aothman

It may be telling that, in response to my "Seed investments follow an alpha < 2 power law" paper, Bryce (whom I have never met) posted something dismissive on twitter whereas Einar reached out to me to discuss how he could validate a similar hypothesis for his own investing.

The fraction of money-losing investments in a population will affect, for instance, whether we would expect the typical investor making five investments at random to make or lose money. But regardless of whether losers are 10%, 50%, or 90% of the investment pool, if the winners are drawing from an unbounded mean power law then broadly indexing raises an investor's expected return.

You can think of the AngelList investment data as being split into three roughly equal-sized groups: markdowns, markups, and no valuation updates.

The reported IRRs are actually relatively high and the return multiples (which are compounded IRRs) are relatively low. That's because there are lots of one- and two-year-old companies in the dataset and---as we show---IRRs and investment durations are negatively correlated.

The algorithm itself is a mashup of a paper I wrote for AAAI: http://www.cs.cmu.edu/~aothman/splines.pdf and this paper: http://link.springer.com/article/10.1007/s10543-005-0028-x

A lot of people at Harvard were uncomfortable there. I wasn't. I loved the place and made great friends. It was a safe place where I could challenge myself intellectually. And the plurality of Harvard students aren't wealthy legacies, they're striver upper middle class kids that worked their asses off in high school.

The interesting contradiction of UTC, to me, is that it had this enormous significance to history despite being terribly written. As a modern reader, I couldn't get any emotion about the book other than it being terrible. James Baldwin trashes the book brutally but fairly in his great essay "Everybody's Protest Novel": http://www.uhu.es/antonia.dominguez/semnorteamericana/protes...

http://www.nytimes.com/2010/01/13/opinion/13friedman.html

50 graduate scholarships is roughly the equivalent of a smaller department at a US research university.

Furthermore, actual Stanford isn't doing any damage to actual Harvard. The data I've found suggest that 70+ or 80+% of undergrads admitted to both Harvard and Stanford pick Harvard.

Sources: http://college.mychances.net/college/tools/college-cross-adm...

http://mathacle.blogspot.com/2008/06/harvard-yale-princeton-...

Harvard's Extension School was designed to teach the greater Boston community, and I think it should be a vehicle to improve town-gown relations, by convincing locals to not perceive Harvard as "the other". I certainly don't think it should have as part of its mission handing out Masters degrees to people from Kansas over the Internet.

In terms of predictions, one of the most interesting teams this year is Kansas (http://kenpom.com/team.php?team=Kansas). They've only lost twice but have a large number of narrow home wins. Depending on how your algorithm treats those wins they either look like a team that will struggle to reach the sweet 16 or like a potential national champion.

Pittsburgh keeps winning most livable city awards today for the same reasons it won in 1985: it's a city that rates good or very good in everything and bad in almost nothing.

"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.

(That said, if you want to talk about your sweet computer vision system that's "coming soon", go right ahead. Just don't call it AI.)

As per this WSJ article http://blogs.wsj.com/digits/2010/08/12/where-did-demand-medi...

if demand media is allowed to amortize out their costs over years (under the assumption every piece of "How to bathe your gerbil" will keep bringing in revenue for the next half-decade) they become strikingly profitable, especially for a company that writes words on the Internet. If not, they're hemorrhaging cash.

Given Google's recently renewed focus on providing quality search results, I'd take the under on all of these companies, mahalo included.

"Every once in a while a window appears - and you realize the past is just the present with different technology and funny clothes"

For instance, one of the new trends in prediction markets has been corporate elicitation (e.g., Inkling Markets YC W06). For instance, a market might ask a pool of employees "When will product X ship?". Now consider the employee's problem. He knows the product will be delayed until November at the current pace, but that if managers see high November probabilities they'll cut back on the project and re-focus development efforts (or, perhaps instead, shift some staffers over to get the project done faster). How should the employee trade in this market?

If your answer is "it's unclear" then you're exactly right. The self-referentiality of prices (they cause actions which then define prices) makes the problem much harder. It's actually possible to design markets which are so self-referential that any trade will be the right one (the market prices are self-predicting prophecies) or that any trade will be the wrong one (e.g., if there's a high price on terrorist attack Z then the government will always spend enough money to prevent Z from happening).

In fact, the only way around the paradox is to have the prices in a prediction market not matter at all - for the managers to take the same course of action regardless of the prices they see in the market. But then why run a market in the first place?

The whole thing is an interesting and seemingly fundamental perversion of the way we normally think about prices. And again, this is without any malicious intent on the part of participants, which will certainly only further confuse things.

Next time you're in a conversation with a group of people you don't know, stop and listen for the laughter - it will be hard to hear because you're so used to tuning it out, but it will be shocking in its frequency when you finally pick up on it. (NB: pointing the laughter out is a good way to alienate yourself from strangers.)

Klosterman talks a bit about his essay here: http://blogs.wsj.com/speakeasy/2009/10/17/chuck-klosterman-o...

One concern I immediately have is overfitting, particularly for claims about how various difficult values have been optimized to be the "best possible". It looks like the parameter space in use is truly enormous and so it would be very easy to come up with hypotheses that perform fantastically on your dataset but terribly in real life. This seems like it would be a first-order concern, while the ability to run tests in a single day seems second-order if those tests are producing garbage outputs.

http://www.cs.cmu.edu/~dst/Secrets/

I believe CMU's lawyers are well-acquainted with responding to legal threats against him.