It shouldn't be about making lots of money, personally I find it great that most good software engineers aren't primarily driven by money, but that mindset shouldn't imply exploitation. It's just a fact that large corporations and companies in general benefit disproportionally from OSS. The value that open source software creates stands in no relation to the price that companies pay for it, even if the maintainers of some projects are perfectly happy with the compensation they receive. If you don't need a large part of the money that your work justifies, then just give it to charity, but don't allow companies to take it.
This whole thread is basically a testament as to why the OSS model, unfortunately, is broken. Very competent people are expected to write widely used software for a salary that basically merely guarantees subsistence. It never ceases to amaze me how great software engineers allow themselves to be exploited in such a way. If you consider the societal impact that some of these projects have and the skill set that some of their maintainers have, which pretty much dwarfs the skill sets of people in other well paid professions such as lawyers or doctors, it's frankly ridiculous that they are expected to work for significantly less money. Not to mention that the abstractions some of these people wrote basically allow a significant number of people to enter the industry in the first place.
$121k is not a lot of money for someone writing compilers. And I would seriously doubt that you could just take your average developer and expect them to be able to contribute to a project like Babel in an equally productive way.
> we've never observed an interstellar object before this, haven't after this, it's reasonable to think that the 99.99%+ of it's life it spent outside of the solar system means it starts with surface conditions unlike what we're used to from comets
In the Lex Friedman Podcast Loeb said that another interstellar object, named Borisov, had been observed since Oumuamua and it had a typical comet shape.
I was sceptical at first too because of the fact that they only restricted buy orders, but it makes perfect sense from the perspective of a broker in this situation. Thomas Peterffy explains it pretty well in [1], basically if you have a short squeeze and the supply is limited because of people holding their shares, the stock would basically go to infinity. Those that sold call options are out of business and the brokers have to take the counterparty risk and are themselves insolvent as a result.
[1] https://youtu.be/kV_P8wnY854
Well I think probably most of the people on wsb have no idea what they're doing, those that trade on margin are extremely reckless and irrational. Having said that I still find absolutely ridiculous and highly corrupt that they solely stopped buy orders. If you stop trading that's reasonable, but to cap this off on one side and allow Wallstreet to make up for losses is unacceptable.
Cut her some slack? She's making extremely harsh accusations without any kind of evidence. Why is it that these people have seemingly unlimited leeway in their statements whereas any just slightly politically incorrect statement can cost other people their careers?
> We're not really comparing flu stats vs. covid stats. I'd argue these things are not good whether they come from a severe flu or covid, so that seems like a red herring?
Thanks for the links. So how do I know these value are abnormally high compared to say a severe flu? I've read in the thread on hn that posted about the mental illness results that the values for a severe flu are 13%. And what implications do, say, a "lower left ventricular ejection fraction, higher left ventricle volumes, and raised native T1 and T2" have? Why is that worse than the risk of potentially (without knowing any probabilities obviously) developing autoimmune conditions as a result of taking the vaccine?
These are all short-term studies. Given that this is a completely new type of vaccine, a certain amount of scepticism about its long-term effects is warranted in my opinion. Personally I fail to see what benefits non-vulnerable people have from immediately being vaccinated.
Has anybody else noticed that negative posts are almost always the most upvoted posts on any kind of success related topic on hn? These posts always mention survivorship bias, which admittedly is in most cases a valid point, but I find it kind of disturbing how in a startup community risk taking is almost completely frowned upon. The Twitter post captures the Startup sentiment fairly well, if you want to do something exceptional you have to be willing to accept that the probability of you never succeeding is quite large and therefore accepting a low status, possibly for your entire life, is something you have to be OK with.
Well, my understanding is that the virus jumped from some species, which was sold under absolutely miserable conditions in a wet market in Wuhan, to humans. I find it almost comical that someone would be completely fine with more animal exploitation given that this was the reason humans were infected in the first place.
The mathematical objects mentioned in the article are way more abstract than something like a sorting algorithm. There aren't any words in our vocabulary which would concisely help to describe these concepts.
They do give an example of a question, in which the model chose an incorrect answer in the adversarial setting:
"The condition of the air outdoors at a certain time ofday is known as (A) friction (B) light (C) force (D)weather[correct](Q) joule (R) gradient[selected](S)trench (T) add heat"
I assume this might be characteristic for other questions as well, although I don't know anything about the Regents Science Exam and whether there are multiple questions about closely related topics.
I found the science exams results interesting and skimmed the paper [1]. They report an accuracy of >90% on the questions. What I found puzzling was that they have a section in the experimental results part where they test the robustness of the results using adverserial answer options, more specifically they used some simple heuristic to choose 4 additional answer options from the set of other questions which maximized 'confusion' for the model. This resulted in a drop of more than 40 percentage points in the accuracy of the model. I find this extremely puzzling, what do these models actually learn? Clearly they don't actually learn any scientific principles.
Is this a joke? He wrote that he liked Arnold's and Wald's books. I have tried to study from typical physics undergrad books and was also missing some rigour which I would assume most people coming from a math background would appreciate more in introductory physics books. Arnold's book or also Spivak's book are much better in that regard.
I never understand this line of reasoning. What do you think is it that comes up with a thought like 'be excited about this task'. When you say that your brain is easily convinced by this you imply that there exists something besides your brain that comes up with thoughts, which makes absolutely no sense at all. It's more likely that your brain just decides a priori to do the task and then comes up with the thought that justifies doing the task.
Those two spots could have gone to students who actually were more qualified and talented. If this is done on a large scale many gifted students cannot get the education they deserve and need, so the world loses out on lots of talents hence making the world worse.
That's so ignorant. How can we possibly know how other animals experience pain or suffering? You just assume that the further you diverge from us in the animal kingdom the less animals are able to suffer. That certainly makes things easier.
Does anybody know how long it would take to train an alphazero go version using one gpu? In [1] they claim that it took 13 hours until the model was able to beat the original alphago version, but they don't state what hardware they used.
Well of course you say that, you're an ML researcher who likely went into the field expecting further steady progress comparable to what we saw between 2012 and 2016. If actual progress in the field would be slower than what is currently still expected by the majority of people this would have dramatic consequences for future research investment, which is why you see most ML researchers reinforcing the hype or at least not talking the hype down.
While the accomplishments in the last 8 years have been impressive and applications of those techniques have and will continue to have impact in the real world I think that there are extremely big obstacles in the way towards something that would be truly transformative and which would put a lot of people out of work, which the $20 trillion industry estimates assume. Unfortunately I have seen no evidence that there are sufficiently good ideas in the field to bypass the upcoming roadblocks.
That is very much an over-generalization and dramatization. While there certainly are people who fit your description, not all people with math or CS backgrounds fit into that category. Also the existence of such people says nothing about the potential benefits of programming for people with different backgrounds. I didn't day that everybody's only tool should be the rigid logic of programming/math, but merely that learning those skills would probably complement most people's skill set nicely.
I highly disagree with this. Programming, if taught correctly, teaches kids how to think, similarly as to how math does. In my opinion people in most professions would benefit tremendously from being able to reason correctly deductively and from knowing how to approach complex problems, which are skills that a good programming education will give you.