skysight-inc/HackerNews huggingface dataset:
Back in the 1960's and 1970's F1 was pure carnage. Things have gotten a lot better.

Here is a summary of improvements: https://www.autosport.com/f1/news/149628/history-of-safety-d...

Here is a quite scary crash that Fernando Alonso walked away from: https://www.youtube.com/watch?v=x45fLUTHCuk

The basic idea is the car is designed to be completely destroyed except for the survival cell for the driver.
China moves to impose controversial Hong Kong security law
The "It's the companies fault for not being prepared" line originated as a satirical jab at the "personal responsibility" rhetoric the right has used for decades to systematically dismantle any semblance of a social safety net in the United States.
Yeah, it works just like TV. There are no unskippable ads or interstitials. Also with unlimited DVR and time-shifting, I rarely watch live, and I can skip all the commercials (albeit manually).
That's an interesting perspective, but it kind of looks like a lot of graphs saying "You should pay for stuff you don't want because otherwise it would incentivize the sellers to price gouge you for the stuff you do want"

I'm kind of confused, if it's an adversarial relationship where the seller is going to extract as much money from you as possible in either scenario, wouldn't some consumers prefer to pay specifically to starve out content they'd prefer not to see?

Maybe I'm not understanding this page, but it looks like it's "good" for me because instead of paying $9-10 for the History Channel alone I'll be paying $11.70 for History Channel + ESPN (which I will literally never watch)?

I'm not a calculus genius but I'd prefer to spend less money and only have what I want. This model assumes that I'd be willing to spend $3 per month on ESPN, but how does the graph look if I were willing to spend $0 per month on it? Or hell, since pricing is arbitrary, I'd like to pay -$3 per month as a courtesy for having to scroll past content I genuinely do not want to consume let alone subsidize?
I would doubt more than 1% are in that category - $50 (now 65) a month raises more mental alarms than your old $11 Netflix subscription.
If you use a Mac, you can create an encrypted HFS disk overlaid on top of another filesystem that TimeMachine can then back up to. Depending on the size of the disk and frequency of the backup, you can keep snapshots that are as long as a year old. You can then take nightly backups of the disk (which is literally just a folder) and store it on a cloud provider or any remote storage. I've been doing this for the past year or so and it's been flawless.
It's misleading to refer to them as "Bengio," on HN, in this context. It definitely created a false impression for me.
> If someone said "Shaq is really tall", would a reasonable response be "No he isn't. Yao Ming is taller"?

If Shaq and Yao played on the same team and Shaq alone started demanding special accommodations due to his height (like extra-large showers or something) that were prohibitively expensive, and refused to play until he got his way, that would be a perfectly valid response. Sound crazy? It's still saner than some people seriously defending cops breaking the law because their job is dangerous. Give me a break.
It was a brilliant $100m bet that backfired for no good reason.
One patient? Could have been over medication or misdiagnosis to begin with.
If there are no threats then how can you argue to increase or even maintain our already massive military budget.
Remembering days when I was a sysadmin, vim or even old-school vi was the default editor on literally every POSIX/UNIX system, and I had to deal with quite a variety of them - BSD family, Solaris (still Sun), AIX - some of them still not connected to the internet! Having advanced vi skills appeared handy in my situation
Why not go find me three links to instances where the police did acknowledge their mistakes?
The solution they seek is to stop pretending that research happens in a vacuum, to think about likely applications of the technologies we develop, and stop taking for granted the idea that the invisible hand of the market will determine the optimum application of the cool shit we make.

This is an extremely unpleasant position to take, if your point of view is empowered within the status quo. It is much extra work for no discernible benefit to the researcher.

If your point of view is subject to disproportionate suffering under the status quo, then reinforcing current practices by implicitly enshrining them in input datasets will make improving your situation even harder.

As an example, consider the case of public school funding. In a hypothetical system where school resources are provided proportionally based on student success, good schools will thrive and bad schools will get worse. If someone points out this isn't fixing the problem, you can reverse the proportions -- this will cause good schools to suffer while bad schools will get additional funding (disincentivizing student success). In cases like this, it's not enough to just have a purely abstract set of metrics on which to base resource allocation: it will always require actual investigation of why good schools produce good results and why students do poorly in specific schools.

This is sort of obvious, of course, but it isn't being translated into terms that some researchers can or will grasp. It's never enough to just tell someone to 'debias the dataset,' as determining that bias is a monumentally difficult challenge that people have failed to achieve for many generations. A key factor in fact is the propensity for this kind of research to get deployed, today, by people who are not experts in a given domain of investigation, with possibly disastrous results in policymaking. These tools are not abstractions that require a team of experts to translate from research paper to the real world; ML researchers put out results that you can shove into your nearest computer and run.

What Timnit and others are getting at is that it requires thoughtful and careful assessment to get real value out of this sort of research. 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.

Yann LeCun and other researchers simply do not believe this is their responsibility; all they want to focus on is the mathematics themselves. I'm sympathetic to this position but I also very much do understand the opposition. One of my favorite movies from childhood, "Real Genius," deals with this sort of issue as the main plot line.
This speaks to me, but I'm not 100% persuaded yet.
I read that. Restrictions on sexual relationships given an uneven power dynamic are common, but an inability to shake someone's hand or put a hand on a shoulder? That's not maintaining professional boundaries, it's paranoia.

It is also a standard that she is clearly imposing on herself, which is what makes it so bizarre. She wasn't just conforming to some overbearing rule of the organization, she thought this was a necessary component of effective leadership. Which makes me question her judgement in general.
This is why free student packs are super valuable. Students typically have little to no money and can't afford to buy expensive software. So the company gives you a temporary free copy so that once you have a job in the field your learning you can afford actually afford to buy it.
> Home ownership rate is not the same as housing costs.

No, but it’s a strong indicator that housing costs are not unaffordable nationwide, but merely in some high cost cities where most people don’t live.

> A lack of transit options is not the same thing as the length of commute.

Talking about “transit options” presupposes a solution to a problem. The problem is getting people to places, not transit per se. The US has chosen to prioritize direct car travel over potentially indirect transit, and the result is that Americans spend less time commuting than almost anyone else in the OECD. Put differently, for most Americans, the fact they have a 25 minute auto commute instead of a 45 minute train commute is not a sign of America being “in decline” but rather an advantage.

As to homicides, other countries do not have the same massive differences in homicide rate between cities and suburbs as the US. My county includes a small city (the state capital). The homicide rate in the nearest big city, 30 minutes away, is almost 15 times higher. By contrast, the homicide rate in London is only 50% higher than in the UK as a whole. Toronto’s homicide rate is actually lower than Canada as a whole. The differences are small enough to ignore for purposes of the point I’m making, which is that many parts of America are quite safe. Large cities have stratospheric levels of violence, but most of the population doesn’t live there.
I’d phrase it that “bias” in the social sense can occur in (1) the task itself based on how it’s defined, (2) preexisting within the problem domain, so representative datasets could still be biased, and (3) in when the system and the people operating it choose to use the outputs of the model. Which is all to say that “bias” problems related to the model can extend beyond the model itself.

The solution isn’t necessarily to fix the data or the model. It might be banning use of facial recognition by police, for example, which some places are doing.
Hire more minorities in ML. One of the few industries where forced diversity is a good thing since your models will be better (wider range of opinions and sources).

See, it's not actually hard. Gebru is just correct in saying you are not listening.
I strongly agree, but would go one step further and just remap caps lock to escape for the entire OS. Caps lock is a relatively useless function that takes up extremely prime real estate.
Sounds like you have a generation 1 Chromecast™ and bad WiFi. We hope you'll try YouTube TV again with a Chromecast Ultra™ and a Nest Wifi™ 3 pack. Take care!
How to build database ecosystem with newSQL in a big-data environment
They could have a C&C server that hands out unique addresses to each person, and when that wallet gets money it sends out a signal to the malware to decrypt the files…
Hey FDSGSG, the situation is definitely not ideal. DHH should not have responded like this, but please consider from his point of view: he and his company just had a moment of existential threat with the Apple IAP rules enforcement, and the disposable addresses are the only way that they manage to be able to still put updates on the App Store. The way you approach this surely feels like another threat to his business. Especially done anonymously.

If I may recommend something that has been echoed in this thread, please try to communicate to him or the company directly and discuss your concerns calmly. Directly threatening to put their whole domain as a spam domain, even with solid concerns, is not a friendly way to do it.
Thank goodness I have economists around to tell me I'm wrong about my own buying preferences.
Vim can jump to definitions and stuff too. It integrates with ctags. It can also do code completion and everything else. I have it bound to clang-format with ctrl-k.
>I expect they're a net gain for the public, but that for an individual who's conscientious and never drives under the influence, tired, or distracted, that they're only marginally useful at best.

Possibly even negatively useful if you consider that the car they come in has significantly reduced visibility compared to it's '00s counterpart.
What is “whole system of biases”?
You need to think about it more holistically - not just YouTube, but Google as a whole (think about all the various Google enterprise products, cloud, etc).
The pressure to keep one's communication brief due to the 240 character limit makes nuanced communication much more laborious, and that results in more conflict.
Figured I'd provide a link to the reddit thread in case anyone else (like me) was interested:

https://www.reddit.com/r/AskReddit/comments/hgsj41/what_soun...
I dream of the day when Excel will have a vim mode... My life will finally be complete.
I'm not sure there is an easy answer for the question of why one language became more popular than the other but there some very interesting parallels that can be drawn between Lua and TCL. One thing that both have in common is that both languages were purposefully designed to be able to be embedded with other languages, such as C. But they do this in very different ways.

The problem of embedding one language inside another is how do you make code written in one language call functions and objects defined in the other language? For example, when an object is transferred from the scripting language to the host language, how does the garbage collector keep track of it? This isn't easy to do and in some languages it is quite cumbersome. For instance, in Python the C programmer has to manually increment and decrement the reference counts for the python objects that they interact with.

TCL solves this problem by making everything a string, taking the "everything is a string" mantra farther than any other programming language. Strings are easy to pass from one language to the other.

Lua went the opposite direction. Lua exposes a rich API that lets other languages interact with Lua. The API lets other languages create Lua objects and call Lua functions, as well as send C objects and functions to Lua. Lua is entirely designed around this API and even the core standard library is built using it, ensuring that every Lua feature is accessible to C code as well. One interesting example of this is exception handling. In most languages this is done using try-catch blocks. But that feature isn't easy to use when embedding one language inside another. How would you write try-catch blocks in C? Because of this, Lua has a different way to do exception handling. Instead of try-catch blocks, there is a "pcall" function that invokes a function and returns whether there was an exception or not. Function calls and if-then-else statements are things that are also available to other languages via the Lua API...

local ok, err = pcall(function()
something_that_may_throw_an_exception()
end)
if not ok then
-- handle exception
end
Is this life ruining thing a thing, or is it a generalizing of that episode that hit the news a week ago, or is it an instantiation of the current zeitgeist according to which there is no ill that can’t be associated with US law enforcement
So basically no real consequences.

I'm sympathetic to people losing their nerve. It's a human reaction and can happen to anyone. But no way should he be allowed to keep his job after he's shown how unfit for it he is.

And the fact that the other cops didn't raise a peep over it tells me that most cops are in fact not heroes. Heroes do the right thing even when it's hard, you see. That's not an insult. I would like a society where policing doesn't have to be heroic. It's just a statement of fact.
overlap crashes tend to happen when the driver attempts to avoid an obstacle but there is not sufficient space to fully do so.
Hm... could you please give an example of how biases of people who build the model affect the biases of the model?
Hey.com Workflows in Notmuch / Emacs Demo [video]
They're maybe trying to use the same law that Locast[1] is: there's a specific carve-out in the law about rebroadcasting for nonprofits: "an exception in U.S. copyright law (17 U.S.C. § 111) for retransmission of television programming by non-profits without charge or any purpose of direct or indirect commercial advantage (besides the costs needed to cover the operations and maintenance of the retransmitter), which was originally intended to cover over-the-air translator stations" [2]

[1] https://www.locast.org

[2] https://en.wikipedia.org/wiki/Locast#Legal_challenge
Hire minorities to work on your machine learning models. At the very least it ensures you won't 'forget' to train the model with black faces.
What We've Learned to Control
So who's doing interesting applied work in scene understanding for AR?
This. The whole network and everything on it are getting nuked. And due to the delay these things have, you have to assume the backup may restore the malware.

Everything gets a clean install and only non-binaries get restored from backup.
Is it at least ad-free for that much?
I need to double check but from what I can recall, while she didn't mention any solutions or alternatives, she did point him to her previous work about the topic. (but she wasn't specific and I don't think she even included a link)
It does not. Enforcing SafetyNet only makes it harder for users to use custom ROMs. It provides no benefit whatsoever to the end user.

It does provide major benefits to app makers who hope to control the user's device.
I love the moxie of some of the smaller rural WISPs, reminds me of community run ISP on Orcas Island[1].

[1] https://arstechnica.com/information-technology/2015/11/how-a...
Sort of. I didn't read the whole exchange, but to me the thing to say would be to be a bit more explicit that the tool will always reflect the bias of it's owners. Hopefully phrased to sound less Marxist, but something along those lines, instead of leaving the impression that the only issue was an inadequate data set.
Gauge activity of market takers based on a order book's exchange
Assuming good faith, I really think there is a gap in understanding why an "objective" viewpoint can be seen as blinkered or prejudiced.

If you're looking at things that correlate, you've got a lot of choices. Saying that your data set is representative, your algorithms are correct, doesn't mean your choices of what correlations to use are objectively or morally correct.

Suppose that you have a system that accurately tells you men have higher car insurance claims on average than women.

The same system might also be able to tell you people with low credit scores have higher claims on average than people with high scores.

Is it right or ok to use the first correlation and ignore the second? Or vice versa? Or maybe both of them are unacceptable? There's nothing objectively inherent in the correlations that tells you it's ok to charge men who are good credit risks the same as men who are bad credit risks. Or optimal economically! Those are independent and difficult questions.
Started at the beginning but all things come to an end. What was once a sports centric + news service has become more and more bloated. Is the audience really cord cutters? As it is probably far cheaper to get a cable+internet package than just internet + yttv.

In honestly, was probably gone once baseball started as they recently dropped YES network. Same goes for many friends in the north east who were also using yttv.
I searched google for "police acknowledgement mistake" and several variations. The closest I could find was people calling for the police to acknowledge their mistakes. I can't find the police doing it themselves.
> How do you de-bias datasets, when the raw data (coming from the real world) starts biased to begin with?

I can think of at least one trick that might work. Build a classifier capable of identifying race from the data, and then reverse its gradient to remove its signals. This would deracialize a dataset, so that there is no detectable latent information in it.

However, whether or not my particular solution works is somewhat beside the point. 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.

> For example, there are already attempts to use expert systems in the courts - to "scientifically" determine flight risk (and thus bail), and even chance of re-offence (and thus punishments). Those are trained on datasets of convictions. But we know that both conviction rates, and arrest rates, are themselves indicative of bias - e.g. some people get arrested more often for a crime, for which other people might get a verbal warning from police.

I totally agree that this is a problem. But it seems to me like the solution is more effort on fixing the biases, not reverting to human judgment. I think it's always important to make sure we understand what the baseline is we're comparing to. Humans are racist. Humans created the biases in these datasets. These algorithmic tools just bring those biases to the surface and make them quantifiable. It seems to me that that represents an incredible opportunity to actually fix the biases in a measurable way.
I think one issue here is that Twitter makes it hard to have good conversations because it motivates people to optimize for pithiness and likeability/retweetability over substance.

That said, I'm disappointed at the lack of professionalism shown by Timnit Gebru in this conversation. I understand that she's frustrated that mainstream AI/ML research doesn't have a good understanding of how models can reflect and reinforce systemic racism. But her response to Yann doesn't attempt to find common ground, doesn't add any meaningful facts to the conversation, and is ad-hominem ("why are YOU too dumb to understand this?"). Timnit is a Research Scientist at Google who leads a group that works on Ethical AI. In that role I'd expect that her mission would be to elevate the collective understanding of ethical problems in AI, and solutions to those problems. Her communication here doesn't achieve that.

This is important to me because I'm also a (fledgling) member of the AI/ML community, and I want us to be able to have civil and constructive conversations.
This happened 15 years ago, December 2004 to February 2005. The headline and editor's insertions about SARS-coronavirus-2 made it seem like she returned in the middle of the pandemic.

https://en.wikipedia.org/wiki/Rachael_Robertson_(writer)#Rob...
> If the water heater engineers designed the water heater to never turn on and keep the water at room temperature while being tested

I think the difference between what they did and this is only a difference in degree and not kind though, right?
I believe the way to "next generation" while keeping the Linux userspace compatibility promise is basically gVisor running on a non-Linux kernel.

If it runs your container just as well, well, it's "Linux" as far as your app is concerned.

Case in point: 1% of Google Cloud Run could just as well run on Fuchsia today and you just wouldn't know. All you see is the inside of the gVisor sandbox -- yet at the same time it'll run pretty much any http-serving docker image.

(Of course, in the real world Linux rules because of its huge collection of drivers, filesystems, etc.)
You give too much credit to the people who build those systems. Having more minorities in ML is a good thing, but I don't think it's gonna change anything regarding bias in ML systems.
Lacking an immediate solution to a complex problem does not mean these problems don’t exist. I’ve noticed often lately that people, when talking past each other, one person is saying, “We really need to consider the implications of problem X.” and the other person will imply “If there is no obvious solution, then no one should consider this a problem.”

When it comes to problems, particularly in complex subjects which aren’t yet well understood, and where there aren’t yet an overabundance of high caliber researchers, this comes across as dismissive of the problem.

This can be even more concerning if we know there are investors lined up who will happily sell something to the world and who will intentionally hide or minimize known ethical concerns. And then play dumb and shocked later when the very same problems manifest.

I see this dismissal of justifiable and real concerns an awful lot in conversations of all kinds lately.

And from what I’ve seen, no one here is anti-ML, no one on either side here is a luddite. 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.

Again, a lack of a neat and tidy solution doesn’t mean the problem doesn’t exist.