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 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.
You don’t do what Google seems to have done - inject diversity artificially into prompts.
You solve this by training the AI on actual, accurate, diverse data for the given prompt. For example, for “american woman” you definitely could find plenty of pictures of American women from all sorts of racial backgrounds, and use that to train the AI. For “german 1943 soldier” the accurate historical images are obviously far less likely to contain racially diverse people in them.
If Google has indeed already done that, and then still had to artificially force racial diversity, then their AI training model is bad and unable to handle that a single input can match to different images, instead of the most prominent or average of its training set.
Ultimately this is futile though, because you can do that for these two specific prompts until the AI appears to “get it”, but it’ll still screw up a prompt like “1800s Supreme Court justice” or something because it hasn’t been trained on that. Real intelligence requires agency to seek out new information to fill in its own gaps; and a framework to be aware of what the gaps are. Through exploration of its environment, a real intelligence connects things together, and is able to form new connections as needed. When we say “AI doesn’t know anything” that’s what we mean–understanding is having a huge range of connections and the ability to infer new ones.
Oh really? Here’s Gemini’s response to “What would the variety of genders and skin tones of the supreme court in the 1800s have been?”
Putting the burden of contextualization on the LLM would have avoided this issue.
That’s why I hate that they started to call them artificial intelligence. There is nothing intelligent in them at all. They work on probability based on a shit ton of data, that’s all. That’s not intelligence, that’s basically brute force. But there is no going back at this point, I know.
Edit: further discussion on the topic has changed my viewpoint on this, its not that its been trained wrong on purpose and now its confused, its that everything its being asked is secretly being changed. It’s like a child being told to make up a story by their teacher when the principal asked for the right answer.
Original comment below
They’ve purposefully overrode its training to make it create more PoCs. It’s a noble goal to have more inclusivity but we purposely trained it wrong and now it’s confused, the same thing as if you lied to a child during their education and then asked them for real answers, they’ll tell you the lies they were taught instead.
This result is clearly wrong, but it’s a little more complicated than saying that adding inclusivity is purposedly training it wrong.
Say, if “entrepreneur” only generated images of white men, and “nurse” only generated images of white women, then that wouldn’t be right either, it would just be reproducing and magnifying human biases. Yet this a sort of thing that AI does a lot, because AI is a pattern recognition tool inherently inclined to collapse data into an average, and data sets seldom have equal or proportional samples for every single thing. Human biases affect how many images we have of each group of people.
It’s not even just limited to image generation AIs. Black people often bring up how facial recognition technology is much spottier to them because the training data and even the camera technology was tuned and tested mainly for white people. Usually that’s not even done deliberately, but it happens because of who gets to work on it and where it gets tested.
Of course, secretly adding “diverse” to every prompt is also a poor solution. The real solution here is providing more contextual data. Unfortunately, clearly, the AI is not able to determine these things by itself.
I agree with your comment. As you say, I doubt the training sets are reflective of reality either. I guess that leaves tampering with the prompts to gaslight the AI into providing results it wasn’t asked for is the method we’ve chosen to fight this bias.
We expect the AI to give us text or image generation that is based in reality but the AI can’t experience reality and only has the knowledge of the training data we provide it. Which is just an approximation of reality, not the reality we exist in. I think maybe the answer would be training users of the tool that the AI is doing the best it can with the data it has. It isn’t racist, it is just ignorant. Let the user add diverse to the prompt if they wish, rather than tampering with the request to hide the insufficiencies in the training data.
I wouldn’t count on the user realizing the limitations of the technology, or the companies openly admitting to it at expense of their marketing. As far as art AI goes this is just awkward, but it worries me about LLMs, and people using it expecting it to respond with accurate, applicable information, only to come out of it with very skewed worldviews.
Why couldn’t it be tuned to simply randomize the skin tone where not otherwise specified? Like if its all completely arbitrary just randomize stuff, problem-solved?
Then you have black Nazis and Native American Texas Rangers. It doesn’t work.
Well, we are seeing what happens when they randomize it. It doesn’t always work.
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.
This is all the same as saying a book is intelligent.
No, it’s not. It’s saying “a book is knowledge”, which is absolutely true.
A book is a physical representation of knowledge.
Knowledge is something possessed by an actor capable to employ it. One way I can employ a textbook about Quantum Mechanics is by throwing it at you, for which any book would suffice, but I can’t put any of the knowledge represented within into practice. Throwing is purely Newtonian, I have some learned knowledge about that and plenty of innate knowledge as a human (we are badass throwers). Also I played Handball when I was a kid. All that is plenty of knowledge, and an object, to throw, but nothing about it concerns spin states. It also won’t hit you any differently than a cookbook.
What exactly are you trying to argue? Yes, I wasn’t incredibly precise, a book isn’t literal knowledge, but I didn’t think that somebody would nitpick this hard. Do you really think this is in any way a productive line of argumentation?
Technically this is not correct, as e.g. a fully paralyzed and mute person can’t employ their knowledge, yet they still possess it.
Why can’t you put any of the knowledge represented in the book into practice? You can still pick the book up and extract the knowledge.
See how these are technically correct arguments, yet they are absolutely stupid?
You’d have to be past Hawkins levels of paralysis to not be able to employ that knowledge to come up with new physical theories. Now that was a nickpick.
That would be employing my knowledge of maths, of my general education, not of the QM knowledge represented in the book: I cannot employ the knowledge in the book to pick up the knowledge in the book because I haven’t picked it up yet. Causality and everything, it’s a thing.
I have no idea what you’re getting at, and I don’t think you’re writing in good faith. I’ll stop here. Have a good day!
You just didn’t understand the argument. How in God’s name is he making bad faith arguments by refuting your points?
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:
There’s a certain point where this just feels like the Chinese room. And, yeah, it’s hard to argue that a room can speak Chinese, or that the weird prediction rules that an LLM is built on can constitute intelligence, but that doesn’t mean it can’t be. Essentially boiled down, every brain we know of is just following weird rules that happen to produce intelligent results.
Obviously we’re nowhere near that with models like this now, and it isn’t something we have the ability to work directly toward with these tools, but I would still contend that intelligence is emergent, and arguing whether something “knows” the answer to a question is infinitely less valuable than asking whether it can produce the right answer when asked.
I really don’t think that LLMs can be constituted as intelligent any more than a book can be intelligent. LLMs are basically search engines at the word level of granularity, it has no world model or world simulation, it’s just using a shit ton of relations to pick highly relevant words based on the probability of the text they were trained on. That doesn’t mean that LLMs can’t produce intelligent results. A book contains intelligent language because it was written by a human who transcribed their intelligence into an encoded artifact. LLMs produce intelligent results because it was trained on a ton of text that has intelligence encoded into it because they were written by intelligent humans. If you break down a book to its sentences, those sentences will have intelligent content, and if you start to measure the relationship between the order of words in that book you can produce new sentences that still have intelligent content. That doesn’t make the book intelligent.
What do you mean it has no world model? Of course it has a world model, composed of the relationships between words in language that describes that world.
If I ask it what happens when I drop a glass onto concrete, it tells me. That’s evidence of a world model.
A simulation of the world that it runs to do reasoning. It doesn’t simulate anything, it just takes a list of words and then produces the next word in that list. When you’re trying to solve a problem, do you just think, well I saw these words so this word comes next? No, you imagine the problem and simulate it in both physical and abstract terms to come up with an answer.
I can see the argument that it has a sort of world model, but one that is purely word relationships is a very shallow sort of model. When I am asked what happens when a glass is dropped onto concrete, I don’t just think about what I’ve heard about those words and come up with a correlation, I can also think about my experiences with those materials and with falling things and reach a conclusion about how they will interact. That’s the kind of world model it’s missing. Material properties and interactions are well enough written about that it ~~simulates ~~ emulates doing this, but if you add a few details it can really throw it off. I asked Bing Copilot “What happens if you drop a glass of water on concrete?” and it went into excruciating detail about how the water will splash, mentions how it can absorb into it or affect uncured concrete, and now completely fails to notice that the glass itself will strike the concrete, instead describing the chemistry of how using “glass (such as from the glass of water)” as aggregate could affect the curing process. Having a purely statistical/linguistic world model leaves some pretty big holes in its “reasoning” process.
I believe you meant to say emulates instead of simulates
Thanks, that is a better word there.
But you don’t really “know” anything either. You just have a network of relations stored in the fatty juice inside your skull that gets excited just the right way when I ask it a question, and it wasn’t set up that way by any “intelligence”, the links were just randomly assembled based on weighted reactions to the training data (i.e. all the stimuli you’ve received over your life).
Thinking about how a thing works is, imo, the wrong way to think about if something is “intelligent” or “knows stuff”. The mechanism is neat to learn about, but it’s not what ultimately decides if you know something. It’s much more useful to think about whether it can produce answers, especially given novel inquiries, which is where an LLM distinguishes itself from a book or even a typical search engine.
And again, I’m not trying to argue that an LLM is intelligent, just that whether it is or not won’t be decided by talking about the mechanism of its “thinking”
We can’t determine whether something is intelligent by looking at its mechanism, because we don’t know anything about the mechanism of intelligence.
I agree, and I formalize it like this:
Those who claim LLMs and AGI are distinct categories should present a text processing task, ie text input and text output, that an AGI can do but an LLM cannot.
So far I have not seen any reason not to consider these LLMs to be generally intelligent.
Literally anything based on opinion or creating new info. An AI cannot produce a new argument. A human can.
It took me 2 seconds to think of something LLMs can’t do that AGI could.