This is equivalent to saying that mobs are not inherently bad - since they often lynch bad people. I think that the behavioral norms of social media are penetrating deeper and deeper into culture. @boredyannlecun -42.0173? data with lower power budgets than computers seem to require. Also, in the case of statistical models, the crafting of the trained features themselves. A very senior person at Google got involved in the discussion and backed Timnit up. 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. "Cyberstalking is the act of using the Internet to systematically and repeatedly harass, threaten or intimidate someone. ML models don't exist in a vacuum, but they do exist in an empirical reality. However, if a convolutional net is confronted with something it wasn't trained for, it will simply have random reactions (it's a robot, it'll send random instructions to the higher levels, meaning if it has a gun, it will extremely likely fire the gun, probably aimed at the first thing it recognizes), a spiking model will try something (which, of course, may be "kill all humans", but it might also decide to wait and see if there are hostile moves, or ...). My understanding is that it was debated, and that eventually executives decided that ethically firing him was the right decision. Your zip code affects insurance rates too, and that’s based on claims. ), The fact that Truenorth can learn approximations is not really surprising, we know that thresholded units can approximate well[1]. I don't know. E.g. Isn’t “Africa” just as bad, if not worse? This slows down the overall computation. It is our job to reduce bias and characterize bias, but it's impossible to have a data set that has zero bias. For a scientist doing a ML system to reconstruct pixelated faces, trained with white faces, why is he/she responsible for "insert larger problem" outside of her/his field? Like, can you even describe a situation where this will be used ethically at all? [1] https://www.pewsocialtrends.org/2018/07/12/income-inequality... but you'll have to manually calculate the percentages. So asking everyone to say "of course I care, of course" before everything they say is laborious. An honest and honorable computer pro who co-developed the Lush programming language, LeCun is equally good-looking and admirable. I was using misuse in the "hold it wrong" sense, but I agree that there's ambiguity there. If you don’t understand that engineering is the fundamental bedrock of IT and the lack of security application during the engineering SDLC is a consistent failure then I don’t know what to tell you except maybe to gain more experience in software engineering and read more about data breaches. You're presupposing the existence of some unbiased objective function which we don't have, and that's at the core of the issue. Race? The message should be: "I might no longer tool in TensorFlow, but I care, and so should you." You subdivide it into 2x2 images (of which you'll have 9), and you send each of those 2x2 images to each neuron. But afaik it hasn't been attempted yet. More 'neurons' this time? Nobody said that imitating our own implementation details will lead to better results than anything else we might want to try. Because I took the time to watch it, because that's the reasonable thing to do when someone suggests that you aren't fully informed on a subject and suggests a resource to improve your understanding. It's really, really hard to erase biases that are deeply systemic. Some of these correlate with race. — Yann LeCun (@ylecun) June 22, 2020. In fact, this is exactly what the authors did. Revenge? LeCun is arguably much more the second kind of researcher than the first. The only exception is the Brazilians, who were brought in as low class workers, have a MUCH higher crime rate, have trouble renting outside of low-income areas, can't get high class jobs, and are generally treated like criminals on-sight. I guess the question is why would that be germane to issuing credit? 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. If you think Yann LeCun's age is not correct, please leave a comment about Yann LeCun's real age and Yann LeCun's actual birthday below. Yann LeCun: Deep Learning, Convolutional Neural Networks, and Self-Supervised Learning | AI Podcast, [Drama] Yann LeCun against Twitter on Dataset Bias, Facebook VP & Chief AI Scientist LeCun on Advancing AI, ICLR 2020: Yann LeCun and Energy-Based Models, Yann LeCun - Self Supervised Learning | ICLR 2020, Yann LeCun: Can Neural Networks Reason? [1] https://twitter.com/ylecun/status/1275162528511860737. Credit Card applications do not ask for race. Therefore, using race (or inferring race) improves prediction quality, but is ethically dubious. An ASIC implementing a convolutional neural net could also be low-power, scalable, and fault-tolerant, while taking advantage of the best currently known learning algorithms and ultimately performing a lot better on real tasks. This is purely a power game in which some individuals have managed to blackmail everyone else into recognising their role or being cancelled. - Why is email security such a dumpster fire? Certainly there’s many cases of it being overused, but in a way it’s sort of denying that there are actual direct people and forces that are fighting tooth and nail against progress. No. >My main criticism is that TrueNorth implements networks of integrate-and-fire spiking neurons...Spiking neurons have binary outputs (like neurons in the brain). Where it does succeed, however, is in low-power computation in an architecture that is scalable and fault tolerant. This is fundamentally a forest/trees type of situation. Since there is no point of comparison, this is not a valid statistical conclusion and is just a racist framing. Do you consider yourself a supporter of "free speech"? No senseless purity testing of engineer versus scientist. Therefore the criticism he receives can be related to how that idea applies to ML overall and not exclusively this problem. Legally, it's quite an important constraint. Approach #1 is to just go to maximize expected value, using all variables, including race. That is, each neuron is one bit, so you have 8 each computing one of the 8 bits he says are necessary? Anger? By prefacing your statements with an explicit allegiance to a team, you're in fact endorsing whoever is presenting him/herself as the team leader. Sending 1 bit of information to 8 processors is not the same as sending 8 bits to one processor. It's an engineering challenge to figure out how to do this, but that isn't an argument that ML is fundamentally bad for these purposes. Huh. Or maybe both of them are unacceptable? I can't recall anyone threatening Damore inside Google. The current nightmare that is cyber security is caused by developers who do not understand that with great power comes great responsibility. There have been a number of fairness and bias workshops and forums in recent ML conferences. I can understand that engineers are not trained to consider those aspects but leaving them entirely to their non-creator or external bureaucrats might also not be the best strategy since they hardly understand the systems as well as engineers do. It's ignoring the broader context of the issue, in favour of a simple answer of "well if people ate less McDonald's, there wouldn't be an obesity crisis.". Very few of them teach them the ethics they'll need to do that in a thoughtful way. And what does this get you? Learning may not happen on-chip, but the network is still learned, and the performance of the chip is dependent on the learning. Gebru et al seem to be talking about a much more abstract, societal trend-oriented, "let's think about if we should not just if we can" type of question, though it's still pretty fuzzy to me. 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. (And, as a minor point, his idea that Senegal is representative of "Africa" as a whole is also... let's say "unfortunate"). Spiking models will adapt (assuming you let them, and I imagine DARPA will let them). This lack of actionable improvements or concrete guidelines reminds me a bit like needing "political officers" in Marxist military units who ensure "compliance". On the flip side - if we were to make a dataset out of criminals in some large and diverse area, we'd need to get our sampling methodology right. That alternative I'd like to see is a general purpose highly parallel chip. Often times there's no intellectual debate happening online, just social posturing on opposed sides. You do. > I'd argue that curve fitting isn't modeling. In September 2018, he received the Harold Pender Award given by the University of Pennsylvania. While I have karma to burn and will happily keep participating, I'm concerned that this trend will have a chilling effect on people who do not. And that's the right way to look at it. Publicly state that the ML scene is optimizing in a myopic way, and invest in doing so less myopically. What it's going to do is bring them to the surface, so that they are quantifiable and we can actually do something about them. Here's what you're doing with spiking nets (more or less). I think it's bad science to just "build a machine with a shit ton of IF neurons and see if it does anything". An ML model isn't suddenly going to solve the cultural issues we have with measuring IQ. But if a bunch of other people agree with me, suddenly I've committed a crime. I'll use the appropriately biased model depending on whether I want to generate white faces, Chinese faces, or black faces. Anyway, the epistemological standards of those questions are basically incompatible (incommensurable? May.29 -- Facebook Vice President and Chief AI Scientist, This week Connor Shorten, Yannic Kilcher and Tim Scarfe reacted to. Are you claiming that there is a difference in the class of problems that can be computer between an 8 bit processor and a network of 1 bit processors? The chip on the other hand really seems to use boolean encoding. Psychological safety is incredibly valuable and is something that a lot of companies don't do enough to foster. Sure, but there are a lot of things that we don't do in the best possible way because it's too expensive. > This is especially true when the difference between either role is highly arbitrary and varies by organization or field. Blaming greed of engineers for security problems? According to the latest Twitter stat on 2020-10-31, Yann LeCun has a total favourites count of 4803 on the Twitter account and Yann LeCun has 229 Thousand followers on the same Twitter account. My main criticism is that TrueNorth implements networks of integrate-and-fire spiking neurons. This general questioning about the implications of bias in the datasets is happening in many fields. For credit reporting this sort of thing creates bad enough externalizes that it needs to be outlawed. Nobody is searching for black zipcodes and using that as an input to deny credit. Yann LeCun Personal and Family Life: Who Is His Wife. However, I believe that exhaustive data sets have a great potential of being less biased that human beings. This isn't someone making some kind of statement about ML research. Seems like a stretch considering there hasn't been a demonstrated benefit to this sort of antagonism, it all just exists to justify itself rather than to facilitate a dialogue. Naive ML won't fix bias in human systems, but that doesn't mean we can't use ML to fix it, if we do so thoughtfully. By stating a few realities, Yann LeCun wrote a brief essay on its capabilities and the hype that has been created around it. It sounds like you're looking at things purely from the point of view of getting the correct average for a group. You keep pointing to a dichotomy between perfect and flawed, while I was talking about relative improvements. Maybe it just so happens that the law enforcement tends to pour all their resources in policing poor areas, where some certain ethnicity is very over represented?
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