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This is really cool. I searched for "Korea" and the third result was a page of Richard Stallman's website about his visit to Korea in 2000, from which I discovered he wrote about an array of big companies and why not to use them---pretty interesting. Last time I met him was in Finland. Such a consistent, profound character.


> "SoftBank routinely selling assets to its Vision fund."

Can anyone help me understand the real reason Softbank does this and whether investors of VF are okay with it? One case I know of is Coupang [1], a Korean E-commerce company, recently "priced" by VF at $9B. Last year, Softbank sold its 20% shares in Coupang to VF at a 30% loss (down-valuation) and then VF poured in an additional $2B to Coupang. It appears Masa did double down on Coupang but why bother to sell its long position at a loss? Not a domain expert on this, so I wonder what is really going on...

https://www.bloomberg.com/news/articles/2018-11-20/softbank-...


It would have been more interesting if they could share deeper, nuanced arguments for setting wealth as the objective function. Sure, wealth past a certain point admits a lot of freedom---but what next beyond that point? But the utility function over wealth (x: wealth, y: utility) is sub-modular in nature. When the curve gets flat, what metric must one use to measure her life's worth? Is there even one as such? Having followed their opinions for quite some time, I know they must have some good opinions on these...


I imagine when you get to that point you focus in transforming x into y or just produce y independent of x.


Usual not, though. People get stuck on X, or just squandering it on expensive luxury.


He has covered these issues with more detail and nuance. For example, see his recent two hour interview on the Joe Rogan podcast. The point is achieving happiness. He's quite philosophical about this, give his material a try.


> The idea that priors are somehow ruining an "objective" model is just absurd to me.

I think some caution can be justified to a certain extent (not the blind "emotional" objections). When establishing priors in a low data regime, one must necessarily be careful. It's a knob whose mass can change a lot in the inference conclusion. That said, if we trust our belief about the region the available data do not inform us well of, why not utilize our domain knowledge/belief?


one vote for BDA. For programmers who learn better by implementing things, this book [1] is also good:

[1]: https://www.amazon.com/Bayesian-Methods-Hackers-Probabilisti...


Parts of that book are available online[1] for free. If not for that book I would never have understood how to apply Bayesian stats to problems that interested me.

[1] http://camdavidsonpilon.github.io/Probabilistic-Programming-...


I am not convinced. The attribution to the simple zoning system is surely not a complete proof, if any. Consider a counter-example: South Korea that has a very similar zoning system. Yet the housing price around Seoul soared roughly by 60% the past 3 years---so did rents (to a smaller but still significant rate). All the while the apartments supply has been at record high in 10 years. That zoning policy may be a factor but clearly not sufficient.


> All the while the apartments supply has been at record high in 10 years.

Has the number of jobs increased? Housing needs to be built in proportion to office/factory/commercial space in order to balance supply and demand.


The employment rate has increased by somewhat unnoticeable amount over the past 10 years [1]. The increased housing supply is mostly due to cost factors (tax breaks from the previous administrations, low interest rate) and the demand due to decline of small cities (demand gets concentrated around Seoul, the capital city; people buy houses around Seoul while living far away in another place on rent). As for the office/factory/commercial space, there has been a sharp increase in supply, and the vacancy rate (spaces that cannot find tenants) is 10-20% in major cities. Folks still buy buildings with a yield of 2.5-4% because the interest rate is so low. I am currently based in Cambridge, Boston. There are empty stores that have been looking for tenants for more than a year.

[1]: http://www.index.go.kr/unify/idx-info.do?idxCd=4013


While switching to the simple option may help the likes of Uber defend themselves on the legal front, the switch may not be that simple to implement and will be costly on the business front (if not making them cease to exist). First, the switch will likely lead to an increase in ride fares and a decrease in user experience. Second, it will be increasingly difficult for the Uber's management to justify its massive operating losses, when they cannot say to their investors "hey, we can always increase the price at will".


A question: if you can make one assumption about Uber (or fix one thing about it) such that Uber will be sustainably profitable in the future. What will it be? Why do you think the assumption implies sustainable profitability in high probability?


I feel like you are fishing for "autonomous driving" but I think the real answer is: Lyft going out of business.


I was just curious, not expecting anything particular---I wondered if Uber's business and its problem space are fundamentally bad as some of the comments here suggest. Re: your opinion, I think it's a good news for Uber. If it just takes Lyft going out of business for Uber to surface above water, I would bet there's a realistic chance.


How much more do you think Uber can charge before a different competitor appears?

My bet is on 0. The market seems more restricted on number of players (drivers can't juggle many applications at the same time) than on price, and if Lyft goes away, another competitor will immediately appear.


Really nice! One suggestion would be to have a list of resources for slightly more theoretical materials, so curious students can be exposed to deeper parts of ML (of course, such materials need links to applied tutorials like this). Perhaps can be done through the pull requests of the community.


I thought so.... until I took a real analysis course. Most calculus tricks build upon on a "small" set of big ideas. The big ideas (such as compactness, convergence, continuity, diff/integration..) are limited in numbers to convince oneself on, yet are generalizable tools to think about A LOT of complicated mathematical phenomena in a concise, clear way. Should they fall short to evaluate a certain math phenomenon, they should do so unambiguously rather than opaquely. Real analysis is a start to developing such tools.


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