3,807 karma · joined April 11, 2009
When you design an algorithm and implement it in code, the computer will not allow you to be ambiguous and imprecise. You made a wrong assumption? Sorry, your program won't work. No partial credit for you. It's tough, but it's fair.
However, I also think the Bourbakists created a monster, which was this notion that the "Greek method" was the only valid one, and that the "Babylonian method" was to be avoided. Even Combinatorics was considered "unworthy" of great minds. All geometrical intuition was frowned upon. Applications were laughed at. All of a sudden, Math became sterile. Interestingly, Turing's work, in a sense, derived from Hilbert's program to make the foundations of Math solid. The fact that Theoretical CS exists outside of traditional Math is nothing more than an historical accident. Computability is pure Math. Computational Complexity is still a bit "dirty" but it's also rather fundamental.
"Computer Science is no more about computers than astronomy is about telescopes."
In my most humble opinion, the value of CS education is not to prepare young people for a job in IT. Instead, its value is in teaching young people how to think in an abstract and rigorous manner. This is much more valuable, and it's useful regardless of what one's future career path is.
These days students think they can hack everything. They think they can BS on their homework essays, they think they can BS on their exams, they abstain from precise reasoning because it's too much work. Well, guess what? You can't BS a computer. All those sub-human morons commenting on the NYTimes article, the ones who work in IT and who are so afraid of outsourcing, should keep in mind that CS education is, at its core, applied philosophy and applied math. The label "Computer Science" is a misnomer. Yet once again, I blame the Bourbakists. If Turing had lived a few decades before, Theoretical CS would be a part of Math, not a separate field.
Saying that "we have a lot to learn from nature" is almost a vacuous statement. Nature is so complex, that there are billions of opportunities to learn from it and to design bio-inspired systems. An example: neural processing is orders of magnitude more power-efficient that CMOS. Sure, our brain can't do arithmetic at high-speed, but if we lose a bunch of neurons, our brain still works. Humans can literally lose parts of their brain and survive and function. It's amazing. By contrast, a dust particle on a Silicon wafer is enough for a CPU to malfunction.
This fascination with nature has a dark side, too. Just because evolution has attained such quasi-perfect designs, it does not mean we can do the same. Neuromorphic electronic systems never got anywhere. People in the 1980s talked so much about analog VLSI and neural networks, and I haven't seen that much coming out of it.
The problem with being fascinated by something is that being in awe is not always the most productive way. Sometimes despizing something works much better. Whatever. I am not saying anything deep, and cheap philosophy never got anyone to actual achievement, to building actual things that actually work. Hence, I shut up.
"I confess that, in 1901, I said to my brother Orville that men would not fly for 50 years. Two years later, we ourselves were making flights. This demonstration of my inability as a prophet gave me such a shock that I have ever since distrusted myself and have refrained from all prediction."
No one has ever been able to predict where technology is going. This one is for you to tame your forecasting proclivities and for the moron who's downvoting my comments without explaining where my argument is weak. Cheers.
In any case, you're picking on the wrong issue. The hummingbird can hover better than a Harrier or a JSF. If you want to start an argument, pick on that.
I would say that open-source is not of great interest in optimization software. The idea is to write as little code as possible, and to trust that everything performance-critical has been optimized. Even speed is not the main issue for me. I want something that is flexible. I want to write little code because the less I write the less bugs there are. Correctness trumps everything else.
If I am using SF to allocate investments, I want to make sure an optimal solution is found, even if it takes a little longer. Computer time is cheap. Buy a bigger computer. Developer time is more precious. There are only 24 hours in a day.
You want to change the source code? With all due respect, but I would speculate that 99,9999% of HN users are not qualified to write numerical optimization code. Looking at it is of little use unless you have a PhD in Applied Math and years and years of experience.
Of limited use to HN readers? To those writing web-apps, perhaps. Those doing Machine Learning will probably be ecstatic to find this.
http://hanselminutes.com/default.aspx?showID=209
Regarding similar software, there's MOSEK and a bunch of others whose logos show up on Solver Foundation's website. If you like to code in Python, there's CVXOPT and CVXMOD. If you're into MATLAB, there's CVX and Yalmip.
Last but not least: never say never, and never predict more than 10 years into the future. In 20 years your predictions might be ridiculed.
For starters, please do note that I wrote flying machines. As far as I know, ostriches do not fly. Besides, supersonic is not that impressive. Hell, a rocket can move at hypersonic speeds. When you design a fighter jet that is as maneuverable and energy-efficient as a hummingbird, please let me know.
http://www.nature.com/nature/journal/v435/n7045/full/nature0...
I found it strange that a guy got downvoted for suggesting that a hedge fund used technical analysis. I hate TA, but then, I hate Quant Finance, too. Whoever thinks that the smart guys at RenTech and the like use that kiddie Stochastic Calculus taught at MFE programs is living in a state of sin. Period.
http://www.bloomberg.com/apps/news?pid=newsarchive&sid=a...
"Volfbeyn said that he was instructed by his superiors to devise a way to 'defraud investors trading through the Portfolio System for Institutional Trading, or POSIT,' an electronic order-matching system operated by Investment Technology Group Inc. Volfbeyn said that he was asked to create an algorithm, or set of computer instructions, to 'reveal information that POSIT intended to keep confidential.'"
Now, you didn't think they used Black-Scholes, did you?! If you did, then: welcome to the real world!
I know a bit of information theory, but I know zero of ASL. It's quite possible that they went in the wrong direction from the start, but someone can still write a paper to point that out and prevent other people from repeating the same mistake. There's value in going in the wrong direction: it serves as a warning to others.
"I guess I'd like to see a little passion in my science. Sorry if that seems too harsh."
Personally, I found the paper's presentation horrible. I would never submit something so visually unappealing under my name. I agree that it sounds like a last-minute rush to finish something. I also agree that there seems to be little passion in it.
However, let us look at the authors: 1st author in an EE undergrad, 2nd author is a post-doc, 3rd author is a professor. Of course, the undergrad did all the work, the post-doc guided him, and the professor secured the grants that paid for the effort. Despite all the paper's flaws, I still think it must be judged for what it is: an EE undergrad trying his luck outside his field... and failing, perhaps.
Sure, TR and Wired are fluff. Except that the article links to an arXiv paper. This is not a standard TR article. Moreover, you can't present scientific research in half a page without making it fluff. That, too, is an interesting information-theoretic problem.
"That would be interesting if they weren't using so poor an approach."
You missed the forest for the trees. In case you didn't notice, the people who wrote the paper are electrical engineers. They used the information-theoretic approach that is used in communications theory to a problem outside the traditional scope of application of the theory. Sure, natural language is hard, but if you start your research by focusing on all the little details you will get nowhere. To me, the paper looks like a first shot at a difficult problem. If you can do better, I would love to hear about it.
"...it sounds like the parameters of the study were determined by someone with very little understanding of how ASL actually works."
That is not the point. The point is that someone who does indeed understand how ASL works can read the paper, find out what is missing and build on it. No one knows everything, and inter-disciplinary work is very hard. Your criticism is too hard, because no one ever built a theory in one single iteration.
Define interesting. Personally, I find the entropic analysis of spoken Engligh vs. sign language pretty interesting, especially so when they relate it to channel capacity and other information-theoretic stuff. Your comment sounds anti-intellectual.
In the real world people care about obtaining results as fast and painlessly as possible. The language is just a tool, not a goal.
I think you're detecting a false pattern there. Russians generally do kick ass in Mathematics. In the West, Mathematics was held back by the Bourbaki fanatics, while in Russia they were never afraid of marrying the pure with the applied, the beautiful with the useful.
There may be a lot of Linear Algebra books translated from Russian, but there are also a lot of other books by Arnold, Kolmogorov, Fomin, Gelfand, etc that were also translated from Russian and that were not on Linear Algebra.