skysight-inc/HackerNews huggingface dataset:
I want to point out that while I disagree with you, you've already engaged in far more actual debate than Gebru did on Twitter.

Like I said, LeCun (and myself) largely agree with most of Gebru's points. But when LeCun went to great lengths to defend/explain his position, Gebru then literally responded with "I don't have time for this". Even before that, she didn't even bother to link the presentation she referred to (which again, didn't even directly address any of the points that Yann was making!).

It's this complete lack of good-faith engagement that prompted LeCun to quit Twitter, not the underlying discussion on ethics itself. LeCun clearly feels that Twitter is not the place for reasonable discussion, and after this episode, I'm inclined to agree.

> If you think there needs to be an argument made to justify that, that's fine, but I don't think it's valuable to assume that people come into a discussion without a basic understanding of the software engineering ecosystem.

I'm not saying that people don't use off-the-shelf models. I'm saying that I don't know if forcing research datasets to be "unbiased" will make any difference to real-world injustice. I don't even know if any of the examples of bias she gave in her tutorial (HireQ/Microsoft/etc) could be ascribed to the use of pretrained models. She could be right. I don't know. You probably don't either.

Going beyond the empirical question, she'd also need to explicitly argue why responsibility should lie at the feet of the researcher, not the engineer. Gebru did neither, which is why I say it's a huge leap of logic.

That, fundamentally, is LeCun's position. He completely agrees that warning labels should be put on these kind of models that say "Model has been trained on biased data and unsuitable for use in real-world applications where racial fairness is expected". In fact, this is exactly what the authors did.

> And the point being made isn't "biased input data isn't responsible for a biased model", it's "you need to look one step further and ask why is the input data biased and how that impacts the world."

And I'd argue you need to account for the context in which your model is deployed. If I'm using StyleGAN to synthesize facial textures for a video game, biased datasets and models are desirable, not something to be eliminated. I'll use the appropriately biased model depending on whether I want to generate white faces, Chinese faces, or black faces.

It's the use case that dictates the risk, hence why LeCun (and I) believe it's the engineer's responsibility, not the researcher.
Maximum penalty is actually death penalty, not just life sentences:

[Article 57] […] execution of penalties and other litigation procedures shall be governed by the relevant laws of the Criminal Procedure Law of the People’s Republic of China

https://transitjam.com/2020/06/30/national-security-law-engl... (unofficial English translation, since no official English translation was provided)
> Collecting evidence is pretty much job #1 for the police.

Excellent point. If denial / lack of evidence is the SoP for any Police Department, what are they even doing? Defense Lawyers would barely have to work for their pay.
Hey, I decided to minimize YouTube and pay for Nebula even before the price hike :-D
But can you control other model developers dataset choices?

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. I'm pretty partial to the idea that ML is a systemic bias footgun. VERY hard to not use incorrectly, and with potentially HUGE social repercussions. E.g. the youtube funnel towards extremism that was well documented a few years ago - visitors start on some innocuous video, and get recommended more and more extreme things, funnelling traffic towards the alt-right in an unhealthy way.
I'm aware of that article. My comment doesn't really pertain to trademarks, but I think my comment pertains equally well to copyright and patent laws. While it may be true that US law treats them differently, they are fundamentally the same thing—a mechanism to reward work that results in ideas, not tangible objects—and it often makes sense to discuss them at the same time, especially when we're broadly discussing how they should be handled as opposed to to the specifics of how US law handles them now.
IIT Madras Launches World’s First-Ever Online B.sc. Degree in Data Science
> Discriminating on the basis of sports fandom, haircut or taste in music is perfectly legal.

IIUC, correlation of any of those with any of the protected classes will land you in trouble though (and I would guess at least the second two from your list are definitely so correlated).

As a result, again IIUC, all the large companies(+) follow, in practice, a European-style approach: there has to be a well-documented, based on some clearly-non-discriminatory criteria (++), reason for firing you (hence performance-improvement-plans, etc, etc).

[+] large companies == worth suing - again IIUC small companies can basically do what you say

[++] I think that still ends up being easier than firing people in many European countries, though
> Yes absolutely this issue needs diverse perspectives and the priorities they bring, but it will fail without objective standards to ensure that technology in the field reliably meets our expectations.

It sounds like we mostly agree. It certainly doesn't hurt to have someone who's been the subject of unfair profiling before to get his spidey sense going at the implications of the system, but it isn't a cure-all.
The cable operators are the only "broadband" ISP for most people in the US. Cable operators want income as both ISPs and as channel providers. The TV channels want income from cable operators and advertisers. Losing one means fewer avenues of revenue.

The reality is cable operators are no longer need to be anything but a dumb pipe. And TV channels are no longer as valuable as they once were due to multitude of other sources of information.
people get surprised all the time by events that have a low percentage chance of happening but are not unique, I mean people get surprised when their spouse cheats on them and that isn't particularly unlikely.

Anyway, I would agree a black swan needs a very low chance of happening, but not that it has to be the first time it has ever happened.

Certainly when the Europeans saw the black swan in Australia it was the first time they had seen one, but probably not the first time they had seen a variation of a species in a non-European location that looked slightly different than expected.

on edit: some clarifications.
Moreso, it's not yet illegal because the problem is an imported one entirely.
Emacs has an edit-over-ssh mode that's pretty useful.
Instead of trying to understand the VC lens, consider the customer lens and solve a problem they have for $.

Any great solution that can be successful under 100M/y probably wouldn't be of interest to VCs, so there's a lot of opportunity out there.
I really don't see a reason to not use both. Even right now, a maintainer of some subsystem could conceivably create a repo on Gitlab* and accept PRs there, which they could then (rebase-?)merge in and sync with git.kernel.org with Linus never having to know about it.

The problem is cultural, as well as organizational. Firstly, I doubt any of the current maintainers would be willing to do that, especially without Linus's blessing and secondly, without a fully managed Gitlab* instance that takes care of sync and whatever else automatically (provided by the Foundation), this would be an unreasonable burden on the maintainers.
I appreciate the offer, thanks for the great response :)

I actually ended up choosing Seattle, it seems like more my vibes (being less laid back for one), I already got a college buddy there, the housing prices are similar, and it's more walkable (don't need a car!).

I'll be reevaluating after a year and maybe hop down to Denver (Seems like it would be great place to combat the 'Seattle freeze' if that's really a thing)
> that the idea that it actually caused net harm may be too much for people to bear

precisely what I was trying to say

> The most prescient, and overlooked researcher on this currently is Sunetra Gupta of Oxford.

loved that interview!

I've been following Levitt from early on, which led me down the same general thread of somewhat disparate online media as you have apparently. I'm aware of some of the research that comes up but never dug too deeply. That said - I'm not sure there's anyone I know that's aware of any of it.

It's a weird headspace; somewhat lonely and maddening to see society act in really draconian ways with such little reflection. It's like the narrative was decided very early on and they're just going with it.

I think I'm more unsettled by how hard it is to mention stuff like this - very strong reactions to even suggesting there's a possibility about lockdown being ineffective, etc. Clearly the cognitive dissonance re: sacrifice and cost is a part, but there's a certain moralism/virtue/hubris (I can't shake the feeling of similarity in the protesting) that's behind it too. It might be a stretch to tie it all together, but we're, for one reason or another, fundamentally lacking open dialogue with others grounded in trust, empathy and reason.
>in the same way the actual world is

I feel like you've missed at least half of what I was trying to communicate, because you're still presenting arbitrary discrimination as reality-based.

Discrimination as you describe is not only unfair, but it costs the lender money, because they are not identifying the true risks of individuals within the group. Because it's suboptimal, it can't be called an unique, objective reflection of the world.

All correlations are inherently imperfect and therefore unfair; if they weren't you'd have an definite causal relationship.

When you use one correlation, or set of correlations, maybe you have completely missed something that would be much better, not just from a moral perspective, but a business perspective.

If you discriminate based on gender, and haven't considered age (hypothetically it being legal), maybe the latter would be a much better proxy for the real causal factors. Your gender model can be correct in itself, and yet from a completely amoral perspective be terrible and uncompetitive. The model is not "the way the world is" just because it's technically correct in isolation.
I would argue that typical desktop linux distros are as difficult if not more to secure than desktop windows.

It's hard to keep track of the current state of things, but issues that come to mind are, all of the design issues with X, such as poor isolation of applications, X server typically running as root.
Desktop apps are typically not deployed with SElinux.
Compromised apps can generally access all of the users' data.

Something like ChromeOS on the other hand, is pretty secure, but that is a whole different beast.
I have experienced caste-based "weirdness" of a slightly different flavor - I look like a brahmin (the highest caste), but am not one. Every once in a while I'll meet some indian-origin guy who's brahmin who will come ask me if I am okay with eating that tortilla since it contains lard. Once I let it deliberately slip that I'm not in-fact brahmin, they will be like, "oh" and change topics. The oh always sounds to me like, "oh, I thought you belonged to the ubermensh Aryan class but you don't so I guess I don't care about your depraved ways!" - sounds like an exaggeration but I have worked too long with too many of these people to be confident that this is what they think. The most rewarding thing for me is when I tell that all my exes were brahmin (it's a trope that they're pissed all their women are being stolen by lower caste men).

Also all of this in the US; it's almost like many indian origin American (even second gen) Brahmins are far more racist than the general Indian population of the same group. Definitely a lesson in anthropology for sure.
That makes a lot of sense for termination. How about hiring? Do you know if the European system specifies what attributes are and aren’t allowed to be considered when it comes to hiring employees?
Why just those two countries then? Why don’t I see a thousand get requests for /wp-login from countries all around the world? Wouldn’t that be significantly more effective since you can’t simply block a range of addresses to mitigate it?
Oh, thank you!
I am now past my middle age, but I so remember as if it were yesterday, the day when I was not more than 7 years old. A completely isolated small puddle of water, all that remained after few days rain and then Sunshine. I stepped on it, creating small waves and a couple of fish emerged writhing on to the dry surface. No one could answer how they came about. All my 7 year brain could think of then was some bird must have dropped them by accident. And now I have the answer!
Name of the hash is a topic. Keys are nanosecond timestamps, values are the packets. You put in the data, you send a pub notify to the listener, listener goes and drains the queue. It acts as a direct exchange queue, recovers fine from a restart and it works wonderfully. For fanout, you can simply use pub sub. For topical, you can keep a list of direct exchange queue hashes. I haven't needed it.
It works, but you have to supply your own sandbox profile (list of allowed/denied operations) and it won’t create a virtual home directory for you like App Sandbox does. I’m not sure whether there’s an easy way to forcibly enable App Sandbox, but one possibility is to compile your own sandboxed app that simply execs the untrusted one. (Sandboxes are inherited by child processes, as they must be for security.)
If you use the right plugins + lsp, there's nothing you can do in vscode that you can't do in vim. Just requires a lot more upfront work, but you can check the config into a git repo.
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"The whole idea around copyright is to encourage the creation of intellectual property. If there is a fundamentally better way to do it that idea should flourish but I have yet to hear a realistic alternative."

How about a basic income so the artists stop starving?
Things this catastrophic are better left to governments for bailouts (which really is just insurance at a societal level) than for companies to save for. Having to save for events this unlikely is incredibly inefficient.
Not to mention the fact that pricing pandemic insurance is going to be very hard. Insurance companies generally don't like to take risks... they like things that are highly predictable and can be modeled. The percentage of people aged 40 who will die in a given year is predictable. So are the chances of car accidents by 17 year old female drivers. When the next pandemic will come is anyone's guess though. And lacking a decent way to model a risk like this, the insurance company will inevitably need to charge a high premium to make this a worthwhile product. Meanwhile, consumers will balk at the high premium and not buy the coverage.
My point is that I am fine with it, I don't want to code on the go on my phone, beyond some toy programming while on the bus, train or waiting for check in.

Coding on Nokia Communicator (even the latest versions) or Nokia's Linux based phones/tablets was also not the best ergonomics.

Also I don't care about having a Linux CLI or something like Termux, any kind of devenv is ok for me, so to extend my list, if you want to develop Android apps on S6, then install AIDE- IDE, it will surely appreciate those 8GB.
Ctrl+arrow is almost as bad as going to the mouse. The thing with vim is you rarely have to leave the home row.

Some other random things I use constantly (I am not the person you're replying to):

C — replace to the end of the line

xp — swap two characters (which I have to use more frequently than I'm willing to admit.)

ci( — replace everything in brackets (like the arguments of a function, this also works with " or ' for strings, { for blocks of code, and so on). The cursor doesn't have to be inside the brackets, it'll jump to the nearest match.

:earlier 10m — time traveling, which comes in handy between commits or with config files, especially if you configure vim to write persistent change history. What did this file look like a month ago? Oh yeah, :earlier 30d

. — repeat the previous command (this is much bigger than it sounds, as it allows you to repeat the same complex manipulation on different data in situations where you can't use multi-cursor, but which don't warrant building a whole macro.)

A ton of custom filetype- and mode-dependent <leader>-based bindings (which is the spacebar in my case). space-space to jump to definition, space-f to reformat the file, that sort of stuff. This can probably be done in vscode.

If you're really interested, there are tons of vim videos on youtube made by real vim pros. I am very far from being one. The best for me are live coding sessions where vim usage is demonstrated by doing the real work, not on some toy examples.
It is not legal to make such a map. In practice, production actuarial systems for loans need to be interpretable and auditable, so this argument is moot.
That's not what I got from LeCun's comment. I read it more like:

LeCun - "ML is biased when datasets are biased. It's not the responsibility of researchers to ensure that ML is used responsibly in all cases, but the responsibility of the engineers of a particular implementation who need to use the correct models for the task at hand."
It is also relatively very wrong to "determine" someone's cast by their looks. You can say you guessed it, but you should stop short of that probably.
no no no: the Publish-or-Perish Community is in trouble. The ML The Community is juuuuust fine :-)
Enhancing Images on the Web
There's an aphantasia subreddit I checked out once. There was a discussion where people mentioned that they could hear a song in their head and play it back, and they could recall a voice or speak in their head, but it had never occurred to them that people could do something like that with images. I asked someone if they could visualize red versus blue in their head and they said they could not, they could only think of the words red and blue and not imagine what the colors looked like without actually looking at something red or blue. One person told me they had absolutely no imagination at all and could not, for instance, recall the sound of someone's voice or imagine warm sand between their toes.
I lived in India for a bit, and no shit they wouldn't share it. It's a horrible form of discrimination, worse than racism in some ways, IMO.

You could, historically, spread your "untouchableness" by contacting people of other castes, for example. Marrying your child to someone from a lower caste is seen as a shameful move to many people.

White people would absolutely not understand this, yes, but what is there to understand? This is just discrimination based on your family + skin tone to some extent, and the sooner it dies, the better.
Names, partly, but again, often comes back to skin color. Darker -> lower caste
There are already libpurple plugins for various chat systems, for example Discord:

https://github.com/EionRobb/purple-discord/
Todoist is excellent. Things for Mac is great as well.
> But I'd expect the state to argue that discrimination against an indian caste is inherently national origin based discrimination

That would be stupid. Caste discrimination is pretty obviously not national origin discrimination. But it is race discrimination! Race and caste are identical concepts -- you get them from your parents.

(It is, however, not discrimination against a race that is recognized by American law.)
A lot of them are probably proprietary variants of XMPP or SIP. ISTR reading Facebook at least is like that.
> Ignoring the (many) arguments that IQ is not really a great marker for intelligence, there's basically no value in trying to predict IQ based on facial structure. Unless it is quite literally 100% accurate (which it is not), you will end up mispredicting people's "intelligence". Even if this is ultimately just randomly distributed (and it probably won't be), you'll be needlessly treating people unfairly for no reason.

The presence of error in a prediction makes that prediction valueless?
To give you a concise answer, USA Federal law only prohibits discrimination based on the following characteristics:

Race.
Color.
Religion or creed.
National origin or ancestry.
Sex.
Age.
Physical or mental disability.
Veteran status.
Genetic information.
Citizenship.

Anything else you can think of is a legal reason to discriminate
Place next door does tower climbing courses.

They’re still busy in the time of COVID, though I assume the class sizes are smaller.
Might be a problem with customization.

THE main problem with JIRA is people breaking it with customization in one way or another.

Or put is on very slow, high network latency servers...

EDIT: Just to be clear, JIRA is never fast. But can be usable if you are not super sensitive to small latency.
This is FUD perpetuated by a certain individual on the FreeNAS forum.
Title is mismatched and misleading. Actual page title: "California Repeal Proposition 209 Affirmative Action Amendment (2020)"
I know. My point is not about IQ. My point is that people find IQ to be a useful measure of things, in certain situations. We can use something else, like the big 5 personality inventory, if you like. The point is just that subjective characteristics of a person are things that we quantify and sometimes care about measuring.
Maybe one day, but for now it's available entirely for free in Apollo Studio
Making open source more inclusive by eradicating problematic language
> Discrimination as you describe is not only unfair, but it costs the lender money, because they are not identifying the people within the group who are better or worse risks.

No model is perfect. Odds are you can better predict risk by (directly or indirectly) attaching bonuses or penalties to given races, all else being equal.

e.g. Say, irrespective of all other measured quantities, people of race A are more likely to default because of other systemic racism against them. As a lender, it'd be completely rational to consider this and make "better" choices. 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. This is the exact kind of thing ML does.

Then, in turn, this becomes self-reinforcing. Because other institutions discriminate against race A, the risks going forward of dealing with race A increase...
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TengineKit is an easy-to-integrate face detection and face landmarks SDK. At present, it can run on various mobile phones at very low latency.