this post was submitted on 22 Feb 2024

485 points (96.2% liked)

Technology

58140 readers

5482 users here now

This is a most excellent place for technology news and articles.

Our Rules

Follow the lemmy.world rules.
Only tech related content.
Be excellent to each another!
Mod approved content bots can post up to 10 articles per day.
Threads asking for personal tech support may be deleted.
Politics threads may be removed.
No memes allowed as posts, OK to post as comments.
Only approved bots from the list below, to ask if your bot can be added please contact us.
Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 1 year ago

MODERATORS

[email protected]

485

Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis (www.theverge.com)

submitted 7 months ago by [email protected] to c/[email protected]

198 comments fedilink hide all child comments

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.

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 130 points 7 months ago (88 children)

I don't know how you'd solve the problem of making a generative AI accurately create a slate of images that both a) inclusively produces people with diverse characteristics and b) understands the context of what characteristics could feasibly be generated.

But that's because the AI doesn't know how to solve the problem.

Because the AI doesn't know anything.

Real intelligence simply doesn't work like this, and every time you point it out someone shouts "but it'll get better". It still won't understand anything unless you teach it exactly what the solution to a prompt is. It won't, for example, interpolate its knowledge of what US senators look like with the knowledge that all of them were white men for a long period of American history.

[–] [email protected] -1 points 7 months ago* (last edited 7 months ago) (44 children)

I'll get the usual downvotes for this, but:

Because the AI doesn't know anything.

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.

[–] [email protected] 12 points 7 months ago (2 children)

I think you might be confusing intelligence with memory. Memory is compressed knowledge, intelligence is the ability to decompress and interpret that knowledge.

[–] [email protected] 2 points 7 months ago

You mean like create world representations from it?

https://arxiv.org/abs/2210.13382

Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello. Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state.

(Though later research found this is actually a linear representation)

Or combine skills and concepts in unique ways?

https://arxiv.org/abs/2310.17567

Furthermore, simple probability calculations indicate that GPT-4's reasonable performance on k=5 is suggestive of going beyond "stochastic parrot" behavior (Bender et al., 2021), i.e., it combines skills in ways that it had not seen during training.

[+] [email protected] -8 points 7 months ago (1 children)

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.

[–] [email protected] 10 points 7 months ago* (last edited 7 months ago) (1 children)

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.

[–] [email protected] -2 points 7 months ago* (last edited 7 months ago) (1 children)

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.

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.

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.

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".

[–] [email protected] 7 points 7 months ago

Where do you get this? What kind of data requires a T3 system to be representable?

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 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'm trying to map your fuzzy terms to something concrete.

I wouldn’t define memory as an adaptable system, so T2 would by my definition be intelligence as well.

My mattress is an adaptable system.

Where do you see “wild hallucination”?

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.

load more comments (41 replies)

load more comments (84 replies)