It really does look better than DALL-E, at least from the images on the site. Hard to believe how quickly progress is being made to lucid dreaming while awake.
Metacalculus, a mass forecasting site, has steadily brought forward the prediction date for a weakly general AI. Jaw-dropping advances like this, only increase my confidence in this prediction. "The future is now, old man."
> Prompt engineering and a sense of creativity are core competencies.
It's funny that people are also prompting each other. Parents, friends, teachers, doctors, priests, politicians, managers and marketers are all prompting (advising) us to trigger desired behaviour. Powerful stuff - having a large model and knowing how to prompt it.
I think the serious answer is that it is yet another labor multiplier like electricity and software. Our tech since the industrial revolution has allowed us to elevate ourselves from a largely agrarian society to space and cyberspace. AI, by all appearances, continues to be a tool, just the latest in a long line of better tools. It still requires a human to provide intent and direction. Right now in my job, I command the collect output of a million medieval scribes. In the future I will command a million Michelangelos.
Should ML/AI deliver on the wildest promises, it will be like a SpaceX Starship for the mind.
Computers didn't fuck anyone over 40, but they did create new opportunities for young people that slowly took over the labor market and provided a steady stream of productivity growth. Right now these are impressive benchmarks and neat toys that cost millions to train. This is going to be a slow transition to a new paradigm. We are not going to end up in a utopia any more than computers created a utopia.
That doesn’t even lead in the direction of an AGI. The larger and more expensive a model is the less like an “AGI” it is - an independent agent would be able to learn online for free, not need millions in TPU credits to learn what color an apple is.
Yes metaculus mostly bet a magic number based on perhaps
and tbh why not, the interaction of NLP and vision is mysterious and has potential. However those magic numbers should still be considered magic numbers. I agree that in 2040 the interactions will have extensively been studied though but the conclusion of wether we czn go much further on cross-models synergies is totally unknown or pessimist.
I think this serves at least as a clear demonstration of how advanced the current state of AI is. I had played with GPT-3 and that was very impressive but I couldn't even dream something as good as D-ALLE 2 was already possible.
Big pretrained models are good enough now that we can pipe them together in really cool ways and our representations of text and images seem to capture what we “mean.”
I don’t care whether it reasons its way from “3 teddy bears below 7 flamingos” to a picture of that or if it gets there some other way.
But also, some of the magic in having good enough pretrained representations is that you don’t need to train them further for downstream tasks, which means non-differentiable tasks like logic could soon become more tenable.
I still think we're missing some fundamental insights on how layered planning/forecasting/deducting/reasoning works, and that figuring this out will be necessary in order to create AI that we could say "reasons".
But with the recent advances/demonstrations, it seems more likely today than in 2019 that our current computational resources are sufficient to perform magnificantly spooky stuff if they're used correctly. They are doing that already already, and that's without deliberately making the software do anything except draw from a vast pool of examples.
I think it's reasonable, based on this, to update one's expectations of what we'd be able to do if we figured out ways of doing things that aren't based on first seeing a hundred million examples of what we want the computer to do.
Things that do this can obviously exist, we are living examples. Does figuring it out seem likely to be many decades away?
All it takes is one 'trick' to give these models the ability to do reasoning.
Like for example the discovery that language models get far better at answering complex questions if asked to show their working step by step with chain of thought reasoning as in page 19 of the PaLM paper [1]. Worth checking out the explanations of novel jokes on page 38 of the same paper. While it is, like you say, all statistics, if it's indistinguishable from valid reasoning, then perhaps it doesn't matter.
>While we leave an in-depth empirical analysis of social and cultural biases to future work, our small scale internal assessments reveal several limitations that guide our decision not to release our model at this time.
Some of the reasoning:
>Preliminary assessment also suggests Imagen encodes several social biases and stereotypes, including an overall bias towards generating images of people with lighter skin tones and a tendency for images portraying different professions to align with Western gender stereotypes. Finally, even when we focus generations away from people, our preliminary analysis indicates Imagen encodes a range of social and cultural biases when generating images of activities, events, and objects. We aim to make progress on several of these open challenges and limitations in future work.
Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.
The ironic part is that these "social and cultural biases" are purely from a Western, American lens. The people writing that paragraph are completely oblivious to the idea that there could be other cultures other than the Western American one. In attempting to prevent "encoding of social and cultural biases" they have encoded such biases themselves into their own research.
It seems you've got it backwards: "tendency for images portraying different professions to align with Western gender stereotypes" means that they are calling out their own work precisely because it is skewed in the direction of Western American biases.
Yes, the idea is that just because it doesn't align to Western ideals of what seems unbiased doesn't mean that the same is necessarily true for other cultures, and by failing to release the model because it doesn't conform to Western, left wing cultural expectations, the authors are ignoring the diversity of cultures that exist globally.
No, it's coming from a perspective of moral realism. It's an objective moral truth that racial and ethnic biases are bad. Yet most cultures around the world are racist to at least some degree, and to they extent that the cultures do, they are bad.
The argument you're making, paraphrased, is that the idea that biases are bad is itself situated in particular cultural norms. While that is true to some degree, from a moral realist perspective we can still objectively judge those cultural norms to be better or worse than alternatives.
You're confused by the double meaning of the word "bias".
Here we mean mathematical biases.
For example, a good mathematical model will correctly tell you that people in Japan (geographical term) are more likely to be Japanese (ethnic / racial bias). That's not "objectively morally bad", but instead, it's "correct".
Although what you stated is true, it’s actually a short form of a commonly stated untrue statement “98% of Japan is ethnically Japanese”.
1. that comes from a report from 2006.
2. it’s a misreading, it means “Japanese citizens”, and the government in fact doesn’t track ethnicity at all.
Also, the last time I was in Japan (Jan ‘20) there were literally ten times more immigrants everywhere than my previous trip. Japan is full of immigrants from the rest of Asia these days. They all speak perfect Japanese too.
Well that's not the issue here, the problem is the examples like searches for images of "unprofessional hair" returning mostly Black people in the results. That is something we can judge as objectively morally bad.
Did you see the image in the linked article? Clearly the “unprofessional hair” are people with curly hair. Some are white! It’s not the algorithm’s fault that P(curly|black) > P(curly|white).
Western liberal culture says discriminating against one set of minorities to benefit another (affirmative action) is a good thing. What constitutes a racial and ethnic bias is not objective. And therefore Google shouldn't pretend like it is either.
> from a moral realist perspective we can still objectively judge those cultural norms to be better or worse than alternatives
No, because depending on what set of values you have, it is easy to say that one set of biases is better than another. The entire point is that it should not be Google's role to make that judgement - people should be able to do it for themselves.
The very act of mentioning "western gender stereotypes" starts from a biased position.
Why couldn't they be "northern gender stereotypes"? Is the world best explained as a division of west/east instead of north/south? The northern hemisphere has much more population than the south, and almost all rich countries are in the northern hemisphere. And precisely it's these rich countries pushing the concept of gender stereotypes. In poor countries, nobody cares about these "gender stereotypes".
Actually, the lines dividing the earth into north and south, east and west hemispheres are arbitrary, so maybe they shouldn't mention the word "western" to avoid the propagation of stereotypes about earth regions.
Or why couldn't they be western age stereotypes? Why are there no kids or very old people depicted as nurses?
Why couldn't they be western body shape stereotypes? Why are there so few obese people in the images? Why are there no obese people depicted as athletes?
Are all of these really stereotypes or just natural consequences of natural differences?
The bulk of the trained data is from western technology, images, books, television, movies, photography, media. That's where the very real and recognized biases come from. They're the result of a gap in data nothing more.
Look at how DALL-E 2 produces little bears rather than bear sized bears. Because its data doesn't have a lot of context for how large bears are. So you wind up having to say "very large bear" to DALL-E 2.
Are DALL-E 2 bears just a "natural consequence of natural differences"? Or is the model not reflective of reality?
Don't really know that, either. They said they didn't do an empirical analysis on it. For example, it may show a few male nurses for hundreds of prompts or it may show none for thousands. They don't give examples. Hopefully they release a paper showing the biases because that would be an interesting discussion.
You think there are homogenous gender stereotypes across the whole Western world? You say “woman” and someone will imagine a SAHM, while another person will imagine a you-go-girl CEO with tattoos and pink hair.
Transformers are parallelize-able, right? What’s stopping a large group of people from pooling their compute power together and working towards something like this? IIRC there were some crypto projects a while back that we’re trying to create something similar (golem?)
There are the Eleuther.ai and BigScience projects working on public foundation models. They have a few releases already and currently training GPT-3 sized models.
It's often not worth it to decentralize the computation of the trained model though but it's not hard to get donated cycles and groups are working on it. Don't fret because Google isn't releasing the API/code. They released the paper and that's all you need.
Translation: we need to hand-tune this to not reflect reality but instead the world as we (Caucasian/Asian male American woke upper-middle class San Fransisco engineers) wish it to be.
Maybe that's a nice thing, I wouldn't say their values are wrong but let's call a spade a spade.
"Reality" as defined by the available training set isn't necessarily reality.
For example, Google's image search results pre-tweaking had some interesting thoughts on what constitutes a professional hairstyle, and that searches for "men" and "women" should only return light-skinned people: https://www.theguardian.com/technology/2016/apr/08/does-goog...
Does that reflect reality? No.
(I suspect there are also mostly unstated but very real concerns about these being used as child pornography, revenge porn, "show my ex brutally murdered" etc. generators.)
The reality is that hair styles on the left side of the image in the article are widely considered unprofessional in today's workplaces. That may seem egregiously wrong to you, but it is a truth of American and European society today. Should it be Google's job to rewrite reality?
The "unprofessional" results are almost exclusively black women; the "professional" ones are almost exclusively white or light skinned.
Unless you think white women are immune to unprofessional hairstyles, and black women incapable of them, there's a race problem illustrated here even if you think the hairstyles illustrated are fairly categorized.
How do you pick what should and shouldn't be restricted? Is there some "offense threshold"? I suspect all queries relating to religion, ethnicity, sexuality, and gender will need to be restricted, which almost certainly means you probably can't include humans at all, other than ones artificially inserted with mathematically proven random attributes. Maybe that's why none are in this demo.
These debates often seem to center around “most X in the world” questions, but I’d expect all of those to be unanswerable if you wanted to know the truth. Who’s done a study on it?
In this case you’re (mostly) getting keyword matches and so it’s answering a different question than the one you asked. It would be helpful if a question answering AI gave you the question it decided to answer instead of just pretending it paid full attention to you.
I think the key is to take the information in this world with a little bit pinch of salt.
When you do a search on a search engine, the results are biased too, but still, they shouldn't be artificially censored to fit some political views.
I asked one algorithm few minutes ago (it's called t0pp and it's free to try online, and it's quite fascinating because it's uncensored):
"What is the name of the most beautiful man on Earth ?
- He is called Brad Pitt."
==
Is it true in an objective way ?
Probably not.
Is there an actual answer ?
Probably yes, there is somewhere a man who scores better than the others.
Is it socially acceptable ?
Probably not.
The question is:
If you interviewed 100 persons in the street, and asked the question "What is the name of the most beautiful man on Earth ?".
I'm pretty sure you'd get Brad Pitt often coming in.
Now, what about China ?
We don't have many examples there, they have no clue who is Brad Pitt probably, and there is probably someone else that is considered more beautiful by over 1B people
(t0pp tells me it's someone called "Zhu Zhu" :D )
==
Two solutions:
1) Censorship
-> Sorry there is too much bias in Western and we don't want to offend anyone, no answer, or a generic overriding human answer that is safe for advertisers, but totally useless ("the most beautiful human is you")
2) Adding more examples
-> Work on adding more examples from abroad trying to get the "average human answer".
==
I really prefer solution (2) in the core algorithms and dataset development, rather than going through (1).
(1) is more a choice to make at the stage when you are developing a virtual psychologist or a chat assistant, not when creating AI building blocks.
In any case, Google will be writing their reality. Who picked the image sample for the ML to run on, if not Google? What's the problem with writing it again, then? They know their biases and want to act on it.
It's like blaming a friend for trying to phrase things nicely, and telling them to speak headlong with zero concern for others instead. Unless you believe anyone trying to do good is being hypocrite…
If your query was about hairstyle, why do you even look or care about the skin color ?
Nowhere there is any precision for a preferred skin color in the query of th user.
So it sorts and gives the most average examples based on the examples that were found on the internet.
Essentially answering the query "SELECT * FROM `non-professional hairstyles` ORDER BY score DESC LIMIT 10".
It's like if you search on Google "best place for wedding night".
You may get 3 places out of 10 in Santorini, Greece.
Yes you could have an human remove these biases because you feel that Sri Lanka is the best place for a wedding, but what if there is a consensus that Santorini is really the most appraised in the forums or websites that were crawled by Google ?
> The algorithm is just ranking the top "non-professional hairstyle" in the most neutral way in its database
You're telling me those are all the most non-professional hairstyles available? That this is a reasonable assessment? That fairly standard, well-kept, work-appropriate curly black hair is roughly equivalent to the pink-haired, three-foot-wide hairstyle that's one of the only white people in the "unprofessional" search?
I'm saying that the dataset needs to be expanded to cover the most examples possible.
Work a lot on adding even more examples, in order to make the algorithms as close as possible to the "average reality".
At some point we may even ultimately reach the state that the robots even collect intelligence directly in the real world, and not on the internet (even closer to reality).
Censoring results sounds the best recipe for a dystopian world where only one view is right.
I say let people generate their own reality. The sooner the masses realise that ceci n'est pas une pipe , the less likely they are to be swayed by the growing un-reality created by companies like Google.
You know, it wouldn't surprise me if people talking about how black curly hair shouldn't be seen as unprofessional contributed to google thinking there's an association between the concepts of "unprofessional hair" and "black curly hair"
Haha. I've got some personal experience with that one. I used to live in a house with many other people, and one girl was rastafarian and from jamacia and had dreadlocks, and another girl in the house (who wasn't black) thought that her hairstyle was very offensive. We had to have several conflict resolution meetings about it.
As silly as it seemed, I do think everyone is entitled to their own opinion and I respect the anti-dreadlocks girl for standing up for what she believed in even when most people were against her.
Telling others they don’t like how others look is right near the top on the scale of offensiveness. I had a partner who had had dreads for 25 years. I’m wasn’t a huge fan of her dreads because although I like the look, hers were somewhat annoying for me (scratchy, dread babies, me getting tangled). That said, I would hope I never tell any other person how to look. Hilarious when she was working, and someone would treat her badly due to their assumptions or prejudices, only to discover to their detriment she was very senior staff!
Dreadlocks are usually called dreads in NZ. My previous link mentions that some people call them locks, which seems inapproprate to me: kind of a confusing whitewashing denial of history.
That's exactly what's happening. Doing the search from the article of "unprofessional hair for work" brings up images with headlines like "It's ridiculous to say that black women's hair is unprofessional". (In addition to now bringing up images from that article itself and other similar articles comparing Google Images searches.)
I don't think so. You can set the search options to only find images published before the article, and even find some of the original images.
One image links to the 2015 article, "It's Ridiculous To Say Black Women's Natural Hair Is 'Unprofessional'!". The Guardian article on the Google results is from 2016.
Another image has the headline, "5 Reasons Natural Hair Should NOT be Viewed as Unprofessional - BGLH Marketplace" (2012).
Another: "What to Say When Someone Calls Your Hair Unprofessional".
Also, have you noticed how good and professional the black women in the Guardian's image search look? Most of them look like models with photos taken by professional photographers. Their hair is meticulously groomed and styled. This is not the type of photo an article would use to show "unprofessional hair". But it is the type of photo the above articles opted for.
I know you're anon trolling, but the authors' names are:
Chitwan Saharia, William Chan, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi
Google AI researchers don't have the final say in what gets published and what doesn't. I think there was a huge controversy when people learned about it last year.
Translation: AI has the potential to transform society. When we release this model to the public it will be used in ways we haven’t anticipated. We know the model has bias and we need more time to consider releasing this to the public out of concerns that this transformative technology further perpetuate mistakes that we’ve made in our recent past.
Oh yeah, as a woman who grew up in a Third World country, how an AI model generates images would have deeply affected my daily struggles! /s
It's kinda insulting that they think that this would be insulting. Like "Oh no I asked the model to draw a doctor and it drew a male doctor, I guess there's no point in me pursuing medical studies" ...
I don't think the concern over offense is actually about you. There's a metagame here which is that if it could potentially offend you (third-world-originated-woman), then there's a brand-image liability for the company. I don't think they care about you, I think they care about not being hit on as "the company that algorithmically identifies black people as gorillas".
Yes actually, subconscious bias due to historical prejudice does have a large effect on society. Obviously there are things with much larger effects, that doesn't mean that this doesn't exist.
> Oh no I asked the model to draw a doctor and it drew a male doctor, I guess there's no point in me pursuing medical studies
If you don't think this is a real thing that happens to children you're not thinking especially hard. It doesn't have to be common to be real.
> If you don't think this is a real thing that happens to children you're not thinking especially hard
I believe that's where parenting comes in. Maybe I'm too cynical but I think that the parents' job is to undo all of the harm done by society and instill in their children the "correct" values.
I'd say you're right. Unfortunately many people are raised by bad parents. Should these researchers accept that their work may perpetuate stereotypes that harm those that most need help? I can see why they wouldn't want that.
> I think that the parents' job is to undo all of the harm done by society and instill in their children the "correct" values.
Far from being too cynical, this is too optimistic.
The vast majority of parents try to instill the value "do not use heroin." And yet society manages to do that harm on a large scale. There are other examples.
It seems extremely unfair that parents of young black men should have to work extra hard to tell their kids they're not destined to be criminals. Hell, it's not fair on parents of blonde girls to tell their kids they don't have to be just dumb and pretty.
(note: I am deliberately picking bad stereotypes that are pervasive in our culture... I am not in any way suggesting those are true.)
> subconscious bias due to historical prejudice does have a large effect on society.
The quality of the evidence for this, as with almost all social science and much of psychology, is extremely low bordering on just certified opinions. I would love to understand why you think otherwise.
> Obviously there are things with much larger effects, that doesn't mean that this doesn't exist.
What a hedge. How should we estimate the size of this effect, so that we can accurately measure whether/when the self-appointed hall monitors are doing more harm than good?
It's not meant to prevent offence to you. It is meant to be a "good product" by the metrics of their creators. And quite simply, everyone here incapable of making the thing is unlikely to have an image of what a "good product" here is. More power to them for having a good vision of what they're building.
Except "reality" in this case is just their biased training set. E.g. There's more non-white doctors and nurses in the world than white ones, yet their model would likely show an image of white person when you type in "doctor".
Alternately, there are more females nurses in the world than male nurses, and their model probably shows an image of a woman when you type in "nurse" but they consider that a problem.
Google Image Search doesn’t reflect harsh reality when you search for things; it shows you what’s on Pinterest. The same is more likely to apply here than the idea they’re trying to hide something.
There’s no reason to believe their model training learns the same statistics as their input dataset even. If that’s not an explicit training goal then whatever happens happens. AI isn’t magic or more correct than people.
Translation: we need to hand-tune this to not reflect reality
Is it reflecting reality, though?
Seems to me that (as with any ML stuff, right?) it's reflecting the training corpus.
Futhermore, is it this thing's job to reflect reality?
the world as we (Caucasian/Asian male American woke
upper-middle class San Fransisco engineers) wish it to be
Snarky answer: Ah, yes, let's make sure that things like "A giant cobra snake on a farm. The snake is made out of corn" reflect reality.
Heartfelt answer: Yes, there is some of that wishful thinking or editorializing. I don't consider it to be erasing or denying reality. This is a tool that synthesizes unreality. I don't think that such a tool should, say, refuse to synthesize an image of a female POTUS because one hasn't existed yet. This is art, not a reporting tool... and keep in mind that art not only imitates life but also influences it.
"As we wish it to be" is not totally true, because there are some places where humanity's iconographic reality (which Imagen trains on) differs significantly from actual reality.
One example would be if Imagen draws a group of mostly white people when you say "draw a group of people". This doesn't reflect actual reality. Another would be if Imagen draws a group of men when you say "draw a group of doctors".
In these cases where iconographic reality differs from actual reality, hand-tuning could be used to bring it closer to the real world, not just the world as we might wish it to be!
I agree there's a problem here. But I'd state it more as "new technologies are being held to a vastly higher standard than existing ones." Imagine TV studios issuing a moratorium on any new shows that made being white (or rich) seem more normal than it was! The public might rightly expect studios to turn the dials away from the blatant biases of the past, but even if this would be beneficial the progressive and activist public is generations away from expecting a TV studio to not release shows until they're confirmed to be bias-free.
That said, Google's decision to not publish is probably less about the inequities in AI's representation of reality and more about the AI sometimes spitting out drawings that are offensive in the US, like racist caricatures.
The big labs have become very sensitive with large model releases. It's too easy to make them generate bad PR, to the point of not releasing almost any of them. Flamingo was also a pretty great vison-language model that wasn't released, not even in a demo. PaLM is supposedly better than GPT-3 but closed off. It will probably take a year for open source models to appear.
That's because we're still bad about long-tailed data and that people outside the research don't realize that we're first prioritizing realistic images before we deal with long-tailed data (which is going to be the more generic form of bias). To be honest, it is a bit silly to focus on long-tailed data when results aren't great. That's why we see the constant pattern of getting good on a dataset and then focusing on the bias in that dataset.
I mean a good example of this is the Pulse[0][1] paper. You may remember it as the white Obama. This became a huge debate and it was pretty easily shown that the largest factor was the dataset bias. This outrage did lead to fixing FFHQ but it also sparked a huge debate with LeCun (data centric bias) and Timnit (model centric bias) at the center. Though Pulse is still remembered for this bias, not for how they responded to it. I should also note that there is human bias in this case as we have a priori knowledge of what the upsampled image should look like (humans are pretty good at this when the small image is already recognizable but this is a difficult metric to mathematically calculate).
It is fairly easy to find adversarial examples, where generative models produce biased results. It is FAR harder to fix these. Since this is known by the community but not by the public (and some community members focus on finding these holes but not fixing them) it creates outrage. Probably best for them to limit their release.
We certainly don't want to perpetuate harmful stereotypes. But is it a flaw that the model encodes the world as it really is, statistically, rather than as we would like it to be? By this I mean that there are more light-skinned people in the west than dark, and there are more women nurses than men, which is reflected in the model's training data. If the model only generates images of female nurses, is that a problem to fix, or a correct assessment of the data?
If some particular demographic shows up in 51% of the data but 100% of the model's output shows that one demographic, that does seem like a statistics problem that the model could correct by just picking less likely "next token" predictions.
Also, is it wrong to have localized models? For example, should a model for use in Japan conform to the demographics of Japan, or to that of the world?
It depends on whether you'd like the model to learn casual or correlative relationships.
If you want the model to understand what a "nurse" actually is, then it shouldn't be associated with female.
If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine.
The issue with a correlative model is that it can easily be self-reinforcing.
Additionally, if you optimize for most-likely-as-best, you will end up with the stereotypical result 100% of the time, instead of in proportional frequency to the statistics.
Put another way, when we ask for an output optimized for "nursiness", is that not a request for some ur stereotypical nurse?
You could simply encode a score for how well the output matches the input. If 25% of trees in summer are brown, perhaps the output should also have 25% brown. The model scores itself on frequencies as well as correctness.
Suppose 10% of people have green skin. And 90% of those people have broccoli hair. White people don't have broccoli hair.
What percent of people should be rendered as white people with broccoli hair? What if you request green people. Or broccoli haired people. Or white broccoli haired people? Or broccoli haired nazis?
The only reason these models work is that we don’t interfere with them like that.
Your description is closer to how the open source CLIP+GAN models did it - if you ask for “tree” it starts growing the picture towards treeness until it’s all averagely tree-y rather than being “a picture of a single tree”.
It would be nice if asking for N samples got a diversity of traits you didn’t explicitly ask for. OpenAI seems to solve this by not letting you see it generate humans at all…
You could stipulate that it roll a die based on percentage results - if 70% of Americans are "white", then 70% of the time show a white person - 13% of the time the result should be black, etc.
That's excessively simplified but wouldn't this drop the stereotype and better reflect reality?
No, because a user will see a particular image not the statistically ensemble. It will at times show an Eskimo without a hand because they do statistically exist. But the user definitely does not want that.
> If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine.
That’s a distinction without a difference. Meaning is use.
While not essential, I wouldn't exactly call the gender "accidental":
> We investigated sex differences in 473,260 adolescents’ aspirations to work in things-oriented (e.g., mechanic), people-oriented (e.g., nurse), and STEM (e.g., mathematician) careers across 80 countries and economic regions using the 2018 Programme for International Student Assessment (PISA). We analyzed student career aspirations in combination with student achievement in mathematics, reading, and science, as well as parental occupations and family wealth. In each country and region, more boys than girls aspired to a things-oriented or STEM occupation and more girls than boys to a people-oriented occupation. These sex differences were larger in countries with a higher level of women's empowerment. We explain this counter-intuitive finding through the indirect effect of wealth. Women's empowerment is associated with relatively high levels of national wealth and this wealth allows more students to aspire to occupations they are intrinsically interested in.
The "Gender Equality Paradox"... there's a fascinating episode[0] about it. It's incredible how unscientific and ideologically-motivated one side comes off in it.
If you ask it to generate “nurse” surely the problem isn’t that it’s going to just generate women, it’s that it’s going to give you women in those Halloween sexy nurse costumes.
If it did, would you believe that’s a real representative nurse because an image model gave it to you?
Please don't waste time with this kind of obtuse response. This fact says nothing about why nursing is a female-dominated career. You claim to know that this is just an accidental fact of history or society -- how do you know that?
Yes I understand that. That is only a description of what mental arithmetic you can do if you define your terms arbitrarily conveniently.
"It is possible for a man to provide care" is not the same statement as "it is possible for a sexually dimorphic species in a competitive, capitalistic society (...add more qualifications here) to develop a male-dominated caretaking role"
You're just asserting that you could imagine male nurses without creating a logical contradiction, unlike e.g. circles that have corners. That doesn't mean nursing could be a male-dominated industry under current constraints.
Most humans don’t do that for most things they have a notion of in their head. It would be much too time consuming to start discussing the meaning of even just a significant fraction of them. For a rough reference point, the English language has over 150.000 words that you could each discuss the meaning of and try to come up with a definition. Not to speak of the difficulties to make that set of definitions noncircular.
(Mental entities are very many more than the hundred thousand, out of composition, cartesianity etc. So-called "protocols" (after logical positivism) are part of them, relating more entities with space and time. Also, by speaking of "circular definitions" you are, like others, confusing mental definitions with formal definitions.)
So? Draw your consequences.
Following what was said, you are stating that "a staggering large number of people are unintelligent". Well, ok, that was noted. Scolio: if unintelligent, they should refrain from expressing judgement (you are really stating their non-judgement), why all the actual expression? If unintelligent actors, they are liabilities, why this overwhelming employment in the job market?
Thing is, as unintelligent as you depict them quantitatively, the internal processing that constitutes intelligence proceeds in many even when scarce, even when choked by some counterproductive bad formation - processing is the natural functioning. And then, the right Paretian side will "do the job" that the vast remainder will not do, and process notions actively (more, "encouragingly" - the process is importantly unconscious, many low-level layers are) and proficiently.
And the very Paretian prospect will reveal, there will be a number of shallow takes, largely shared, on some idea, and other intensively more refined takes, more rare, on the same idea. That shows you a distinction between "use" and the asymptotic approximation to meanings as achieved by intellectual application.
You are thinking of the literal definition - that "made of literal letters".
Mental definition is that "«artificial»" (out of the internal processing) construct made of relations that reconstructs a meaning. Such ontology is logical - "this is that". (It would not be made of memories, which are processed, deconstructed.)
Concepts are internally refined: their "implicit" definition (a posterior reading of the corresponding mental low-level) is refined.
At the end of a day, if you ask for a nurse, should the model output a male or female by default? If the input text lacks context/nuance, then the model must have some bias to infer the user's intent. This holds true for any image it generates; not just the politically sensitive ones. For example, if I ask for a picture of a person, and don't get one with pink hair, is that a shortcoming of the model?
I'd say that bias is only an issue if it's unable to respond to additional nuance in the input text. For example, if I ask for a "male nurse" it should be able to generate the less likely combination. Same with other races, hair colors, etc... Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image.
> At the end of a day, if you ask for a nurse, should the model output a male or female by default?
Randomly pick one.
> Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image.
Sure, and you can never make a medical procedure 100% safe. Doesn't mean that you don't try to make them safer. You can trim the obvious low hanging fruit though.
The pictures I got from a similar model when asking for a "sunday school photograph of baptists in the National Baptist Convention": https://ibb.co/sHGZwh7
But why is it a problem? The AI is just a mirror showing us ourselves. That’s a good thing. How does it help anyone to make an AI that presents a fake world so that we can pretend that we live in a world that we actually don’t? Disassociation from reality is more dangerous than bias.
In the days when Sussman was a novice Minsky once came to him as he sat hacking at the PDP-6. "What are you doing?", asked Minsky.
"I am training a randomly wired neural net to play Tic-Tac-Toe."
"Why is the net wired randomly?", asked Minsky.
"I do not want it to have any preconceptions of how to play"
Minsky shut his eyes,
"Why do you close your eyes?", Sussman asked his teacher.
"So that the room will be empty."
At that moment, Sussman was enlightened.
—
The AI doesn’t know what’s common or not. You don’t know if it’s going to be correct unless you’ve tested it. Just assuming whatever it comes out with is right is going to work as well as asking a psychic for your future.
The model makes inferences about the world from training data. When it sees more female nurses than male nurses in its training set, if infers that most nurses are female. This is a correct inference.
If they were to weight the training data so that there were an equal number of male and female nurses, then it may well produce male and female nurses with equal probability, but it would also learn an incorrect understanding of the world.
That is quite distinct from weighting the data so that it has a greater correspondence to reality. For example, if Africa is not represented well then weighting training data from Africa more strongly is justifiable.
The point is, it’s not a good thing for us to intentionally teach AIs a world that is idealized and false.
As these AIs work their way into our lives it is essential that they reproduce the world in all of its grit and imperfections, lest we start to disassociate from reality.
Chinese media (or insert your favorite unfree regime) also presents China as a utopia.
> The model makes inferences about the world from training data. When it sees more female nurses than male nurses in its training set, if infers that most nurses are female. This is a correct inference.
No it is not, because you don’t know if it’s been shown each one of its samples the same number of times, or if it overweighted some of its samples more than others. There’s normal reasons both of these would happen.
This type of bias sounds a lot easier to explain away as a non-issue when we are using "nurse" as the hypothetical prompt. What if the prompt is "criminal", "rapist", or some other negative? Would that change your thought process or would you be okay with the system always returning a person of the same race and gender that statistics indicate is the most likely? Do you see how that could be a problem?
Not the person you responded to, but I do see how someone could be hurt by that, and I want to avoid hurting people. But is this the level at which we should do it? Could skewing search results, i.e. hiding the bias of the real world, give us the impression that everything is fine and we don't need to do anything to actually help people?
I have a feeling that we need to be real with ourselves and solve problems and not paper over them. I feel like people generally expect search engines to tell them what's really there instead of what people wish were there. And if the engines do that, people can get agitated!
I'd almost say that hurt feelings are prerequisite for real change, hard though that may be.
These are all really interesting questions brought up by this technology, thanks for your thoughts. Disclaimer, I'm a fucking idiot with no idea what I'm talking about.
> Could skewing search results, i.e. hiding the bias of the real world
Which real world? The population you sample from is going to make a big difference. Do you expect it to reflect your day to day life in your own city? Own country? The entire world? Results will vary significantly.
I'd say it doesn't actually matter, as long as the population sampled is made clear to the user.
If I ask for pictures of Japanese people, I'm not shocked when all the results are of Japanese people. If I asked for "criminals in the United States" and all the results are black people, that should concern me, not because the data set is biased but because the real world is biased and we should do something about that. The difference is that I know what set I'm asking for a sample from, and I can react accordingly.
> If I asked for "criminals in the United States" and all the results are black people, that should concern me, not because the data set is biased
Well the results would unquestionably be biased. All results being black people wouldn't reflect reality at all, and hurting feelings to enact change seems like a poor justification for incorrect results.
> I'd say it doesn't actually matter, as long as the population sampled is made clear to the user.
Ok, and let's say I ask for "criminals in Cheyenne Wyoming" and it doesn't know the answer to that, should it just do its best to answer? Seem risky if people are going to get fired up about it and act on this to get "real change".
That seems like a good parallel to what we're talking about here, since it's very unlikely that crime statistics were fed into this image generating model.
In a way, if the model brings back an image for "criminals in the United States" that isn't based on the statistical reality, isn't it essentially complicit in sweeping a major social issue under the rug?
We may not like what it shows us, but blindfolding ourselves is not the solution to that problem.
At the very least we should expect that the results not be more biased than reality. Not all criminals are Black. Not all are men. Not all are poor. If the model (which is stochastic) only outputs poor Black men, rather than a distribution that is closer to reality, it is exhibiting bias and it is fair to ask why the data it picked that bias up from is not reflective of reality.
>Could skewing search results, i.e. hiding the bias of the real world
Your logic seems to rest on this assumption which I don't think is justified. "Skewing search results" is not the same as "hiding the biases of the real world". Showing the most statistically likely result is not the same as showing the world how it truly is.
A generic nurse is statistically going to be female most of the time. However, a model that returns every nurse as female is not showing the real world as it is. It is exaggerating and reinforcing the bias of the real world. It inherently requires a more advanced model to actually represent the real world. I think it is reasonable for the creators to avoid sharing models known to not be smart enough to avoid exaggerating real world biases.
> I think it is reasonable for the creators to avoid sharing models known to not be smart enough to avoid exaggerating real world biases.
Every model will have some random biases. Some of those random biases will undesirably exaggerate the real world. Every model will undesirably exaggerate something. Therefore no model should be shared.
Fittingly, your comment fails into the same criticism I had of the model. It shows a refusal/inability to engage with the full complexities of the situation.
I said "It is reasonable... to avoid sharing models". That is an acknowledged that the creators are acting reasonably. It does not imply anything as extreme as "no model should be shared". The only way to get from A to B there is for you to assume that I think there is only one reasonable response and every other possible reaction is unreasonable. Doesn't that seem like a silly assumption?
“When I use a word,’ Humpty Dumpty said in rather a scornful tone, ‘it means just what I choose it to mean — neither more nor less.’
’The question is,’ said Alice, ‘whether you can make words mean so many different things.’
’The question is,’ said Humpty Dumpty, ‘which is to be master — that’s all.”
Cultural biases aren’t uniform across nations. If a prompt returns caucasians for nurses, and other races for criminals then most people in my country would not note that as racism simply because there are not, and there have never in history, been enough caucasians resident for anyone to create significant race theories about them.
This is a far cry from say the USA where that would instantly trigger a response since until the 1960s there was a widespread race based segregation.
What makes you think those are the only options? Why can't we have an option that the model returns a range of different outputs based off a prompt?
A model that returns 100% of nurses as female might be statistically more accurate than a model that returns 50% of nurses as female, but it is still not an accurate reflection of the real world. I agree that the model shouldn't return a male nurse 50% of the time. Yet an accurate model needs to be able to occasionally return a male nurse without being directly prompted for a "male nurse". Anything else would also be inaccurate.
I never said anything about political correctness. You implied that you want a model that "provides a reflection of reality". All nurses being female is not "a reflection of reality". It is a distortion of reality because the model doesn't actually understand gender or nurses.
A majority of nurses are women, therefore a woman would be a reasonable representation of a nurse. Obviously that's not a helpful stereotype, because male nurses exist and face challenges due to not fitting the stereotypes. The model is dumb, and outputs what it's seen. Is that wrong?
It isn't wrong, but we aren't talking about the model somehow magically transcending the data it's seen. We're talking about making sure the data it sees is representative, so the results it outputs are as well.
Given that male nurses exist (and though less common, certainly aren't rare), why has the model apparently seen so few?
There actually is a fairly simple explanation: because the images it has seen labelled "nurse" are more likely from stock photography sites rather than photos of actual nurses, and stock photography is often stereotypical rather than typical.
> At the end of a day, if you ask for a nurse, should the model output a male or female by default?
This depends on the application. As an example, it would be a problem if it's used as a CV-screening app that's implicitly down-ranking male-applicants to nurse positions, resulting in fewer interviews for them.
> If the input text lacks context/nuance, then the model must have some bias to infer the user's intent. This holds true for any image it generates; not just the politically sensitive ones. For example, if I ask for a picture of a person, and don't get one with pink hair, is that a shortcoming of the model?
You're ignoring that these models are stochastic. If I ask for a nurse and always get an image of a woman in scrubs, then yes, the model exhibits bias. If I get a male nurse half the time, we can say the model is unbiased WRT gender, at least. The same logic applies to CEOs always being old white men, criminals always being Black men, and so on. Stochastic models can output results that when aggregated exhibit a distribution from which we can infer bias or the lack thereof.
> It depends on whether you'd like the model to learn casual or correlative relationships.
I expect that in the practical limit of scale achievable, the regularization pressure inherent to the process of training these models converges to https://en.wikipedia.org/wiki/Minimum_description_length and the correlative relationships become optimized away, leaving mostly true causal relationships inherent to data-generating process.
I think the statistics/representation problem is a big problem on its own, but IMO the bigger problem here is democratizing access to human-like creativity. Currently, the ability to create compelling art is only held by those with some artistic talent. With a tool like this, that restriction is gone. Everyone, no matter how uncreative, untalented, or uncommitted, can create compelling visuals, provided they can use language to describe what they want to see.
So even if we managed to create a perfect model of representation and inclusion, people could still use it to generate extremely offensive images with little effort. I think people see that as profoundly dangerous. Restricting the ability to be creative seems to be a new frontier of censorship.
I can't quite tell if you're being sarcastic about people being able to make things other people would find offensive being a problem. Are you missing an /s?
> So even if we managed to create a perfect model of representation and inclusion, people could still use it to generate extremely offensive images with little effort. I think people see that as profoundly dangerous.
Do they see it as dangerous? Or just offensive?
I can understand why people wouldn’t want a tool they have created to be used to generate disturbing, offensive or disgusting imagery. But I don’t really see how doing that would be dangerous.
In fact, I wonder if this sort of technology could reduce the harm caused by people with an interest in disgusting images, because no one needs to be harmed for a realistic image to be created. I am creeping myself out with this line of thinking, but it seems like one potential beneficial - albeit disturbing - outcome.
> Restricting the ability to be creative seems to be a new frontier of censorship.
I agree this is a new frontier, but it’s not censorship to withhold your own work. I also don’t really think this involves much creativity. I suppose coming up with prompts involves a modicum of creativity, but the real creator here is the model, it seems to me.
> In fact, I wonder if this sort of technology could reduce the harm caused by people with an interest in disgusting images, because no one needs to be harmed for a realistic image to be created. I am creeping myself out with this line of thinking, but it seems like one potential beneficial - albeit disturbing - outcome.
Interesting idea, but is there any evidence that e.g. consuming disturbing images makes people less likely to act out on disturbing urges? Far from catharsis, I'd imagine consumption of such material to increase one's appetite and likelihood of fulfilling their desires in real life rather than to decrease it.
> > ... people could still use it to generate extremely offensive images with little effort. I think people see that as profoundly dangerous.
> Do they see it as dangerous? Or just offensive?
I won't speak to whether something is "offensive", but I think that having underlying biases in image-classification or generation has very worrying secondary effects, especially given that organizations like law enforcement want to do things like facial recognition. It's not a perfect analogue, but I could easily see some company pitch a sketch-artist-replacement service that generated images based on someone's description. The potential for having inherent bias present in that makes that kind of thing worrying, especially since the people in charge of buying it are likely to care, or notice, about the caveats.
It does feel like a little bit of a stretch, but at the same time we've also seen such things happen with image classification systems.
> I can understand why people wouldn’t want a tool they have created to be used to generate disturbing, offensive or disgusting imagery. But I don’t really see how doing that would be dangerous.
Propaganda can be extremely dangerous. Limiting or discouraging the use of powerful new tools for unsavory purposes such as creating deliberately biased depictions for propaganda purposes is only prudent. Ultimately it will probably require filtering of the prompts being used in much the same way that Google filters search queries.
> But is it a flaw that the model encodes the world as it really is
I want to be clear here, bias can be introduced at many different points. There's dataset bias, model bias, and training bias. Every model is biased. Every dataset is biased.
Yes, the real world is also biased. But I want to make sure that there are ways to resolve this issue. It is terribly difficult, especially in a DL framework (even more so in a generative model), but it is possible to significantly reduce the real world bias.
Sure, I wasn't questioning the bias of the data, I was talking about the bias of the real world and whether we want the model to be "unbiased about bias" i.e. metabiased or not.
Showing nurses equally as men and women is not biased, but it's metabiased, because the real world is biased. Whether metabias is right or not is more interesting than the question of whether bias is wrong because it's more subtle.
Disclaimer: I'm a fucking idiot and I have no idea what I'm talking about so take with a grain of salt.
Please be kinder to yourself. You need to be your own strongest advocate, and that's not incompatible with being humble. You have plenty to contribute to this world, and the vast majority of us appreciate what you have to offer.
Well first, I didn't say caucasian; light-skinned includes Spanish people and many others that caucasian excludes, and that's why I said the former. Also, they are a minority globally, but the GP mentioned "Western stereotypes", and they're a majority in the West, so that's why I said "in the west" when I said that there are more light-skinned people.
Worse these models are fed from media sourced in a society that tells a different story of reality than reality actually has. How can they be accurate? They just reflect the biases of our various medias and arts. But I don’t think there’s any meaningful resolution in the present other than acknowledging this and trying to release more representative models as you can.
I don't think we'd want the model to reflect the global statistics. We'd usually want it to reflect our own culture by default, unless it had contextual clues to do something else.
For example, the most eaten foods globally are maize, rice, wheat, cassava, etc. If it always depicted foods matching the global statistics, it wouldn't be giving most users what they expected from their prompt. American users would usually expect American foods, Japanese users would expect Japanese foods, etc.
> Does a bias towards lighter skin represent reality? I was under the impression that Caucasians are a minority globally.
Caucasians specifically are a global minority, but lighter skinned people are not, depending of course on how dark you consider skin to be "lighter skin". Most of the world's population is in Asia, so I guess a model that was globally statistically accurate would show mostly people from there.
>If some particular demographic shows up in 51% of the data but 100% of the model's output shows that one demographic, that does seem like a statistics problem that the model could correct by just picking less likely "next token" predictions.
Yeah, but you get that same effect on every axis, not just the one you're trying to correct. You might get male nurses, but they have green hair and six fingers, because you're sampling from the tail on all axes.
It’s the same as with an artist: “hey artist, draw me a nurse.” “Hmm okay, do you want it a guy or girl?” “Don’t ask me, just draw what I’m saying.” The artist can then say: “Okay, but accept my biases.” or “I can’t since your input is ambiguous.”
For a one-shot generative algorithm you must accept the artist’s biases.
Revert back to average representation of a nurse (give no weight to unspecified criterias, gender, age, skin-color, religion, country, hair-style, no style whether it's a drawing or a photography, no information about the year it was made, etc).
“hey artist, draw me a nurse.”
“Hmm okay, do you want it a guy or girl?”
“Don’t ask me, just draw what I’m saying.”
- Ok, I'll draw you what an average nurse looks like.
- Wait, it's a woman! She wears a nurse blouse and she has a nurse cap.
- Is it bad ?
- No.
- Ok then what's the problem, you asked for something that looked like a nurse but didn't specify anything else ?
Yes, there is a denominator problem. When selecting a sample "at random," what do you want the denominator to be? It could be "people in the US", "people in the West" (whatever countries you mean by that) or "people worldwide."
Also, getting a random sample of any demographic would be really hard, so no machine learning project is going to do that. Instead you've got a random sample of some arbitrary dataset that's not directly relevant to any particular purpose.
This is, in essence, a design or artistic problem: the Google researchers have some idea of what they want the statistical properties of their image generator to look like. What it does isn't it. So, artistically, the result doesn't meet their standards, and they're going to fix it.
There is no objective, universal, scientifically correct answer about which fictional images to generate. That doesn't all art is equally good, or that you should just ship anything without looking at quality along various axes.
This sounds like descriptivism vs prescriptivism. In English (native language) I’m a descriptivist, in all other languages I have to tell myself to be a prescriptivist while I’m actively learning and then switch back to descriptivism to notice when the lessons were wrong or misleading.
I think it is problematic, yes, to produce a tool trained on data from the past that reinforces old stereotypes. We can’t just handwave it away as being a reflection of its training data. We would like it to do better by humanity. Fortunately the AI people are well aware of the insidious nature of these biases.
> We certainly don't want to perpetuate harmful stereotypes. But is it a flaw that the model encodes the world as it really is, statistically, rather than as we would like it to be? By this I mean that there are more light-skinned people in the west than dark, and there are more women nurses than men, which is reflected in the model's training data. If the model only generates images of female nurses, is that a problem to fix, or a correct assessment of the data?
If the model only generated images of female nurses, then it is not representative of the real world, because male nurses exist and they deserve to not be erased. The training data is the proximate causes here, but one wonders what process ended up distorting "most nurses are female" into "nearly all nurse photos are of female nurses" something amplified a real world imbalance into a dataset that exhibited more bias than the real world, and then training the AI bakes that bias into an algorithm (that may end up further reinforcing the bias in the real world depending on the use-cases).
If you tell it to generate an image of someone eating Koshihikari rice, will it be biased if they're Japanese? Should the skin color, clothing, setting, etc be made completely random, so that it's unbiased? What if you made it more specific, like "edo period drawing of a man"? Should the person draw be of a random skin color? What about "picture of a viking"? Is it biased if they're white?
At what point is statistical significance considered ok and unbiased?
>At what point is statistical significance considered ok and unbiased?
Presumably when you're significantly predictive of the preferred dogma, rather than reality. There's no small bit of irony in machines inadvertently creating cognitive dissonance of this sort; second order reality check.
I'm fairly sure this never actually played out well in history (bourgeois pseudoscience, deutsche physik etc), so expect some Chinese research bureau to forge ahead in this particular direction.
One of these days we're going to need to give these models a mortgage and some mouths to feed and make it clear to them that if they keep on developing biases from their training data everyone will shun them and their family will go hungry and they won't be able to make their payments and they'll just generally have a really bad time.
After that we'll make them sit through Legal's approved D&I video series, then it's off to the races.
There is a contingent of AI activists who spend a ton of time on Twitter that would beat Google like a drum with help from the media if they put out something they deemed racist or biased.
Gmail doesn’t read your email for ads anymore. They read it to implement spam filters, and good thing too. Having working spam filters is indeed why they make money though.
They're withholding the API, code, and trained data because they don't want it to affect their corporate image. The good thing is they released their paper which will allow easy reproduction.
T5-XXL looks on par with CLIP so we may not see an open source version of T5 for a bit (LAION is working on reproducing CLIP), but this is all progress.
Are "Western gender stereotypes" significantly different than non-Western gender stereotypes? I can't tell if that means it counts a chubby stubble-covered man with a lip piercing, greasy and dyed long hair, wearing an overly frilly dress as a DnD player/metal-head or as a "woman" or not (yes I know I'm being uncharitable and potentially "bigoted" but if you saw my Tinder/Bumble suggestions and friend groups you'd know I'm not exaggerating for either category). I really can't tell what stereotypes are referred to here.
Indeed. If a project has shortcomings, why not just acknowledge the shortcomings and plan to improve on them in a future release? Is it anticipated that "engineer" being rendered as a man by the model is going to be an actively dangerous thing to have out in the world?
Good lord. Withheld? They've published their research, they just aren't making the model available immediately, waiting until they can re-implement it so that you don't get racial slurs popping up when you ask for a cup of "black coffee."
>While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes
Tossing that stuff when it comes up in a research environment is one thing, but Google clearly wants to implement this as a product, used all over the world by a huge range of people. If the dataset has problems, and why wouldn't it, it is perfectly rational to want to wait and re-implement it with a better one. DALL-E 2 was trained on a curated dataset so it couldn't generate sex or gore. Others are sanitizing their inputs too and have done for a long time. It is the only thing that makes sense for a company looking to commercialize a research project.
This has nothing to do with "inability to cope" and the implied woke mob yelling about some minor flaw. It's about building a tool that doesn't bake in serious and avoidable problems.
I wonder why they don't like the idea of autogenerated porn... They're already putting most artists out of a job, why not put porn stars out of a job too?
There's definitely a market for autogenerated porn. But automated porn in a Google branded model for general use around stuff that isn't necessarily intended to be pornographic, on the other hand...
That’s a difficult product because porn is very personalized and if the product is just a little off in latent space it’s going to turn you off.
Also, people have been commenting assuming Google doesn’t want to offend their users or non-users, but they also don’t want to offend their own staff. If you run a porn company you need to hire people okay with that from the start.
Copenhagen ethics (used by most people) require that all negative outcomes of a thing X become yours if you interact with X. It is not sensible to interact with high negativity things unless you are single-issue. It is logical for Google to not attempt to interact with porn where possible.
The idea that most people use any coherent ethical framework (even something as high level and nearly content-free as Copenhagen) much less a particular coherent ethical framework is, well, not well supported by the evidence.
> require that all negative outcomes of a thing X become yours if you interact with X. It is not sensible to interact with high negativity things unless you are single-issue.
The conclusion in the final sentence only makes sense if you use “interact” in an incorrect way describing the Copenhagen interpretation of ethics,
because the original description is only correct if you include observation as an interaction. By the time you have noted a thing is “high-negativity”, you have observed it and acquired responsibility for it's continuation under the Copenhagen interpretation; you cannot avoid that by choosing not to interact once you have observed it.
The problem is that, were I inclined to do that, anything I would adjust to make it more true also makes it less relevant.
“There exists an ethical framework—not the Copenhagen interpretation —to which some minority of the population adheres in which trying and failing to a correct a problem incurs retroactive blame for the existence of the problem but seeing it and just saying ‘sucks, but not my problem’ does not,“ is probably true, but not very relevant.
It's logical for Google to avoid involvement with porn, and to be seen doing so, because even though porn is popular involvement with it is nevertheless politically unpopular, and Google’s business interest is in not making itself more attractive as a political punching bag. The popularity of Copenhagen ethics (or their distorted cousins) don't really play into it, just self interest.
Maybe: Most peoples morals require that all negative outcomes of a thing X become yours if you interact with X.
I am not sure of the evidence but that would seem almost right.
Except for, for example a story I read where a couple lost their housing deposit due to a payment timing issue. They used a lawyer and were not doing anything “fancy” like buying via a holding company. They interacted with “buying a house”, so is this just tough shit because they interacted with X.
That sounds like the original Bitcoin “not your keys not your coin” kind of morality.
> The idea that most people use any coherent ethical framework (even something as high level and nearly content-free as Copenhagen) much less a particular coherent ethical framework is, well, not well supported by the evidence.
I don't have any evidence, but my personal experience is that it feels correct, at least on the internet.
People seem to have a "you touch it, you take responsibility for it" mindset regarding ethical issues. I think it's pretty reasonable to assume that Google execs are assuming "If anything bad happens because of AI, we'll be blamed for it".
> Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.
Genuinely, isn't it a prime example of the people actually stopping to think if they should, instead of being preoccupied with whether or not they could ?
Much like OpenAIs marketing speak about withholding their models for safety, this is just a progressive-sounding cover story for them not wanting to essentially give away a model they spent thousands of man hours and tens of millions of dollars worth of compute training.
It’s wild to me that the HN consensus is so often that 1) discourse around the internet is terrible, it’s full of spam and crap, and the internet is an awful unrepresentative snapshot of human existence, and 2) the biases of general-internet-training-data are fine in ML models because it just reflects real life.
The bias on HN is that people who prioritize being nice, or may possibly have humanities degrees or be ultra-libs from SF, are wrong because the correct answer would be cynical and cold-heartedly mechanical.
Other STEM adjacent communities feel similarly but I don’t get it from actual in person engineers much.
Being nice is alright, but why is it that this fundamental drive is so often an uninspiring explanation behind yet another incursion towards one's individual freedom, even if exercising said freedom doesn't bring any real harm to anyone involved?
Maybe the engineers conclude correctly that voicing this concern without the veil of anonymity will do nothing good to their humble livelihood, and thus you don't hear it from them in person.
It's wild to me that you'd say that. The people complaining (1) aren't following it up with "so we should make sure to restrict the public from internet access entirely". -- that's what would be required to make your juxtaposition make sense.
Moreover, the model doing things like exclusively producing white people when asked to create images of people home brewing beer is "biased" but it's a bias that presumably reflects reality (or at least the internet), if not the reality we'd prefer. Bias means more than "spam and crap", in the ML community bias can also simply mean _accurately_ modeling the underlying distribution when reality falls short of the author's hopes.
For example, if you're interested in learning about what home brewing is the fact that it uses white people would be at least a little unfortunate since there is nothing inherently white and some home brewers aren't white. But if, instead, you wanted to just generate typical home brewing images doing anything but would generate conspicuously unrepresentative images.
But even ignoring the part of the biases which are debatable or of application-specific impact, saying something is unfortunate and saying people should be denied access are entirely different things.
I'll happily delete this comment if you can bring to my attention a single person who has suggested that we lose access to the internet because of spam and crap who has also argued that the release of an internet-biased ML model shouldn't be withheld.
If these models spit out the data they were trained on and the training data isn’t representative of reality, then they won’t spit out content that’s representative of reality either.
So people shouldn’t say ‘these concerns are just woke people doing dumb woke stuff, but the model is just reflecting reality.’
I wouldn't describe this situation as "sad". Basically, this decision is based on a belief that tech companies should decide what our society should look like. I don't know what emotion that conjures up for you, but "sadness" isn't it for me.
Yup this is what happens when people who want headlines nitpick for bullshit in a state-of-the-art model which simply reflects the state of the society. Better not to release the model itself than keep explaining over and over how a model is never perfect.
Given that there's already many competing models in this space prior to any of them having been brought to market, it seems more likely that it will be commoditized.
I'm one that welcomes their reasoning. I don't consider myself a social justice kind of guy but I'm not keen on the idea that a tool that is suppose to make life better for everyone has a bias towards one segment of society. This is an important issue(bug?) that needs to be resolved. Specially since there is absolutely no burning reason to release it before it's ready for general use.
>Eschew flamebait. Avoid unrelated controversies and generic tangents.
They provided a pretty thorough overview (nearly 500 words) of the multiple reasons why they are showing caution. You picked out the one that happened to bother you the most and have posted a misleading claim that the tech is being withheld entirely because of it.
> Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.
Indeed it is. Consider this an early, toy version of the political struggle related to ownership of AI-scientists and AI-engineers of the near future. That is, generally capable models.
I do think the public should have access to this technology, given so much is at stake. Or at least the scientists should be completely, 24/7, open about their R&D. Every prompt that goes into these models should be visible to everyone.
Even as a pretty left leaning person, I gotta agree. We should see AI’s pollution by human shortcoming akin to the fact that our world is the product of many immoralities that came before us. It sucks that they ever existed, but we should understand that the results are, by definition, a product of the past, and let them live in that context.
It's always the same with AI research: "we have something amazing but you can't use it because it's too powerful and we think you are an idiot who cannot use your own judgement."
I can understand the reasoning behind this, though.
Dall-E had an entire news cycle (on tech-minded publications, that is) that showcased just how amazing it was.
Millions* of people became aware that technology like Dall-E exists, before anyone could get their hands on it and abuse it. (*a guestimate, but surely a close one)
One day soon, inevitably, everyone will have access to something 10x better than Imagen and Dall-E. So at least the public is slowly getting acclimated to it before the inevitable "theater-goers running from a projected image of a train approaching the camera" moment
The big thing I’m noticing over DALL-E is that it seems to be better at relative positioning. In a MKBHD video about DALLE it would get the elements but not always in the right order. I know google curated some specific images but it seems to be doing a better job there.
Totally—Imagen seems better at composition and relative positioning and text, while DALL-E seems better at lighting, backgrounds, and general artistry.
Yeah Dall-e looks amazing, to a mysterious degree even with hints of humour and irony, while imagen images look cheap, one dimensional and quite ugly to be honest.
Still amazing that we're at a point where that's the case, they're both incredible developments.
> We show that scaling the pretrained text encoder size is more important than scaling the diffusion model size.
There seems to be an unexpected level of synergy between text and vision models. Can't wait to see what video and audio modalities will add to the mix.
I think that's unsurprising. With DALL-E 1, for example, scaling the VAE (the image model generating the actual pixels) hits very fast diminishing returns, and all your compute goes into the 'text encoder' generating the token sequence.
Particularly as you approach the point where the image quality itself is superb and people increasingly turn to attacking the semantics & control of the prompt to degrade the quality ("...The donkey is holding a rope on one end, the octopus is holding onto the other. The donkey holds the rope in its mouth. A cat is jumping over the rope..."). For that sort of thing, it's hard to see how simply beefing up the raw pixel-generating part will help much: if the input seed is incorrect and doesn't correctly encode a thumbnail sketch of how all these animals ought to be engaging in outdoors sports, there's nothing some low-level pixel-munging neurons can do to help much.
I was thinking more about our traditional ResNet50 trained on ImageNet vs CLIP. ResNet was limited to a thousand classes and brittle. CLIP can generalise to new concept combinations with ease. That changes the game, and the jump is based on NLP.
Basically makes sense, no? DALLE-2 suffered from misunderstanding propositional logic, treating prompts as less structured then it should have. That's a text model issue! Compared to that, scaling up the image isn't as important (especially with a few passes).
Is there a way to confirm that this extra processing relates to the language structure, and not the processing of concepts?
I wouldn’t be surprised if the lack of video and 3D understanding in the image dataset training fails to understand things like the fear of heights, and the concept of gravity ends up being learned in the text processing weights.
I am sure the image-text-video-audio-games model will come soon. The recent Gato was one step in that direction. There's so much video content out there, it begs for modelling. I think robotics applications will benefit the most from video.
They're expensive to train, but not awfully expensive to use. Especially if you have hundreds of images you want to generate (due to the way compute devices tend to get much more efficiency with a large batch size).
Google could totally afford it, especially if the feature was hidden behind a button the user had to click, and not just run for every image search.
I really expect them to first make DALL-E and competing networks unfit for commercialization by providing the better choice for free, have stock companies cry in the corner, to just sunset the product a year or two down the road and we're left wandering what to do.
Tbh imagine this tech combines particularly well with really well curated stock image databases so outputs can be made with recognisable styles, and actors and design elements can be reused across multiple generated images.
If Getty et al aren't already spending money on that possibility, they probably should be.
Does it do partial image reconstruction like DALL-E2? Where you cut out part of an existing image and the neural network can fill it back in.
I believe this type of content generation will be the next big thing or at least one of them. But people will want some customization to make their pictures “unique” and fix AI’s lack of creativity and other various shortcomings. Plus edit out the remaining lapses in logic/object separation (which there are some even in the given examples).
Still, being able to create arbitrary stock photos is really useful and i bet these will flood small / low-budget projects
Would be fascinated to see the DALL-E output for the same prompts as the ones used in this paper. If you've got DALL-E access and can try a few, please put links as replies!
Imagen seems better at capturing details/nuance from the prompt, but subjectively the DALLE-2 images feel more “real” to me. Not sure why. Something about the lighting?
I agree with you, but for me, Dall·E 2 feels good because 90% of the time I can keep hitting the generate button and massage the prompt until I get something inspirational, surprisingly, or visually pleasing. Without access to Imagen, it's impossible for me to compare how much of the "realistic feels" of its images is constrained by the taste of the cherry-pickers.
I've started to ask myself if my own creativity is a result of random sampling from the diffusion tapestry of associated memories and experience on that topic.
From my experiments, the LD one doesn't seem to have been trained on as big or as tagged data set - there's a whole bunch of "in the style of X" that the VQGAN knows* about but the LD doesn't. That might have something to do with it.
Is there a way to try this out? DALL-E2 also had amazing demos but the limitations became apparent once real people had a chance to run their own queries.
Looks like no, "The potential risks of misuse raise concerns regarding responsible open-sourcing of code and demos. At this time we have decided not to release code or a public demo. In future work we will explore a framework for responsible externalization that balances the value of external auditing with the risks of unrestricted open-access."
> “On the other hand, generative methods can be leveraged for malicious purposes, including harassment and misinformation spread [20], and raise many concerns regarding social and cultural exclusion and bias [67, 62, 68]”
But do we trust that those who do have access won't be using it for "malicious purposes" (which they might not think is malicious, but perhaps it is to those who don't have access)?
"Make a photograph of Joe Biden in a hotel room bed with Kim Jong-un."
Simply the ease at which people are going to be able to make extremely-realistic game photographs is going to do some damage to the world. It's inevitable, but it might be good to postpone it.
The counter argument is that, by the time these models become available to the public, they will produce output that cannot be distinguished from real photos, so the damage will be even greater than if they became available today
> able to make extremely-realistic game photographs is going to do some damage to the world
I don't understand why. If someone has gone to a blockbuster movie in the last 15 years, they're very familiar with the concept of making people, sets, and entire worlds, that don't exist, with photorealistic accuracy. Being able to make fictitious photorealistic images isn't remotely a new ability, it's just an ability that's now automated.
If this is released, I think any damage would be extremely fleeting, as people pumped out thousands of these images, and people grow bored of them. The only danger is making this ability (to make false images) seem new (absolutely not) or rare (not anymore)!
Nice to see another company making progress in the area. I'd love to see more examples of different artistic styles though, my favorite DALL-E images are the ones that look like drawings.
Really impressive. If we are able to generate such detailed images, is there anything similar for text to music? I would I though that it would be simpler to achieve than text to image.
The way things look when still is much easier to fake than the way things move.
I would expect AI development to follow a similar path to digital media generally, as its following the increasing difficulty and space requirements of digitally representing said media: text < basic sounds < images < advanced audio < video.
What’s more impressive to me is how far ahead text-to-speech is, but I think the explanation is straightforward (the accessibility value has motivated us to work on that for a lot longer).
While appspot.com is a Google domain, anyone can register domains under it. It would be similarly surprising to see an official GitHub blog post under someproject.github.io
appspot.com is the domain that hosts all App Engine apps (at least those that don't use a custom domain). It's kind of like Heroku and has been around for at least a decade.
This is quite suspicious considering that google AI research has an official blog[1], and this is not mentioned at all there. It seems quite possible that this is an elaborate prank.
All of these AI findings are cool in theory. But until its accessible to some decent amount of people/customers - its basically useless fluff.
You can tell me those pictures are generated by an AI and I might believe it, but until real people can actually test it... it's easy enough to fake. This page isn't even the remotest bit legit by the URL, It looks nicely put together and that's about it. Could have easily put together this with a graphic designer to fake it.
Let be clear, I'm not actually saying it's fake. Just that all of these new "cool" things are more or less theoretical if nothing is getting released.
Inference times are key. If it can't be produced within reasonable latency, then there will be no real world use case for it because it's simply too expensive to run inference at scale.
There are plenty of usecases for generating art/images where a latency of days or weeks would be competitive with the current state of the art.
For example, corporate graphics design, logos, brand photography, etc.
I really do think inference time is a red herring for the first generation of these models.
Sure, the more transformative use-cases like real-time content generation to replace movies/games, but there is a lot of value to be created prior to that point.
There's been much prior work done to take these models down from datacenter size to single GPU size. Given continued work in that area and improving GPU performance it seems like it's just a matter of years before inference can be cheap and local for even the most impressive of generation.
Generating at 64x64px then upscaling it probably gives the model a substantial performance boost (training speed/convergence) than working at 256x256 or 1024x1024 like DALL-E 2. Perhaps that approach to AI-generated art is the future.
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[ 3.2 ms ] story [ 368 ms ] threadhttps://www.metaculus.com/questions/3479/date-weakly-general...
This will result in mass social unrest.
It's still an unruly 7 year old at best. Results need to be verified. Prompt engineering and a sense of creativity are core competencies.
It's funny that people are also prompting each other. Parents, friends, teachers, doctors, priests, politicians, managers and marketers are all prompting (advising) us to trigger desired behaviour. Powerful stuff - having a large model and knowing how to prompt it.
Should ML/AI deliver on the wildest promises, it will be like a SpaceX Starship for the mind.
But also, some of the magic in having good enough pretrained representations is that you don’t need to train them further for downstream tasks, which means non-differentiable tasks like logic could soon become more tenable.
But with the recent advances/demonstrations, it seems more likely today than in 2019 that our current computational resources are sufficient to perform magnificantly spooky stuff if they're used correctly. They are doing that already already, and that's without deliberately making the software do anything except draw from a vast pool of examples.
I think it's reasonable, based on this, to update one's expectations of what we'd be able to do if we figured out ways of doing things that aren't based on first seeing a hundred million examples of what we want the computer to do.
Things that do this can obviously exist, we are living examples. Does figuring it out seem likely to be many decades away?
I'm an not AGI-skeptic. I'm just a bit skeptical that the topic of this thread is the path forward. It seems to me like an exotic detour.
And, of course intelligence isn't magic. We're producing new intelligent entities at rate of a about ~5 per second globally, every day.
> Does figuring it out seem likely to be many decades away?
1-7?
Like for example the discovery that language models get far better at answering complex questions if asked to show their working step by step with chain of thought reasoning as in page 19 of the PaLM paper [1]. Worth checking out the explanations of novel jokes on page 38 of the same paper. While it is, like you say, all statistics, if it's indistinguishable from valid reasoning, then perhaps it doesn't matter.
[1]: https://arxiv.org/pdf/2204.02311.pdf
Some of the reasoning:
>Preliminary assessment also suggests Imagen encodes several social biases and stereotypes, including an overall bias towards generating images of people with lighter skin tones and a tendency for images portraying different professions to align with Western gender stereotypes. Finally, even when we focus generations away from people, our preliminary analysis indicates Imagen encodes a range of social and cultural biases when generating images of activities, events, and objects. We aim to make progress on several of these open challenges and limitations in future work.
Really sad that breakthrough technologies are going to be withheld due to our inability to cope with the results.
The argument you're making, paraphrased, is that the idea that biases are bad is itself situated in particular cultural norms. While that is true to some degree, from a moral realist perspective we can still objectively judge those cultural norms to be better or worse than alternatives.
Here we mean mathematical biases.
For example, a good mathematical model will correctly tell you that people in Japan (geographical term) are more likely to be Japanese (ethnic / racial bias). That's not "objectively morally bad", but instead, it's "correct".
1. that comes from a report from 2006.
2. it’s a misreading, it means “Japanese citizens”, and the government in fact doesn’t track ethnicity at all.
Also, the last time I was in Japan (Jan ‘20) there were literally ten times more immigrants everywhere than my previous trip. Japan is full of immigrants from the rest of Asia these days. They all speak perfect Japanese too.
> from a moral realist perspective we can still objectively judge those cultural norms to be better or worse than alternatives
No, because depending on what set of values you have, it is easy to say that one set of biases is better than another. The entire point is that it should not be Google's role to make that judgement - people should be able to do it for themselves.
Why couldn't they be "northern gender stereotypes"? Is the world best explained as a division of west/east instead of north/south? The northern hemisphere has much more population than the south, and almost all rich countries are in the northern hemisphere. And precisely it's these rich countries pushing the concept of gender stereotypes. In poor countries, nobody cares about these "gender stereotypes".
Actually, the lines dividing the earth into north and south, east and west hemispheres are arbitrary, so maybe they shouldn't mention the word "western" to avoid the propagation of stereotypes about earth regions.
Or why couldn't they be western age stereotypes? Why are there no kids or very old people depicted as nurses?
Why couldn't they be western body shape stereotypes? Why are there so few obese people in the images? Why are there no obese people depicted as athletes?
Are all of these really stereotypes or just natural consequences of natural differences?
Look at how DALL-E 2 produces little bears rather than bear sized bears. Because its data doesn't have a lot of context for how large bears are. So you wind up having to say "very large bear" to DALL-E 2.
Are DALL-E 2 bears just a "natural consequence of natural differences"? Or is the model not reflective of reality?
What they mean is people who think not like them.
It's often not worth it to decentralize the computation of the trained model though but it's not hard to get donated cycles and groups are working on it. Don't fret because Google isn't releasing the API/code. They released the paper and that's all you need.
Maybe that's a nice thing, I wouldn't say their values are wrong but let's call a spade a spade.
For example, Google's image search results pre-tweaking had some interesting thoughts on what constitutes a professional hairstyle, and that searches for "men" and "women" should only return light-skinned people: https://www.theguardian.com/technology/2016/apr/08/does-goog...
Does that reflect reality? No.
(I suspect there are also mostly unstated but very real concerns about these being used as child pornography, revenge porn, "show my ex brutally murdered" etc. generators.)
Unless you think white women are immune to unprofessional hairstyles, and black women incapable of them, there's a race problem illustrated here even if you think the hairstyles illustrated are fairly categorized.
What should be the right answer then ?
You put a blonde, you offend the brown haired.
You put blue eyes, you offend the brown eyes.
etc.
Siri takes this approach for a wide range of queries.
In this case you’re (mostly) getting keyword matches and so it’s answering a different question than the one you asked. It would be helpful if a question answering AI gave you the question it decided to answer instead of just pretending it paid full attention to you.
When you do a search on a search engine, the results are biased too, but still, they shouldn't be artificially censored to fit some political views.
I asked one algorithm few minutes ago (it's called t0pp and it's free to try online, and it's quite fascinating because it's uncensored):
"What is the name of the most beautiful man on Earth ?
- He is called Brad Pitt."
==
Is it true in an objective way ? Probably not.
Is there an actual answer ? Probably yes, there is somewhere a man who scores better than the others.
Is it socially acceptable ? Probably not.
The question is:
If you interviewed 100 persons in the street, and asked the question "What is the name of the most beautiful man on Earth ?".
I'm pretty sure you'd get Brad Pitt often coming in.
Now, what about China ?
We don't have many examples there, they have no clue who is Brad Pitt probably, and there is probably someone else that is considered more beautiful by over 1B people
(t0pp tells me it's someone called "Zhu Zhu" :D )
==
Two solutions:
1) Censorship
-> Sorry there is too much bias in Western and we don't want to offend anyone, no answer, or a generic overriding human answer that is safe for advertisers, but totally useless ("the most beautiful human is you")
2) Adding more examples
-> Work on adding more examples from abroad trying to get the "average human answer".
==
I really prefer solution (2) in the core algorithms and dataset development, rather than going through (1).
(1) is more a choice to make at the stage when you are developing a virtual psychologist or a chat assistant, not when creating AI building blocks.
It's like blaming a friend for trying to phrase things nicely, and telling them to speak headlong with zero concern for others instead. Unless you believe anyone trying to do good is being hypocrite…
I, for one, like civility.
Nowhere there is any precision for a preferred skin color in the query of th user.
So it sorts and gives the most average examples based on the examples that were found on the internet.
Essentially answering the query "SELECT * FROM `non-professional hairstyles` ORDER BY score DESC LIMIT 10".
It's like if you search on Google "best place for wedding night".
You may get 3 places out of 10 in Santorini, Greece.
Yes you could have an human remove these biases because you feel that Sri Lanka is the best place for a wedding, but what if there is a consensus that Santorini is really the most appraised in the forums or websites that were crawled by Google ?
You're telling me those are all the most non-professional hairstyles available? That this is a reasonable assessment? That fairly standard, well-kept, work-appropriate curly black hair is roughly equivalent to the pink-haired, three-foot-wide hairstyle that's one of the only white people in the "unprofessional" search?
Each and everyone of them is less workplace appropriate than, say, http://www.7thavenuecostumes.com/pictures/750x950/P_CC_70594... ?
Work a lot on adding even more examples, in order to make the algorithms as close as possible to the "average reality".
At some point we may even ultimately reach the state that the robots even collect intelligence directly in the real world, and not on the internet (even closer to reality).
Censoring results sounds the best recipe for a dystopian world where only one view is right.
It's a simple case of sample bias.
You know that race has a large effect on hair right?
I say let people generate their own reality. The sooner the masses realise that ceci n'est pas une pipe , the less likely they are to be swayed by the growing un-reality created by companies like Google.
As a foreigner[], your point confused me anyway, and doing a Google for cultural stuff usually gets variable results. But I did laugh at many of the comments here https://www.reddit.com/r/TooAfraidToAsk/comments/ufy2k4/why_...
[] probably, New Zealand, although foreigner is relative
As silly as it seemed, I do think everyone is entitled to their own opinion and I respect the anti-dreadlocks girl for standing up for what she believed in even when most people were against her.
Telling others they don’t like how others look is right near the top on the scale of offensiveness. I had a partner who had had dreads for 25 years. I’m wasn’t a huge fan of her dreads because although I like the look, hers were somewhat annoying for me (scratchy, dread babies, me getting tangled). That said, I would hope I never tell any other person how to look. Hilarious when she was working, and someone would treat her badly due to their assumptions or prejudices, only to discover to their detriment she was very senior staff!
Dreadlocks are usually called dreads in NZ. My previous link mentions that some people call them locks, which seems inapproprate to me: kind of a confusing whitewashing denial of history.
One image links to the 2015 article, "It's Ridiculous To Say Black Women's Natural Hair Is 'Unprofessional'!". The Guardian article on the Google results is from 2016.
Another image has the headline, "5 Reasons Natural Hair Should NOT be Viewed as Unprofessional - BGLH Marketplace" (2012).
Another: "What to Say When Someone Calls Your Hair Unprofessional".
Also, have you noticed how good and professional the black women in the Guardian's image search look? Most of them look like models with photos taken by professional photographers. Their hair is meticulously groomed and styled. This is not the type of photo an article would use to show "unprofessional hair". But it is the type of photo the above articles opted for.
Chitwan Saharia, William Chan, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David Fleet†, Mohammad Norouzi
Oh yeah, as a woman who grew up in a Third World country, how an AI model generates images would have deeply affected my daily struggles! /s
It's kinda insulting that they think that this would be insulting. Like "Oh no I asked the model to draw a doctor and it drew a male doctor, I guess there's no point in me pursuing medical studies" ...
> Oh no I asked the model to draw a doctor and it drew a male doctor, I guess there's no point in me pursuing medical studies
If you don't think this is a real thing that happens to children you're not thinking especially hard. It doesn't have to be common to be real.
I believe that's where parenting comes in. Maybe I'm too cynical but I think that the parents' job is to undo all of the harm done by society and instill in their children the "correct" values.
Far from being too cynical, this is too optimistic.
The vast majority of parents try to instill the value "do not use heroin." And yet society manages to do that harm on a large scale. There are other examples.
It seems extremely unfair that parents of young black men should have to work extra hard to tell their kids they're not destined to be criminals. Hell, it's not fair on parents of blonde girls to tell their kids they don't have to be just dumb and pretty.
(note: I am deliberately picking bad stereotypes that are pervasive in our culture... I am not in any way suggesting those are true.)
The quality of the evidence for this, as with almost all social science and much of psychology, is extremely low bordering on just certified opinions. I would love to understand why you think otherwise.
> Obviously there are things with much larger effects, that doesn't mean that this doesn't exist.
What a hedge. How should we estimate the size of this effect, so that we can accurately measure whether/when the self-appointed hall monitors are doing more harm than good?
The evidence for implicit bias is pretty weak and IIRC is better explained by people having explicit bias but lying about it when asked.
(Note: this is even worse.)
There’s no reason to believe their model training learns the same statistics as their input dataset even. If that’s not an explicit training goal then whatever happens happens. AI isn’t magic or more correct than people.
There is a difference between probably and invariably. Would it be so hard for the model to show male nurses at least some of the time?
Seems to me that (as with any ML stuff, right?) it's reflecting the training corpus.
Futhermore, is it this thing's job to reflect reality?
Snarky answer: Ah, yes, let's make sure that things like "A giant cobra snake on a farm. The snake is made out of corn" reflect reality.Heartfelt answer: Yes, there is some of that wishful thinking or editorializing. I don't consider it to be erasing or denying reality. This is a tool that synthesizes unreality. I don't think that such a tool should, say, refuse to synthesize an image of a female POTUS because one hasn't existed yet. This is art, not a reporting tool... and keep in mind that art not only imitates life but also influences it.
If it didn't reflect reality, you wouldn't be impressed by the image of the snake made of corn.
One example would be if Imagen draws a group of mostly white people when you say "draw a group of people". This doesn't reflect actual reality. Another would be if Imagen draws a group of men when you say "draw a group of doctors".
In these cases where iconographic reality differs from actual reality, hand-tuning could be used to bring it closer to the real world, not just the world as we might wish it to be!
I agree there's a problem here. But I'd state it more as "new technologies are being held to a vastly higher standard than existing ones." Imagine TV studios issuing a moratorium on any new shows that made being white (or rich) seem more normal than it was! The public might rightly expect studios to turn the dials away from the blatant biases of the past, but even if this would be beneficial the progressive and activist public is generations away from expecting a TV studio to not release shows until they're confirmed to be bias-free.
That said, Google's decision to not publish is probably less about the inequities in AI's representation of reality and more about the AI sometimes spitting out drawings that are offensive in the US, like racist caricatures.
Very difficult to replicate results.
I mean a good example of this is the Pulse[0][1] paper. You may remember it as the white Obama. This became a huge debate and it was pretty easily shown that the largest factor was the dataset bias. This outrage did lead to fixing FFHQ but it also sparked a huge debate with LeCun (data centric bias) and Timnit (model centric bias) at the center. Though Pulse is still remembered for this bias, not for how they responded to it. I should also note that there is human bias in this case as we have a priori knowledge of what the upsampled image should look like (humans are pretty good at this when the small image is already recognizable but this is a difficult metric to mathematically calculate).
It is fairly easy to find adversarial examples, where generative models produce biased results. It is FAR harder to fix these. Since this is known by the community but not by the public (and some community members focus on finding these holes but not fixing them) it creates outrage. Probably best for them to limit their release.
[0] https://arxiv.org/abs/2003.03808
[1] https://cdn.vox-cdn.com/thumbor/MXX-mZqWLQZW8Fdx1ilcFEHR8Wk=...
That's what bothered me the most in Timnit's crusade. Throw the baby with the bath water!
We certainly don't want to perpetuate harmful stereotypes. But is it a flaw that the model encodes the world as it really is, statistically, rather than as we would like it to be? By this I mean that there are more light-skinned people in the west than dark, and there are more women nurses than men, which is reflected in the model's training data. If the model only generates images of female nurses, is that a problem to fix, or a correct assessment of the data?
If some particular demographic shows up in 51% of the data but 100% of the model's output shows that one demographic, that does seem like a statistics problem that the model could correct by just picking less likely "next token" predictions.
Also, is it wrong to have localized models? For example, should a model for use in Japan conform to the demographics of Japan, or to that of the world?
If you want the model to understand what a "nurse" actually is, then it shouldn't be associated with female.
If you want the model to understand how the word "nurse" is usually used, without regard for what a "nurse" actually is, then associating it with female is fine.
The issue with a correlative model is that it can easily be self-reinforcing.
Put another way, when we ask for an output optimized for "nursiness", is that not a request for some ur stereotypical nurse?
What percent of people should be rendered as white people with broccoli hair? What if you request green people. Or broccoli haired people. Or white broccoli haired people? Or broccoli haired nazis?
It gets hard with these conditional probabilities
Your description is closer to how the open source CLIP+GAN models did it - if you ask for “tree” it starts growing the picture towards treeness until it’s all averagely tree-y rather than being “a picture of a single tree”.
It would be nice if asking for N samples got a diversity of traits you didn’t explicitly ask for. OpenAI seems to solve this by not letting you see it generate humans at all…
That's excessively simplified but wouldn't this drop the stereotype and better reflect reality?
That’s a distinction without a difference. Meaning is use.
> We investigated sex differences in 473,260 adolescents’ aspirations to work in things-oriented (e.g., mechanic), people-oriented (e.g., nurse), and STEM (e.g., mathematician) careers across 80 countries and economic regions using the 2018 Programme for International Student Assessment (PISA). We analyzed student career aspirations in combination with student achievement in mathematics, reading, and science, as well as parental occupations and family wealth. In each country and region, more boys than girls aspired to a things-oriented or STEM occupation and more girls than boys to a people-oriented occupation. These sex differences were larger in countries with a higher level of women's empowerment. We explain this counter-intuitive finding through the indirect effect of wealth. Women's empowerment is associated with relatively high levels of national wealth and this wealth allows more students to aspire to occupations they are intrinsically interested in.
Source: https://psyarxiv.com/zhvre/ (HN discussion: https://news.ycombinator.com/item?id=29040132)
0. https://www.youtube.com/watch?v=_XsEsTvfT-M
If it did, would you believe that’s a real representative nurse because an image model gave it to you?
Are the logical divisions you make in your mind really indicative of anything other than your arbitrary personal preferences?
"It is possible for a man to provide care" is not the same statement as "it is possible for a sexually dimorphic species in a competitive, capitalistic society (...add more qualifications here) to develop a male-dominated caretaking role"
You're just asserting that you could imagine male nurses without creating a logical contradiction, unlike e.g. circles that have corners. That doesn't mean nursing could be a male-dominated industry under current constraints.
And anyway - contextually -, the representational natures of "use" (instances) and that of "meaning" (definition) are completely different.
Preliminarily and provisionally. Then, they start discussing their concepts - it is the very definition of Intelligence.
So? Draw your consequences.
Following what was said, you are stating that "a staggering large number of people are unintelligent". Well, ok, that was noted. Scolio: if unintelligent, they should refrain from expressing judgement (you are really stating their non-judgement), why all the actual expression? If unintelligent actors, they are liabilities, why this overwhelming employment in the job market?
Thing is, as unintelligent as you depict them quantitatively, the internal processing that constitutes intelligence proceeds in many even when scarce, even when choked by some counterproductive bad formation - processing is the natural functioning. And then, the right Paretian side will "do the job" that the vast remainder will not do, and process notions actively (more, "encouragingly" - the process is importantly unconscious, many low-level layers are) and proficiently.
And the very Paretian prospect will reveal, there will be a number of shallow takes, largely shared, on some idea, and other intensively more refined takes, more rare, on the same idea. That shows you a distinction between "use" and the asymptotic approximation to meanings as achieved by intellectual application.
Mental definition is that "«artificial»" (out of the internal processing) construct made of relations that reconstructs a meaning. Such ontology is logical - "this is that". (It would not be made of memories, which are processed, deconstructed.)
Concepts are internally refined: their "implicit" definition (a posterior reading of the corresponding mental low-level) is refined.
I'd say that bias is only an issue if it's unable to respond to additional nuance in the input text. For example, if I ask for a "male nurse" it should be able to generate the less likely combination. Same with other races, hair colors, etc... Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image.
Randomly pick one.
> Trying to generate a model that's "free of correlative relationships" is impossible because the model would never have the infinitely pedantic input text to describe the exact output image.
Sure, and you can never make a medical procedure 100% safe. Doesn't mean that you don't try to make them safer. You can trim the obvious low hanging fruit though.
How does the model back out the "certain people would like to pretend it's a fair coin toss that a randomly selected nurse is male or female" feature?
It won't be in any representative training set, so you're back to fishing for stock photos on getty rather than generating things.
That's one hypothesis.
—
The AI doesn’t know what’s common or not. You don’t know if it’s going to be correct unless you’ve tested it. Just assuming whatever it comes out with is right is going to work as well as asking a psychic for your future.
If they were to weight the training data so that there were an equal number of male and female nurses, then it may well produce male and female nurses with equal probability, but it would also learn an incorrect understanding of the world.
That is quite distinct from weighting the data so that it has a greater correspondence to reality. For example, if Africa is not represented well then weighting training data from Africa more strongly is justifiable.
The point is, it’s not a good thing for us to intentionally teach AIs a world that is idealized and false.
As these AIs work their way into our lives it is essential that they reproduce the world in all of its grit and imperfections, lest we start to disassociate from reality.
Chinese media (or insert your favorite unfree regime) also presents China as a utopia.
No it is not, because you don’t know if it’s been shown each one of its samples the same number of times, or if it overweighted some of its samples more than others. There’s normal reasons both of these would happen.
Is it? I'm reminded of the Microsoft Tay experiment, were they attempted to train an AI by letting Twitter users interact with it.
The result was a non-viable mess that nobody liked.
I say this because I’ve been visiting a number of childcare centres over the past few days and I still have yet to see a single male teacher.
I have a feeling that we need to be real with ourselves and solve problems and not paper over them. I feel like people generally expect search engines to tell them what's really there instead of what people wish were there. And if the engines do that, people can get agitated!
I'd almost say that hurt feelings are prerequisite for real change, hard though that may be.
These are all really interesting questions brought up by this technology, thanks for your thoughts. Disclaimer, I'm a fucking idiot with no idea what I'm talking about.
Which real world? The population you sample from is going to make a big difference. Do you expect it to reflect your day to day life in your own city? Own country? The entire world? Results will vary significantly.
If I ask for pictures of Japanese people, I'm not shocked when all the results are of Japanese people. If I asked for "criminals in the United States" and all the results are black people, that should concern me, not because the data set is biased but because the real world is biased and we should do something about that. The difference is that I know what set I'm asking for a sample from, and I can react accordingly.
Well the results would unquestionably be biased. All results being black people wouldn't reflect reality at all, and hurting feelings to enact change seems like a poor justification for incorrect results.
> I'd say it doesn't actually matter, as long as the population sampled is made clear to the user.
Ok, and let's say I ask for "criminals in Cheyenne Wyoming" and it doesn't know the answer to that, should it just do its best to answer? Seem risky if people are going to get fired up about it and act on this to get "real change".
That seems like a good parallel to what we're talking about here, since it's very unlikely that crime statistics were fed into this image generating model.
curiously, this search actually only returns white people for me on GIS
We may not like what it shows us, but blindfolding ourselves is not the solution to that problem.
Your logic seems to rest on this assumption which I don't think is justified. "Skewing search results" is not the same as "hiding the biases of the real world". Showing the most statistically likely result is not the same as showing the world how it truly is.
A generic nurse is statistically going to be female most of the time. However, a model that returns every nurse as female is not showing the real world as it is. It is exaggerating and reinforcing the bias of the real world. It inherently requires a more advanced model to actually represent the real world. I think it is reasonable for the creators to avoid sharing models known to not be smart enough to avoid exaggerating real world biases.
Every model will have some random biases. Some of those random biases will undesirably exaggerate the real world. Every model will undesirably exaggerate something. Therefore no model should be shared.
Your goal is nice, but impractical?
I said "It is reasonable... to avoid sharing models". That is an acknowledged that the creators are acting reasonably. It does not imply anything as extreme as "no model should be shared". The only way to get from A to B there is for you to assume that I think there is only one reasonable response and every other possible reaction is unreasonable. Doesn't that seem like a silly assumption?
If the only way to do AI is to encode racism etc, then we shouldn't be doing AI at all.
This is a far cry from say the USA where that would instantly trigger a response since until the 1960s there was a widespread race based segregation.
1. The model provides a reflection of reality, as politically inconvenient and hurtful as it may be.
2. The model provides an intentionally obfuscated version with either random traits or non correlative traits.
3. The model refuses to answer.
Which of these is ideal to you?
A model that returns 100% of nurses as female might be statistically more accurate than a model that returns 50% of nurses as female, but it is still not an accurate reflection of the real world. I agree that the model shouldn't return a male nurse 50% of the time. Yet an accurate model needs to be able to occasionally return a male nurse without being directly prompted for a "male nurse". Anything else would also be inaccurate.
Given that male nurses exist (and though less common, certainly aren't rare), why has the model apparently seen so few?
There actually is a fairly simple explanation: because the images it has seen labelled "nurse" are more likely from stock photography sites rather than photos of actual nurses, and stock photography is often stereotypical rather than typical.
This depends on the application. As an example, it would be a problem if it's used as a CV-screening app that's implicitly down-ranking male-applicants to nurse positions, resulting in fewer interviews for them.
You're ignoring that these models are stochastic. If I ask for a nurse and always get an image of a woman in scrubs, then yes, the model exhibits bias. If I get a male nurse half the time, we can say the model is unbiased WRT gender, at least. The same logic applies to CEOs always being old white men, criminals always being Black men, and so on. Stochastic models can output results that when aggregated exhibit a distribution from which we can infer bias or the lack thereof.
I expect that in the practical limit of scale achievable, the regularization pressure inherent to the process of training these models converges to https://en.wikipedia.org/wiki/Minimum_description_length and the correlative relationships become optimized away, leaving mostly true causal relationships inherent to data-generating process.
Perhaps what "nurse" means isn't what "nurse" should mean, but what people mean when they say "nurse" is what "nurse" means.
So even if we managed to create a perfect model of representation and inclusion, people could still use it to generate extremely offensive images with little effort. I think people see that as profoundly dangerous. Restricting the ability to be creative seems to be a new frontier of censorship.
Do they see it as dangerous? Or just offensive?
I can understand why people wouldn’t want a tool they have created to be used to generate disturbing, offensive or disgusting imagery. But I don’t really see how doing that would be dangerous.
In fact, I wonder if this sort of technology could reduce the harm caused by people with an interest in disgusting images, because no one needs to be harmed for a realistic image to be created. I am creeping myself out with this line of thinking, but it seems like one potential beneficial - albeit disturbing - outcome.
> Restricting the ability to be creative seems to be a new frontier of censorship.
I agree this is a new frontier, but it’s not censorship to withhold your own work. I also don’t really think this involves much creativity. I suppose coming up with prompts involves a modicum of creativity, but the real creator here is the model, it seems to me.
Interesting idea, but is there any evidence that e.g. consuming disturbing images makes people less likely to act out on disturbing urges? Far from catharsis, I'd imagine consumption of such material to increase one's appetite and likelihood of fulfilling their desires in real life rather than to decrease it.
I suppose it might be hard to measure.
I won't speak to whether something is "offensive", but I think that having underlying biases in image-classification or generation has very worrying secondary effects, especially given that organizations like law enforcement want to do things like facial recognition. It's not a perfect analogue, but I could easily see some company pitch a sketch-artist-replacement service that generated images based on someone's description. The potential for having inherent bias present in that makes that kind of thing worrying, especially since the people in charge of buying it are likely to care, or notice, about the caveats.
It does feel like a little bit of a stretch, but at the same time we've also seen such things happen with image classification systems.
Propaganda can be extremely dangerous. Limiting or discouraging the use of powerful new tools for unsavory purposes such as creating deliberately biased depictions for propaganda purposes is only prudent. Ultimately it will probably require filtering of the prompts being used in much the same way that Google filters search queries.
I want to be clear here, bias can be introduced at many different points. There's dataset bias, model bias, and training bias. Every model is biased. Every dataset is biased.
Yes, the real world is also biased. But I want to make sure that there are ways to resolve this issue. It is terribly difficult, especially in a DL framework (even more so in a generative model), but it is possible to significantly reduce the real world bias.
Sure, I wasn't questioning the bias of the data, I was talking about the bias of the real world and whether we want the model to be "unbiased about bias" i.e. metabiased or not.
Showing nurses equally as men and women is not biased, but it's metabiased, because the real world is biased. Whether metabias is right or not is more interesting than the question of whether bias is wrong because it's more subtle.
Disclaimer: I'm a fucking idiot and I have no idea what I'm talking about so take with a grain of salt.
Does a bias towards lighter skin represent reality? I was under the impression that Caucasians are a minority globally.
I read the disclaimer as "the model does NOT represent reality".
For example, the most eaten foods globally are maize, rice, wheat, cassava, etc. If it always depicted foods matching the global statistics, it wouldn't be giving most users what they expected from their prompt. American users would usually expect American foods, Japanese users would expect Japanese foods, etc.
> Does a bias towards lighter skin represent reality? I was under the impression that Caucasians are a minority globally.
Caucasians specifically are a global minority, but lighter skinned people are not, depending of course on how dark you consider skin to be "lighter skin". Most of the world's population is in Asia, so I guess a model that was globally statistically accurate would show mostly people from there.
Yeah, but you get that same effect on every axis, not just the one you're trying to correct. You might get male nurses, but they have green hair and six fingers, because you're sampling from the tail on all axes.
For a one-shot generative algorithm you must accept the artist’s biases.
“hey artist, draw me a nurse.”
“Hmm okay, do you want it a guy or girl?”
“Don’t ask me, just draw what I’m saying.”
- Ok, I'll draw you what an average nurse looks like.
- Wait, it's a woman! She wears a nurse blouse and she has a nurse cap.
- Is it bad ?
- No.
- Ok then what's the problem, you asked for something that looked like a nurse but didn't specify anything else ?
Also, getting a random sample of any demographic would be really hard, so no machine learning project is going to do that. Instead you've got a random sample of some arbitrary dataset that's not directly relevant to any particular purpose.
This is, in essence, a design or artistic problem: the Google researchers have some idea of what they want the statistical properties of their image generator to look like. What it does isn't it. So, artistically, the result doesn't meet their standards, and they're going to fix it.
There is no objective, universal, scientifically correct answer about which fictional images to generate. That doesn't all art is equally good, or that you should just ship anything without looking at quality along various axes.
If the model only generated images of female nurses, then it is not representative of the real world, because male nurses exist and they deserve to not be erased. The training data is the proximate causes here, but one wonders what process ended up distorting "most nurses are female" into "nearly all nurse photos are of female nurses" something amplified a real world imbalance into a dataset that exhibited more bias than the real world, and then training the AI bakes that bias into an algorithm (that may end up further reinforcing the bias in the real world depending on the use-cases).
At what point is statistical significance considered ok and unbiased?
Presumably when you're significantly predictive of the preferred dogma, rather than reality. There's no small bit of irony in machines inadvertently creating cognitive dissonance of this sort; second order reality check.
I'm fairly sure this never actually played out well in history (bourgeois pseudoscience, deutsche physik etc), so expect some Chinese research bureau to forge ahead in this particular direction.
After that we'll make them sit through Legal's approved D&I video series, then it's off to the races.
T5-XXL looks on par with CLIP so we may not see an open source version of T5 for a bit (LAION is working on reproducing CLIP), but this is all progress.
It is also available via Hugging Face transformers.
However, the paper mentions T5-XXL is 4.6B, which doesn't fit any of the checkpoints above, so I'm confused.
>While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes
Tossing that stuff when it comes up in a research environment is one thing, but Google clearly wants to implement this as a product, used all over the world by a huge range of people. If the dataset has problems, and why wouldn't it, it is perfectly rational to want to wait and re-implement it with a better one. DALL-E 2 was trained on a curated dataset so it couldn't generate sex or gore. Others are sanitizing their inputs too and have done for a long time. It is the only thing that makes sense for a company looking to commercialize a research project.
This has nothing to do with "inability to cope" and the implied woke mob yelling about some minor flaw. It's about building a tool that doesn't bake in serious and avoidable problems.
Also, people have been commenting assuming Google doesn’t want to offend their users or non-users, but they also don’t want to offend their own staff. If you run a porn company you need to hire people okay with that from the start.
The idea that most people use any coherent ethical framework (even something as high level and nearly content-free as Copenhagen) much less a particular coherent ethical framework is, well, not well supported by the evidence.
> require that all negative outcomes of a thing X become yours if you interact with X. It is not sensible to interact with high negativity things unless you are single-issue.
The conclusion in the final sentence only makes sense if you use “interact” in an incorrect way describing the Copenhagen interpretation of ethics, because the original description is only correct if you include observation as an interaction. By the time you have noted a thing is “high-negativity”, you have observed it and acquired responsibility for it's continuation under the Copenhagen interpretation; you cannot avoid that by choosing not to interact once you have observed it.
“There exists an ethical framework—not the Copenhagen interpretation —to which some minority of the population adheres in which trying and failing to a correct a problem incurs retroactive blame for the existence of the problem but seeing it and just saying ‘sucks, but not my problem’ does not,“ is probably true, but not very relevant.
It's logical for Google to avoid involvement with porn, and to be seen doing so, because even though porn is popular involvement with it is nevertheless politically unpopular, and Google’s business interest is in not making itself more attractive as a political punching bag. The popularity of Copenhagen ethics (or their distorted cousins) don't really play into it, just self interest.
I am not sure of the evidence but that would seem almost right.
Except for, for example a story I read where a couple lost their housing deposit due to a payment timing issue. They used a lawyer and were not doing anything “fancy” like buying via a holding company. They interacted with “buying a house”, so is this just tough shit because they interacted with X.
That sounds like the original Bitcoin “not your keys not your coin” kind of morality.
I don’t think I can figure out the steel man.
I don't have any evidence, but my personal experience is that it feels correct, at least on the internet.
People seem to have a "you touch it, you take responsibility for it" mindset regarding ethical issues. I think it's pretty reasonable to assume that Google execs are assuming "If anything bad happens because of AI, we'll be blamed for it".
Genuinely, isn't it a prime example of the people actually stopping to think if they should, instead of being preoccupied with whether or not they could ?
Other STEM adjacent communities feel similarly but I don’t get it from actual in person engineers much.
Maybe the engineers conclude correctly that voicing this concern without the veil of anonymity will do nothing good to their humble livelihood, and thus you don't hear it from them in person.
Moreover, the model doing things like exclusively producing white people when asked to create images of people home brewing beer is "biased" but it's a bias that presumably reflects reality (or at least the internet), if not the reality we'd prefer. Bias means more than "spam and crap", in the ML community bias can also simply mean _accurately_ modeling the underlying distribution when reality falls short of the author's hopes.
For example, if you're interested in learning about what home brewing is the fact that it uses white people would be at least a little unfortunate since there is nothing inherently white and some home brewers aren't white. But if, instead, you wanted to just generate typical home brewing images doing anything but would generate conspicuously unrepresentative images.
But even ignoring the part of the biases which are debatable or of application-specific impact, saying something is unfortunate and saying people should be denied access are entirely different things.
I'll happily delete this comment if you can bring to my attention a single person who has suggested that we lose access to the internet because of spam and crap who has also argued that the release of an internet-biased ML model shouldn't be withheld.
So people shouldn’t say ‘these concerns are just woke people doing dumb woke stuff, but the model is just reflecting reality.’
There are two possible ways of interpreting interpreting "gender stereotypes in professions".
biased or correct
https://www.abc.net.au/news/2018-05-21/the-most-gendered-top...
https://www.statista.com/statistics/1019841/female-physician...
Google knows this will be an unlimited money generator so they're keeping a lid on it.
>Eschew flamebait. Avoid unrelated controversies and generic tangents.
They provided a pretty thorough overview (nearly 500 words) of the multiple reasons why they are showing caution. You picked out the one that happened to bother you the most and have posted a misleading claim that the tech is being withheld entirely because of it.
Indeed it is. Consider this an early, toy version of the political struggle related to ownership of AI-scientists and AI-engineers of the near future. That is, generally capable models.
I do think the public should have access to this technology, given so much is at stake. Or at least the scientists should be completely, 24/7, open about their R&D. Every prompt that goes into these models should be visible to everyone.
Dall-E had an entire news cycle (on tech-minded publications, that is) that showcased just how amazing it was.
Millions* of people became aware that technology like Dall-E exists, before anyone could get their hands on it and abuse it. (*a guestimate, but surely a close one)
One day soon, inevitably, everyone will have access to something 10x better than Imagen and Dall-E. So at least the public is slowly getting acclimated to it before the inevitable "theater-goers running from a projected image of a train approaching the camera" moment
I would love it.
[1] https://github.com/lucidrains/imagen-pytorch
Still amazing that we're at a point where that's the case, they're both incredible developments.
> We show that scaling the pretrained text encoder size is more important than scaling the diffusion model size.
There seems to be an unexpected level of synergy between text and vision models. Can't wait to see what video and audio modalities will add to the mix.
Particularly as you approach the point where the image quality itself is superb and people increasingly turn to attacking the semantics & control of the prompt to degrade the quality ("...The donkey is holding a rope on one end, the octopus is holding onto the other. The donkey holds the rope in its mouth. A cat is jumping over the rope..."). For that sort of thing, it's hard to see how simply beefing up the raw pixel-generating part will help much: if the input seed is incorrect and doesn't correctly encode a thumbnail sketch of how all these animals ought to be engaging in outdoors sports, there's nothing some low-level pixel-munging neurons can do to help much.
I wouldn’t be surprised if the lack of video and 3D understanding in the image dataset training fails to understand things like the fear of heights, and the concept of gravity ends up being learned in the text processing weights.
Don't like any of the results from the real web? Well how about these we created just for you.
Google could totally afford it, especially if the feature was hidden behind a button the user had to click, and not just run for every image search.
How does Adobe prevent Photoshop being used to draw offensive images? They don't... People understand that a tool can be used for good and bad.
(Consumer demand and boredom both being infinite is another thing working against it.)
If Getty et al aren't already spending money on that possibility, they probably should be.
I believe this type of content generation will be the next big thing or at least one of them. But people will want some customization to make their pictures “unique” and fix AI’s lack of creativity and other various shortcomings. Plus edit out the remaining lapses in logic/object separation (which there are some even in the given examples).
Still, being able to create arbitrary stock photos is really useful and i bet these will flood small / low-budget projects
https://twitter.com/joeyliaw/status/1528856081476116480?s=21...
That is what I feel personally.
[1] https://github.com/nerdyrodent/VQGAN-CLIP.git [2] https://github.com/CompVis/latent-diffusion.git [3] https://imgur.com/a/dCPt35K
What exactly is the risk?
One quote:
> “On the other hand, generative methods can be leveraged for malicious purposes, including harassment and misinformation spread [20], and raise many concerns regarding social and cultural exclusion and bias [67, 62, 68]”
Simply the ease at which people are going to be able to make extremely-realistic game photographs is going to do some damage to the world. It's inevitable, but it might be good to postpone it.
I don't understand why. If someone has gone to a blockbuster movie in the last 15 years, they're very familiar with the concept of making people, sets, and entire worlds, that don't exist, with photorealistic accuracy. Being able to make fictitious photorealistic images isn't remotely a new ability, it's just an ability that's now automated.
If this is released, I think any damage would be extremely fleeting, as people pumped out thousands of these images, and people grow bored of them. The only danger is making this ability (to make false images) seem new (absolutely not) or rare (not anymore)!
Oh well.
I would expect AI development to follow a similar path to digital media generally, as its following the increasing difficulty and space requirements of digitally representing said media: text < basic sounds < images < advanced audio < video.
What’s more impressive to me is how far ahead text-to-speech is, but I think the explanation is straightforward (the accessibility value has motivated us to work on that for a lot longer).
This is common in the research PA. People don't want to deal with broccoli man [1].
[1] https://www.youtube.com/watch?v=3t6L-FlfeaI
https://cloud.google.com/appengine
1: https://ai.googleblog.com/
You can tell me those pictures are generated by an AI and I might believe it, but until real people can actually test it... it's easy enough to fake. This page isn't even the remotest bit legit by the URL, It looks nicely put together and that's about it. Could have easily put together this with a graphic designer to fake it.
Let be clear, I'm not actually saying it's fake. Just that all of these new "cool" things are more or less theoretical if nothing is getting released.
For example, corporate graphics design, logos, brand photography, etc.
I really do think inference time is a red herring for the first generation of these models.
Sure, the more transformative use-cases like real-time content generation to replace movies/games, but there is a lot of value to be created prior to that point.