>Figure 2: Non-cherry picked Imagen samples
Hooray! Non-cherry-picked samples should be the norm.
Imagen, a text-to-image diffusion model
111–120 of 661 posts
Re: Imagen, a text-to-image diffusion model
#112Earlier quoted context omitted.
It seems you've got it backwards: "tendency for images portraying different professions to align with Western gender stereotypes" means that they are calling out their own work precisely because it is skewed in the direction of Western American biases.
Yes, the idea is that just because it doesn't align to Western ideals of what seems unbiased doesn't mean that the same is necessarily true for other cultures, and by failing to release the model because it doesn't conform to Western, left wing cultural expectations, the authors are ignoring the diversity of cultures that exist globally.
No, it's coming from a perspective of moral realism. It's an objective moral truth that racial and ethnic biases are bad. Yet most cultures around the world are racist to at least some degree, and to they extent that the cultures do, they are bad.
The argument you're making, paraphrased, is that the idea that biases are bad is itself situated in particular cultural norms. While that is true to some degree, from a moral realist perspective we can still objectively judge those cultural norms to be better or worse than alternatives.
Re: Imagen, a text-to-image diffusion model
#113Earlier quoted context omitted.
Translation: AI has the potential to transform society. When we release this model to the public it will be used in ways we haven’t anticipated. We know the model has bias and we need more time to consider releasing this to the public out of concerns that this transformative technology further perpetuate mistakes that we’ve made in our recent past.
> it will be used in ways we haven’t anticipated Oh yeah, as a woman who grew up in a Third World country, how an AI model generates images would have deeply affected my daily struggles! /s It's kinda insulting that they think that this would be insulting. Like "Oh no I asked the model to draw a doctor and it drew a male doctor, I guess there's no point in me pursuing medical studies" ...
I don't think the concern over offense is actually about you. There's a metagame here which is that if it could potentially offend you (third-world-originated-woman), then there's a brand-image liability for the company. I don't think they care about you, I think they care about not being hit on as "the company that algorithmically identifies black people as gorillas".
Re: Imagen, a text-to-image diffusion model
#114Earlier quoted context omitted.
Rolling this into Google Docs seems like a nobrainer.
Or rolling this into Google Image Search to create images that match users' search queries on the fly. Don't like any of the results from the real web? Well how about these we created just for you.
[deleted]
Re: Imagen, a text-to-image diffusion model
#115>While we leave an in-depth empirical analysis of social and cultural biases to future work, our small scale internal assessments reveal several limitations that guide our decision not to release our model at this time. Some of the reasoning: >Preliminary assessment also suggests Imagen encodes several social biases and stereotypes, including an overall bias towards generating images of people with lighter skin tones…
This seems bullshit to me, considering Google translate and google images encode the same biases and stereotypes, and are widely available.
Aren't those old systems?
Re: Imagen, a text-to-image diffusion model
#116Earlier quoted context omitted.
Translation: AI has the potential to transform society. When we release this model to the public it will be used in ways we haven’t anticipated. We know the model has bias and we need more time to consider releasing this to the public out of concerns that this transformative technology further perpetuate mistakes that we’ve made in our recent past.
> it will be used in ways we haven’t anticipated 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" ...
Postmodernism is what postmodernism does.
Re: Imagen, a text-to-image diffusion model
#117Why is this seemingly official Google blog post on this random non-Google domain?
You mean one of Google's domains? # 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…
While appspot.com is a Google domain, anyone can register domains under it. It would be similarly surprising to see an official GitHub blog post under someproject.github.io
Re: Imagen, a text-to-image diffusion model
#118>While we leave an in-depth empirical analysis of social and cultural biases to future work, our small scale internal assessments reveal several limitations that guide our decision not to release our model at this time. Some of the reasoning: >Preliminary assessment also suggests Imagen encodes several social biases and stereotypes, including an overall bias towards generating images of people with lighter skin tones…
Indeed. If a project has shortcomings, why not just acknowledge the shortcomings and plan to improve on them in a future release? Is it anticipated that "engineer" being rendered as a man by the model is going to be an actively dangerous thing to have out in the world?
Re: Imagen, a text-to-image diffusion model
#119Reading a relatively-recent Machine Learning paper from some elite source, and after multiple repititions of bragging and puffery, in the middle of the paper, the charts show that they had beaten the score of a high-ranking algorithm in their specific domain, moving the best consistant result from 86% accuracy to 88% accuracy, somewhere around there. My response was: they got a lot of attention within their world by beating the previous score, no matter how small the improvement was.. it was a "winner take all" competition against other teams close to them; the accuracy of less than 90% is really of questionable value in a lot of real world problems; it was an enormous amount of math and effort for this team to make that small improvement.
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.
Re: Imagen, a text-to-image diffusion model
#120Why is this seemingly official Google blog post on this random non-Google domain?
appspot.com is the domain that hosts all App Engine apps (at least those that don't use a custom domain). It's kind of like Heroku and has been around for at least a decade. https://cloud.google.com/appengine
Spring 2008: 14 years!