Never thought about it from that perspective, but I think you're right. It is by design, not deceptive intent, just the infinite monkeys theorem where we've replaced randomness with pattern matching trained on massive datasets.
Another way to look at it is everything a LLM creates is a 'hallucination', some of these 'hallucinations' are more useful than others.
I do agree with the parent post. Calling them hallucinations is not an accurate way of describing what is happening and using such terms to personify these machines is a mistake.
This isn't to say the outputs aren't useful, we see that they can be very useful...when used well.
The key idea is the model doesn't have any signal on "factual information." It has a huge corpus of training data and the assumption humans generally don't lie to each other when creating such a corpus.
... but (a) we do, and (b) there's all kinds of dimensions of factuality not encoded in the training data that can only be unreliably inferred (in the sense that there is no reason to believe the algorithm has encoded a way to synthesize true output from the input at all).
Seems so easy! You only need the entire world even tangentially related to video to rely solely on your project for a task and you too can have all the developers you need to work on performance!
ffmpeg has competition. For the longest time it wasn't the best audio encoder for any codec[0], and it wasn't the fastest H.264 decoder when everyone wanted that because a closed-source codec named CoreAVC was better[1].
ffmpeg was however, always the best open-source project, basically because it had all the smart developers who were capable of collaborating on anything. Its competition either wasn't smart enough and got lost in useless architecture-astronauting[2], or were too contrarian and refused to believe their encoder quality could get better because they designed it based on artificial PSNR benchmarks instead of actually watching the output.
[0] For complicated reasons I don't fully understand myself, audio encoders don't get quality improvements by sharing code or developers the way decoders do. Basically because they use something called "psychoacoustic models" which are always designed for the specific codec instead of generalized. It might just be that noone's invented a way to do it yet.
[1] I eventually fixed this by writing a new multithreading system, but it took me ~2 years of working off summer of code grants, because this was before there was much commercial interest in it.
[2] This seems to happen whenever I see anyone try to write anything in C++. They just spend all day figuring out how to connect things to other things and never write the part that does anything?
> They just spend all day figuring out how to connect things to other things and never write the part that does anything?
I see a lot of people write software like this regardless of language. Like their job is to glue pieces of code together from stack overflow. Spending more time looking for the right code that kinda sorta works than it would take to write the code which will just work.
I was thinking about two types of people; one gets distracted and starts writing their own UI framework and standard library and never gets back to the program. The other starts writing a super-flexible plugin system for everything because they're overly concerned with developing a community to the point they don't want to actually implement anything themselves.
(In this space the first was a few different mplayer forks and the second was gstreamer.)
Sometimes they get there but a lot of times not too.
I'm pretty sure there are a lot more types and the two you wrote aren't the copy-pasters either. Me, I try to follow the Unix philosophy[0] though I think there's plenty of exceptions to be made. Basically just write a bunch of functions and make your functions simple. Function overhead calls are usually cheap so this allows things to be very flexible. Because the biggest lesson I've learned is that the software is going to change so it is best to write with this in mind. The best laid plans of mice and men and all I guess. So write for today but don't forget about tomorrow.
Then of course there are those that love abstractions, those that optimize needlessly, and many others. But I do feel the copy-pasters are the most common type these days.
No one is forcing them to produce code for free. There is something toxic about giving things away for free with the ulterior motive of getting money for it.
It’s market manipulation, with the understanding that free beats every other metric.
Once the competition fails, the value extraction process can begin. This is where the toxicity of our city begins to manifest. Once there is no competition remaining we can begin eating seeds as a pastime activity.
The toxicity of our city; our city. How do you own the world? Disorder.
You know friend, if open source actually worked like that I wouldn’t be so allergic to releasing projects. But it doesn’t - a large swath of the economy depends on unpaid labour being treated poorly by people who won’t or can’t contribute.
As an American, I obviously can get behind it, but I can easily see how a declared goal of superiority of others would rub those others the wrong way (and possibly prevent their contribution)
>a declared goal of superiority of others would rub those others the wrong way
So what? Does that change anything in how things work in reality? Everyone knows it, so why pussyfoot around it?.
Why are people nowadays so sensitive about saying the truth of how things work? Have people been coddled that much that they've can't handle reality? A good life lesson is that the world does not revolve around your feelings.
It's not my feelings mate, if you don't live outside the US and have not been subjected to their unipolar attitude you will probably never understand and there is literally nothing I'm going to say to convince you of the objective reality the rest of us face.
I have to disagree. Anyone that says LLMs do not qualify as AI are the same people who will continue to move the goal posts for AGI. "Well it doesn't do this!". No one here is trying to replicate a human brain or condition in its entirety. They just want to replicate the thinking ability of one. LLMs represent the closest parallel we have experienced thus far to that goal. Saying that LLMs are not AI feel disingenuous at best and entirely purposely dishonest at the worst (perhaps perceived as staving off the impending demise of a profession).
The sooner people stop worrying about a label for what you feel fits LLMs best, the sooner they can find the things they (LLMs) absolutely excel at and improve their (the user's) workflows.
Stop fighting the future. Its not replacing right now. Later? Maybe. But right now the developers and users fully embracing it are experiencing productivity boosts unseen previously.
> the developers and users fully embracing it are experiencing productivity boosts unseen previously
This is the kind of thing that I disagree with. Over the last 75 years we’ve seen enormous productivity gains.
You think that LLMs are a bigger productivity boost than moving from physically rewiring computers to using punch cards, from running programs as batch processes with printed output to getting immediate output, from programming in assembly to higher level languages, or even just moving from enterprise Java to Rails?
Even learning your current $EDITOR and $SHELL can be a great productivity booster. I see people claiming AI is helping them and you see them hunting for files in the file manager tree instead of using `grep` or `find` (Unix).
The studies I've seen for AI actually improving productivity are a lot more modest than what the hype would have you believe. For example: https://www.youtube.com/watch?v=tbDDYKRFjhk
Skepticism isn't the same thing as fighting the future.
I will call something AGI when it can reliably solve novel problems it hasn't been pre-trained on. That's my goal post and I haven't moved it.
> For all intents and purposes of the public. AI == LLM. End of story. Doesn't matter what developers say.
This is interesting, because it's so clearly wrong. The developers are also the people who develop the LLMs, so obviously what they say is actually the factual matter of the situation. It absolutely does matter what they say.
But the public perception is that AI == LLM, agreed. Until it changes and the next development comes along, when suddenly public perception will change and LLMs will be old news, obviously not AI, and the new shiny will be AI. So not End of Story.
People are morons. Individuals are smart, intelligent, funny, interesting, etc. But in groups we're moronic.
>Do you produce good results every time, first try?
Almost always, yes, because I know what I'm doing and I have a brain that can think. I actually think before I do anything, which leads to good results. Don't assume everyone is a junior.
If you always use your first output then you are not a senior engineer, either your problem space is THAT simple that you can fit all your context in your head at the same time first try, or quite frankly you just bodge things together in non-optimal way.
It always takes some tries at a problem to grasp edge cases and to easier visualize the problem space.
Depends on how you define "try". If someone asks me to do something I don't come back with a buggy piece of garbage and say "here, I'm done!", the first deliverable will be a valid one, or I'll say I need more to do it.
When I'm confident something will work it almost always works, that is very different from these models.
Sure sometimes I do stuff I am not confident about to learn but then I don't say "here I solved the problem for you" without building confidence around the solution first.
Every competent senior engineer should be like this, if you aren't then you aren't competent. If you are confident in a solution then it should almost always work, else you are over confident and thus not competent. LLM are confident in solutions that are shit.
In cybernetics, this label has existed for a long time.
Unfortunately, discourse has followed an epistemic trajectory influenced by Hollywood and science fiction, making clear communication on the subject nearly impossible without substantial misunderstanding.
> Anyone that says LLMs do not qualify as AI are the same people who will continue to move the goal posts for AGI.
I have the complete opposite feeling. The layman understanding of the term "AI" is AGI, a term that only needs to exist because researchers and businessmen hype their latest creations as AI.
The goalposts for AI don't move but the definition isn't precise but we know it when we see it.
AI, to the layman, is Skynet/Terminator, Asimov's robots, Data, etc.
The goalposts moving that you're seeing is when something the tech bubble calls AI escapes the tech bubble and everyone else looks at it and says, no, that's not AI.
The problem is that everything that comes out of the research efforts toward AI, the tech industry calls AI despite it not achieving that goal by the common understanding of the term. LLMs were/are a hopeful AI candidate but, as of today, they aren't but that doesn't stop OpenAI from trying to raise money using the term.
AI has had many, many lay meanings over the years. Simplistic decision trees and heuristics for video games is called AI. It is a loose term and trying to apply it with semantic rigour is useless, as is trying to tell people that it should only be used to match one of its many meanings.
If you want some semantic rigour use more specific terms like AGI, human equivalent AGI, super human AGI, exponentially self improving AGI, etc. Even those labels lack rigour, but at least they are less ambiguous.
LLMs are pretty clearly AI and AGI under commonly understood, lay definitions. LLMs are not human level AGI and perhaps will never be by themselves.
> LLMs are pretty clearly AI and AGI under commonly understood, lay definitions.
That's certainly not clear. For starters, I don't think there is a lay definition of AGI which is largely my point.
The only reason people are willing to call LLMs AI is because that's how they are being sold and the shine isn't yet off the rose.
How many people call Siri AI? It used to be but people have had time to feel around the edges where it fails to meet their expectations of AI.
You can tell what people think of AI by the kind of click bait surrounding LLMs. I read an article not too long ago with the headline about an LLM lying to try and not be turned off. Turns out it was intentionally prompted to do that but the point is that that kind of self preservation is what people expect of AI. Implicitly, they expect that AI has a "self".
AI and AGI are broad umbrella terms. Stuff like Alpha Zero is AI but not AGI while LLMs are both.
Engaging in semantic battles to try to change the meanings of those terms is just going to create more confusion, not less. Instead why not use more specific and descriptive labels to be clear about what you are saying.
Self-Aware AGI, Human Level AGI, Super-Human ANI, are all much more useful than trying to force general label to be used a specific way.
> I've never seen someone state, as fact, that LLMs are AGI before now.
Many LLMs are AI that weren't designed / trained to solve a narrow problem scope. They can complete a wide range of tasks with varying levels of proficiency. That makes them artificial general intelligence or AGI.
You are confused because lots of people use "AGI" as a shorthand to talk about "human level" AGI that isn't limited to a narrow problem scope.
It's not wrong to use the term this way, but it is ambiguous and vague.
Even the term "human level" is poorly defined and if I wanted to use the term "Human level AGI" for any kind of discussion of what qualifies, I'd need to specify how I was defining that.
I'm not confused at all. Your own personal definitions just further my point that tech people have a much different classification system that the general populous and that the need for those excessive classifications is that way ambitious CEOs keep using the term incorrectly in order to increase share prices.
It's actually very funny to me that you are stating these definitions so authoritatively despite the terms not having any sort if rigor attached to either their definition or usage.
> It's actually very funny to me that you are stating these definitions so authoritatively despite the terms not having any sort if rigor attached to either their definition or usage.
Huh? My entire point was that AI and AGI are loose, vague terms and if you want to be clear about what you are talkng about, you should use more specific terms.
> The sooner people stop worrying about a label for what you feel fits LLMs best, the sooner they can find the things they (LLMs) absolutely excel at and improve their (the user's) workflows.
This is not a fault of the users. These labels are pushed primarily by "AI" companies in order to hype their products to be far more capable than they are, which in turn increases their financial valuation. Starting with "AI" itself, "superintelligence", "reasoning", "chain of thought", "mixture of experts", and a bunch of other labels that anthropomorphize and aggrandize their products. This is a grifting tactic old as time itself.
From Sam Altman[1]:
> We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence
Apologists will say "they're just words that best describe these products", repeat Dijkstra's "submarines don't swim" quote, but all of this is missing the point. These words are used deliberately because of their association to human concepts, when in reality the way the products work is not even close to what those words mean. In fact, the fuzzier the word's definition ("intelligence", "reasoning", "thought"), the more valuable it is, since it makes the product sound mysterious and magical, and makes it easier to shake off critics. This is an absolutely insidious marketing tactic.
The sooner companies start promoting their products honestly, the sooner their products will actually benefit humanity. Until then, we'll keep drowning in disinformation, and reaping the consequences of an unregulated marketplace of grifters.
It seems to me like this would also be illegal. You are giving one gender an option you aren't giving the other gender. And you are making it so one gender has more potential customers than the other, which is effectively giving them more money.
But whether the law is enforced is a whole other question.
> You are giving one gender an option you aren't giving the other gender.
A simple solution then is to make the feature a `custom request for the same sex driver/passenger`. Then males can request males and females can request females. Or they (driver/passenger) can simply use it as
The more of this kind of natural discrimination we make illegal the less meaningful public markets will be and the more people will choose to:
1) Just not socialize
2) Do things under the table.
Let people pay the premium for what they want. Sometimes there are good reasons for it. Stop pretending to have an apodictic understanding of both the world and morality.
It's not blatant - "a driver not threatening to women" is not a job both genders can do. It's very easy to delineate. We have hundreds of jobs like that already that are quite mundane and legal, like worker at Victoria's Secret.
> "a driver not threatening to women" is not a job both genders can do.
Imagine if I were to make an analogous claim with races rather than genders. You wouldn't even care whether there were any kind of statistical basis for the claim (I am explicitly not claiming any statistical basis for any claim of that form). You would immediately and correctly deem the claim to be racist.
Feeling threatened by the mere existence of another person, on the basis of that person's sex, race or anything else is not generally considered a rational or socially acceptable response. It's the sort of thing that results either from past life trauma or from explicit bigotry.
