skysight-inc/HackerNews huggingface dataset:
I think it's important to note that human pattern recognition is basically black-box as well.

We can't "explain" how we recognize a particular person's face, for example.

Robust pattern recognition based on 100+'s of factors is just inherently black box.

Even when people make decisions, they're generally a weighted combination of a bunch of "feelings". We attempt to explain a kind of simple, logical rationalization afterwards, but psychologists have shown that this is often a bunch of post-hoc fiction.

Black box doesn't necessarily mean bad. I think the relevant question is: how do we ensure machine learning is trained and verified in ways that don't encode bias, and only used with "intended" inputs, so a model isn't being trusted to make predictions on things it wasn't trained for?

And also: when do we want to decide to use alternatives like decision trees instead -- because despite being less accurate, they can have designers who can be held legally accountable and liable, which can be more important in certain situations?
You don’t think an machines can learn to classify situations as good or bad for them ?
I just looked at two different emails from Instacart and can see the unsubscribe link.
Thanks for sharing this. I'm inclined to agree with your observations/reflections. Moreover, I suspect this might be common to the post-colonial experience [fn0] , if we account for: (a) what life was like under colonial rule; (b) how stratified such societies were before/after independence; (c) the forms of government (statist or plutocratic/military regimes) that replaced the colonial structures. These points and your overall observations ring particularly true when I consider the case of people (usually older generations) I've met from India (and South Asia generally) with the risk averse, at times counterproductive frugal, mindset that the Nehruvian state encouraged. [fn1]

[fn0]: Here, I may be incorrectly inferring your origins to be in a former colony.

[fn1]: For Indian readers, I hope this comment doesn't initiate a partisan discussion. :-)
> If you really want to do syscall filtering to confine an untrusted process (as opposed to reducing attack surface from potential bugs in otherwise-permissible syscalls, which is I think Docker's goal), you need to start from empty and allow syscalls instead of starting from full and blocking them. Alternatively, maybe just run the untrusted code as another user account or something.

It looks like the gVisor approach for containers (reimplementing parts of the kernel syscalls in userspace and disallowing the rest)
https://www.amazon.com/Ingenious-Language-Nine-Reasons-Greek...
>How do you know? Not to be rude but why should I trust you more than some author of a "self-help" book?

For one, because he doesn't try to sell you anything.

Second, you don't get to "know" because you are hand-fed some hard proofs or figures.

You either know it already (and agree) or can see the point the parent is making, correlate it with your experiences and observations, and recognise it's true -- or, of course, you don't (and e.g. you have a different experience).
The masses never blame the super-rich. Instead, they hate their immediate bosses (who hate theirs). Politically, we mostly resent the party of the middle managers.

We are a "managed population", following Edward Bernays's program from one century ago.
“Explainability” is a subjective idea that allows people to act as gatekeepers about the use of a model through arbitrary politics.

“The Mythos of Model Interpretability” is good reading on this.

https://arxiv.org/pdf/1606.03490.pdf
I think reading books correlates with IQ, but it does not increase it; it provides information and stimulate imagination and analytical thinking, but there is no known method to increase IQ.
> It was just a tool to use against people they suspected being involved espionage. Just another reason to put you in a cell.
https://simple.wikipedia.org/wiki/RSA_algorithm

The wiki link you provided does not have the words "export" or "espionage". Are you trying to imply that R,S, and A were suspected of espionage? You'll need a real source for that..
> They weren't really allowed viable property access until the last 50 years or so. 100's of years of catch up to make up for.

You don’t need property ownership to have a decent upbringing. It does mean there isn’t a lot of generational wealth to go around, but that’s no longer specific to being black because it applies to huge swaths of the population.
The problem here is that sunk costs affect emotions, and humans are emotional beings. It's not all pure rationality.

It's all well and good to try and convince yourself that you made a rational decision against an emotional fallacy, but that doesn't always work.
Location: Lagos, Nigeria

Remote: Preferred

Willing to relocate: Not yet, still studying in Lagos

Technologies: Javascript, Node.js, React, MongoDb, PostgreSQL, Python(intermediate), C++(I use it for solving programming challenges)

Resume: available on request

Email: olusola.samuel.oluwatobi@gmail.com

Github username: olusamimaths
Their model didn't win. IBM's model won, based on actual metrics around useful insights.
This seems unlikely. It's more likely it will continue to grow, but probably not as much as Apple, Microsoft, or Amazon.
Viewing from mobile if that would make a difference.
>Hard work is as much of a racket as easy magic.

Really? Perhaps you need to think long and hard about what you wrote above.

We got to the moon and in 2020 style civilization, from the technology involved to the social issues and personal problems we overcame, with hard work, not with "easy magic".
Photon Vs. Phonon
At our company, we are working with "mechanistic" or mathmatical models rather than stastical approches. I have observed :

1. It is hard than it should be to explain the concept to people (particularly VCs)
2 . people struggle to understand that a mechanistic model could have more utlity than a machine learning black box
3. people think you are doing something wrong if you are not using a neural network
4. The less people understand about neural networks, the more they seem to believe they are appropriate for all predictive / modelling problems
5. There is generally quite a low understanding of scientific method in the startup / VC space (speaking as someone who has worked in and around academia for years) vs how "scientific" people believe they are because it sounds good to be data driven and scientific about running startups and funding them.
Ask HN: Is competing based on price only viable SaSS strategy?
Hello HN,
I have a product, that I could launch with same features that competition have and at least same quality for half price. Is competing solely on price a viable business strategy for a bootstrapped startup ?
That would just be another example of someone peddling malware from the Edge New Tab page...
If your compiler generates something huge for the most basic test, how can you trust it's doing well on actual programs? Most real programs are far too complicated to analyze. It may be a toy example, but if your language can't handle a toy, it's probably bad on real world programs also.
Right, the optic nerve between the eye and the human brain has finite bandwidth. As a designer you want to minimize the information traveling across the wire to maximize efficiency.

An arrow represents the minimum amount of strokes needed to convey the needed information. I shall call this minimal form of design "minimalism!"
Also:

https://physics.stackexchange.com/questions/92185/photons-an...

And:

https://en.wikipedia.org/wiki/Photon

https://en.wikipedia.org/wiki/Phonon
That was a great refutation.

Allow me to add one piece: Girard comes close to refuting himself. Why? Because, if Girard is correct, then why do people believe Girard? Not because he is correct, but only because they are memetically infected by some model. And why did that model believe it? Same reason. Nobody believes it (or anything) on the basis of the actual truthfulness of the idea.

And then, where did Girard get it? Not from a careful examination of the evidence, but because he was memetically infected by some model. So it's not original, and it's not based on evidence.

So if it's true, it destroys our ability to know whether anything is actually true, including itself. That is pretty strong grounds to not believe it, in my book.
Driverless cars somewhat arrive (in limited city areas). The number of robot taxis drastically increases in the city centers, clogging the streets. Time based road tolls for robot cars are introduced a few years after their general availability mostly solving this problem. The taxes further incentivize robot car pooling, which from then on gradually start to replace public transport buses improving the attractiveness of public transport in Europe.
The first mover advantage that Uber and FreeNow have built up turns out to be moot, as the producer of the first robot car can choose who to partner with in the first few years. Customers happily switch apps to whoever offers the best service.
German car industry suffers first from switch to electric and very late in the decade from a decrease in sales due to increasing availability of robot taxis.
This throws Germany into political and economical turmoil leaving a power vacuum in the EU(by 2035).

Medical apps could by now replace most routine doctor consultations, but don't, because of strict regulation. With an aging population, leads to an overloaded and expensive medical system.

Africa becomes a big economical player, with many countries under heavy Chinese influence. Some African countries can build up an industry around drones due to lax regulations regarding air traffic.
First big passenger airliner to emergency land / crash due to a drone accident.

The actions that are taken against climate change as the effects become palpable can be summarized as too late, too little.

Household robots continue to be good for one task only.

The US looses its global dominance, as trump is reelected and continues isolationist politics while degrading US democracy.
How does that compare to others in this space? That's nearly $1.5 B per month. How do you ramp up and spend at that level without a noticeable amount/percentage being pure waste?

Yes, it's bold. But perhaps too much so?
HN is specifically because they offer a bookmarklet. For the others, it makes sense that they'd want you to be able to include share links in your newsletters.
If true, this sounds like the kind of company that needs to be named and shamed and investigated.
I had a friend who also read that book. I don't know if he picked up smoking again but I remember him telling that the book basically hammers it into you that smoking is bad for 150 pages and that you just have to give up smoking because it's so annoying to read about how bad it is.
Bloody Harvest–How Everyone Ignored the Crime of the Century
Pros of long written form:

- more in-depth content about a topic

- presents a structured approach

- can make you think about problems you didn't know you had (vs googling for a problem you have right now)

Cons of long written form:

- usually not up to date (some topics are less affected by this than others)

- you have to pay
Just to be clear, I’m referring to the popsci.com article linked by the GP, not to the NYT article you posted.

Pretty much the second half of the article. It claims that GNG prevents ketosis. It does not. It also claims that any amount of protein beyond the minimum results in GNG. Wrong again. The body is always undergoing GNG, and protein intake he nothing to do with it.

Furthermore, there is a metabolic pathway by which ketones actually regulate GNG, preventing it from exceeding a certain level.

See the article I linked. It has a good summary as well as links to the various papers on this subject.
If it tries to sell you something (item, product, experience) where the paid message's intent is to generate revenue, it's not free speech, it's marketing.
For a CS job? Very, very surprised.
Turns out it can be made quite efficient with a little effort put into arithmetic power reduction. In the outer loop, it is possible to calculate a `dr` value from the existing `dx` and `dy`. Then you just increment the radius traveled at the same time as `px` and `py`, and replace the inner loop condition that puts a bound on `z` with a new condition that puts a bound on the radius.

That fixes the rotation artifacts, and then makes it really easy to add a distance-based fog effect to hide the cut-off where new terrain comes into view as well.
An abbreviated history of JavaScript package managers
Historical population estimates necessarily have a very large admixture of conjecture.

Direction of changes, short term, are more reliable where there is information, but with long periods without information, so only rare baselines.
5 Places World War III Could Start in 2020
A sister-in-law who is South African shared that American culture has so influenced that country that some of her classmates in high school referred to themselves at African American because they believed it to be more politically correct than saying black.
I will spend $100bn in future of bs by 2050. Both statements have equal weight. Mine may not get as much exposure.
Math books are a solid investment and do not become obsolete. Looking at a digital math book is inconvenient, since learning from a math book often requires flipping between pages and the index. That is why a physical math book is more convenient. For many math topics, books from 50 or 100 years ago will also provide useful insights that are still entirely valid and true today and into the future. Books from non-math genres may not age as well, because the information may become obsolete or outdated. However, for pure math books this is not the case, which makes physical math books a good investment that could last.
Does anyone care what the opinion of OSI is anymore? If you care enough about whether an agreement is "open source", you probably care enough to read the agreement itself and make your own opinion.
Entire civilizations have been destroyed before, notably the Roman Empire. Humans came back stronger and more well off.
Looking at the chart of the climate summit targets, we don't really seem to have a history of doing the right thing. I would love to share your optimism of "some hope" but to me it seems that as a species we're simply incapable.

https://miro.medium.com/max/696/1*NHFNUj3lns_y-3srODj-zg.jpe...
The Future of [audio]Books
Well, may be. I'm not sure if I should trust USA after their lies about Iraq in early 00s.
This article seems to misrepresent a number of important issues, and as a result significantly overstates their claims. I'll pick just one illustrative (but important) example:

> "For instance, when ProPublica journalists tried to explain what was in the proprietary COMPAS model for recidivism prediction, they seem to have mistakenly assumed that if one could create a linear model that approximated COMPAS and depended on race, age, and criminal history, that COMPAS itself must depend on race. However, when one approximates COMPAS using a nonlinear model, the explicit dependence on race vanishes, leaving dependence on race only through age and criminal history. This is an example of how an incorrect explanation of a black box can spiral out of control."

The concern about the strong relationship between race and COMPAS predictions is not largely based on a concern about whether there is an explicit dependence in the model. The concern is whether there's a relationship either explicitly or implicitly. And in particular, whether such a relationship results in unfair outcomes. The findings of the ProPublica study (https://www.propublica.org/article/how-we-analyzed-the-compa...) strongly suggested this was the case:

"- Black defendants were often predicted to be at a higher risk of recidivism than they actually were. Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent).

- White defendants were often predicted to be less risky than they were. Our analysis found that white defendants who re-offended within the next two years were mistakenly labeled low risk almost twice as often as black re-offenders (48 percent vs. 28 percent).

- The analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 45 percent more likely to be assigned higher risk scores than white defendants."

I understand the desire of the MIT researchers to promote the value of their work, but in this case they appear to be doing so in a potentially damaging way.
Lovely news
> at sufficiently high latitude

I assume you mean altitude?
In my own read Roosevelt was clearly heavily involved in the first attempt. Second one he mostly took credit for things he wish he could do.

Wiki might be incomplete but compared to npr agitprop it is much better
YouTubers are being hijacked for crypto scams
It seems like you're frame of reference for everything is about lists of things that have been recommended to you for some reason, whether it's online content or books that you've gotta read.

Just throw that out, erase your list of stuff, and go to a bookstore or a library and pick something up. Some books will catch you, some won't. You wouldn't very well deny going to a movie with friends because you can find the synopsis online and that's more efficient.
I would also add in that blackbox ML solutions are deceptively "simple" approaches. Like a miraj in the desert for software developers: No domain expertise neccessary - or even the counter narriatives may come in... "domain expertise holds you back - you need to be an outsider with a new method", where as many problems require domain expertise for a realistic understanding of the complexity of the problem-solution.

Other narriatives that can creep in "once we get enough data..." which for some problems may as well be never.

Also, for some systems pertubing the system changes the problem meaning your dataset and model may not reflect changes in a dyanmic / complex system.

Also, blackbox ML approaches struggle to combine data from multiple modalities which is often relevant to many real world problems (at least with the majority of algorithms which are realistically implementable off the shelf).
I was specifically replying to their commentary on Asian women's dating preferences. I should've been more clear.
chrome:// pages do. But Microsoft didn't use a chrome:// URL, they instead gave the privileges to an https:// URL, breaking the security model.
MONITOR/MWAIT will get you the part where a thread will pause and the cpu can rest until a write on a store on an address range, then the waiting thread is allowed to continue. You can't wait on a specific value though.
> your model might be incredibly close for a huge portion of the population (or for the biased sample you unknowingly have), but then wildly off somewhere else and you won't know until it's too late because it's not science

But all science works like this. All scientific models can have errors, and presumably they all do. Even really basic empirical science is only reliable to the extent that our models of how photons and our visual systems work, and those models have errors we know about and probably others we don’t know about.

Fallibility does not mean that something is not science, on the contrary, denying that some theory or model is fallible is profoundly unscientific.

But of course, that doesn’t mean we should accept “black box” algorithms as the end of the story. We should strive to develop explanations for those things just like for all other things.
Show HN: TextPiper – shareable Unix-like text processing pipeline
Yes, but if you're comparing black performance to the performance of other races - you can't leave wealth accumulation out of the picture.

Sure, there are plenty of poor people who are white, but when you are looking at aggregate statistics, the gap is extremely clear.

In my city, Boston, the average household wealth of a white family (excluding the value of the home) is $200,000. The average household wealth of a black family is $5. (and no, I didn't forget to put a thousand after the 5)
Way to go! Would you consider a blog post about the self-publishing experience?
What is the history of the Hello World example program? I'm not sure why it's become this defacto example of syntax for every language. Maybe there is something else we could use as an example that would be more indicative of syntax but perhaps I'm overthinking it.
I agree with that. They'll slow considerably, not collapse. The stagnation of their growth is basically inevitable, considering they'll be hitting $200 billion in annual sales soon (two more fiscal years). Right now there are seven companies in the US over $200b in sales. Globally there are 17 total over that line (seven of them are essentially state-backed energy companies). None of them are growing at a consequential real rate other than Amazon.
> My understanding is...

That's how two finserv companies would transact off-chain with each other, but when I go to buy a cup of coffee with a bitcoin, I'm not opening up a payment channel with them for one transaction, that would defeat the whole point. The coffee shop will use a payment processor, who isn't going to deal with me off-chain unless I'm the KYC'd customer of them or some other finserv they trust. (please correct me if I'm wrong here)
New Hosting Company with Free Hosting Shared Web Hosting
Oh cool. Interesting!
It matters when. 50 million dying per year is the baseline, say a billion in 20. So, if six billion die over the next 20 years, that is a problem.

If it happens, we can at least be certain that 6 billion more will not die in the subsequent period.
It’s also a real open question whether any of the interpretable models are actually interpretable, or even if they are in any well-defined sense more interpretable than “black box” alternatives.

In practice the answer is a massive “no” so far. Some of the least interpretable models I’ve had the misfortune to deal with in practice are misspecified linear regression models, especially when non-linearities in the true covariate relationships causes linear models to give wildly misleading statistical significance outputs and classical model fitting leads to estimating coefficients of the wrong sign.

Real interpretability is not a property of the mechanism of the model, but rather consistent understanding of the data generating process.

Unfortunately, people like to conflate the mechanism of the model for some notion of “explainability” because it’s politically convenient and susceptible to arguments from authority (if you control the subjective standards of “explainability”).

If your model does not adequately predict the data generating process, then your model absolutely does not explain it or articulate its inner working.
Yeah. Near as I can tell, it says "to safely publish something that can be considered illicit, don't put it on the internet." But to the obvious response, "ok, where then?" it doesn't answer, as far as I can tell.
Doesn't quantization harm model quality?

It's hard to quantify how much damage quantization does, but natural language generation is very subtle.
Click on the slash to see a list of supported commands. You can compose a sequence of text processing operations, and share the resulting link with your coworkers/friends. Let me know if you wish to see certain operations supported!
I wonder how much of that is the legacy of redlining given that home ownership is the major vehicle for long term middle class wealth accumulation. (House as financial instrument is itself a problem, but that's another matter.)
Greater percentage of black men in prison today than in the jim crow era. If you're in prison, you can't be there for your children.

In my hometown, DC, one out of every three black men will be imprisoned in their lifetime.