There is no such thing as "just words". That's a position only sustainable from privilege.
495 karma · joined August 18, 2015
... found that Ellen Pao could not prove her case on the balance of probabilities. That's not the same thing as you just said.
So much of Radiohead's bleakness is in their use of irony.
> the cheeriest, 15 Step
The title line: "fifteen steps and then a sheer drop". A song which makes explicit references to, well, execution by hanging is...
(And "you used to be alright; what happened?" is a repeated motif.)
If your culture is one where that's necessary then the entire management chain should be fired.
It's always been the fluff which drew the ads; the lifestyle and sports content.
What newspapers had was a lock on the business now owned by Craigslist, with the transport and fulfillment network (newsagents and delivery vans!) being the key asset. Once that went away they were no longer businesses.
They didn't see it that way, but that's because their core business was so good they could afford to delude themselves - it didn't matter that they didn't know what they were really doing, the business model was that powerful. Google is remarkably similar in many ways.
What a cool business. Congratulations.
This is really smart as long as you can pull it off (and you have the network to get the capital and debt together to get it off the ground).
Credentials can be abused, sure, and they're a shorthand for a more complex set of concepts, but they are on the whole a very good thing.
Thank you for saying this.
It feels like what we're seeing here from HN leadership is denial - or abdication – of moral responsibility. That's a choice you can make, and it's known to be an argument going on within Facebook right now. It doesn't impress me much, but in all honesty, HN doesn't; this place has real problems with sexism and racism.
Speaking personally, I guess it means I'm unlikely to ever apply to YC, because I weigh these moral – call it social justice, that's fine with me – considerations pretty highly in choosing who I work with. Other people will find different personal calculuses here. Many YC founders are my friends and they speak highly of the people involved; this just happens to be over my personal line.
Nevertheless, I'm still just... disappointed.
Yes! It's a useful lens – just as Hayek is. No-one has a monopoly on absolute truth in the social sciences, so understanding (and empathizing with) all the framing narratives, most all of which have some kind of a point, is crucial to being able to navigate these kinds of discussion.)
Understanding Marxist (and Hayekian) thought has been very helpful to me in framing my own politics – which, ironically enough, wind up being moderate market/social liberalism in the European tradition.
And, behold, we see someone rediscovering the central tenet of Marxist economic theory.
(Entirely serious; not an insult. HN readers could really do with a more balanced economic diet – reading more Marxist theory and less Chicago School...)
I think the intuition is something like this, and I'm going to be really informal here so please indulge me while I have a go at the pop-science version (and experts, please correct me!)
One of the ways deep learning is useful, compared to other feature extraction schemes, is that (in most formulations) it's a multi-scale method (it learns features at various length scales from very short to very long, specifically as you ascend/descend layers in the network).
The renormalization group is a mathematical framework for reasoning about how physics changes between length scales. Very informally, the hardest unsolved problem in physics is building a single theory which works for both the very short and the very big – very big problems (astrophysics) have an effective theory in the form of general relativity, very very short length scales have effective theory in the form of quantum field theory (basically all of modern particle physics), and the rest of physics (e.g. quantum mechanics, which from a purely theoretical perspective just is the entirety of chemistry and materials science...) is likewise somewhere in between on the great cosmic measuring stick.
So far so Powers of Ten, right?
The challenge is building a mathematical structure for transforming between these length scales in such a way as to have a coherent single theory which works for all of them. This is what people mean when they talk about "unification" in physics. The best mathematical framework we have for that thus far is the renormalization group.
So this paper – which I have no more than skimmed – purports to show that these two mathematical structures are (in some deep sense) more or less the same shape, which would make sense given that the problems they are solving are the same kind of shape (making sense of something at a range of length scales).
So it could be wrong, but it's not obviously misguided. Is it useful? In terms of practical machine learning applications right now, probably not (and ditto, but more strongly, in terms of theoretical physics). But if it means a bunch of mathematical techniques worked out in one domain can be ported over to the other (in practice, from particle physics to analyzing deep neural networks), that'd be useful!
And yes; I want working with Thiel to be a large net economic loss, because I believe that's the language he understands. This is the only effective tool I have to try and do that. It's nowhere near enough, but it's what I've got.