skysight-inc/HackerNews huggingface dataset:
> vim vs vi: most obvious difference (IMHO) feature wise is that vim adds vimscript
IMHO, the most obvious difference is that in vim you can kinda just press "i" and pretend its a normal text editor. You know, the kind anyone coming from another OS would already be familiar with. Try doing this in regular vi, and you'll be cursing and pulling your hair out.

Today, I don't put much stock into anyone expressing an opinion in the "vi vs emacs" wars, because 90% of the people claiming a preference for vi seem to actually be vim users. Using vim is a much more pleasant experience than using good old classic vi.
I'm in the group which was upset over the insinuation that ML Researchers should not care about fairness or ethics as much as ML Engineers. The distinction between researcher and engineer should not be grounds to care or not care about safe and ethical model building. This is especially true when the difference between either role is highly arbitrary and varies by organization or field.

For the sake of ethical R&D, it's counter-productive to build a hierarchy of investment into the problem. Admittedly, the responsibility of end-results can differ, but the consensus that this ethical work is important should ideally be universal. That said, there should not be some ivory tower where you wash your hands of the ethical problems of your field.
> [...] there are many of us who would not be where we are today, as productive, value-creating citizens, without having learned our trade with pirated software.

Agreed. I learned to program with GW-BASIC which if I remember correctly was included in a (pirated) copy of MS-DOS. Then I graduated to pirated Turbo C and Borland C++, and (I think) only then to the genuinely free DJGPP (a port of GCC for DOS).

If it weren't for pirated software, I wouldn't be a programmer today.
It's actually such a waste devoting any time to discussing this kind of nonsense. Everyone involves knows that they're blowing up a very matter-of-fact statement of opinion into some contentious """sOcIaL jUsTiCe""" issue. Society has rapidly degenerated over the last decade to the point where online midwits view righteous indignation like the coin of the realm. What he was talking about is hardly insensitive, it's an opinion on a technical matter. His real misdeed is being ostensibly male and white while weighing in on a matter which impacts non-white members of the population.
Is there a torrent going?
>Rectangle selection and deletion? Check. Regex operations?

These are vital features from my perspective, but completely orthogonal to whether an editor has a post 1980s interface.

And it's also nice to be able to handle large files.
I agree that some people appear to believe this. But what is the justification for it? I don't really get how it cashes out in concrete harm, assuming you de-bias the datasets.

To be clear, I do get arguments that ML might increase the efficiency of policing, and there is some inherent tension between the efficiency of the state and freedom, to the extent that imperfect enforcement is a tacit feature of our legal system. But, this is not an inherently racial point.
Neither Ford nor Coca-Cola pulled out of Nazi Germany until ~1941. IS Facebook really that bad?
It always depends on how you stretch the term indeed, HN has less features than most old-style phpbb forums so that sounds strange to count it as social media.
Yes I agree with you but what’s the point? Everyone should be like Jeff Dean? Many people don’t have those qualities and don’t desire to have them.

I have no idea who this person is, but it sounds like he’s not very good at engaging with the public on Twitter, so the best strategy for him is to leave the platform.
Vimscript has the worst comment syntax, hands down. Comments start with the " character, except when they cannot (see :h map-comments)
I cannot remember the last time I wanted to use CapsLock--more than 30 years ago, anyway.

By far the most irritating thing about others' keyboards is they have not disabled it yet. Second most is that Ctrl isn't there instead.
ZBrush is $40/month. Makes me sad you can’t afford that.
I think Yann and his critics are focusing on different questions. IIUC Yann is looking at the narrow engineering perspective of how to build an fair ML system. Gebru et al seem to be talking about a much more abstract, societal trend-oriented, "let's think about if we should not just if we can" type of question, though it's still pretty fuzzy to me. Anyway, the epistemological standards of those questions are basically incompatible (incommensurable?), so discussion is impossible unless they agree on which one they're trying to answer. I'm starting to notice this kind of thing a lot.
What actual task are you performing that requires you to generate a high-resolution photo of a face given a low-resolution photo of a face? If it’s just for fun, then sure, bias may not matter by definition. But if it’s used to make decisions that have real consequences for people’s lives, then it sounds like a really bad idea no matter what the training set is.
Balanced training data? Kind of an oxymoron.

Why not just edit the end results to show what you really want?
Guns don't kill people, its the people who keep loading them with bullets and shooting them at people.

ML isn't biased, its the people who keep loading them with biased data, and then running them against photos of people.
Using appearance and superficial features to predict personality or aptitude is phrenology.
> As a result,

How was causation established?
Paraphrasing heavily. Some models, regards of how much care is taken to unbias them, do more harm than good.
As much as I've had my disagreements with dang and the team, they're doing a great job keeping this community together.

If you steer clear of certain topics[1], this place is absolutely civil.

[1]: Not the usual suspects and not what divides the rest of the world...
Well, what are you going to use this technique for? What’s the use case for generating a high-res photo of a face from a low-res photo of a face? It’s hard to think of any use case with real-world consequences that wouldn’t be a bad idea regardless of the training set.
I agree! While maybe it won't be everyone , it does feel like there's a cracking of positive energy around these decentralized, or at least federated efforts.
Do you have any tips for explaining to stakeholders that things in a Redis cache expire on async schedules?
My concern with Twitter became reality when news media started using tweets as part of coverage.

Between APIs, bots and selective coverage it creates an ability to amplify messages as if they are representative of public opinion. Look at the services that will sell followers as an example.

I look at Twitter less than once a month but because of coverage (like the above article) I know about all sorts of stuff that’s happening on Twitter.
Vi users RTFM.
This analogy really doesn't work, because taking something deprives someone of something they would've otherwise had. If you have the means to buy a piece of software and pirate instead to save money, then sure, I think you can make a case that it is 'taking'. When you're pirating something that you would've never been able to buy, who are you depriving of anything? I guess you're depriving those who have the means to buy that software of the exclusivity of their position, but is that something that they get to 'own'?
Maybe! My point about the support system is that it represents the "outside state". You don't have that person inside your organization, so that's they need a channel into your process. A support ticket needs to be triaged, prioritized, and executed, but that belongs in project management land.

I guess the overarching idea of my philosophy is that all pieces of a company's workings need to be owned by someone. Software like issue trackers don't work because you can't replace ownership with software. You may pretend that all issues going into the system are important, but they aren't, which is how we have ended up with Github bots that go around closing "stale" issues.

Thanks for reminding me about Bors. It's something we've been thinking of using.
I haven't paid attention to the yearly goals, but the story you are telling is quite believable (and also sad). To me it sounds like a disconnect between people who do planning and people who implement things. Thank you for sharing!
“Be extremely subtle even to the point of formlessness. Be extremely mysterious even to the point of soundlessness. Thereby you can be the director of the opponent's fate.”
― Sun Tzu

I speak not of confrontation with the central government but confrontation with many aspects of the things in "the system" that currently underlines and defines how the cerntral government thinks and operates.

The ambiguities introduced by the word "system" here is in no means unintentional in that it needs not even be political or geopolitical: it can be cultural, it can be like you said ideological, it can be legalistic, it can be infrastructural or componential, it can even be technological, transnational, game theoretical, self-regulatory, etc. It's a board spectrum having to do with the emergent phenomena in the power structures that spread across different industries and establishments in and outside of China. As much as the Chinese central government is central and monolithic in nature, there are many shades to it, and for everyone involved in running this conglomerate (including those at the top) it is a constrained optimization problem to survive, diversify future risks, etc. Ultimately we are dealing with human nature and the status quo here. There are some beauties and there are lots of ugliness. But it is definitely not immutable. It will continue to change.

What they are currently doing to Hong Kong is shooting themselves in their own foot. It is a very short-sighted move and likely they are aware of the many cons that come with it too. Now the question is what they are going to do next to minimise damage and how the system that constraints them can be confronted to encourage more freedom of Chinese expression.

In the next twenty years China will probably overtake the States in terms of market sizes, but very unlikely in terms of science, art, technologies, etc. And without all those wonderful human endeavors China as of now is and will continue to be (if there are no changes) pretty much just one giant sweatshop, except for small parts of Beijing and Shanghai, etc where the manufactured consent are being examined and there are still traces of liberal arts (and also maybe parts of Yunnan where the hippies live). One could argue that it was necessary for China to position itself as a sweatshop in the post-Mao era (1975~1989) under the darwinism-inspired mindset on national progress [1], but as times change we are coming to a point where "the system" needs to be adjusted to encourage more freedom of Chinese expression and confrontations challenging the status quo. The act of postponing it or doing things aimed to diminish it is very short-sighted, backward-thinking, self-mutilating, and sad, and it would continue to screw up the reward systems of capitalism for innovation domestically, and to an increasing degree around the world due to its sheer market size. And I hope that is not the direction we as a civilisation is heading towards.

“The supreme art of war is to subdue the enemy without fighting.” ― Sun Tzu, The Art of War

Confrontation needs not be violent.

[1]: https://www.jstor.org/stable/j.ctt1tg5mmd
You have the reasoning wrong for why they broadcast over the air via antennas, it was (still is I think?) mandated by the FCC.
> Yellow sticky notes on a wall are awesome. They're a great deterrent to keep the PHBs from generating reports and dashboards for bothering people with KPIs.

Wow! You should buy a .io domain name throw up a landing page and start selling this as a feature! (On a subscription model, naturally.) Seriously though I love this point.
Part of the problem is that "it's just the dataset" is being used as an excuse (witness the "just"). It doesn't really matter to possible victims of, say, the use of AI in law enforcement where exactly the problem originates.

I doubt that anybody was accusing the creators of that upsampling model to have intentionally tweaked the model to default to white people. In that sense, YLC is attacking a straw man.

Given the publicity such problems have gained in the community, one would expect publishers of any model to verify it doesn't fail with the most obvious examples. Not doing so is negligent at best.

If we lack datasets to train AI that doesn't spectacularly fail any test for racial biases, we lack datasets for anything that could be considered fit for use. At that point it doesn't matter if the model is flawed, or if it's "just" the available data. Such models shouldn't be published, and we should instead invest in either better data, or come up with better methods of training.

(And, as a minor point, his idea that Senegal is representative of "Africa" as a whole is also... let's say "unfortunate")
In that Reddit thread the topic starter mentions that his CS faculty has a diversity issue. According to the Reddit TS because 30% of undergrads and 15% of the professors are women.

I was triggered by that and wanted to comment on it because I can imagine specific parts of CS are currently so new, evolving and hard to master that I can hardly imagine that diversity is considered an issue.

I can imagine society should already be happy with anyone who is able to complete an university CS study.These studies often require a specific mindset, passion and probably also a specific 'beta' brain.

I might be totally misunderstanding diversity anyway, but in my eyes all society can do is provide equal opportunities to everyone and the outcome can be a result of other factors.

In some specific industries like healthcare (specially nurses) and childcare the far majority is women. That is not because men don't have the opportunity to become one, but there are other factors that create this specific outcome where men are outnumbered.

In e.g. construction the majority is men. Also here women have the opportunity to get such a job, but women probably less likely apply for heavily physical construction jobs because of different physical conditions.

Therefore I think diversity itself should never be a goal, but making sure that _everyone_ gets equal chances/opportunities should be? And then accept the outcome.

In case of the Reddit TS it seems improvements on equal opportunities could perhaps be made on how is dealt with paternal leaves.

And once we can
I don't understand what Timnit and these other people are really even advocating. She says "diverse datasets are not enough". Structural problems can't be ignored etc.

Ok, so what is the solution then? Does she have a concrete set of steps or goals to address the problems she sees? Is there a list somewhere of things that would appease her, and in her mind make ML fair? Honestly interested.
The core distinction is understanding that bias in ML is not _just_ bias in the data. It may be true that we can reduce bias in this model by reducing bias in the training data, but there is a deeper, more fundamental problem of bias that will not be solved just by changing the training data. Marginalizing the discussion by pointing out that this case would benefit from less biased data is unproductive.
Maybe we shouldn’t use Ai for anything in critical situations. Perhaps there is no perfect model for AI training. Nuance is valuable and perhaps current AI systems are not valid answers to the problems we face. Money will say otherwise but it’s hard to support these kinds of solutions. Better trained people are a solution that is unpopular currently.
as a customer, I always like that if something goes wrong, eg. product not delivered, there's always the chargeback option.
Well said, but I believe the “thinking” problem is because we are not generally wired to think in a statistical sense. We can assess the severity fairly well, but we have all kinds of cognitive biases that get in the way of accurate probability assessments. [1]

[1] https://en.m.wikipedia.org/wiki/List_of_cognitive_biases
This was the kind of reasoning that led gun makers to develop safety mechanisms.

It isn't clear what a similar safety mechanism would look like for an ML algorithm. Modern ML algorithms are nothing more than generic pattern extractors. What would a safety mechanism look like?
My org allows profpics for email or messaging or stuff like that. Some people have old, tiny profpics that are pixellated at various sizes. This technique could help the pictures look nicer.
I was thinking along the lines (based on skimming documentation) of CGAL using arbitrary precision integer based rationals, which are slow, and using floating point with the error correction might potentially be faster.

Unfortunately, it's probably way beyond my ability to delve in to it.
MathsGee Launches Interactive Online Python Course for Everyone
But you also paid 2x more for it, so it evens out.
For instance Pixel 4 is 74000JPY with 3 years of updates = 24667JPY/year
Galaxy S20 is 78000JPY with 4 years of updates = 19500JPY/year
iPhone 11 Pro is 120000JPY with 5 years of updates = 24000JPY/year
Your description of being capable of simulating "hearing the words" resonates with me.

When I think about it more, I think I almost feel the sounds on my tongue, which is to say, when I try to simulate the sounds, I do so by imagining how I would make the sounds with my mouth.

Also your point about casting choices for movie adaptations resonates. When a movie is made of a book I've been reading I'm almost always just so pleased by finally being able to see what the character looks like, and have never felt disappointment.

If anything while reading about characters, my mind cycles over possible physical attributes wondering which a character might have, but never really building anything resembling imagery.
Twitter curates content and pretend to be neutral the way you said.

They push politics and constantly push you away from content you like. So everyone is disappointed and angry but thinks they enjoy it and some people think it’s all up to users.
Highlighting the initial { of a function and then typing 'va}' ('v' for entering visual mode, and 'a}' moves to the matching close brace. Similar commands work the [], (), and <> pairs) selects the entire function body. From there, a regular s command will operate on the entire selected text.
Not sure why you're being downvoted. I haven't been following this and I appreciated the summary.
If you have time to play:
https://m.youtube.com/watch?v=_QdPW8JrYzQ
I asked another commenter as well, but what are the proposed solutions then? People are obviously upset about ML and bias, is there a place I can get a summary of actionable next steps to lessen bias in ML?
Show HN: Simple, quick and affordable way to add analytics to anything
> Twitter makes it super easy to find the worst examples of the other side/take them out of context, and circulate what they said among 'your people' to get massive retweets. Twitter does this...

i mean, i can give plenty others that do exactly what you said. twitter is not unique and certainly not the problem.

i read his final(?) tweets and the reasons why he decided to leave twitter. and i don't think or remember him blaming the medium.
If that is the case they should notice they are talking past each other and reset the conversation... But I guess that is very hard/impossible once it gets personal. Thus I think the fact that it became personal is the main problem...
All the little operators that make things nice like += are missing. Their object implementation is meta-tables, so every single codebase does stuff in fundamentally different paradigms. Operator overloading in such a dynamic language makes things extremely hard to understand. While `"0" == 0` is false, the language will still sloppily convert between floats and ints and will also convert strings to numbers in other places. Worst of all, array indexes are base 1.
That even makes it weirder that a no-name Chinese phone with an unknown ROM is considered more secure than a stock LineageOS just because it's not unlocked...
I think part of the exhaustion is that there's no in-between phase from work to home. A commute helps, because I have time to mentally cool down after ending the day. WFH, I get jumped by my kids as soon as I leave my office, but my brain is still working on that software architecture design document.

What helped me was to aim to stop working about 30 minutes before leaving the home office. During that time, just read a book, post on HN, answer/delete emails, all this lighter behavior.
You think Youtube is getting strongarmed by Comcast? That’s surprising. Google could easily hold out for a la carte if they wanted to.
I can't speak for Timnit, but for me it would have sufficed to see Yann start with "I care about ethics and so should researchers and engineers." Instead it took so long to get there, so many "buts" and so many implications that researchers shouldn't be as invested, that I was disappointed.

As for Timnit and LeCun: a user above notes that the epistemological frameworks they're using to analyze this problem are not aligned. I found that comment pretty eye-opening, honestly.
Has anyone put together a interactive VIM tutorial?
https://en.wikipedia.org/wiki/Hong_Kong_Basic_Law

It does, article one states HK should have high degree of autonomy and etc...

Furthermore, article 23 states that HK should make their own national security law, not China.
>> Email-based collaboration is most welcoming to new people

What? Aren't new collaborators way more likely to be younger and acquainted with Web-based tools like GitHub/GitLab?
The level of discourse on Twitter is noticeably worse than every other major social platform (except YouTube comments if you count those). I'm sure people will have tough questions for him elsewhere, but the sheer nastiness is a distinctly Twitter phenomenon.
I know we're having a laugh directing the would-be pirates to where they can get "free software" from, but you already know the answer to your question, right?

Online proprietary software sells you to advertisers for money, or locks you into an ecosystem so they can sell you to other third parties, and they use some of that money to fund development.

With free software you either have to improve it yourself (the source code is on the internet) or give them money so they can hire someone to do it (they all have donation links). If you're dissatisfied with the user interface, have you tried doing the things that cause it to be improved?
MIT has a python workshop https://libguides.mit.edu/c.php?g=176295&p=1161397
"I can't imagine that anyone would be ready to fork over $1k for something that you can't really try out"

That's pretty much what the normal experience is for buying prescription glasses.
You try on the frame to see if it is comfortable, but you can't try out the prescription in the frame until a week after you have ordered them.
What I learnt about Product Management
Ask HN: How to get eyes on our SaaS product?
We have built an MVP for an app to help restaurants keep records of customers to comply with new laws around COVID-19 contact tracing in Australia: https://hosposafe.com.au/

We are struggling to get the app in front of potential users. We have a few friends who work in hospitality (who've responded positively), but we don't have connections to restaurant owners. We're using Google Ads, but we're hardly getting any clicks. So we were wondering: how do people find their first users for a bootstrapped product like this?

At the moment many restaurants are just collecting details with pens and paper. We also have a handful of competitor apps - most of them are free and pretty janky. One competitor is slicker, but they're also a lot more expensive. So we believe there's a big market.