https://www.metaculus.com/questions/3479/date-weakly-general...
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.
> 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.
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.
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
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.)
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?
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
At what point is statistical significance considered ok and unbiased?
After that we'll make them sit through Legal's approved D&I video series, then it's off to the races.
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.
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.
Don't like any of the results from the real web? Well how about these we created just for you.
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.
Very difficult to replicate results.
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.
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 ?
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" ...
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... ?
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.
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=...
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.
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.
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.
:format(webp)/cdn.vox-cdn.com/uploads/chorus_image/image/66972412/face_depixelizer_obama.0.jpg){kind=link}
Translation: we need to hand-tune this to not reflect reality
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
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.
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.
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.
# whois appspot.com
[Querying whois.verisign-grs.com]
[Redirected to whois.markmonitor.com]
[Querying whois.markmonitor.com]
[whois.markmonitor.com]
Domain Name: appspot.com
Registry Domain ID: 145702338_DOMAIN_COM-VRSN
Registrar WHOIS Server: whois.markmonitor.com
Registrar URL: http://www.markmonitor.com
Updated Date: 2022-02-06T09:29:56+0000
Creation Date: 2005-03-10T02:27:55+0000
Registrar Registration Expiration Date: 2023-03-10T00:00:00+0000
Registrar: MarkMonitor, Inc.
Registrar IANA ID: 292
Registrar Abuse Contact Email: abusecomplaints@markmonitor.com
Registrar Abuse Contact Phone: +1.2086851750
Domain Status: clientUpdateProhibited (https://www.icann.org/epp#clientUpdateProhibited)
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Domain Status: serverDeleteProhibited (https://www.icann.org/epp#serverDeleteProhibited)
Registrant Organization: Google LLC
Registrant State/Province: CA
Registrant Country: US
Registrant Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
Admin Organization: Google LLC
Admin State/Province: CA
Admin Country: US
Admin Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
Tech Organization: Google LLC
Tech State/Province: CA
Tech Country: US
Tech Email: Select Request Email Form at https://domains.markmonitor.com/whois/appspot.com
Name Server: ns4.google.com
Name Server: ns3.google.com
Name Server: ns2.google.com
Name Server: ns1.google.comIt'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.
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".
Put another way, when we ask for an output optimized for "nursiness", is that not a request for some ur stereotypical nurse?
Siri takes this approach for a wide range of queries.
It's a simple case of sample bias.
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.
Hooray! Non-cherry-picked samples should be the norm.
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.
What I see is semi-poverty mindset among very smart people who appear to be treated in a way such that the winners get promotion, and everyone else is fired. That this sort of analysis with ML is useful for massive data sets at scale, where 90% is a lot of accuracy, not at all for the small sets of real world, human-scale problems where each result may matter a lot. The amount of years of training that these researchers had to go through, to participate in this apparently ruthless environment, are certainly like a lottery ticket, if you are in fact in a game where everyone but the winner has to find a new line of work. I think their masters live in Redmond, if I recall.. not looking it up at the moment.
You know that race has a large effect on hair right?
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?
>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.
If it didn't reflect reality, you wouldn't be impressed by the image of the snake made of corn.
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.
That’s a distinction without a difference. Meaning is use.
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 ?
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
> 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 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).
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 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.
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
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.
What they mean is people who think not like them.
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?
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.
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…
https://twitter.com/joeyliaw/status/1528856081476116480?s=21...
And anyway - contextually -, the representational natures of "use" (instances) and that of "meaning" (definition) are completely different.
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]”
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.
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.
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...
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.
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.
That's excessively simplified but wouldn't this drop the stereotype and better reflect reality?
This is common in the research PA. People don't want to deal with broccoli man [1].
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.
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.
> 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)
Other STEM adjacent communities feel similarly but I don’t get it from actual in person engineers much.
For a one-shot generative algorithm you must accept the artist’s biases.
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.
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".
(Consumer demand and boredom both being infinite is another thing working against it.)
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.
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.
“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 ?
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.
Google knows this will be an unlimited money generator so they're keeping a lid on it.
gwern can maybe comment here.
An actually scary thing is that AIs are getting okay at reproducing people’s voices.
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.
In practice, my guess is that even though Dall-e level performance in music generation would be stunning and incredible, it would also be tiresome and predictable to consume on any extended basis. I mean- that's my reaction to Dall-e- I find the images astonishing and magical but can only look at them for limited periods of time. At these early stages in this new world the outputs of real individual brains are still more interesting.
But having tools like this to facilitate creation and inspiration by those brains- would be so so cool.
I mean, from my perspective, the skill in these (and DALL-E's) image reproductions is truly astonishing. Just looking for more information about how the software actually works, even if there are big chunks of it that are "this is beyond your understanding without taking some in-depth courses".
Preliminarily and provisionally. Then, they start discussing their concepts - it is the very definition of Intelligence.
That said, you can download Dream by Wombo from the app store and it is one of the top smartphone apps, even though it is a few generations behind state of the art.
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's mountains of ai-generated inauthentic content that companies (including Google) have to filter out of their services. This content is used for spam, click farms, scamming, and even state propaganda operations. GPT-2 made this problem orders of magnitude worse than it used to be, and each iteration makes it harder to filter.
The industry term is (generally) "Coordinated Inauthentic Behavior" (though this includes uses of actual human content). I think Smarter Every Day did a good videos (series?) on the topic, and there are plenty of articles on the topic if you prefer that.
Each box you see there has a section in the paper explaining it in more detail.
There is a Google Colab workbook that you can try and run for free :)
This is the image-text pairs behind: https://laion.ai/laion-400-open-dataset/
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.
Are the logical divisions you make in your mind really indicative of anything other than your arbitrary personal preferences?
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 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.
>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.
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.
If Getty et al aren't already spending money on that possibility, they probably should be.
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?
Without a fairly deep grounding in this stuff it’s hard to appreciate how far ahead Brain and DM are.
Neither OpenAI nor FAIR ever has the top score on anything unless Google delays publication. And short of FAIR? D2 lacrosse. There are exceptions to such a brash generalization, NVIDIA’s group comes to mind, but it’s a very good rule of thumb. Or your whole face the next time you are tempted to doze behind the wheel of a Tesla.
There are two big reasons for this:
- the talent wants to work with the other talent, and through a combination of foresight and deep pockets Google got that exponent on their side right around the time NVIDIA cards started breaking ImageNet. Winning the Hinton bidding war clinched it.
- the current approach of “how many Falcon Heavy launches worth of TPU can I throw at the same basic masked attention with residual feedback and a cute Fourier coloring” inherently favors deep pockets, and obviously MSFT, sorry OpenAI has that, but deep pockets also non-linearly scale outcomes when you’ve got in-house hardware for multiply-mixed precision.
Now clearly we’re nowhere close to Maxwell’s Demon on this stuff, and sooner or later some bright spark is going to break the logjam of needing 10-100MM in compute to squeeze a few points out of a language benchmark. But the incentives are weird here: who, exactly, does it serve for us plebs to be able to train these things from scratch?
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.
A basic part of it is that neural networks combine learning and memorizing fluidly inside them, and these networks are really really big, so they can memorize stuff good.
So when you see it reproduce a Shiba Inu well, don’t think of it as “the model understands Shiba Inus”. Think of it as making a collage out of some Shiba Inu clip art it found on the internet. You’d do the same if someone asked you to make this image.
It’s certainly impressive that the lighting and blending are as good as they are though.
Actually, I think they made InstructGPT even better at erotica because it’s trained to be “helpful and friendly”, so in other words they made it a sub.
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?
Should ML/AI deliver on the wildest promises, it will be like a SpaceX Starship for the mind.
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 it did, would you believe that’s a real representative nurse because an image model gave it to you?
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.
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.
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.
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?
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.
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?
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.
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.
The kind of early 2010's, over the top description of something that's ridiculous
What I don't understand is how they do the composition. E.g. for "A giant cobra snake on a farm. The snake is made out of corn." I think I could understand how it could reproduce the "A giant cobra snake on a farm" part. What I don't understand is how it accurately pictured "The snake is made out of corn." part, when I'm guessing it has never seen images of snakes made out of corn, and the way it combined "snake" with "made out of corn", in a way that is pretty much how I imagined it would look, is the part I'm baffled by.
Still amazing that we're at a point where that's the case, they're both incredible developments.
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.
So people shouldn’t say ‘these concerns are just woke people doing dumb woke stuff, but the model is just reflecting reality.’
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.)
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'm not sure it matters. The history of computing shows that within the decade we will all have the ability to train and use these models.
To the extent that they get used for making bored ape images or whatever meme du juor, it says much more about the kind of pictures people want to see.
I personally find the weird deep dreaming dogs with spikes coming out of their heads more mathematically interesting, but I can understand why that doesn’t sell as well.
An impressive advance would be a small model that’s capable of working from an external memory rather than memorizing it.
For example: the high-frequency trading industry is estimated to have made somewhere between 2-3 billion dollars in all of 2020, profit/earnings. That’s a good weekend at Google.
HFT shops pay well, but not much different to top performers at FAANG.
People work in HFT because without taking a pay cut they can play real ball: they want to try themselves against the best.
Heavy learning people are no different in wanting both a competitive TC but maybe even more to be where the action is.
That’s currently Blade Runner Industries Ltd, but that could change.
Google clearly demonstrates their unrivaled capability to leverage massive quantities of data and compute, but it’s premature to declare that they’ve secured victory in the AI Wars.
> 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.
And I don’t think whatever iteration of PaLM was cooking at the time GPT-3 started getting press would have looked to shabby.
I think Google crushed OpenAI on both GPT and DALL-E in short order because OpenAI published twice and someone had had enough.
But if you’re interested I’m happy to (attempt) answers to anything that was jargon: by virtue of HN my answers will be peer-reviewed in real time, and with only modest luck, a true expert might chime in.
But in general it is likely more due in part to the fact that it's going to happen anyway, if we can share our approaches and research findings, we'll just achieve it sooner.
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.
It doesn't output it outright, it basically forms it slowly, finding and strengthening more and more finer-grained features among the dwindling noise, combining the learned associations of memorized convolutional texture primitives vs encoded text embeddings. In the limit of enough data the associations and primitives turn out composable enough to suffice for out-of-distribution benchmark scenes.
When you have a high-quality encoder of your modality into a compressed vector representation, the rest is optimization over a sufficiently high-dimensional, plastic computational substrate (model): https://moultano.wordpress.com/2020/10/18/why-deep-learning-...
It works because it should. The next question is: "What are the implications?".
Can we meaningfully represent every available modality in a single latent space, and freely interconvert composable gestalts like this https://files.catbox.moe/rmy40q.jpg ?
If I were going to cite evidence for Alphabet’s “supremacy” in AI, I would’ve picked something more novel and surprising such as AlphaFold, or perhaps even Gato.
It’s not clear to me that Google has anything which compares to Reality Labs, although this may simply be my own ignorance.
Nvidia surely scooped Google with Instant Neural Graphics Primitives, in spite of Google publishing dozens of (often very interesting) NeRF papers. It’s not a war, all these works build on one another.
I’ve got no interest in moralizing on this, but if any of the big actors wanted to they could put a meaningful if not overwhelming subset of the corpus on S3, put the source code on GitHub, and you could on a modest budget see an epoch or 3.
I’m not holding my breath.
Almost there, the Apple Laserwriter nailed it at 300 dpi.
Sometimes sneaked an issue of the "SF-Lovers Digest" in between code printouts.
The bits and pieces I saw first hand tie out reasonably well with that account.
curiously, this search actually only returns white people for me on GIS
And to be equally clear, I have no inside baseball on how Brain/DM choose when to publish. I have some watercooler chat on the friendly but serious rivalry between those groups, but that’s about it.
I’m looking from the outside in at OpenAI getting all the press and attention, which sounds superficial but sooner or later turns into actual hires of actual star-bound post docs, and Google laying a little low for a few years.
Then we get Gato, Imagen, and PaLM in the space of like what, 2 months?
Clearly I’m speculating that someone pulled the trigger, but I don’t think it’s like, absurd.
b) It has seen images with descriptions of "corn," "cobra," "farm," and it has seen images of "A made out of B" and "C on a D." To generate a high-scoring image, it has to make something that scores well on all of them put together.
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.
People tend to really underestimate just how big these models are. Of course these models aren't simply "really really big" MLPs, but the cleverness of the techniques used to build them is only useful at insanely large scale.
I do find these models impressive as examples of "here's what the limit of insane amounts of data, insane amounts of compute can achieve with some matrix multiplication". But at the same time, that's all they are.
What saddens me about the rise of deep neural networks is it is really is the end of the era of true hackers. You can't reproduce this at home. You can't afford to reproduce this one in the cloud with any reasonable amount of funding. If you want to build this stuff your best bet is to go to top tier school, make the right connections and get hired by a mega-corp.
But the real tragedy here is that the output of this is honestly only interesting it if it's the work of some hacker fiddling around in their spare time. A couple of friend hacking in their garage making images of raccoon painting is pretty cool. One of the most powerful, well funded, owners of the likely the most compute resources on the planet doing this as their crowning achievement in AI... is depressing.
It’s Boltzmann and Szilard that did the original “kT” stuff around underlying thermodynamics governing energy dissipation in these scenarios, and Rolf Landaeur (I think that’s how you spell it) who did the really interesting work on how to apply that thermo work to lower-bounds on energy-expenditure in a given computation.
I said Maxwell’s Demon because it’s the best known example of a deep connection between useful work and computation. But it was sloppy.
This is ... very incorrect. I am very certain (95%+) that Google had nothing even close to GPT-3 at the time of its release. It's been 2 full years since GPT-3 was released, and even longer since OpenAI actually trained it.
That's not to talk about any of the other things OpenAI/FAIR has released that were SOTA at the time of release (Dall-E 1, JukeBox, Poker, Diplomacy, Codex).
Google Brain and Deepmind have done a lot of great work, but to imply that they essentially have a monopoly on SOTA results and all SOTA results other labs have achieved are just due to Google delaying publication is ridiculous.
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.
Good lord we are screwed. And yet somehow I bet even this isn't going to kill off the they're just statistical interpolators meme.
[1] https://www.deepmind.com/blog/tackling-multiple-tasks-with-a...
You could’ve had the same reaction years ago when Google published GoogleNet followed by a series of increasingly powerful Inception models - namely that Google would wind up owning the DNN space. But it didn’t play out that way, perhaps because Google dragged its feet releasing the models and training code, and by the time it did, there were simpler and more powerful models available like ResNet.
Meta’s recent release of the actual OPT LLM weights is probably going to have more impact than PaLM, unless Google can be persuaded to open up that model.
They’re all fundamentally anthropocentric: people argue until they are blue in the face about what “intelligent” means but it’s always implicit that what they really mean is “how much like me is this other thing”.
Language models, even more so than the vision models that got them funded have empirically demonstrated that knowing the probability of two things being adjacent in some latent space is at the boundary indistinguishable from creating and understanding language.
I think the burden is on the bright hominids with both a reflexive language model and a sex drive to explain their pre-Copernican, unique place in the theory of computation rather than vice versa.
A lot of these problems just aren’t problems anymore if performance on tasks supersedes “consciousness” as the thing we’re studying.
I think it's fair to say that this is the way it's always been. In 1990, you couldn't hack on an accurate fluid simulation at home, you needed to be at a university or research lab with access to a big cluster. But then, 10 years later, you could do it on a home PC. And then, 10 years after that, you could do it in a browser on the internet.
It's the same with this AI stuff.
I think if we weren't in the midst of this unique GPU supply crunch, the price of a used 1070 would be about $100 right now -- such a card would be state of the art 10 years ago!
Their slider with examples at the top showed a prompt along the lines of "a chrome plated duck with a golden beak confronting a turtle in a forest" and the resulting image was perfect - except the turtle had a golden shell.
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?
"A photo of a Shiba Inu dog Wearing a (sic) sunglasses And black leather jacket Playing guitar In a garden"
The Shiba Inu is not playing a guitar.
I think it’s in everyone’s benefit if we start planning for a world where a significant portion of the experts are stubbornly wrong about AGI. As a technology, generally intelligent ML has the potential to change so many aspects of our world. The dangers of dismissing the possibility of AGI emerging in the next 5-10 years are huge.
Again, I think we should consider "The Human Alignment Problem" more in this context. The transformers in question are large, heavy and not really prone to "recursive self-improvement".
If the ML-AGI works out in a few years, who gets to enter the prompts?
Google is not a hobby project anymore: "don't do evil" or whatever they whittered on about back in the day.
I don’t know what “we should grab a coffee or a beer sometime” means in the hyper-global post-C19 era, but I’d love to speak more on this without dragging a whole HN comment thread through it.
Drop me a line if you’re inclined: ben.reesman at gmail
Other funding models are possible as well, in the grand scheme of things the price for these models is small enough.
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.
“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.
“Oh our tech is so dangerous and amazing it could turn the world upside down” yet we hand it to random Bluechecks on Twitter.
It’s just marketing
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?
Convolutional filters lend themselves to rich combinatorics of compositions[1]: think of them as of context-dependent texture-atoms, repulsing and attracting over the variations of the local multi-dimensional context in the image. The composition is literally a convolutional transformation of local channels encoding related principal components of context.
Astronomical amounts of computations spent via training allow the network to form a lego-set of these texture-atoms in a general distribution of contexts.
At least this is my intuition for the nature of the convnets.
1. https://microscope.openai.com/models/contrastive_16x/image_b...
Music, I'm afraid, appears stuck in the doldrums of small one-offs doing stuff like MIDI. Nothing like the breadth & quality of Jukebox has come out since it, even though it's super-obvious that there is a big overhang there and applying diffusion & other new methods would give you something like much like DALL-E 2 / Imagen for general music.
They have an example “horse riding an astronaut” that no model produces a correct image for. It’d be interesting if models could explain themselves or print the caption they understand you as saying.
Nothing in a Transformer's perplexity in predicting the next token tells you that at some point it suddenly starts being able to write flawless literary style parodies, and this is why the computer art people become virtuosos of CLIP variants and are excited by new ones, because each one attacks concepts in slightly different ways and a 'small' benchmark increase may unlock some awesome new visual flourish that the model didn't get before.
TL;DR generative story site creators employ human moderation after horny people inevitably use site to make gross porn; horny people using site to make regular porn justifiably freaked out
Bring your popcorn
The TLDR is that people had been trying for ages to capture long-distance (in the input or output, not the black box) relationships in a way that was amenable to traditional neural-network training techniques, which is non-obvious how to do because your basic NN takes an input without a distance metric, or put more plainly: it can know all the words in a sentence but struggles with what order they are in without some help.
The state of the art for awhile was something called an LSTM, and those gadgets are still useful sometimes, but have mostly been obsoleted by this attention/transformer business.
That paper had a number of cool things in it but two stand out:
- by blinding an NN to some parts of the input (“masking”) you can incentivize/compel it to look at (“attend to”) others. That’s a gross oversimplification, but it gets the gist of it I think. People have come up with very clever ways to boost up this or that part of the input in a context-dependent way.
- by playing with some trigonometry you can get a unique shape that came be expressed as a sun on something else that gives the model its “bearings” so to speak as to “where” it is in the input. such a word is closer to the beginning of a paragraph sort of a thing. people have also gotten very clever about how to do this, but the idea is the same: how do I tell a neural network that there’s structure in what would otherwise be a pile of numbers.
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.
"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.
For image generation, it's obviously all fiction. Which is fine and mostly harmless if you you know what you're getting. It's going to leak out onto the Internet, though, and there will be photos that get passed around as real.
For text, it's all fiction too, but this isn't obvious to everyone because sometimes it's based on true facts. There's often not going to be an obvious place where the facts stop and the fiction starts.
The raw Internet is going to turn into a mountain of this stuff. Authenticating information is going to become a lot more important.
—
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 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.)
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
I did a bit of disclaimer on my original post but not enough to withstand detailed scrutiny. This is sort of the trouble with trying to talk about cutting-edge research in what amounts to a tweet: what’s the right amount of oversimplified, emphatic statement to add legitimate insight but not overstep into being just full of shit.
I obviously don’t know that publication schedules at heavy-duty learning shops are deliberate and factor-in other publications. The only one I know anything concretely about is FAIR and even that’s badly dated knowledge.
I was trying to squeeze into a few hundred characters my very strong belief that Brain and DM haven’t let themselves be scooped since ResNet, based on my even stronger belief that no one has the muscle to do it.
To the extent that my oversimplification detracted from the conversation I regret that.
- If you made that picture with actors or in MS Paint, politics boomers on Facebook wouldn’t care either way. They’d just start claiming it’s real if they like the message.
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.
GPT is, opaque. It’s somewhere between common knowledge and conspiracy theory that it gets a helping hand from Turks when it gets in over its head.
The exact details of why a BERT-style transformer, or any of the zillion other lookalikes, isn’t just over-fitting Wikipedia the more corpus and compute you feed to its insatiable maw has always seemed a little big on claims and light on reproducibility.
I don’t think there are many attention skeptics in language modeling, it’s a good idea that you can demo on a gaming PC. Transformers demonstrably work, and a better beam-search (or whatever) hits the armchair Turing test harder for a given compute budget.
But having seen some of this stuff play out at scale, and admittedly this is purely anecdotal, these things are basically asking the question: “if I overfit all human language on the Internet, is that a bad thing?”
It’s my personal suspicion that this is the dominant term, and it’s my personal belief that Google’s ability to do both corpus and model parallelism at Jeff Dean levels while simultaneously building out hardware to the exact precision required is unique by a long way.
But, to be more accurate than I was in my original comment, I don’t know most of that in the sense that would be required by peer-review, let alone a jury. It’s just an educated guess.
“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.”
I work for a big org myself, and I’ve wondered what it is exactly that makes people in big orgs so bad at saying things.
Someone tried to say there were ethics committees etc the other day...what a bad joke. Who checks the ethics committee is making ethical decisions?
I was told I "didn't know what" I was talking about, excuse from some over-important know-it-all who didn't know what ethics was, i.e. they don't know what they are talking about.
Luckily, training from scratch will hopefully be obsoleted by fine-tuning - if someone else releases a generally capable model then you can turn that into another one for lower cost.
The irony is that if you had a great discriminator to separate the wheat from the chaff, that it would probably make its way into the next model and would no longer be useful.
My only recommendation is that OpenAI et al should be tagging metadata for all generated images as synthetic. That would be a really interesting tag for media file formats (would be much better native than metadata though) and probably useful across a lot of domains.
I can see the future as being devoid of any humanity.
Print me a racoon in a leather jacket riding a skateboard.
All of these models seem to require a human to evaluate and edit the results. Even Co-Pilot. In theory this will reduce the number of human hours required to write text or create images. But I haven't seen anyone doing that successfully at scale or solving the associated problems yet.
I'm pessimistic about the current state of AI research. It seems like it's been more of the same for many years now.
https://www.wired.com/2016/04/can-draw-bikes-memory-definite...
... ... ...
Obviously "/s", obviously joking, but meant to highlight that there are a few parties that would all answer "me" and truly mean it, often not in a positive way.
I think you’re right, and it’s unlikely that we (society) will convince people to label their AI content as such so that scraping is still feasible.
It’s far more likely that companies will be formed to provide “pristine training sets of human-created content”, and quite likely they will be subscription based.
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.
Neil Stephenson covered this briefly in "Fall; or Dodge In Hell." So much 'net content was garbage, AI-generated, and/or spam that it could only be consumed via "editors" (either AI or AI+human, depending on your income level) that separated the interesting sliver of content from...everything else.
https://nonint.com/2022/05/04/friends-dont-let-friends-train...
I guess the concern would be: If one of these recipe websites _was_ generated by an AI, the ingredients _look_ correct to an AI but are otherwise wrong - then what do you do? Baking soda swapped with baking powder. Tablespoons instead of teaspoons. Add 2tbsp of flower to the caramel macchiato. Whoops! Meant sugar.
I feel like it would've been more than reasonable for them to have taken the position that the AI might output something distasteful, and implement a filter for people who were afraid of it.
well, we do have organic/farmed/handcrafted/etc. food. One can imagine information nutrition label - "contains 70% AI generated content, triggers 25% of the daily dopamine release target".
I think this will introduce unavoidable background noise that will be super hard to fully eliminate in future large scale data sets scraped from the web, there's always going to be more and more photorealistic pictures of "cats" "chairs" etc. in the data that are close to looking real but not quite, and we can never really go back to a world where there's only "real" pictures, or "authentic human art" on the internet.
AI was expected to grow like a child. Somehow blurting out things that would show some increasing understanding on a deep level but poor syntax.
In fact we get the exact opposite. AI is creating texts that are syntaxically correct and very decently articulated and pictures that are insanely good.
And these texts and images are created from a text prompt?! There is no way to interface with the model other than by freeform text. That is so weird to me.
Yet it doesn’t feel intelligent at all at first. You can’t ask it to draw “a chess game with a puzzle where white mates in 4 moves”.
Yet sometimes GPT makes very surprising inferences. And it starts to feel like there is something going on a deeper level.
DeepMind’s AlphaXxx models are more in line with how I expected things to go. Software that gets good at expert tasks that we as humans are too limited to handle.
Where it’s headed, we don’t know. But I bet it’s going to be difficult to tell the “intelligence” from the “varnish”
Lying about ethics or misattributing their actions to some misguided sense of "social" responsibility puts google in a far worse light in my eyes. I can't help but wonder how many skilled employees were driven off from accepting a position at google because of lies like these.
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.
Cheap books, cheap TV and cheap music will be generated.
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.
Imagine that instead of having cheap labor from Southeast Asia churn out these videos, that instead they are just spit out as fast as possible using AI.
I don't think they would host this for fun then.
Unless you assume there are bad actors who will crop out the tags. Not many people now have access to Dall-E2 or will have access to Imagen.
As someone working in Vision, I am also thinking about whether to include such images deliberately. Using image augmentation techniques is ubiquitous in the field. Thus we introduce many examples for training the model that are not in the distribution over input images. They improve model generality by huge margins. Whether generated images improve generality of future models is a thing to try.
Damn I just got an idea for a paper writing this comment.
Unrelated to the main topic, but this is exactly why I think cryptocurrencies will only be used for illegal activities, or things you may want to hide, and nothing else. Because that's where it has found its usecase in porn.
But I have not tried making generative models with out-of-distribution data before. Distributions other than main training data.
There are several indie attempts that I am aware of. Mentioning them to the reply of this comment. (In case the comment gets deleted)
The first layers should be general. But the later layers should not behave well to porn images. As they are more specialist layers learning distribution specific visual patters.
Transfer learning is posssible.
https://www.google.com/search?q=chess+puzzle+mate+in+4&tbm=i...
It would be surprising if AI couldn't do the same search and produce a realistic drawing out of any one of the result puzzles.
2. hentAI automates the process: https://github.com/natethegreate/hent-AI
3. [NSFW] Should look at this person on Twitter: https://twitter.com/nate_of_hent_ai
4. [NSFW] PornHub released vintage porn videos upscaled to 4k with AI a while back. The called it the "Remastured Project": https://www.pornhub.com/art/remastured
5. [NSFW] This project shows the limit of AI-wthout-big-tech-or-corporate-support projects. This project creates female genitalia that don't exist in the real world. Project is "This Vagina Does Not Exist": https://thisvaginadoesnotexist.com/about.html
That is what I feel personally.
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.
Of course, working in a golden lab at Google may twist your views on society.
I don't know why people do that but lots of randoms on the internet do that and they're not even bad actors per se. The removed signatures from art posted online became a kind of a meme itself. Especially when comic strips are reposted on Reddit. So yeah, we'll see lots of them.
If the AI models can't consume it, it can't be commoditised and, well, ruined.
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 would love it.
“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.”A bit far out there in terms of plot but the notion of authenticating based on a multitude of factors and fingerprints is not that strange. We've already started doing that. It's just that we currently still consume a lot of unsigned content from all sorts of unreliable/untrustworthy sources.
Fake news stops being a thing as soon as you stop doing that. Having people sign off on and vouch for content needs to start becoming a thing. I might see Joe Biden saying stuff in a video on Youtube. But how do I know if that's real or not?
With deep fakes already happening, that's no longer an academic question. The answer is that you can't know. Unless people sign the content. Like Joe Biden, any journalists involved, etc. You might still not know 100% it is real but you can know whether relevant people signed off on it or not and then simply ignore any unsigned content from non reputable sources. Reputations are something we can track using signatures, blockchains, and other solutions.
Interesting with Neal Stephenson that he presents a problem and a possible solution in that book.
If the only way to do AI is to encode racism etc, then we shouldn't be doing AI at all.
We may not like what it shows us, but blindfolding ourselves is not the solution to that problem.
If you want to be trivial about it, you can just have white back-rank mate with a rook, and black has 4 pieces to block with.
[1] https://github.com/CompVis/latent-diffusion.git [2] https://imgur.com/a/Sl8YVD5
A digital picture of an oil painting != an actual oil painting
Of course once someone trains an AI with a robotic arm to do the actual painting, then your worry holds firm.
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 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".
Naturally there's a python library [1] with some algorithms that are resistant to lossy compression, cropping, brightness changes, etc. Scaling seems to be a weakness though.
Sure, it's only 2%, but if it's on a problem where everyone else has been trying to make that improvement for a long time, and that improvement means big economic or social gains, then it's worth it.
For example, what kind of source images are used for the snake made of corn[0]? It's baffling to me how the corn is mapped to the snake body.
[0] https://gweb-research-imagen.appspot.com/main_gallery_images...
We can worry about two things at once. We can be especially worried that at some point (maybe decades away, potentially years away), we'll have nuclear weapons and rampant AGI.
[1] https://github.com/nerdyrodent/VQGAN-CLIP.git [2] https://github.com/CompVis/latent-diffusion.git [3] https://imgur.com/a/dCPt35K
You gave an example of a still image, but it's going to end up with an AI generating a full video according to a detailed text prompt. The porn industry is going to be utterly destroyed.
Less common opinion: this is also how you end up with models that understand the concept of themselves, which has high economic value.
Even less common opinion: that's really dangerous.
So text -> text representation -> most likely noised image space -> iteratively reduce noise N times -> upsample result
Something like that, please correct anything I'm missing.
Re: the snake corn question, it is mapping the "concept" of corn to the concept of a body as represented by intermediary learned vector representations.
Perhaps what "nurse" means isn't what "nurse" should mean, but what people mean when they say "nurse" is what "nurse" means.
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.
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.
As AI advances, a lot of people will look after experiencing life outside the digital world.
Even digital communication will not be trustworthy anymore with deepfaces and everything else, so people will want to get together more often.
Edit: for the lazy ones, yeah, digital will be a sad and heartless environment...
maybe I misunderstood, but I had it that people used generative AI models that would transform the media they produced. The generated content can be uniquely identified, but the creator (or creators) retains anonymity. Later these generative AI models morphed into a form of identity since they could be accurately and uniquely identified.
> 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.
I can see the argument here. It would be super fun to test this model's ability to generate arbitrary images, but "arbitrary" also contains space for a lot of distasteful stuff. Add in this point:
> 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. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. As such, there is a risk that Imagen has encoded harmful stereotypes and representations, which guides our decision to not release Imagen for public use without further safeguards in place.
That said, I hope they're serious about the "framework for responsible externalization" part, both because it would be really fun to play with this model and because it would be interesting to test it outside of their hand-picked examples.
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 usually consider myself fairly intelligent, but I know that when I read an AI research paper I'm going to feel dumb real quick. All I managed to extract from the paper was a) there isn't a clear explanation of how it's done that was written for lay people and b) they are concerned about the quality and biases in the training sets.
Having thought about the problem of "building" an artificial means to visualize from thought, I have a very high level (dumb) view of this. Some human minds are capable of generating synthetic images from certain terms. If I say "visualize a GREEN apple sitting on a picnic table with a checkerboard table cloth", many people will create an image that approximately matches the query. They probably also see a red and white checkerboard cloth because that's what most people have trained their models on in the past. By leaving that part out of the query we can "see" biases "in the wild".
Of course there are people that don't do generative in-mind imagery, but almost all of us do build some type of model in real time from our sensor inputs. That visual model is being continuously updated and is what is perceived by the mind "as being seen". Or, as the Gorillaz put it:
… For me I say God, y'all can see me now
'Cos you don't see with your eye
You perceive with your mind
That's the end of it…
Miika Aittala at Nvidia Research has done several presentations on taking a model (imagined as a wireframe) and then mapping a bitmapped image onto it with a convolutional neural network. They have shown generative abilities for making brick walls that looks real, for example, from images of a bunch of brick walls and running those on various wireframes.
Maybe Imagen is an example of the next step in this, by using diffusion models instead of the CNN for the generator and adding in semantic text mappings while varying the language models weights (i.e. allowing the language model to more broadly use related semantics when processing what is seen in a generated image). I'm probably wrong about half that.
Here's my cut on how I saw this working from a few years ago: https://storage.googleapis.com/mitta-public/generate.PNG
Regardless of how it works, it's AMAZING that we are here now. Very exciting!
Meanwhile, Nvidia sees no problem with yeeting stylegan and and models that allow real humans to be realistically turned into animated puppets in 3d space. The inevitable end result of these scientific achievements will be orders of magnitude worse than deepfakes.
Oh, or a panda wearing sunglasses, in the desert, digital art.
It’s an old fear for sure but it seems to be getting closer and closer every day, and yet most of the discussion around these things seems to be variations of “isn’t this cool?”
(Also, hello readers from the year 2032 when all of these predictions sound silly.)
Oh wait.
Google: "it's too dangerous to release to the public"
OpenAI: "we are committed to open source AGI but this model is too dangerous to release to the public"
That's what bothered me the most in Timnit's crusade. Throw the baby with the bath water!
So we can't have this model because of ... the mere possibility of stereotypes? With this logic, humans should all die, as we certainly encode some nasty stereotypes in our brains.
This level of dishonesty to not give back to the community is not unexpected at this point, but seeing apologists here is.
The harder part here will be getting access to the compute required, but again, the folks involved in this project have access to lots of resources (they've already trained models of this size). We'll likely see some trained checkpoints as soon as they're done converging.
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 loved that he extended the concept of identity as an individualized pattern of events and activities to the real world: the innovation of face masks with seemingly random but unique patterns to foil facial recognition systems but still create a unique identity.
Like you say, the story itself had horrible flaws (I'm still not sure if I liked it in its totality, and I'm a Stephenson fan since reading Snow Crash on release in '92), but still had fascinating and thought provoking content.
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.
Considering how many of the readers of said blog will be scrapers and bots, who will use the results to generate more spammy "content", I think you are right.
[0] https://creativecloud.adobe.com/discover/article/how-to-use-...
Which means that it is always you that decides is you'll be offended or not.
Not to mention the weirdness that random strangers on the internet feel the need to protect me, another random stranger on the internet, from being offended. Not to mention that you don't need to be a genius to find pornography, racism and pretty much anything on the internet...
I'm really quite worried by the direction it's all going at. More and more the internet is being censored and filtered. Where are the times of IRC where a single refresh erased everything that was said~
I have a friend who used to have an abuser who talked like that. Every time she said or did something that hurt him, it was his fault for feeling that way, and a real man wouldn't have any problem with it.
I'm all for mindfulness and metacognition as valuable skills. They helped me realize that a bad grade every now and then didn't mean I was lazy, stupid, and didn't belong in college.
But this argument that people should indiscriminately suppress emotional pain is dangerous. It entails that people ought to tolerate abuse and misuse of themselves and of other people. And that's wrong.
I can see a past where this already happened, to paraphrase Douglas Adams ;)
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 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)!
It's been done, starting from plotter based solutions years ago, through the work of folks like Thomas Lindemeier:
https://scholar.google.com/citations?user=5PpKJ7QAAAAJ&hl=en...
Up to and including actual painting robot arms that dip brushes in paint and apply strokes to canvas today:
https://www.theguardian.com/technology/2022/apr/04/mind-blow...
The painting technique isn't all that great yet for any of these artbots working in a physical medium, but that's largely a general lack of dexterity in manual tool use rather than an art specific challenge. I suspect that RL environments that physically model the application of paint with a brush would help advance the SOTA. It might be cheaper to model other mediums like pencil, charcoal, or even airbrushing first, before tackling more complex and dimensional mediums like oil paint or watercolor.
Also, given the processing power and data requirements to create one, there are only a few candidates out there who can get there firstish.
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).
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.
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.
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.
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?
