You think the Chicago PD is lawless? It's the criminals, crime and general lawlessness in Chicago that is causing over 40 people to routinely be shot each weekend in Chicago.
The rampant lies and misrepresentations about police will only cause more crime, deaths and lawlessness as police choose to retire and decent people who may have been considering becoming police will decide to not subject themselves to the constant and unjustified abuse meted out by the public and politicians which police have to endure.
Hi, Chicago resident here. The CPD is under a consent decree that they do not follow, their officers have rioted against protesters and stood by watching looting occur, and every year they incur tens (if not hundreds) of millions of dollars of debt for the city and it's taxpayers for police misconduct settlements. So yeah, I think it's fair to say CPD is lawless.
As for your assertion that "rampant lies and misrepresentations" caused the decline of the police profession, the idiom isn't "criticism of the apple barrel spoils the whole bunch." The police do damage to their own reputation by not removing the "bad apples," moreso than anybody who critices the police.
>"Defund the police and use the money for mental health and human services."
That doesn't address the cancer of gang violence and other violence that police need to deal with constantly.
"Mental health and human services" will not address these problems. Most people committing crime had an opportunity to go to school and participate in legal society. They choose not to.
The future of America is that of a lawless gangland like Mexico if the police are "defunded".
Even police departments don't send their paramilitary groups (eg. "SWAT") to every scene, police don't need to be at every emergency call. In fact, this is already the case -- when you call 911 for a medical emergency, an ambulance or medical fire department unit shows up (usually alone). In the same way, "defund the police" means use police more judiciously, not necessarily eliminate police.
> The future of America is that of a lawless gangland like Mexico if the police are "defunded".
Citation needed.
Gang violence in the US has been declining for more than 2 decades, even as the population grows and US police case closure rates are far below 50%. Police don't have as much effectiveness or as good of a reputation with innocent civilians in gang territory as I imagine you think. Also, a significant portion of the US already has and carries guns, so it's not like gang violence would just expand boundless even if there were no police.
However DACA itself is unlawful in that it is an Executive Order undermining Federal immigration law, so the administration's rescinding it on the basis of its unlawfulness should be sufficient enough justification. The problem is that the Court's ruling indicates that an EO which subverts existing law cannot be overturned by subsequent administrations simply on the basis of its illegality. Justice Thomas' dissent says exactly this.
That's incredibly problematic. According to the recent ruling therefore, a President has the power to issue and EO which prevents the enforcement of certain laws.
I believe it was an upside down triangle... That's a common symbol and just happened to match an obscure Nazi symbol I think very few people alive today have ever seen before.
Just more propaganda repeated over and over. "Nope, it wasn't an upside down triangle, it was a Nazi symbol. Everyone using upside down triangles is hereafter deemed to be making Nazi dogwhistles!"
So ok, maybe we can give the benefit of the doubt. By why not use the actual symbol that's very predominantly used in Antifacist protests? It's very easy to find and much more familiar and relevant. Honestly it would have made for a better ad.
And in isolation this is a single mistake, but the administration has been repeatedly criticized for using fascist symbolism and language, including "America First." At what point do these things stop being coincidental?
I'm not saying it isn't xenophobic and pandering to a nationalist base but it seems like a stretch to call "America First" fascist, especially when the phrase is generally accompanied by (dubious) accusations that the opposing party failed to prioritize the needs of the people. I could see the argument that the slogan was perhaps used as a tool to further fascist policies but "America First" represents fascism about as much as the Che Guervarra hats that Hot Topic used to sell represented communism.
It's not the phrase "America First" on its own, but its history of use.
"While the America First Committee had a variety of supporters in the United States, 'the movement was marred by anti-Semitic and pro-fascist rhetoric.'"
And again, this is another example where they could have literally picked anything else... but they didn't. When pressed about its history Trump literally backed up the phrase's use by stating "I like the expression."
It's isolationist, nationalistic, xenophobic nonsense with a tinge of antisemitism. It's been denounced by multiple jewish scholars and the Anti-defamation league.
Exactly. It's so blatantly political propaganda. The scary part is that "respected" outlets like NPR and others pick up the headline and run with it knowing that the link to Nazi imagery is tenuous at best.
This is pulled directly from an NPR article denouncing the triangle as a Nazi symbol:
"Trump campaign spokesman Tim Murtaugh said that some products are sold online that use the inverted red triangle in antifa imagery, though experts said it is not a commonly adopted symbol among anti-fascist activists.
"We would note that Facebook still has an inverted red triangle emoji in use, which looks exactly the same, so it's curious that they would target only this ad," Murtaugh said"
Which is a perfectly reasonable explanation that's ignored by the Democratic Party's political propagandists in the media.
> To claim like the campaign that this would be the symbol, or even a really common one – in the US – seems to not have been true. This changed obviously in a reverse-Streisand now.
That same answer goes on to show all of the imagery dating back to WWII using the red triangle. Plus images of modern Antifa paraphernalia including T-Shirts, Stickers and more that include that symbol.
Seems, to me, the upside down red triangle is definitely a symbol of Antifa, although perhaps not the most commonly used symbol in the US.
Regardless, the claim that the Trump campaign picked an obvious Nazi symbol just to make Antifa look bad seems very clearly garbage. I, as a sample of one, have never seen that symbol used in a Nazi context before, but that's just me.
> Regardless, the claim that the Trump campaign picked an obvious Nazi symbol just to make Antifa look bad seems very clearly garbage.
That is not anyone's claim. Explain why you think that the Trump campaign's adoption of the symbol the Nazis used to brand and kill political prisoners somehow makes antifa look bad?
That top answer is editorializing but their own analysis indicates that in Europe the upside red triangle is incredibly common in resistance movement imagery as well as having precedented use in US Antifa branches.
As far as symbols go, it's fairly good. Simple is better than complex. A red triangle has no difficult lines, curves or fine detail. You can spray paint it, print it, draw it... and it will look pretty much the same every time.
The three flag symbol isn't nearly as easy to make the same everywhere every time without some official stencil or something.
The triangle wasn't the only Nazi reference. They ran 88 variations of the ad, and the first sentence had 14 words. Both numbers are well-known references used by neo-Nazis for decades.
Only by overcoming the concept of race can we actually overcome social division. Divisions among whites have melted away as they stopped seeing each other as "English" or "Italian" and instead came to unite. Similarly for us as citizens to unite we need to stop seeing each other as "white" and "black" and instead overcome these superficial differences.
Injustice can easily be rooted out while also overcoming the concept of race.
The problem is that race-oriented thinkers obsess over race and reify it, actually making racial tension worse.
Through the mixing pot of America we can come together and overcome our natural tendency towards division. This is part of what makes America a great nation.
Again, the racially-oriented thinkers often just exacerbate division.
America's history has a continuous component of oppression. At no point since the founding of the country have all folk present on this land been equal and free. Pretending that past (and current) atrocities didn't happen, and ignoring whom they targeted, means that the inequalities they created will linger.
Even today, COVID 19 affects Black and Latino folks at greater rates. That is a real, physical, manifestation of racial injustice. Ignoring that means ignoring structural problems in America.
This is the case for every single country on Earth, and is actually still the case for most countries on Earth. Show me a country today and I'll show you a set of groups who are oppressed in that country.
However it is America that has come the farthest in overcoming racial discrimination, which is why people from around the world want to come here in droves.
Those groups are disproportionately affected because they are disproportionately living in cities, which has nothing to do with racial oppression.
The Latino population has swelled in recent decades and new immigrants to the US are almost always poorer than the general population. That's been true for the last 200 years and there's nothing wrong with that.
Yet racialists like yourself ignore this and try to claim discrimination. It's intellectually dishonest.
As for Blacks in America, the only thing that will bring them up and negate past discrimination is a improvement of their culture and focusing on the future. Most of black poverty today is caused by broken homes and other cultural issues, not oppression.
Students whose parents went to Harvard tend to grow up aiming for Harvard themselves. It's likely that a high percentage of the Harvard applicant pool are legacies in the first place. If their stats are as good or better than the average of the admitted class, then what's the problem?
Is there clear evidence that legacies have an easier time getting in on a per case basis compared to a similar student without that that designation, as is the case for applicants with the URM designation?
The US allows over 1 million immigrants per year. Maybe less the past year, but still extremely high on an immigrant per capita basis.
H1B was being abused and needed to be reigned in.
There are still plenty of people coming into the country through various means, the US is still extremely generous with immigration.
There are plenty of qualified citizens here and in the zero sum game that is hiring national immigration policy should put their needs first before anyone else's.
>the US is still extremely generous with immigration.
Everything I read about it convinces me it's a byzantine and capricious process, and largely down to chance.
My process of gaining permanent residence in the Netherlands is a cakewalk by comparison. University MSc, then free access to the job market for a year, then after some years of employment and passing a relatively simple integration exam, I have long-term European residence. At no point was this up to the arbitrary whims of some immigration officer or a far-fetched lottery chance.
To me the idea that immigration is zero sum is repugnant and wrong.
> the US is still extremely generous with immigration.
US is the WORST country on earth for immigration. I came to US >10 years ago, still NOWHERE near getting a Green Card. Will probably won't get well into my 30s or 40s. It's practically impossible to get it in the next 5 years.
The same time I started undergrad in US, my friend started undergrad in Germany. He was already a German citizen by the time we were working. I was still on F visa. This was like half a decade ago.
My biggest regret EVER was doing this in the US. If I got a PhD in Europe instead of US, I wouldn't be a person who doesn't have a country.
I understand that American people don't want immigrants. But let's not pretend immigration into US is easy. I've been trying my entire life to live here without getting kicked out and it's pure luck. One mistake and you need to wrap your entire life, leave loved ones and go back home. Again, I'm NOT saying I'm entitled to living in the US, I'm saying this is an extremely long, and complex process.
Could the waiting be attributed to your country of citizenship? I know there's a long wait for citizens of a few countries because there are per-country limits on green cards.
Nope, I'm not from China or India (so I'm in the global pool). If you're from India, it's impossible to get citizenship this way (the waitlist is too long), you need to marry a US citizen if you want citizenship.
That's horrible. I have several friends from different origin countries (including USA) who have received Swiss citizenships (which the American right wing holds up as some sort of uber-restrictive standard) in half that time.
The time to naturalize in Switzerland is now 10 years (it used to be a bit longer) unless married to a Swiss citizen, in which case it's shorter. Include that plus the processing time and assuming your friends were in one of the right residence status categories during their entire time in Switzerland, it would have taken them at least 11 years or so.
> There are plenty of qualified citizens here and in the zero sum game that is hiring national immigration policy should put their needs first before anyone else's.
This exact sort of anti-competitive nationalism has been tried before, and over and over again it has been abandoned. It's a failed policy that gives other countries a competitive advantage over your own.
It's better to learn from others' mistakes than to repeat them yourself.
Could you point out those various means to me? Forgive me if that sounds rhetorical, but I'm serious - it would be life changing for some close friends. I only ask because as far as I'm aware, the only available option right now is marriage.
> H1B was being abused and needed to be reigned in.
Nothing has been reigned in. H1B quota got full early on this year and H1bs were allotted through a lottery of qualified candidates. Transfers are still being allowed. If your argument is that H1B gets abused then right way is to pass a reasonable bill and not blanket ban everything.
Yet again, F1 visa already prohibit students a 100% online school from entering the country. So your impression of immigration being "reigned in" is pretty misguided.
H1B abuse doesn't exist. It's made up. You multiply the quota by 100x and it wouldn't exist as a concept, same as J1 nanny visas being abused doesn't exist.
Even if you are anti-immigration, your biggest issue should be tourist visas which is how illegal immigrants stay in the US, and those are unlimited and in the millions. H1B quotas are stupid by design.
"the way Yann LeCun talked about biases and fairness topics was insensitive"
Insensitive according to who? The most sensitive 5% of people? All statements will be deemed insensitive by at least one person somewhere. It's silly to allow the most extremely (often unreasonably) sensitive people to set the threshold for what is sensitive or insensitive speech.
> It's silly to allow the most extremely (often unreasonably) sensitive people to set the threshold for what is sensitive or insensitive speech.
Well, that's one of the drawbacks of social media. Offended people can band together, amplify their voices, and spark nation-wide outrage. Whether the outrage is "real" or just "perceived" (i.e. the media says everyone is outraged so it must be so) is a different debate.
Insensitive to anyone who has a moderate amount of understanding of machine learning and social empathy.
You can't plug your ears and say "it's just your training set" as a response to unfairness in ML algorithms. Real life is biased. Any real life data in our world is going to be biased. If you train algorithms on this data, they will cement any existing divides in society. So, with the understanding that researchers need to be more circumspect about ML algorithms than worrying about just the training data, consider that the upsampling algorithm in question only worked for white people because they fed it a huge amount of white faces. Claiming "it's just the training data" is one of those "well yes, but actually no" situations where ML researchers tend to miss the broader picture of how ML algorithms are used in real life, and just makes Yann look ignorant.
Sure. Your comment's language equivalent is something along the lines of "Care to explain how words are racist?" Which yes, they are just a collection of words. They possess no consciousness and cannot be racist by themselves.
Similarly, a gradient is just a collection of vectors. It's just numbers. However, like language, it's what they represent that matters.
For example, I can create a machine learning algorithm to determine who should get a home loan. I create a gradient to optimize the algorithm to give loans to people who I think are unqualified.
The gradient can easily be racist if it optimizes heavily on something like race. Minorities tend to be lower income and so can be seen as less qualified as higher income individuals. However that's the easy argument, and also quite illegal. If you exclude race, there's 2nd degree variables that are proxies for race. Things like zip codes, job titles, whether they rent or buy. These are not explicitly illegal to filter on, though the end result is illegal if they exclude certain protected statuses. It can even be no fault of the researchers who implement the algorithm, because controlling for bias using real world data is extremely difficult. But we must do it, since it is the ethical thing to do.
And so, it's easy to see that one can optimize ML algorithms to exclude certain protected statues, which is what can make the algorithms racist.
Maybe I'm not explaining it very well. Look, so things have meaning deeper than their face value. To use a really basic example, The number 14 means nothing, it's a number. The number 88 means nothing. In the same context, they mean something not good.
There are English words that as pieces, they don't mean anything except their face value. I can string words together that mean bad things that are harmful to real humans.
Gradients are not racist by themselves, they're just math. It's like saying multiplication is racist.
But I can use multiplication as a tool in a chain to create weighted averages to create a naive Bayesean classifier to reject people for home loans.
And so too can I misapply gradient descent as a part of a larger ML model that is racially biased. For instance, I could choose a loss function that when minimized, gives biased output despite less biased input. Or, I could accidentally settle on a local minimum on the gradient in my model. There's many naive implementations of an algorithm that will just be biased no matter the unbiased inputs.
So in summary, a gradient is just math and is not racist by itself. It's being used in an algorithmic tool chain that researchers are frequently using which potentially will always produce biased output no matter the inputs (but more often than not also with biased input).
It should be self-evident that if you add race as a variable the resulting function at the very least could easily end up racist. If you add biases to a function it will be biased. Which is fine, sometimes the biases are necessary to solve difficult problems!
Even if you insist that a gradient or mathematical function is unbiased and can never have negative impact based on race or gender or other demographics, you have to explain any resulting negative impact somehow. Saying that the function or gradient is racially biased is a generous interpretation of the situation because it allows the creators to deflect blame towards an error in their mathematics or training set. If you insist on claiming that the training set and mathematics are infallible, one of the only remaining explanations is that the creator intended to discriminate. I'd rather not assume that!
I'm not disagreeing with your basic idea, but it seems you're nitpicking and talking past Yann's point.
A model's only link to the real world is the training data, so saying it's sufficient to "worry about the training data" captures all the concerns we may have about bias, because from the model's POV there is no other relevant interface with the real world.
Saying "we need to do more" is devoid of meaning when by addressing the training data we are truly doing all we can as model builders and trainers.
A huge problem in the field is that we must use the previous benchmarks. This is because how do you know if the needle moves or not if you just change your data constantly?
So. In order to tackle this problem, someone with more resources than me needs to create training sets that are less biased. THEN, new academic papers need to benchmarked against the old biased sets, and also the new "less biased" (I don't think it's possible to ever get 0% bias, the world just isn't that clean) sets. And progress needs to be eventually transitioned to be measured on the new less biased sets.
The upsampling algorithm used pictures of celebrities. And the researchers put a blurb in their paper that was basically a "We know this is biased but everyone uses it so we must also". I feel like this is less useful science than an algorithm trained on more of a mix of actual real-world humans.
I admit it's quite challenging and probably impossible to do in some areas. I mean, how do you make a field whose end algorithmic goal is generalization, not use real world data to generalize people? But I think the issue can be worked on, and the need to use celebrity photos to train a set is a good place to start.
All this is going to do is researchers not releasing data and code when publishing their articles so that the public doesn't meme biases/mistakes of their data/code into twitter hate mobs.
We'll probably go back to the 2000s model where you have to email the authors for code and data. The authors will delay by saying they are preparing it and then release it a few years later when it becomes irrelevant for public discourse.
ML is a huge field outside of modelling humans and their behavior. For instance, image recognition of vehicles, financial data prediction and analytics, and weather forecasting, to name a couple examples. Those don't draw scrutiny. The problem comes with generalizing humans. And generalizing using biased data. And applying generalized algorithms in areas that cause a lot of harm. I think these researchers should properly be placed under the microscope since they have the potential to be very hurtful to society. I do not think they should be subject to death threats or loss of income or whatever the social media mob throws at them these days, but I don't think researchers should be cavalier in creating algorithms that generalize humans without taking very careful steps to not create bias in the end result.
I think it's more appropriate to hold companies, governments, organizations that use these algorithm on the general public under scrutiny. Research that doesn't materially impact anyone shouldn't be placed under such scrutiny.
I understand that the research is what is driving this and vice versa, because companies and governments are funding a lot of this research (face recognition research specifically had significantly increased funding due to 9/11). It's the companies and governments who should be scrutinized and put under pressure instead of researchers who are trying to get ahead in academia or publish their next article or are incentivized by funding.
Sorry, but was the trained ML model to be implemented and used, as is, in public, like in an airport? Or was it to become the next standard or the next "ML for dummies" book? Or was it just research or an experiment?
If it was an experiment, then let it be. Perhaps the researcher was looking for something else, circumscribing the data, model, whatever to the experiment itself.
> researchers need to be more circumspect about ML algorithms
What does entitle you to tell what to study or how?
Your entire comment is correct, but still missing the bigger picture. It's understood that it's way easier to detect features in pictures of white faces than black faces due to the fact that it's easier to resolve lines and shadows. These lighting differences show up once the image is pixelated, and gives something for PULSE to lock on to when it attempts the upscale. I'm questioning whether or not the algorithm even works for cases where these lighting differences are difficult or impossible to resolve.
If the researchers created a toy, then great, it's a cool project and is a neat algorithm. But they didn't create a toy. It's an academic paper to attempt to move the needle forward in ML academia. And they are doing the exact same thing as a lot of other researchers, which is basing their research on old biased benchmarks. If the bedrock of the field is based on biased data and everyone builds on top of that, your research down the line will skew more and more in favor of the bias.
>What does entitle you to tell what to study or how?
Nothing entitles me. It is my opinion based on the facts in front of me. The ML field has a bias problem, researchers toss a "oh this is biased" blurb in their papers, and then continue using the biased data. Everyone looks at the cool demos, and then the research gets slurped up and implemented without regard to the science. More algorithms get based on previous biased algorithms.
> doing the exact same thing as a lot of other researchers, which is basing their research on old biased benchmarks
They might have a reason. I can understand if they want to compare the result of the model with a past experiment. That's normal.
> attempt to move the needle forward
Completely agree, so just let them work.
By the way, I don't see "evil" in these experiments and I want a 100% free from bias model too, but I wouldn't dare to attribute the result to lazyness, stupidity or racism. If I come with something completely new then I would try to compare it with something that already exists too.
The witch hunt as a means of enforcing female subservience in the context of an emergent capitalism is a just-so story that is driven more by a desire to demonize capitalism than anything else.
Gender roles have remained pretty static throughout European history so it doesn't make sense to pin down witch paranoia on a changing of gender roles or attitudes that never changed in the first place.
The better explanation is the one offered by the OP, that a pre-existing desire by religious authorities to root out heresy found fertile ground in the fervor of the reformation era. The result was witch hysteria.
>Gender roles have remained pretty static throughout European history
I've generally heard the inverse statement. Compare the women in Chaucer to the women in Shakespeare, and you'll immediately notice a huge difference in behaviour.
Usually, people say that women's rights took a precipitous dive in the 1600s, with the invention of things like brutal punishments for 'nagging', and so on. Which would fit with the idea it was related to primitive accumulation.
I don't really agree with the grandparent thesis, but I think the chronology is correct.
In my reading of history, hysteria is often driven by plagues or famines. Look at the Mayan culture of human sacrifice. It's very similar to witch hunting. I think when a formerly thriving society is struck by a series of disasters that threaten the social order, false enemies get created as scapegoats. Either it's an angry god or a witch's curse.
The rampant lies and misrepresentations about police will only cause more crime, deaths and lawlessness as police choose to retire and decent people who may have been considering becoming police will decide to not subject themselves to the constant and unjustified abuse meted out by the public and politicians which police have to endure.