704 karma · joined April 14, 2010
Former: Warby Parker, Polynumeral, OkCupid
It is a great school. But until recently, rankings weren't really something that the administration paid much attention to.
There are at least three ways that money can have an effect that come to mind. The first is determining who wins; this is the kind you're talking about. The second is determining who can run; the winners of the primaries in both parties have as far as I can remember also been the top fundraisers going in to the primaries. Is it sticky which is the cause and which is the effect? Of course. But I have no doubt it's an amplification.
The third way is, I think, the most perverse. Only a very narrow range of political perspectives are capable of making it to the mainstream, since they first have to win the money election in order to make it anywhere.
Frankly we have no idea what the political landscape would look like with citizen funded elections. Maybe it would look exactly the same, in which case your hypothesis that the government we have is already representative would be proven correct. Right now we have no idea, and that's why I support citizen funded elections.
2) Most of what the government does is not big issues, it's small ones that actually have the biggest effect on our lives. How should airlines, financial firms, telecommunications, etc., be regulated? Most people have very vague ideas about what they want, and are strongly swayed by poll wording. Organizations being regulated, however, have very strong ideas about what should and shouldn't happen. In theory, they should be one voice of many; in practice, we get regulatory capture.
For those of us who find the synchronous style to be a sufficient motivator, it's a great benefit. For people who fall behind but want to cover the material, they're welcome to do so. The grades don't actually matter, remember? Coursera is already "gamified." It's just that the game is done so well that most of the time we're not aware of it.
* Problem definition
* Infrastructure
* Data transformation
* Exploratory analysis (arguably part of model work)
* Results presentation
Then again, this is an ongoing disagreement I have with the Kaggle folks over what constitutes "data science," where I'm pretty confident that "applied machine learning" is a better explanation of what their contests are about.
Your web pages become live views of the underlying controllers. Modifying state in one client automatically modifies it in another. And I'm pretty sure it supports hot-swapping.
They lost their founder only a few months ago. I have no inside information, but I can't imagine that this is unrelated.
I wish the whole team the best of luck! They did a great job with Everyblock. I hope they do something else amazing soon.
I can't speak for other people, but the effort it took to maintain a Facebook presence (deidentify unflattering photos, take a few minutes every day to get caught up on people's minutiae) was far greater than the benefit it brought me.
People who want to talk to me know my email address and phone number.
(edit: clarification, first paragraph)
Of course, that means that countries that don't want to play by US rules (North Korea, Iran, Cuba) get shafted, but it's still a better explanation of the crazy military costs than either fear or corruption.
The ostensible header ("Become the designer...") is almost invisible (I didn't see it for the first 30 seconds). My eyes keep wanting to drift back down to where the contrast is higher.
So paint me as skeptical.
Now all we need is SciPy and matplotlib and we'll be in business...
The two examples have the same mean and median, but differ substantially in their min/max/standard deviation/median absolute deviation. Pick your spread, they differ. I got this by poking around in iPython.
Find the standard deviation of A. Reshuffle A and B together, taking half of the resulting list, calculate the standard deviation, record it and repeat that a thousand times. See that you basically never get a standard deviation as large as you did for A alone, so they meaningfully differ.
Repeat on each of the data sets, and you see that D and E are plausibly similar to A whereas C and F are not. Repeat for B to see if we're being fucked with, and lo and behold it all seems to work out, though D is a little iffy.
I used numpy and ten lines of glue code. Happy to post if there is interest.
This really struck a chord with me:
> What the learner acquires through experience is not represented at all but is presented to the learner as more and more finely discriminated situations, and, if the situation does not clearly solicit a single response or if the response does not produce a satisfactory result, the learner is led to further refine his discriminations, which, in turn, solicit more refined responses.
Comparing that to the meaning of "learn" commonly used in ML to mean "finding optimal weights for some features" and you can see the gulf pretty starkly.
Basically, we haven't developed the social norms needed to put some space between our brains and maximal connectedness, but we can look to history and personal experience to try and figure out what some of them would look like.
It's got some good historical bits about how different great thinkers have dealt with technological change (writing, printing press, telegraph were as disruptive as the Internet). It's also got some personal anecdotes of the author's decision to have an 'Internet Sabbath' every weekend, where from Friday night to Monday morning there is no connectivity in his house.
I'm not sure that engineering (including social engineering) is going to work in a situation like this, but it's probably not very harmful to try.
That's what I took away, anyway.
If you're tired of Google-as-platform, just wait a few years.