Am I the only one getting agitated by the word AI?
Am I the only one getting agitated by the word AI (Artificial Intelligence)?
Real AI does not exist yet,
atm we only have LLMs (Large Language Models),
which do not think on their own,
but pass turing tests
(fool humans into thinking that they can think).
Imo AI is just a marketing buzzword,
created by rich capitalistic a-holes,
who already invested in LLM stocks,
and now are looking for a profit.
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”?
What would a “real AGI” be able to do that an LLM cannot?
edit: again, the smartest men in the room loudly proclaiming their smartness, until someone asks them the simplest possible question about what they’re claiming
GPT3 was cheating and playing poorly, but original GPT4 played already in level of relatively good player, even in mid game (not found in the internet, do require understanding the game, not just copying). GPT4 turbo probably isn’t so good, openai had to make it dummer (read: cheaper)
Artificial intelligence might be really good, perhaps even superhuman at one thing, for example driving a car but that same competence doesn’t apply over variety of fields. Your self-driving car can’t help with your homework. With artificial general intelligence however, it does. Humans posses general intelligence; we can do math, speak different languages, know how to navigate social situations, know how to throw a ball, can interpret sights, sounds etc.
With a real AGI you don’t need to develop different versions of it for different purposes. It’s generally intelligent so it can do it all. This also includes writing its own code. This is where the worry about intelligence explosion origins from. Once it’s even slightly better than humans at writing its code it’ll make a more competent version of itself which will then create even more competent version and so on. It’s a chain reaction which we might not be able to stop. After all it’s by definition smarter than us and being a computer; also million times faster.
Edit: Another feature that AGI would most likely, though not neccessarily posses is consciousness. There’s a possibility that it feels like something to be generally intelligent.
It’s a computer science term that’s been used for this field of study for decades, it’s like saying calling a tomato a fruit is a marketing decision.
Yes it’s somewhat common outside computer science to expect an artificial intelligence to be sentient because that’s how movies use it. John McCarthy’s which coined the term in 1956 is available online if you want to read it
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.
But what do you call a robot that teaches itself how to walk
In it’s current state,
I’d call it ML (Machine Learning)
A human defines the desired outcome,
and the technology “learns itself” to reach that desired outcome in a brute-force fashion (through millions of failed attempts, slightly inproving itself upon each epoch/iteration), until the desired outcome defined by the human has been met.
My AI professor back in the early 90’s made the point that what we think of as fairly routine was considered the realm of AI just a few years earlier.
I think that’s always the way. The things that seem impossible to do with computers are labeled as AI, then when the problems are solved, we don’t figure we’ve created AI, just that we solved that problem so it doesn’t seem as big a deal anymore.
LLMs got hyped up, but I still think there’s a good chance they will just be a thing we use, and the AI goal posts will move again.
on the other hand calculators can do things more quickly than humans, this doesn’t mean they’re intelligent or even on the intelligence spectrum. They take an input and provide and output.
The idea of applying intelligence to a calculator is kind of silly. This is why I still prefer words like “algorithms” to “AI” as its not making a “decision”. Its making a calculation, its just making it very fast based on a model and is prompt driven.
Actual intelligence doesn’t just shut off the moment its prompted response ends - it keeps going.
I think we’re misaligned on two things. First, I’m not saying doing something quicker than a human can is what comprises “intelligence.” There’s an uncountable number of things that can do some function faster than a human brain, including components of human physiology.
My point is that intelligence as I define it involves adaptation for problem solving on the part of a complex system in a complex environment. The speed isn’t really relevant, although it’s obviously an important factor in artificial intelligence, which has practical and economic incentives.
So I again return to my question of whether we consider a dog or a dolphin to be “intelligent,” or whether only humans are intelligent. If it’s the latter, then we need to be much more specific than I’ve been in my definition.
Got it. As someone who has developed computational models of complex biological systems, I’d like to know specifically what you believe the differences to be.
It’s the ‘why’. A robot will only teach itself to walk because a human predefined that outcome. A human learning to walk is maybe not even intelligence - Motor functions even operate in a separate area of the brain from executive function and I’d argue the defining tasks to accomplish and weighing risks is the intelligent part. Humans do all of that for the robot.
Everything we call “AI” now should be called “EI” or “extended intelligence” because humans are defining the both the goals and the resources in play to achieve them. Intelligence requires a degree of autonomy.
Okay, I think I understand where we disagree. There isn’t a “why” either in biology or in the types of AI I’m talking about. In a more removed sense, a CS team at MIT said “I want this robot to walk. Let’s try letting it learn by sensor feedback” whereas in the biological case we have systems that say “Everyone who can’t walk will die, so use sensor feedback.”
But going further - do you think a gazelle isn’t weighing risks while grazing? Do you think the complex behaviors of an ant colony isn’t weighing risks when deciding to migrate or to send off additional colonies? They’re indistinguishable mathematically - it’s just that one is learning evolutionarily and the other, at least theoretically, is able to learn theoretically.
Is the goal of reproductive survival not externally imposed? I can’t think of any example of something more externally imposed, in all honesty. I as a computer scientist might want to write a chatbot that can carry on a conversation, but I, as a human, also need to learn how to carry on a conversation. Can we honestly say that the latter is self-directed when all of society is dictating how and why it needs to occur?
Things like risk assessment are already well mathematically characterized. The adaptive processes we write to learn and adapt to these environmental factors are directly analogous to what’s happening in neurons and genes. I’m really just not seeing the distinction.
AI, as a term, was coined in the mid-50s by a computer scientist, John McCarthy. Yes, that John McCarthy, the one who invented LISP and helped develop Algol 60.
It’s been a marketing buzzword for generations, born out of the initial optimism that AI tasks would end up being pretty easy to figure out. AI has primarily referred to narrow AI for decades and decades.
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.
I’ve the that confusion, too. I saw someone saying AI shouldn’t be controversial because we’ve already had AI in video games for years. It’s a broad and blanket term encompassing many different technologies, but people act like it all means the same thing.
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.
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.
Maybe I’m just a little bit too familiar with it, but I don’t find LLMs particularly convincing of anything I would call “real AI”. But I suppose that entirely depends on what you mean with “real”. Their flaws are painfully obvious. I even use ChatGPT 4 in hopes of it being better.
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 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.
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.
This used to be my opinion, then I started using local models to help me write code. It’s very useful for that, to automate rote work like writing header files, function descriptions etc. or even to spit out algorithms so that I don’t have to look them up.
However there are indeed many applications that AI is completely useless for, or is simply the wrong tool.
While a diagnostic AI onboard in my car would be “useful”, what is more useful is a well-documented industry standard protocol like OBD-II, and even better would be displaying the fault right on the dashboard instead of requiring a scan tool.
Conveniently none of these require a GPU in the car.
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.
I remember when OpenAI were talking like they had discovered AGI or were a couple weeks away from discovering it, this was around the time Sam Altman was fired. Obviously that was not true, and honestly we may never get there, but we might get there.
Good list tbh.
Personally I’m excited and cautious about the future of AI because of the ethical implications of it and how it could affect society as a whole.
You can still check if its a real human if you do something really stupid or speak or write giberisch. Almost every AI will try to reply to it or say “Sorry i couldnt understand it” or recent events ( most of the LLMs arent trained on the newest events )
You’re not the only one but I don’t really get this pedantry, and a lot of pedantry I do get. You’ll never get your average person to switch to the term LLM. Even for me, a techie person, it’s a goofy term.
Sometimes you just have to use terms that everyone already knows. I suspect we will have something that functions in every way like “AI” but technically isn’t for decades. Not saying that’s the current scenario, just looking ahead to what the improved versions of chat gpt will be like, and other future developments that probably cannot be predicted.
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