This presentation has got to be the funniest TED talk (if not, please provide a link in the comments).
But this of course is just a bonus point, because as most (or all) TED talks, this one's very insightful.
This is a talk by Sir Ken Robinson (wkp), about creativity, particularly how our typical school systems undermine most of the innate creativity children have.
"(...) the whole purpose of public education, throughout the world, is to produce university professors"
He goes on to say (after a few good jokes about university professors) why this is so. It appears that that our public education in a whole is centered about the idea of academicability. I still have to sleep on that for a while, but maybe this is one of those cases where something is too obvious to be noticed.
It was around the 19th century that public education was "invented (...) to meet the needs of industrialism". He goes on with other interesting insights, but there's enough spoilers from me for now.
On a related video, another good tip for those of you who like to think about creativity. Here's Amy Tan (wkp).
Of course, I still want to know what Creativity *really* is, which always brings me to what the hell Intelligence realy is. A friend of mine is now completely obsessed with AGI, and thanks to a tip I gave him from a Scientific American podcast, Singularity (wkp), so probably in a while I'll have some insights from him about these interesting topics.
2009-04-28
2009-04-26
Nostalgic rant of the interweb
I miss those good old days when HTML was white background, black foreground and blue links, not these devilish flash websites and heavy webapps we have today.
I miss the good old days of ASCII emails, not these HTML embedded with attached images, off-site images. Oh yeah, those spam-free text-only simple and efficient e-mail, that was email. Who the hell needs scripts being auto-executed when reading emails?
Heck, I miss the good old mainframe days, when men were real men, and coded their own interfaces, patched and recompiled the programs they used, until they worked to satisfaction. Of course, this was done in macho-language C, not these bogus unreliable unpredictable script languages, that don't enforce variable definitions. These modern languages that let ignorants remain ignorants.
Maybe I'm 20 years older than my real age.
Disclaimer: I'm not old enough to actually have used - or even seen, FTM - a mainframe, but that's beside the point. I can't code in C, although I have some basic familiarity with the syntax. I have seen someone use Python, but I don't grok it either. </hypocrisy> I wish I had enough skills to fix my own issues, but there's only so much time.
I miss the good old days of ASCII emails, not these HTML embedded with attached images, off-site images. Oh yeah, those spam-free text-only simple and efficient e-mail, that was email. Who the hell needs scripts being auto-executed when reading emails?
Heck, I miss the good old mainframe days, when men were real men, and coded their own interfaces, patched and recompiled the programs they used, until they worked to satisfaction. Of course, this was done in macho-language C, not these bogus unreliable unpredictable script languages, that don't enforce variable definitions. These modern languages that let ignorants remain ignorants.
Maybe I'm 20 years older than my real age.
Disclaimer: I'm not old enough to actually have used - or even seen, FTM - a mainframe, but that's beside the point. I can't code in C, although I have some basic familiarity with the syntax. I have seen someone use Python, but I don't grok it either. </hypocrisy> I wish I had enough skills to fix my own issues, but there's only so much time.
Vampire eco dynamics
What do you get when you mix fiction with science? You may have said sci-fi, but that's not all of it. You might also get fun science (as in, not necessarilly useful, purposeful or publishable science, just the kind of fun stuff you do in your free time).
Linked from pharyngula, I got the tip for a nice fun article someone posted in his (her?) family site. It looks like it may be a follow up to one of those meaningless conversations everyone likes to have among loved one and friends, but this guy actually did his homework to make his point - he did the math for the model, and simulations to get the equilibrium points. But that's not what I liked the most about it (although I like this kind of stuff a lot). I liked it that it's a funny
Just take a look at what I'm talking about:
In principle, ecologists might employ two basic strategies to get at a problem like this. The empiricists would go out and find a field site where they could actually observe predators and their prey, and just tally the results over time. The theoreticians would chuckle at the empiricists, and construct mathematical models that probably approximate the behavior of populations in the field, keeping their hands more or less clean in the process.
In real life, most ecologists use both strategies off and on. Unfortunately, I don’t know of any real life vampire populations in the field, so we’re going to have to pretend that we are strict theoreticians. That means that we’ll be using math: some algebra, some calculus, and some matrix theory. This is O.K.! It hurts a lot less than, say, getting bitten by a vampire as you’re trying to fit the bugger with a radio collar.
PDF of the article.
He could display little of the math and simulations, although I can see why he didn't. I would at least give the option to the curious reader to learn more, say, put the dirt in a supplemental PDF, or an appendix. Even though, it's
Linked from pharyngula, I got the tip for a nice fun article someone posted in his (her?) family site. It looks like it may be a follow up to one of those meaningless conversations everyone likes to have among loved one and friends, but this guy actually did his homework to make his point - he did the math for the model, and simulations to get the equilibrium points. But that's not what I liked the most about it (although I like this kind of stuff a lot). I liked it that it's a funny
Just take a look at what I'm talking about:
In principle, ecologists might employ two basic strategies to get at a problem like this. The empiricists would go out and find a field site where they could actually observe predators and their prey, and just tally the results over time. The theoreticians would chuckle at the empiricists, and construct mathematical models that probably approximate the behavior of populations in the field, keeping their hands more or less clean in the process.
In real life, most ecologists use both strategies off and on. Unfortunately, I don’t know of any real life vampire populations in the field, so we’re going to have to pretend that we are strict theoreticians. That means that we’ll be using math: some algebra, some calculus, and some matrix theory. This is O.K.! It hurts a lot less than, say, getting bitten by a vampire as you’re trying to fit the bugger with a radio collar.
PDF of the article.
He could display little of the math and simulations, although I can see why he didn't. I would at least give the option to the curious reader to learn more, say, put the dirt in a supplemental PDF, or an appendix. Even though, it's
2007-06-22
Citizendium: a scholarly wikipedia
Some of you may have heard that Wikipedia is not suitable for serious research, for many reasons. That's true. Here is the article where they officially agree with this fact. And here are some replies to the most common objections perceived, such as it being prone to e-vandalism, and being written mostly by non-experts.
Despite this fact, I personally have for quite some time considered it a valuable source of pointers for further reading. Its articles often provide succint introductions to common or modern terms, facts or people, making it also a useful (IMHO) definitions dictionary (besides Google's 'define:' label, of course). Also, in my experience, most articles have valid links to useful reading, supporting at least most of the article claims.
So, one might think it would be enough to make it reputable to assign qualified editors to each section or area (by some definition of qualification), and have them moderate edits, while still accepting general public contributions. Guess what, our wait might have ended. As per this hint from Science I just found out, some people (formerly?) tied to wikipedia created a fork project
Bookmark this:
http://citizendium.org/
And, yes, citizendium has an article on wikipedia and vice versa, just like Uncyclopedia has one. Not as funny, though.
Despite this fact, I personally have for quite some time considered it a valuable source of pointers for further reading. Its articles often provide succint introductions to common or modern terms, facts or people, making it also a useful (IMHO) definitions dictionary (besides Google's 'define:' label, of course). Also, in my experience, most articles have valid links to useful reading, supporting at least most of the article claims.
So, one might think it would be enough to make it reputable to assign qualified editors to each section or area (by some definition of qualification), and have them moderate edits, while still accepting general public contributions. Guess what, our wait might have ended. As per this hint from Science I just found out, some people (formerly?) tied to wikipedia created a fork project
Bookmark this:
http://citizendium.org/
And, yes, citizendium has an article on wikipedia and vice versa, just like Uncyclopedia has one. Not as funny, though.
The System (of how not to waste time when studying)
In trying to organize my reading ToDo list, I developed a SYSTEM in which I put my faith upon, that will quickly give me the means to learn the important things I must, in order to pass the first step of the doctoral routine.
It consists in classifying topics or articles or discussions into one of four categories, namely:
1- EFFICIENT
2- USEFUL
3- LOGICAL
4- INTERESTING
The above-mentioned categories are rated in decreasing order of importance. Now some explaining is due. In the following, I will refer to (topic|article|discussion|conversation) simply as "topic".
EFFICIENT means the topic will make be better in doing something I can already do, or avoid doing something in an inefficient, or unprofessional way. A simple example is when you learn to program your first algorithms in scientific computing (say, in Matlab/Octave for starters), the first thing you learn (or at least it should be) after it works, is to vectorize the code. Following this simple example, the EFFICIENT kind of stuff is of prime importance, hence it's number 1 priority in the list.
USEFUL is similar. The topic in question is important to learn, but maybe not ASAP. It will probably be used in a somewhat near future, so it receives number 2 priority.
LOGICAL is as in Spock's usual saying. I may not need the topic itself, but it broadens the understanding of an EFFICIENT or USEFUL topic, so it just makes sense to seek some degree of deep understanding in the later, by reading the LOGICAL topics.
INTERESTING is something I would really like to read or learn, but has whatsoever no positive impact in the above mentioned framework. It must be dealt with in the spare time. Actually I created this mostly to accommodate music listening and geek stuff I like.
If a topic doesn't fit in any of the above categories, it must be discarded at once.
(I can imagine Dr. Spock saying: "This is illogical").
There. The system. The method.
If a more complex framework is needed, one can adapt it to use weights into each category. Maybe some Linear Programming can be done in order to decide how much time exactly one should spend on each topic in one's list. This would certainly be interesting. Unfortunately, for me, not logical, so...
It consists in classifying topics or articles or discussions into one of four categories, namely:
1- EFFICIENT
2- USEFUL
3- LOGICAL
4- INTERESTING
The above-mentioned categories are rated in decreasing order of importance. Now some explaining is due. In the following, I will refer to (topic|article|discussion|conversation) simply as "topic".
EFFICIENT means the topic will make be better in doing something I can already do, or avoid doing something in an inefficient, or unprofessional way. A simple example is when you learn to program your first algorithms in scientific computing (say, in Matlab/Octave for starters), the first thing you learn (or at least it should be) after it works, is to vectorize the code. Following this simple example, the EFFICIENT kind of stuff is of prime importance, hence it's number 1 priority in the list.
USEFUL is similar. The topic in question is important to learn, but maybe not ASAP. It will probably be used in a somewhat near future, so it receives number 2 priority.
LOGICAL is as in Spock's usual saying. I may not need the topic itself, but it broadens the understanding of an EFFICIENT or USEFUL topic, so it just makes sense to seek some degree of deep understanding in the later, by reading the LOGICAL topics.
INTERESTING is something I would really like to read or learn, but has whatsoever no positive impact in the above mentioned framework. It must be dealt with in the spare time. Actually I created this mostly to accommodate music listening and geek stuff I like.
If a topic doesn't fit in any of the above categories, it must be discarded at once.
(I can imagine Dr. Spock saying: "This is illogical").
There. The system. The method.
If a more complex framework is needed, one can adapt it to use weights into each category. Maybe some Linear Programming can be done in order to decide how much time exactly one should spend on each topic in one's list. This would certainly be interesting. Unfortunately, for me, not logical, so...
Greetings
This is going to be the space I'll be using to keep record of interesting academic articles and related news.
Areas I am mainly interested at present include Mathematics, Statistics, Genomics and Evolution & Selection. Actually, in the reversed order.
I often allow myself to divert to farther topics such as Astronomy & Cosmology, Physics and Palenteology, which are both intriguing and misterious, given my almost absolute ignorance in the field. These and other fields might appear ocasionally, but I intend my focus to be related to my current work, whatever that may be.
Feel free to share thoughts in the comments area. I am always willing to learn.
But please, if you cite facts, be kind enough to include some (valid|authoritary) sources.
Areas I am mainly interested at present include Mathematics, Statistics, Genomics and Evolution & Selection. Actually, in the reversed order.
I often allow myself to divert to farther topics such as Astronomy & Cosmology, Physics and Palenteology, which are both intriguing and misterious, given my almost absolute ignorance in the field. These and other fields might appear ocasionally, but I intend my focus to be related to my current work, whatever that may be.
Feel free to share thoughts in the comments area. I am always willing to learn.
But please, if you cite facts, be kind enough to include some (valid|authoritary) sources.
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