Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.
I’ll get the usual downvotes for this, but:
is untrue, because current AI fundamentally is knowledge. Intelligence fundamentally is compression, and that’s what the training process does - it compresses large amounts of data into a smaller size (and of course loses many details in the process).
But there’s no way to argue that AI doesn’t know anything if you look at its ability to recreate a great number of facts etc. from a small amount of activations. Yes, not everything is accurate, and it might never be perfect. I’m not trying to argue that “it will necessarily get better”. But there’s no argument that labels current AI technology as “not understanding” without resorting to a “special human sauce” argument, because the fundamental compression mechanisms behind it are the same as behind our intelligence.
Edit: yeah, this went about as expected. I don’t know why the Lemmy community has so many weird opinions on AI topics.
Part of the problem with talking about these things in a casual setting is that nobody is using precise enough terminology to approach the issue so others can actually parse specifically what they’re trying to say.
Personally, saying the AI “knows” something implies a level of cognizance which I don’t think it possesses. LLMs “know” things the way an excel sheet can.
Obviously, if we’re instead saying the AI “knows” things due to it being able to frequently produce factual information when prompted, then yeah it knows a lot of stuff.
I always have the same feeling when people try to talk about aphantasia or having/not having an internal monologue.
Yes and the Excel sheet knows. There’s been some stick up your ass CS folks in the past railing about “computers don’t know things, sorting algorithms don’t understand how to sort”, they’ve long since given up. They claimed that saying such things is representative of a bad understanding of how things work yet people casually employing that kind of language often code circles around people who don’t, fact of the matter is many people’s minds like to think of actor forces as animated. “If the light bridge is tripped the machine knows you’re there and stops because we taught it not to decapitate you”.
I can ask AI models specific questions about knowledge it has, which it can correctly reply to. Excel sheets can’t do that.
That’s not to say the knowledge is perfect - but we know that AI models contain partial world models. How do you differentiate that from “cognizance”?
Omg give me a break with this complete nonsense. LLMs are not an intelligence. They are language processors. They do not “think” about anything and don’t have any level of self awareness that implies cognizance. A cognizant ai would have recognized that the Nazis it was creating looked historically inaccurate, based on its training data. But guess what, it didn’t do that because it’s fundamentally incapable of thinking about anything.
So sick of reading this amateurish bullshit on social media.
This gets the question…how do we think? Are we not just language (and other inputs as well) processors? I’m not sure the answer is “no.”
I also listened to an interesting podcast, I believe it was this American life or some other npr one, about whether ai has intelligence. To avoid the just “compressed knowledge” they came up with questions that the ai almost certainly would not have found in the web. Early ai models were clearly just predicting the next word, and the example was asking it to stack a list of objects. And it just said to stack them one on top of another, in a way that would no way be stable.
However when they asked a new model to do the same, with the stipulation that it explain it’s reasoning, it stacked the objects in a way that would likely be stable. Even noting that the nail on top should be placed on the head so it doesn’t roll around, and laying eggs down in a grid between a book and a plank of wood so they wouldn’t roll out.
Another experiment they did was take a language model and asked it to use some obscure programming language to draw a picture of a unicorn. Now this is a language model, not trained on any images.
And you know what it did? It produced a picture of a unicorn. Just in rough shapes, but even when they moved the horn and flipped it around, it was able to put it back. Without even ever seeing a unicorn, or anything even, it was able to draw a picture of one.
I don’t think the answer is as simple and clear as you want it to be. And the fact that it “fucked up” on a vague prompt doesn’t really prove anything. Even humans do stupid shit like this if they learn something incorrectly.
Do you understand that the model is specifically prompted to create “historically inaccurate looking Nazis”? Models aren’t supposed to inject their own guidelines and rules, they simply produce output for your input. If you tell it to produce black Hitler it will produce a black Hitler. Do you expect the model to instead produce white Hitler?
I think you might be confusing intelligence with memory. Memory is compressed knowledge, intelligence is the ability to decompress and interpret that knowledge.
You mean like create world representations from it?
https://arxiv.org/abs/2210.13382
(Though later research found this is actually a linear representation)
Or combine skills and concepts in unique ways?
https://arxiv.org/abs/2310.17567
No. On a fundamental level, the idea of “making connections between subjects” and applying already available knowledge to new topics is compression - representing more data with the same amount of storage. These are characteristics of intelligence, not of memory.
You can’t decompress something if you haven’t previously compressed the data.
Our current AI systems are T2, and T1 during interference. They can’t decide how they represent data that’d require T3 (like us) which puts them, in your terms, at the level of memory, not intelligence.
Actually it’s quite intuitive: Ask StableDiffusion to draw a picture of an accident and it will hallucinate just as wildly as if you ask a human to describe an accident they’ve witnessed ten minutes ago. It needs active engagement with that kind of memory to sort the wheat from the chaff.
Where do you get this? What kind of data requires a T3 system to be representable?
I don’t think I’ve made any claims that are related to T2 or T3 systems, and I haven’t defined “memory”, so I’m not sure how you’re trying to put it in my terms. I wouldn’t define memory as an adaptable system, so T2 would by my definition be intelligence as well.
I just did this:
Where do you see “wild hallucination”? Yeah, it’s not perfect, but I also didn’t do any kind of tuning - no negative prompt, positive prompt is literally just “accident”.
It’s not about the type of data but data organisation and operations thereon. I already gave you a link to Nikolic’ site feel free to read it in its entirety, this paper has a short and sweet information-theoretical argument.
I’m trying to map your fuzzy terms to something concrete.
My mattress is an adaptable system.
All of it. Not in the AI but conventional term: Nothing of it ever happened, also, none of the details make sense. When humans are asked to recall an accident they witnessed they report like 10% fact (what they saw) and 90% bullshit (what their brain hallucinates to make sense of what happened). Just like human memory the AI is taking a bit of information and then combining it with wild speculation into something that looks plausible. But which, if reasoning is applied, quickly falls apart.
Knowledge is a bit more than just handling data, and in terms of intelligence it also involves understanding. I don’t think knowledge in an intelligent sense can be reduced to summarising data to keywords, and the reverse.
In those terms an encyclopaedia is also knowledge, but not in an intelligent way.
I’m not saying knowledge is summarising data to keywords, where did you get that?
Intelligence is compression, and the training process compresses data. There is no “summarising” here.
“Intelligence is compression” is it?
So are you going to like… explain how that makes any sense at all, or how to deal with the many, many obvious counterexamples?
Do I need to spell this out? Like… a ZIP algorithm is not what anyone would call “intelligent”. Nor is the ZIP archive.
Do you really think you can just say “intelligence is compression” and expect people to believe you?
I could gladly provide research that supports my position! But I don’t think you’re interested in that - you didn’t ask for an explanation or for evidence, instead you’re discounting the idea with snark, so I’ll save myself the time.
I’d like to see the research.
I’ll look it up once I’m off work and reply with another comment :)
Edit: with all the downvotes, I’m not interested in continuing this broader discussion. Lemmy isn’t a good place to talk about anything close to AI. So I won’t spend time to find resources, sorry!
Would it be accurate so say that while current AI does have the knowledge, it lacks the reasoning skills needed to apply the knowledge correctly?
No, it can solve word problems that it’s never seen before with fairly intricate reasoning. LLMs can even play chess at Grandmaster levels without ever duplicating games in the training set.
Most of Lemmy has no genuine idea about the domain and hasn’t actually been following the research over the past year which invalidates the “common knowledge” on the topic you often see regurgitated.
For example, LLMs build world models from the training data, and can combine skills from the data in ways that haven’t been combined in the training data.
They do have shortcomings - being unable to identify what they don’t know is a key one.
But to be fair, apparently most people on Lemmy can’t do that either.
I don’t think it’s generally true, because current AI can solve some reasoning tasks very well. But it’s definitely something where they are lacking.
It isn’t reasoning about anything. A human did the reasoning at some point, and the LLM’s dataset includes that original information. The LLM is simply matching your prompt to that training data. It’s not doing anything else. It’s not thinking about the question you asked it. It’s a glorified keyword search.
It’s obvious you have no idea how LLMs work at a fundamental level, yet you keep talking about them like you’re an expert.
So if I find a single example of an AI doing a reasoning task that’s not in its training material, would you agree that you’re wrong and AI does reason?
You won’t find one. LLMs are literally incapable of the kind of reasoning you’re talking about. All of their solutions are based on training data, no matter how “original” your problem might seem.
You didn’t answer my question.
That’s fair, I have seen AI reason at a low level, but it seems to me that it is lacking higher levels of reasoning and context
It definitely is lacking for now, but the question is: are these differences in degrees, or fundamental differences? I haven’t seen research suggesting that it’s the latter so far.
Lemmy hasn’t met a pitchfork it doesn’t pick up.
You are correct. The most cited researcher in the space agrees with you. There’s been a half dozen papers over the past year replicating the finding that LLMs generate world models from the training data.
But that doesn’t matter. People love their confirmation bias.
Just look at how many people think it only predicts what word comes next, thinking it’s a Markov chain and completely unaware of how self-attention works in transformers.
The wisdom of the crowd is often idiocy.
Thank you very much. The confirmation bias is crazy - one guy is literally trying to tell me that AI generators don’t have knowledge because, when asking it for a picture of racially diverse Nazis, you get a picture of racially diverse Nazis. The facts don’t matter as long as you get to be angry about stupid AIs.
It’s hard to tell a difference between these people and Trump supporters sometimes.
To me it feels a lot like when I was arguing against antivaxxers.
The same pattern of linking and explaining research but having it dismissed because it doesn’t line up with their gut feelings and whatever they read when “doing their own research” guided by that very confirmation bias.
The field is moving faster than any I’ve seen before, and even people working in it seem to be out of touch with the research side of things over the past year since GPT-4 was released.
A lot of outstanding assumptions have been proven wrong.
It’s a bit like the early 19th century in physics, where everyone assumed things that turned out wrong over a very short period where it all turned upside down.
Exactly. They have very strong feelings that they are right, and won’t be moved - not by arguments, research, evidence or anything else.
Just look at the guy telling me “they can’t reason!”. I asked whether they’d accept they are wrong if I provide a counter example, and they literally can’t say yes. Their world view won’t allow it. If I’m sure I’m right that no counter examples exist to my point, I’d gladly say “yes, a counter example would sway me”.
Yall actually have any research to share or just gonna talk about it?
Yall actually have any research to share or just gonna talk about it?
Both
Jsyk I can’t see that comment from your link.
Weird, works fine for me. It’s their response to the comment in this thread with this content:
Thanks!