9,643 karma · joined May 29, 2007
The efficient-market hypothesis does not have an asterisk excluding cryptocurrencies.
MtGox has said they were subject to DDoS attacks: https://twitter.com/MagicalTux/status/317423311174909954
> If you looked at the log plot[2], it still looked exponential!
Trying to divine future price movement from looking at a graph has been proven definitively, repeatedly to be foolish.
Have you read any of Brian Boyd's work? I think you'd really like this: http://www.amazon.com/gp/product/0691089574/ref=as_li_ss_tl?...
if you like puzzles, it might be better not to spoil the fun by reading the wikipedia article
Yes please. It's absolutely better that way — the most fun book I've ever read. Just pick it up and start reading.
edit: the link should be more obvious now
I'm curious: you've mentioned a lot of features of Google Drawings. What do you actually use it for?
There is a lot of uncertainty here. Intelligent, rational people disagree wildly on the future of Bitcoin. There are all kinds of risks: legal, technical, and competitive.
If the exchange rate crashes, and takes a long time to recover, you'll feel justified in calling all buyers "speculators feeding the hype machine", when in actuality many of them were simply wrong, or early.
2. False dichotomy. Some people are speculating. Others are using it to exchange value. Inventions don't need teleologies.
Your concern, if I'm understanding you, is that speculators are harming Bitcoin's potential as an alternative to the current economy. I wouldn't worry about that. The exchange rate is either going to $0 or $1000+, and the behavior of the community short-term isn't going to affect that.
Obviously, I'm long Bitcoin. See this blog post from 2011: http://andrewbadr.com/log/10/bitcoins-and-why-im-buying/
Consider the formal grammar that might be used to implement a programming language. When we use the word 'grammar' here, it's obvious that Lisp has less of it than, say, Python. It's roughly equivalent to saying that Lisp has less syntax. So, his argument is independent of any VSO/SVO distinction.
Ben is arguing that Python's additional syntax, by formalizing common structures, allows us to (in some sense) externalize the inherent complexity of a problem (i.e. out of our brain). The downside is that it introduces some rigidity into the language. Lisp makes a different tradeoff: we are forced to handle all of the complexity ourselves, but in return the language is flexible enough that we can build exactly the right abstractions for our problem. There's a kind of conservation law.
* Heterogeneity of object abstractions: in order of complexity, you use namedtuples, classes, and metaclasses. In Lisp you'd use the same kind of abstraction across complexity levels.
* Similarly, there are lambdas, plain functions, and bound methods, all for pretty much the same thing.
* Which do you use when? How hard is it to change? http://lukeplant.me.uk/blog/posts/djangos-cbvs-were-a-mistak...
* The way so many configuration variables in Django are strings, that just "coincidentally" point to a class, module, or function.
I've been using Python for 10 years, and Django since pre-1.0. My current startup is built on it. The parts start to show.
# Adapted from http://autotelicum.github.com/Smooth-CoffeeScript/literate/partial.html
F.partial = (func, a...) -> (b...) ->
func (for arg in a then arg ?= b.shift())..., b...
For example, do `parseInt10 = F.partial(parseInt, undefined, 10)`, then `[].map(parseInt10)` does the right thing.First, he points out that Kurzweil is extrapolating, and extrapolations can be wrong. This seems obvious. And it doesn't do much towards Paul's "isn't".
Second, he says that software and hardware will have to keep improving, and that might not happen. Again, this seems intellectually equivalent to saying "you might be wrong". No evidence is provided that progress will slow.
Third, he says that the singularity will require either a bottom-up biologically inspired model, or a non-biologically inspired "AI" system.
The complexity of the former will take a long time to overcome. Since Allen concedes that sufficient computational power is already here, he seems to be arguing that it will take over a hundred years for us to have a detailed model of the human brain. Looking at the progress of science, this seems terribly conservative, yet little justification is offered. Allen's posited "complexity brake" seems positioned tendentiously — why are we going to start hitting it now, instead of fifty years ago?
As for the AI route, he writes: "But when we step back, we can see that overall AI-based capabilities haven’t been exponentially increasing either, at least when measured against the creation of a fully general human intelligence." What does it even mean to be exponential relative to a binary variable? He is right to say that current methods are limited, and don't achieve generalized intelligence, but offers no reason why that won't happen. His argument seems equivalent to simply saying it hasn't happened yet.
This piece offers little intellectual contribution, and the only reason we're reading it piece is because of the author. It reads like he started from the conclusion he wanted, and threw some paragraphs in that direction. His argument, essentially, is that he's betting against continued progress in science, software, and hardware. That just seems crazy.