Large Language Vulture Model?
https://en.wikipedia.org/wiki/LLVM
Released approx 20 years before ASGSI (Artificial Super General Super Intelligence)
Isn't ASGSI just a marketing term, while ASSGSI is the one as smart as a human?
On a very high level, the role of deep learning here seems similar to AlphaGo (which is also the combination of a less novel generic optimization algorithm, Monte Carlo tree search, with deep learning-provided predictions). I don't think anyone would debate that AlphaGo is fundamentally an AI system. Maybe if we are to be really precise, both of these systems are optimization guided by heuristics provided by deep learning.
At least, that used to be the case before the current AI summer and hype.
But I'm skeptical of calling most optimizers AI.
Perhaps one day we'll understand what goes on in our brains to the same extent.
I would not say the bar is moving along with "things computers can do".
Which one? Fuzzy logic, ANNs, symbol processing, expert systems, ...?
It's always entertaining to watch the hype cycles. Hopefully this one will have a net positive impact on society.
Marvin Minsky -- father of classical AI -- pointed out that intelligence is a "suitcase word" [1] which can be stuffed with many different meanings.
Basically, we call things AI, that we are too stupid to understand.
It probably uses a relatively simple hill climbing algorithm, but I would agree that it could still be classified as machine learning. AI is just the new, hip term for ML.
(Note - I may have misunderstood your meaning btw, if so apologies!)
https://www.cnbc.com/2017/12/21/long-island-iced-tea-micro-c...
Now they start being called AI, because AI is artificial human thought and those things are that.
What changed? Our perception of the meaning of "thought".
A fairly simple set of if statements is AI (an "expert system" specifically).
AI is _not_ just talking movie robots.
And you have to be doing something rather specific with a pile of if statements for it to count as an expert system.
Clippy was an AI but he wasn't an AI.
For some of us, your objection sounds as silly as if we were to tell some student they didn't use algebra, because what they wrote down isn't "an algebra".
This is "just" an optimizer being used in conjunction with a simulation, which we've been doing for a long, long time. It's cool, but it's not AI.
Optimization is a branch of mathematics concerned with optimization techniques, and the analysis and quality of possible solutions. An optimizer is an algorithm concerned with finding optima of functions. You don't get to rewrite decades of mathematical literature because it gives you AI vibes.
Yeah, you need an optimizer to train AI, but it's not the AI part. Most people would refer to and understand AI as being the thing they interact with. You can't interact with an optimizer, but you can interact with the function that is being optimized.
I'm honestly stunned that this is even a controversial position.
The CS literature has used AI to refer to nearly any advanced search algorithm, e.g. during the prior AI boom and bust cycle around symbolic AI. In this literature, it is idiomatic that AI techniques are the broad category of search and optimization techniques. There wasn't necessarily any "training" involved, as machine learning was considered part of the AI topic area but not its entirety.
It's always been acknowledged that various disciplines had significant crossover, e.g. ML and operations research, but I've never seen anyone claim that optimization is AI until recently.
Ian Goodfellow's book is, what, 10 years old at this point? The fundamentals in that book cover all of ML from classical to deep learning, and pretty clearly enumerate the different components necessary to do ML, and there's no doubt that optimization is one of them. But to say that it is AI in the way that most people would probably understand it? It's a stretch, and hinges on whether you're using AI to refer to the collection of techniques or the discipline, as opposed to the output (i.e. the "intelligence"). I, and I'd argue most people, use AI to refer to the latter, but I guess the distinction between the discipline and the product is vague enough for media hype.
And to be clear, I'm not trying to take away from the authors. Optimization is one of the tools I like to throw around, both in my own projects and professionally. I love seeing cool applications of optimization, and this definitely qualifies. I just don't agree that everything that uses optimization is AI, because it's an unnecessary blurring of boundaries.
I'm actually getting old and grumpy. And I'm talking about common usage since I was an undergraduate taking AI courses 30+ years ago and surveying CS literature, some of which predated my courses by another 10-30 years... I recall one visiting professor who was an "AI researcher" at NASA, and his work was planners and optimizers for spacecraft operations.
But getting back to the basic point: there is a semantic difference of having a grammatical article, which for others here carries more significance than you seem to admit. "Artificial intelligence" vs "An artificial intelligence".
AI as a term was invented to describe exactly this. Any usage of the term AI which does not include this is a misunderstanding of the term. You don't get to rewrite decades of computer science literature because it fails to give you AI vibes.
> Most people would refer to and understand AI as being the thing they interact with. You can't interact with an optimizer, but you can interact with the function that is being optimized.
I have no idea what you mean by "interact with" in this context. You can use a non AI optimizer to train an AI. You can also create an AI that serves the function of an optimizer. Optimization is a task, artificial intelligence is an approach to tasks. A neural network trained to optimize chip design is exactly as much an AI as a neural network trained to predict protein folding or translate speech.
> "The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements.[4][2][1] Edward Geist credits John McCarthy for coining the term "AI effect" to describe this phenomenon.[4]
> McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the 'failures', the tough nuts that couldn't yet be cracked."[5] It is an example of moving the goalposts.[6]
> Tesler's Theorem is:
> AI is whatever hasn't been done yet.
> — Larry Tesler
On the contrary. The "AI effect" is an example of attempting to hold others to goalposts that they never agreed to in the first place.
Instead of saying "this is AI and if you don't agree then you're shifting the goalposts" instead try asking others "what future developments would you consider to be AI" and see what sort of answers you get.
Meanwhile I do not consider gradient descent (or biased random walk, or any number of other algorithms) to be AI.
The exact line is fuzzy. I don't feel like most simple image classifiers qualify, whereas style transfer GANs do feel like a very weak form of AI to me. But obviously it's becoming quite subjective at that point.
As to my own goalposts, those haven't changed in quite some time. If I did happen to disagree with you though, well, someone disagreeing with you does not on its own imply that they've recently moved their own goalposts.
You seem to be assuming that there's some universally accepted definition that's been changing over time but that doesn't seem to be the case to me. What I see is a continual stream of bogus and overhyped claims that get rebuked.
If anything it's the people trumpeting AI this and AI that who are trying very hard to shift the goalposts for their own benefit.
The original goalpost was AI. You moved the goal post to general/strong AI. No one was claiming to be working on that back in the day even if they hoped their efforts would be along the path to that eventually. If you asked someone even just a few years ago when general AI would become possible, I think most people would have said a date after 2050 if they thought it was possible at all.
> You seem to be assuming that there's some universally accepted definition that's been changing over time but that doesn't seem to be the case to me
I can guarantee you that if you asked someone what AI meant in 1995 it would be radically different than what someone would answer in 2025. Obviously at both periods of time the boundaries of the definition were fuzzy, but the entire fuzzy blob has undeniably shifted radically.
> What I see is a continual stream of bogus and overhyped claims that get rebuked. If anything it's the people trumpeting AI this and AI that who are trying very hard to shift the goalposts for their own benefit.
People claiming AI is more capable than it really is moves the goal posts further away. If I falsely claim I have an AI that can out-litigate the best lawyers in the world, that certainly makes a real AI that can get a passing grade in an introductory law school class a lot less impressive. No one makes any money from claiming their product will underdeliver compared to what people expect.
Even if we happened to disagree about that clarification (although we appear to agree?) that would not on its own imply that any shifting of goalposts had occurred. The "original goalpost" of which you speak is not attributed to me. To imply that I once held a particular view is to straw man me. If you want to know what I thought in the past then just ask!
I'd also like to point out that a change in the common usage of a term is not the same thing as the shifting of goalposts. I don't believe that happened here but it bears pointing out nonetheless.
> asked someone what AI meant in 1995
Who is the someone? I wouldn't expect a layman, either then or now, to have an even remotely rigorous working definition. This is important because if you are going to claim that goalposts have shifted then you're going to need to be clear about whose goalposts and what exactly they were.
> the boundaries of the definition were fuzzy, but the entire fuzzy blob has undeniably shifted radically
It seems we have a fundamental disagreement then. From my perspective the term "AI" in popular culture brought to mind an expert system conversing proficiently in natural language in both the 90s and today. That is to say, a strong and general AI. I think that most laymen today would classify something like chatgpt as AI, but if pressed with examples of some of its more egregious logical failures would probably become confused about the precise definition of the term.
Meanwhile the technical definition has always been much more nuanced and similarly appears to me to have remained largely unchanged. If anything I think the surprising revelation has been that you can have such extensive and refined natural language processing capabilities without the ability to reason logically.
You consider the example listed to be AI, but not general AI. You believe that the pop culture definition of AI is general AI. Thus you don't believe those things meet the pop culture definition of AI. The discrepancy between your definition of AI and the pop culture definition of AI proves that somebody moved the goalposts.
I disagree with your assumption that the association of natural language processing with AI means that this has always been exclusively what AI referred to. However, working with that assumption, you still acknowledge that when presented with a system that does exactly that, people feel their definition needs to change to exclude it.
Realistically though, I think pop culture largely thought of AI the way it was presented in scifi stories - a cold, calculating thing that analyzed data and came to decidedly non-human conclusions based on it. Skynet's probably the canonical example. The idea that an AI needs to understand information in the same way we do was definitely not always the case.
since 2021/whenever LLM applications got popular everyone has been mentioning AI. this happened before during the previous mini-hype cycle around 2016-ish where everyone was claiming neural networks were “AI”. even though, historically, they were still referred to by academics as machine learning.
no-one serious, who actually works on these things; isn’t interested in making hoardes of $$$ or getting popular on social media, calls this stuff AI. so if there were a wikipedia link one might want to include on this thread, I’d say it would be this one — https://en.m.wikipedia.org/wiki/Advertising
because, let’s face it, advertising/marketing teams selling products using linear regression as “AI” are the ones shifting the definition into utter meaninglessness.
so it’s no surprise people on HN, some of whom actually know stuff about things, would be frustrated and annoyed and get tetchy about calling things “AI” (when it isn’t) after 3 sodding years of this hype cycle. i was sick of it after a month. imagine how i feel!
- edit, removed line breaks.
The reason why the "AI" goalposts always seem to shift -- is not because people suddenly decide to change the definition, but because the definition gets watered down by advertising people etc. Most people who know anything call this stuff deep learning/machine learning to avoid that specific problem.
Personally, I can't wait for people who work in advertising to get put on the same spaceship as the marketers and telephone sanitizers. (It's not just people in advertising. i just don't like advertising people in particular).
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I'd argue machine learning is actually a sub-field within statistics. but then we're gonna get into splitting hairs about whether Serena Williams is an athlete, or a professional sports player. which wasn't really the point I was making and isn't actually that important. (also, it can be a sub-field of both, so then neither of us is wrong, or right. isn't language fun!).