanced technology»): it is in the complexity of the intermediate that built the final product, when that intermediate is not human. The pocket calculator is majestic, yes - but there is the strong point: it was human made. That is human intelligence at work. Study how to have it blueprinted by a machine, and if it works properly, you'll attribute a simulation of intelligence to the automated blueprinter - that is artificial intelligence.

> used words that describe what we're actually talking about

Look, people who follow me here know I place radical importance to language and to the awareness of language. It should be one of the aspects I would be most dreaded for.

Surely, most people are unaware of what they say to a large extent.

But in the case of "Artificial Intelligence", it seems you are underplaying the concept of directions - "simple algorithms" vs "advanced algorithms"; "houses" vs "skyscrapers"; "flying machines" vs "air force fighters". There is continuity and yet different position. And intelligence surely can be implemented at different levels.

Another thing (I am strongly selecting what I could reply, and I am forced to be concise). There is also a concept of "unintelligence" - the dire opposite of intelligence is also a thing (if Eliza is ~0, you can go below that). Understanding what intelligence is helps recognize its opposite, which is an experienced pitfall in the area.

Game AI uses behaviour trees, usually coded by hand. Decision trees are used for classification and are normally learned from data. The latter are a traditional AI technique from the early days of the modern machine learning era, in the 1990's.

I have been practicing saying ML for traditional machine learning and LLMs for LLMs for just this reason. Trying not to say AI anymore. Too ambiguous. Sometimes I'm talking about game AI even, I'll try to use shorthand for whatever algorithm I think the AI is using (often I'll talk about its flowchart, though not always sure it's literally using that under the hood).

What is ChatGPT then? Sure it's an LLM, but I can give the app pictures and audio, and it can generate pictures for me. Do we distinguish between the bits of the architecture to accomplish those features separately from the LLM part of the product?

Yes? Or just call it a chatbot if you don't care about the implementation details.

"AI" is a term cursed by cool sci-fi implications. It makes it a kick ass marketing term because most people are going to have some familiarity with sci-fi AI and "X media predicted Y technology" is a pretty widespread belief for a lot of values of X (star trek, Hitchhikers Guide to the Galaxy, Arthur C. Clarke) and Y (internet, cell phones, VR). If you want to tell someone we're making big strides in something, linking it into some popsci understanding of sci-fi being the great predictor of human achievement is low effort and high impact for quite a few people.

People aren't trying to communicate accurately if their first priority is getting you excited about the thing!

I miss "predictive analytics". Too boring and honest for marketers though.

> AI can do a looot of things

AI is not a real thing or a natural kind but a perspective. Whether something qualifies as "AI" or not cannot be decided by the objective features of the thing. Ergo, it can be defined at the author's pleasure.

> conflating established and morally neutral activities in ML

LLMs are no more or less morally neutral than other ML techniques.

Recently I heard some people conflate procedural generation and generative AI and I had to explain why there isn't some legal or ethical issue with what breaks down to essentially scattering some points.

It's really getting annoying having to have these conversations.

Just as more successful machine learning fields distanced themselves from the term during the AI winter, I suppose we will (and perhaps are?) be seeing them adopt it again, now that we are in an "AI summer".

As always, it's a matter of funding. Both inside academia and outside of it. I remember when nanotechnology was all the rage. Everyone flocked to writing grant proposals about their "nano" technology that was thousands or millons of nanometers, aka micrometers or even millimeters. Stupid but if it works it's not stupid. The old joke is what do you call AI that works? Machine Learning.

The real question is how much compute do you need. With LLMs getting popular, so is compute. That's the real win for non-LLM technologies. The sheer availability of GPU capacity. Yes, it's expensive, but time in a GB300 supercomputer isn't even possible if they don't exist.

Alexnet succeeded for many reasons but a big reason is that computers got good enough to apply those algorithms and techniques in practice. Outside of LLMs, what new AI/ML systems await us in the future? The LLM bubble popping, if it ever does, is going to leave us with supercomputer capacity going unused and available for cheap, meaning experiments that were once infeasibly expensive become practical. I can't afford $10 million to run a weather simulation, but at $1,000 for the same amount of compute, a lot more experimentation becomes practical.

Reinforcement learning can solve a Rubik’s Cube. A LLM that hasn’t been trained to solve a Rubik’s Cube can not.

People who work in marketing do not seem particularly concerned with accuracy, or even just making logical sense.

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