skysight-inc/HackerNews huggingface dataset:
There's too many little things to mention, they do all add up to be quite significant in the course of programming.

Here's a tiny sample of some stuff I use constantly:

Move cursor forward word at a time: w ignoring punctuation: W reverse: b B

Move cursor to next occurrence of character Z: fZ reverse: FZ

Delete forward to next occurrence of character Z: dfZ reverse: dFZ

Change forward to next occurrence of character Z: cfZ reverse: cFZ (like delete but leaves in insert mode afterwards)

Repeat latest edit from current cursor position: .

A lot of the vi commands are composable, as illustrated to some extent above.

To search for say the next line starting with void: /^void

To delete from the current line up to the next line starting with void: d/^void

Note the similarity to dfZ/fZ from above, it's not 100% consistent but most times you can take a chance composing things as you'd expect and things work correctly.

There are far more sophisticated things too of course, but I'm not writing a vim tutorial in an HN comment.

You can do things like rename a function while rearranging its arguments either file-wide, or on a number of lines, or on an interactively selected visual region, or a section selected similar to d/^void... I presume other editors have such capabilities, but I'm most familiar with vim. In vim the actual renaming and rearranging is done using a regular expression containing backreferences:

s/old_func(\([^,]*\), \([^)]*\))/new_func(\2, \1)/

You may constrain that to a subset of lines a variety of ways, and on top of that can suffix a 'c' on the end of the substitution regexp to make it conditional and you'll get a chance to interactively yay/nay the substitution at every match in the considered lines. The conditional suffix is a godsend as it lets you be fast and sloppy with your regexps and just skip the false positives.
My favorite way to use jq is as follows:

`tail -f my-log-file.json | jq`

This can be great if you've setup Nginx to log as JSON.
Yes, but that doesn't mean representative of all minorities. Very few Black people and Native Americans, for instance, even if Asian people are represented well.
Why is it only that the US seemingly has this problem? Other developed nations (UAE, Qatar, Kuwait, Canada, etc.) don't have the so called "war on drugs" that wasted billions of millions of dollars.
The underlying (racially biased) superresolution algorithm was called PULSE and the code was released as an artifact of this paper:

Paper: https://arxiv.org/pdf/2003.03808v1.pdf

Here are the authors: Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin

They conveniently captured an image of them all in one place for us here - https://cdn.telanganatoday.com/wp-content/uploads/2020/06/Au...

Pretty diverse team if you ask me. Sprinkling underrepresented (and therefore 'underpowered') representation into engineering teams will solve a lot of issues, but I think it has yet to be proven that it's going to help ML bias.
Does this dataset include people with voice or speech disorders (or other disabilities)? I don’t see any mention of it in this announcement or the forums, though I haven’t looked thoroughly (yet).

Examples: dysphonias of various kinds, dysarthria (e.g. from ALS / cerebral palsy), vocal fold atrophy, stuttering, people with laryngectomies / voice prosthesis, and many more.

Altogether, this represents millions of people for whom current speech recognition systems do not work well. This is an especially tragic situation, since people with disabilities depend more heavily on assistive technologies like ASR. Data/ML bias is rightfully a hot topic lately, so I feel that the voices of people w/ disabilities need to be amplified as well (npi).
I assume they want cancellations so they can shutter it with less friction.
Totally. I also find that using it, I'm more willing to externalize my thought process, put code on the screen to reason about it and quickly scrap or modify it if it doesn't work.
Still a work in progress which is why I haven't posted it to Show HN, but I have been building this for about a year now:

https://viz.chat/
>I've always trusted CyanogenMod/LineageOS more then than a device manufacturer, but after >10 years of using Android I think it's finally time for me to switch to an iPhone.

I'm not trying to go inside Android vs iOS rabbit hole, as it's common knowledge that android's default privacy features pale in comparison to iOS.

But in Android you can leverage the trust of an individual app publisher as well and not forced to 'trust the manufacturer'.

e.g.

Tasker is trusted, it can automate workflows unimaginable in an iOS ecosystem and when Google's API changes broke some of its features; Google listened to the community and whitelisted it.

Don't trust default messaging app? Let Signal handle messages. If there's an SMS based exploit, rest assured Signal update to patch that would arrive faster than android update.

Got 7 year old android device, which hasn't received any system updates in say (cough) 7 years? But you only use it for web browsing, you can still use latest Firefox for android(with its own engine). Which will support latest PWA API.

This can go on and on without rooting android; none of these would be possible in iOS at least not until jailbreak(which compromises security akin to rooting) and probably until Apple steals couple of tricks from the jailbreak community.

Finally, If you are talented but poor developer from a village in India/Nigeria/any other place you can develop a PWA web app on a Raspberry Pi and release it for the world to see without any additional charges and probably profit out of it; It would work fine on Android, KaiOS, perhaps upcoming suite of pure Linux smartphone OS, but if you want it to function on iOS you'll have to invest hundreds if not thousands of dollars in equipment, pay yearly $99 fee, develop new native app with probably a new programming language, Give 30% cut to Apple when you make money out of it.
By the way many methods are excellent at explaining their decisions. The favorite is decision trees which break the decision up into discrete decisions for individual features. E.g. a person was put in class 1 because their income was above X, their savings was above Y, etc. Other "shallow" methods like regression are fairly easy to interpret as well.

It is mainly deep learning for which this is difficult. 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.
Google never had a ten year lead, that's pretty naive. Google had a ten year _lag_. Microsoft already had office when Google appeared, and while fans have numerous reasons to like Google docs & friends, they were never better or bigger than Office.

Chrome OS has a tremendous lag on windows and Mac.

It's really hard to innovate once you start to be compared to a established brand. Go ask Bing.

Unless duck duck go becomes larger than Google, they never blew any lead, only were defeated in places opponents were stronger.
One of my favorite essays of all time is "A Muscular Empathy": https://www.theatlantic.com/national/archive/2011/12/a-muscu...

It explores this idea, that we like to think that we would be heroes if things got tough... even though we don't do anything heroic in our normal, cushy lives.

We seem to think we would some how be better people if things are hard, when in reality it is often the opposite. This pandemic has really driven home this point.

To quote the article:

> Still, we are, in the main, ordinary people living in plush times. We are smart enough to get by, responsible enough to raise a couple of kids, thrifty to sock away for a vacation, and industrious enough to keep the lights on. We like our cars. We love a good cheeseburger. We'd die without air-conditioning. In the great mass of humanity that's ever lived, we are distinguished only by our creature comforts, and we are, on the whole, mediocre.

That mediocrity is oft-exemplified by the claim that though we are unremarkable in this easy world, something about enslavement, degradation and poverty would make us exemplary. We can barely throw a left hook--but surely we would have beaten Mike Tyson.
Aka “Silence is violence”.
To be fair, LeCun started a discussion around bias in ML -- Gebru's reply is on topic, since statistical bias in ML is a narrow way of understanding the social impact of an algorithm. Gebru is one of the foremost scholars on that issue.
This is reckless and compromises the security of the Bitcoin network. A single transaction worth a billion dollars starts to create incentive for miners to 51% attack and reorg the chain.
I love emacs, but I taught myself to use vi because I know when I ssh into a remote server there will have a vi.
This is immaterial. What about blind folks or quadriplegics or folks with chromosomal defects. Adding a black person to a team and expecting them to somehow fix anything beyond what they are trained at working on is unfair and unrealistic.

I mentioned before but this is the team that created the PULSE algorithm that made Obama look like a white guy from Arizona - https://cdn.telanganatoday.com/wp-content/uploads/2020/06/Au...
A Roundtable on Covid-19 and the Attention Economy
This sounds like cable TV packages with extra steps — and the precedent there is to still have ads even after gouging the cusotmer.
The real issue is people are trying to steal it and getting fooled by a malicious site.
Yann clearly points out that the dataset was biased(or rather, not representative of the (presumed) US population).
[citation required]

I’ve seen no evidence of guns from the protestors, just the racist Karen and Ken.
You don't actually "send money to a wallet/address" in Bitcoin. You broadcast a transaction that includes a public key puzzle (the address) that someone can spend if they have the corresponding private key.

So you can generate a unique private key for each person, give them the pubkey (the address), and watch the network for 1:1 reconciliation.
A bit off topic, but the audio book "Ice Bound: A Doctor's Incredible Battle for Survival at the South Pole" about the Dr who got cancer while serving at the base is quite an interesting ride. That book kept me wide awake on a long road trip a couple years ago and the couple people I lent it to had nothing but good things to say about it too.
I can’t see a single legal justification put forward here or anywhere else. They talk extensively about CDL, but this lawsuit isn’t about CDL, it’s about un-CDL. Their moral justification is quite straightforward (though not without its own issues), but the legal system doesn’t exist to enforce a system of morals, it exists to enforce a system of laws.

I can’t imagine what their legal strategy could possibly be...
"What I believe" is completely irrelevant, because it has zero grounding in research or evidence. She's claiming that we should listen to her because she's an expert, but then provides no evidence or explanation between the two for a causal link.

I'm not saying she's wrong, I'm saying we don't know because she defaulted to the argument that "you should listen to minorities", not "here is the evidence".

What's more, every single example of injustice in her tutorial was an image recognition/classification problem - entirely different from the generative model that originally sparked the debate.
You don't actually "send money to a wallet/address" in Bitcoin. You broadcast a transaction that includes a public key puzzle (the address) that someone can spend if they have the corresponding private key.

So you can generate N private keys, give the pubkeys (the address) to N people, and watch the network for 1:1 reconciliation.
Caddy Dnspod.cn DNS Provider Module
I could not disagree more. This is a probably for me almost every waking hour. Perhaps this is a US-centric problem, but I have groups of friends on WhatsApp (mostly foreign), Hangouts, MMS/SMS, Telegram, and Messenger. Then there's Slack for work.

Ever since my Blackberry had my emails, SMS, and BBMs (iMessage-like service for BlackBerry users) all in the same inbox with threads, I have been dying for a service where I can just say, "I want to send a message to [person]," and let the app decide how to contact them or continue my existing thread.

I have paid for Franz and Station, both of which were unusable garbage, and now use Hamsket (a fork or Rambox)[1].

1. https://github.com/TheGoddessInari/hamsket
> Instead, people chose "dataset bias affects machine learning models", the most basic Machine Learning 101 fact ever, to try and debate against.

No one disagrees with that statement. The point that the ethicists are making is that that isn't the whole story, and that focusing only on the lowest hanging fruit (dataset bias) is ultimately harmful since it shifts focus away from the other, deeper problems.
When the rationale is that having someone with an experience of being disadvantaged might be particularly inclined to help build a less biased system, one hardly refutes it by pointing out there are a lot of [not very disadvantaged] minorities on the team.

But one might earn some pedantry points with the argument.
Regardless of the answers, wouldn't the problem be placed on the feet of "industry", and not the researchers?

Should cryptography researchers backdoor their own papers, because terrorists or pedophiles might use it?
This is very true! After I'd mastered drawing to the point where I can pay my rent doing it, it became a lot easier to learn other stuff.

My pole dance teacher once pointed out how different my style of training was: most students would try a difficult new move a few times, then go back to practicing stuff they were ultra-confident at for a while, while I'd be more likely to keep on trying the new move with a bunch of different little variations, and to pay a lot of attention to her when she'd come over and point out things I was doing wrong, especially if it was a wrong thing that would make me more likely to hurt myself!

I kinda feel like I can do this because I remember how I improved my art by the long, painful process of analyzing what I was doing wrong. And also because I have much less ego invested in the new thing - I already have a thing I can do the heck out of, I don't give a shit if I look like a bumbling beginner when that is what I am.

"How to learn" is a skillset, which you have to learn along with everything else you learn in the first two or three decades of your life. Once you have it down it's a lot easier to learn stuff if you're willing to put the energy into doing it right.
Why the options don't include also the "100" function (aka 1s orbital)? I guess the error is in https://github.com/c0nrad/hydrogen/blob/209ffe5cf14879a4679b...
That video is amazing!
Bitcoin's strong point is that it will allow rich people to maintain their wealth when the inevtiable tyrants take over and try to redistribute wealth.
:vimtutor
This was the first time I've watched live broadcast US TV since leaving the country ten years ago. Are there any shows on, or is every channel besides PBS just advertisements all day?
Not an expert since I don't really watch cable TV, but it seems like Spectrum's app is available for iPhone too - so not just available from home.

https://apps.apple.com/us/app/spectrum-tv/id420455839
> very high probability of climate change.

There is a 100% probability. It has been happening since there was a climate. The question is how much can we really affect it or even if we should.
it's not that incredible. most going concerns already have the working capital, current assets, and receivables to hold out 3-6 months.

and the cost of capital isn't the right rate to be applying here. a better estimate is the spread between the cost of debt and their short-term securities rate (like the interest rate on current assets), which could even be negative (that is, making money). certain businesses can even be financed via their receivables alone.

damodaran is a good reference. his book was the basis of my finance and valuation classes. but it's dense and not easy to implement without going all in. it really taught me that finance and valuation are arguments, not analytical solutions.
That’s a frequently used formula for security, and it’s dumb there too, manipulated to explain away problems or get money.

Impact is more like “perceived impact to me” and risk is over a timeline that matters to the assessor of risk.

War hawks see nuclear war as medium probability, low impact. Most people see climate change as something impactful in a timeframe they don’t give a shit about, and terrorism is medium or high impact with medium or high probability in a timeframe that matters to them.
Sure, and in some cases that does matter. But it seems unlikely that the PULSE model was biased towards white people because of latent racism, given that 3 of the 5 researchers who built it weren't white.
That is not a la cart. I want the option to subscribe to individual channels - ideally with multiple tiers.
just shows, some people are more interested in the fight than working to actually address the issue
There are lots of things about appearance that are deliberate signaling. It's all subjective but it's not meaningless or pseudoscience to read those signals.
The right answer may not always be the risk compensated answer. The social gains to be had from ignoring the risk could be worthwhile in the broader picture, even if some groups fail.
It is a manufactured war started in reaction to the civil rights movement by Nixon and escalated by Reagan with the explicit intent to disrupt and imprison as many blacks and leftists as possible.
The PULSE paper was done by this team - https://cdn.telanganatoday.com/wp-content/uploads/2020/06/Au...

If they are in here I hope they don't misconstrue my linking this image. I think they explained themselves very well and I feel bad that they were thrust into the middle o f this controversy.

My point is that while diversity might help, you need a lot more than that to address this problem.
i tried to switch to emacs+evil, but i can't bring myself to do it. partly because I'd have to invest a lot of time in getting an equivalently productive environment, but partly because of the following

1. vi is everywhere, and...

2. vi with no plugins is still super powerful, which is helpful on random hosts / coworker's machines / basically anywhere, and...

3. since vi is still so powerful out of the box, i only need a few lightweight plugins to close the loop and make the most powerful editor i can imagine needing. 99% of my plugin usage is fzf and ale w/lsp, which are arguably two very powerful plugins, but the cognitive overhead is very low, and at least among peers my editor doesn't seem to be slowing me down at all

i would also pose the controversial opinion/hot take that using a less "powerful" (non-IDE) editor helps you write better code. humans can only fit so much into their working memory at a time. vim doesn't really let me do crazy refactors or jump through endless chains of class hierarchies (well, with lsp it can, but...). compared to my eclipse/intellij days (and codebases worked on by other people using those tools) i am forced to tame complexity by defining good module boundaries and abstraction barriers.
FCC designates Huawei, ZTE as risks to national security
always wondered if netflix couldn't have had warehouses full of dvd drives to avoid licensing fees. wouldn't work for new releases with a lot of peak demand but maybe it would be more feasible for the back catalog of older titles.
It's strange that the people who disagree with my "security theater" comment don't seem to be alarmed about the bug I disclosed, which is a very serious one if you really do believe that macOS privacy protections are important.
Intelligence and Investigation Talent Moves (June 2020)
My judgement says not to execute programs I can't read the source to or hold the publisher legally accountable for.
Nobody gives a sentence like that as a deterrent. You give a sentence like that out of cruelty toward people you don't like who you want to stick it to.
Matt Levine has a great take on this in regards to the airline industry in this pandemic. He talks about all the people upset about all the stock buybacks they did during the boom years and how they should have saved cash for a pandemic.

He runs the basic math, and even if the airlines completely cease to exist, it was still the financially sound decision to return the money to investors. Even if they knew the pandemic was going to happen this year, they made the right decision... and given that this was such a rare event, it would have been extremely foolish to try to maintain a cash cushion for this.
How many people can afford a “good lawyer”?
From the the perspective of a technical person, such as a software developer or AI scientist, it's easy to think about what we do as working with data and algorithms. While we might step back and look at a system as a whole, a lot of technical people can't communicate the birds eye view of what they're creating. They might have the capacity, but easier for them to rely on the technical description - yeah we can just make sure the data is good and the problem is fixed. However, that doesn't communicate fully and allow another person to understand the larger picture or that you even recognize it yourself.

As an example, consider the architectural view of a system where you need availability, performance, reliability, and scalability scalability. Over time we've learned to add Security. In recent years we've begun to learn how important how to include accessibility and privacy - we're learning and don't always get it right. All these things are often implied and we expect people to believe that we already know to include them. Now, lets look at more dimensions that are affecting what we build in the area of equality, diversity, and inclusion, which are part of our discussion, especially when we're talking about AI where we lack explainability.

So, it's true that we have to look at the data and that's part of the data science associated with AI work. When we are doing data science, it's more than just munging data to try to get a validation set match. We have to do the same thing we do with all other software and look at the domain of the problem, what it's purpose is, who are the users, and what are we trying to accomplish. If we examine the data set these things will help inform us on the appropriateness of the data being used, which takes analysis, just like other software problems.

It's simple to say, let's just make sure we have an equal amount of data from each represented group. While that isn't bad, it isn't enough. Imagine hiring or loan application program. We have demographics like name, location, occupation, sex, race, etc. Some obvious discriminants are sex and race. However, think about things like location - is it possible for someone to be denied a loan because they live in a part of town considered high-risk? Maybe a human wouldn't make that connection, but a machine learning algorithm with historical data that discriminates will also use that data to automate the discrimination.

We have to look at the history, current status, and goals of what we want to do to ensure we're thinking the problem through, rather than some mechanical input-process-output steps. That's why simplifying the discussion with "just change the data" doesn't communicate the source of the problem or how it should be fixed. It would have been nice for both people to ask "BTW, what did you mean by ...?" to open the conversation so we all could learn.
There are already a number of opinionated issue trackers, like Clubhouse and Pivotal Tracker.
You really should try that Google thing

https://www.washingtonpost.com/opinions/2019/01/24/ocasio-co...