What do you mean? Appearance includes hair, piercings, tattoos.
Any names (projects/people/protocols) come to mind?  https://wiki.c2.com/?TheKenThompsonHack. Do I think that IQ as a measure of IQ is flawed? Psychology researchers are consistently finding new and interesting ways that IQ tests are socially/culturally/environmentally influenced and that exams may not be fair. But we know that both conviction rates, and arrest rates, are themselves indicative of bias - e.g. >>Don't worry, Twitter isn't real life. According to internet, Yann LeCun's height is 1.75m. There are a whole host of ethical discussions that need to happen to even begin to flesh out what “balanced” might even mean for, say, facial recognition software intended for use in law-enforcement, but the same biases that lead people to skip right past those discussions and begin training are often the very ones that result in the biased data to begin with. Directly suppressing flame is different than discrediting a speaker to silence them.
What exactly is balanced training data, who decides it is balanced? Similar to how the US intelligence is (I have no doubts about it) profiling muslims and middle eastern immigrants- not because it has anything against those groups per se, but because it has reasons to believe terrorists might hide in their ranks. To argue they are their models are always wrong on their terms is to argue that there is a Ken Thompson-like hack in their mathematics. Those are trained on datasets of convictions. Because it's suboptimal, it can't be called an unique, objective reflection of the world. And sorry, but I think that what happened is extremely plain, and it requires a lot of ideological contorsions to deny the anger, the harassment and the revenge, all based on vague purported "feelings", "asked for drastic action but not harassed", etc. > No, this is only technically correct but actually wrong. Ideally, in Timnit's assessment, the researchers themselves would put effort into identifying possible calamities and put as much effort into mitigating them as they do into publicizing the work itself. In practice, multiple advertisers have been in trouble for breaking this class of rule in the past 2 years. One of my favorite movies from childhood, "Real Genius," deals with this sort of issue as the main plot line. More likely, the reason that bias isn't routinely fixed is that it isn't easy, and these kinds of biases do make it into production systems. Do you believe that industry uses pre-made datasets that researchers promote in their work? It’s easy to fix bias in the data for a model developer, but one can’t control other people’s dietary choices. I was only pointing out that even bias is a matter of bias: if white faces had been depixelated to black faces instead of the other way around, the authors could have still been accused of a racist bias (because of the hypothetical scenario I've described above).
The issue at hand was her lack of good-faith engagement on Twitter and the subsequent pile-on from the mob. And what's more - if we were to transfer that model, which is trained on data from my small homogeneous place, it would probably generalize very poorly in areas with more diversity. So, you deny that the people who asked for him to be fired were angry?
If adding race-- or inferring race-- makes the model substantially better in predicting outcomes, is it right to do so? She says "diverse datasets are not enough". It doesn't really matter to possible victims of, say, the use of AI in law enforcement. > By the way, ML is not nearly as new as you seem to think.
Who's going to risk their ass just to be caught with some unknown bias? He co-developed the Lush programming language with Léon Bottou. And research progress is one of those things that could help us ultimately address some of these issues, because as the original argument makes clear: improving the quality of datasets is not enough. A variant of this is that both ppl are answering the same question but on different time scales. The goal is to increase revenue by giving as much credit as possible with the least risk possible, so it wouldn’t even make sense.
This slows down the overall computation. Ways to attack ML systems in order bias their behavior? He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Facebook.He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. Well it depends. This chip doesn't obviously improve the state of the art on an arbitrary (but standard) benchmark, so LeCun dismisses it.
What’s the use case for generating a high-res photo of a face from a low-res photo of a face? Imo these are researchers. Or perhaps more specifically, can you present an example of cancelling that was done "specifically [...] for reasons of anger, revenge or control", and not due to other reasons, and that includes embarrassment and humiliation, economic control like ruining the credit score, harassing family and friends or employers, or scare tactics to instill fear? ML tools are developed, and filter down into the general developer population where they are used without fully comprehending the biases they contain or can contain if used incorrectly. And if you're not allowed to measure race, it'd be completely rational to find other variables that don't have an obvious causal relationship to credit risk, but predict race and thus have some information about credit risk. A better response is more along the lines of "Not in MY Army" which makes it everyone's responsibility at every level. Yann LeCun est professeur à l’Université de New York et directeur de Facebook AI Research (FAIR), le nouveau centre de recherche européen de Facebook basé à Paris et dédié à l’intelligence artificielle.
"Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. We're not talking about 100% segregated populations like during slavery or patriarchy, the means between the groups are very close and there's a huge overlap between the distributions. I commented on his doc before it was leaked to the press. But like so many conversations online, we should quit talking past each other and likely need to quit trying to paint people as if their concerns don’t have very real ethical implications which will, if left unaddressed, manifest in all kinds of negative ways throughout society. I read it and ignored it as a completely arbitrary constraint that you added. Yup, so, academics should never research this stuff or publish? Part of the problem is that "it's just the dataset" is being used as an excuse (witness the "just"). Il devient chercheur aux Bell Laboratories en 1988 et est nommé directeur de département aux laboratoires AT&T en 1996. If it's so easy to fix the bias, then why isn't it fixed, always? The main criticism should be that these neurons are not like real neurons, because integrate-and-fire is an oversimplification of neurons. In most US jurisdictions, Harassment requires the (credible) threat of violence. My entire point, this entire time, is that individuals should be free to express the opinions they want, and companies should be able to act on those opinions by choosing to associate with who and how they want based on the company's values. You're presupposing the existence of some unbiased objective function which we don't have, and that's at the core of the issue. They may be less inscrutable than deep learning, but can easily still draw a box around most of the black people in a clever, non-obvious way. And that's the right way to look at it.
and dark-skinned women. I wonder how often apologies like this are genuine, versus simply bending the knee to the mob out of fear for one's livelihood.
There isn't even necessarily a disagreement there. His name was originally spelled Le Cun from the old Breton form Le Cunff meaning literately "nice guy" and was from the region of Guingamp in northern Brittany. > China might be using AI not to "round up" Uighurs, but as an intelligence measure to prevent more terror attacks. I agree with your gist but let's not pretend that Yann LeCun has been "taken down." Trying to make a list of possible domains where social bias is a factor: The classic (multi-decade problem repeated again, and again, and again...) is face detection. Which is to say that if an AI crunches the numbers in a objective fashion with the aim to make decisions based on various correlations, that can fundamentally problematic regardless of the bias of the original data or people. It's far easier to invent gender frameworks and equity rhetoric than to actually solve problems like predominantly one parent households or the seething xenophobia and sexism of the trans community (Latin X imperialism, treatment of black comedians, bigotry towards safe spaces for women, etc). Of course she's right about all the things that everyone agrees on. POC in particular should not be quiet when it comes to some of the issues around the questionable use of ML in relation to race and issues that are ultimately surrounding race. There’s not one neat trick to make it go away. There might be a logical proof of this somewhere. If I'm using StyleGAN to synthesize facial textures for a video game, biased datasets and models are desirable, not something to be eliminated.
Is amount of money in someone's bank account a subjective trait? Which I'm sure is the method in the twitter discussion, but that is a different kind of problem where interpretability doesn't have a clear use. Yes - the ML engineers and scientists are responsible for building good generalized models. One possible approach is to make a model that besides its intended prediction also predicts race, and penalise its ability to predict race with probability larger than random. And we absolutely can incrementally reduce bias in systems that involve humans. China is using face detection for surveilling ethnic minorities. For any commonly used definition of race, there is a lot of intra-group variety.
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