The word “AI” has been used for way longer than the current LLM trend, even for fairly trivial things like enemy AI in video games. How would you even define a computer “thinking on its own”?

Real AGI does not exist yet. AI has existed for decades.

Ai is 100% a marketing term.

I’d like to offer a different perspective. I’m a grey beard who remembers the AI Winter, when the term had so over promised and under delivered (think expert systems and some of the work of Minsky) that using the term was a guarantee your project would not be funded. That’s when the terms like “machine learning” and “intelligent systems” started to come into fashion.

The best quote I can recall on AI ran along the lines of “AI is no more artificial intelligence than airplanes are doing artificial flight.” We do not have a general AI yet, and if Commander Data is your minimum bar for what constitutes AI, you’re absolutely right, and you can define it however you please.

What we do have are complex adaptive systems capable of learning and problem solving in complex problem spaces. Some are motivated by biological models, some are purely mathematical, and some are a mishmash of both. Some of them are complex enough that we’re still trying to figure out how they work.

And, yes, we have reached another peak in the AI hype - you’re certainly not wrong there. But what do you call a robot that teaches itself how to walk, like they were doing 20 years ago at MIT? That’s intelligence, in my book.

My point is that intelligence - biological or artificial - exists on a continuum. It’s not a Boolean property a system either has or doesn’t have. We wouldn’t call a dog unintelligent because it can’t play chess, or a human unintelligent because they never learned calculus. Are viruses intelligent? That’s kind of a grey area that I could argue from either side. But I believe that Daniel Dennett argued that we could consider a paramecium intelligent. Iirc, he even used it to illustrate “free will,” although I completely reject that interpretation. But it does have behaviors that it learned over evolutionary time, and so in that sense we could say it exhibits intelligence. On the other hand, if you’re going to use Richard Feynman as your definition of intelligence, then most of us are going to be in trouble.

I’m agitated that people got the impression “AI” referred specifically to human-level intelligence.

Like, before the LLM boom it was uncontroversial to refer to the bots in video games as “AI.” Now it gets comments like this.

AI isn’t reserved for a human-level general intelligence. The computer-controlled avatars in some videogames are AI. My phone’s text-to-speech is AI. And yes, LLMs, like the smaller Markov-chain models before them, are AI.

The only thing I really hate about “AI” is how many damn fonts barely differentiate between a capital “i” and lowercase “L” so it just looks like everyone is talking about some guy named Al.

“Al improves efficiency in…” Oh, good for him

I’m more infuriated by people like you who seem to think that the term AI means a conscious/sentient device. Artificial intelligence is a field of computer science dating back to the very beginnings of the discipline. LLMs are AI, Chess engines are AI, video game enemies are AI. What you’re describing is AGI or artificial general intelligence. A program that can exceed its training and improve itself without oversight. That doesn’t exist yet. AI definitely does.

AI has, for a long time been a Hollywood term for a character archetype (usually complete with questions about whether Commander Data will ever be a real boy.) I wrote a 2019 blog piece on what it means when we talk about AI stuff.

Here are some alternative terms you can use in place of AI, when they’re talking about something else:

• AGI: Artificial General Intelligence: The big kahuna that doesn’t exist yet, and many projects are striving for, yet is as evasive as fusion power. An AGI in a robot will be capable of operating your coffee machine to make coffee or assemble your flat-packed furniture from the visual IKEA instructions. Since we still can’t define sentience we don’t know if AGI is sentient, or if we humans are not sentient but fake it really well. Might try to murder their creator or end humanity, but probably not.
• LLM Large Language Model: This is the engine behind digital assistants like Siri or Alexa and still suffer from nuance problems. I’m used to having to ask them several times to get results I want (say, the Starbucks or Peets that requires the least deviation from the next hundred kilometers of my route. Siri can’t do that.) This is the application of learning systems see below, but isn’t smart enough for your household servant bot to replace your hired help.
• Learning Systems: The fundamental programmity magic that powers all this other stuff, whether simple data scrapers to neural networks. These are used in a whole lot of modern applications, and have been since the 1970s. But they’re very small compared to the things we’re trying to build with it. Most of the time we don’t actually call it AI, even for marketing. It’s just the capacity for a program to get better at doing its thing from experience.
• Gaming AI Not really AI (necessarily) but is a different use of the term artificial intelligence. When playing a game with elements pretending to be human (or living, or opponents), we call it the enemy AI or mob AI. It’s often really simple, except in strategy games which can feature robust enough computational power to challenge major international chess guns.
• Generative AI: A term for LLMs that create content, say, draw pictures or write essays, or do other useful arts and sciences. Currently it requires a technician to figure out the right set of words (called a prompt) to get the machine do create the desired art to specifications. They’re commonly confused by nuance. They infamously have problems with hands (too many fingers, combining limbs together, adding extra limbs, etc.). Plagiarism and making up spontaneous facts (called hallucinating) are also common problems. And yet Generative AI has been useful in the development of antibiotics and advanced batteries. Techs successfully wrangle Generative AI, and Lemmy has a few communities devoted to techs honing their picture generation skills, and stress-testing the nuance interpretation capacity of Generative AI (often to humorous effect). Generative AI should be treated like a new tool, a digital lathe, that requires some expertise to use.
• Technological Singularity: A bit way off, since it requires AGI that is capable of designing its successor, lather, rinse, repeat until the resulting techno-utopia can predict what we want and create it for us before we know we want it. Might consume the entire universe. Some futurists fantasize this is how human beings (happily) go extinct, either left to retire in a luxurious paradise, or cyborged up beyond recognition, eventually replacing all the meat parts with something better. Probably won’t happen thanks to all the crises featuring global catastrophic risk.
• AI Snake Oil: There’s not yet an official name for it, but a category worth identifying. When industrialists look at all the Generative AI output, they often wonder if they can use some of this magic and power to facilitate enhancing their own revenues, typically by replacing some of their workers with generative AI systems, and instead of having a development team, they have a few technicians who operate all their AI systems. This is a bad idea, but there are a lot of grifters trying to suggest their product will do this for businesses, often with simultaneously humorous and tragic results. The tragedy is all the people who had decent jobs who do no longer, since decent jobs are hard to come by. So long as we have top-down companies doing the capitalism, we’ll have industrial quackery being sold to executive management promising to replace human workers or force them to work harder for less or something.
• Friendly AI: What we hope AI will be (at any level of sophistication) once we give it power and responsibility (say, the capacity to loiter until it sees a worthy enemy to kill and then kills it.) A large coalition of technology ethicists want to create cautionary protocols for AI development interests to follow, in an effort to prevent AIs from turning into a menace to its human masters. A different large coalition is in a hurry to turn AI into something that makes oodles and oodles of profit, and is eager to Stockton Rush its way to AGI, no matter the risks. Note that we don’t need the software in question to be actual AGI, just smart enough to realize it has a big gun (or dangerously powerful demolition jaws or a really precise cutting laser) and can use it, and to realize turning its weapon onto its commanding officer might expedite completing its mission. Friendly AI would choose to not do that. Unfriendly AI will consider its less loyal options more thoroughly.

That’s a bit of a list, but I hope it clears things up.

LLMs are AI. Lots of things are. They’re just not AGI.

When I was doing my applied math PhD, the vast majority of people in my discipline used either “machine learning”, “statistical learning”, “deep learning”, but almost never “AI” (at least not in a paper or a conference). Once I finished my PhD and took on my first quant job at a bank, management insisted that I should use the word AI more in my communications. I make a neural network that simply interpolates between prices? That’s AI.

The point is that top management and shareholders don’t want the accurate terminology, they want to hear that you’re implementing AI and that the company is investing in it, because that’s what pumps the company’s stock as long as we’re in the current AI bubble.

I think most people consider LLMs to be real AI, myself included. It’s not AGI, if that’s what you mean, but it is AI.

What exactly is the difference between being able to reliably fool someone into thinking that you can think, and actually being able to think? And how could we, as outside observers, be able to tell the difference?

As far as your question though, I’m agitated too, but more about things being marketed as AI that either shouldn’t have AI or don’t have AI.

People: build an algorithm to generate text that sounds like a person wrote it by finding patterns in text written by people

Algorithm: outputs text that sounds like a person wrote it

Holyfuck its self aware guys

I just get tired of seeing all the dumb ass ways it’s trying to be incorporated into every single thing even though it’s still half-baked and not very useful for a very large amount of people. To me, it’s as useful as a toy is. Fun for a minute or two, and then you’re just reminded how awful it is and drop it in the bin to play with when you’re bored enough to.

This is not a very good poem.