I have often maintained that people who refuse to believe the mountains of accumulated scientific knowledge in domains where they have counter-scientific but unyielding faith aren't necessarily behaving irrationally. At the risk of being quoted out of context as supporting religious dogma over science (I don't, never have - if you quote this post, please quote its entirety), let me show you what I mean by that.
One of the intellectually rich contributions of computational thinking is the notion of Bayesian belief -- the idea that a rational agent must update its beliefs about possible events in the world based on some prior information and the evidence it sees. Sounds pretty straightforward, doesn't it? Well, maybe it doesn't until things are defined more rigorously. 'Belief' that X in this case is a probability the rational agent maintains in its mind that X is true. Classic example: do I believe I should take an umbrella with me today, as I go out to work (where else!?)? Well, that depends on what I a priori believe about the weather today (the forecast was for 40% chance of rain - maybe I shouldn't) and on what I see when I look out the window (evidence: menacing clouds ride low, gusts of wind seem to indicate a thunderstorm is on the way - oh-oh, maybe I should).
How exactly does one arrive at that decision, at that degree of belief? The beautiful thing is: there's a theorem about that. The best anyone can do was written down by a British Presbyterian minister by the name of Thomas Bayes in the 18th century. Bayes' theorem says that the likelihood of something is equal to its prior probability times the evidence, normalized. The trick is: if you a priori believe something to be 100% true (or false), then no amount of evidence to the contrary will sway you. To see that for yourself, check out the awesome intuitive introduction to Bayesian reasoning by Eliezer Yudkowsky (or the intuitive and short version by Kalid at BetterExplained), then try updating a prior probability of 1 in any of the examples.
It turns out that there is much evidence that our brain operates in a fashion consistent with Bayesian updating, on many levels from immediate visual perception (I can see the letters I'm typing but the higher-level prior context provided by my brain will prevent me from spotting some typos), to common-sense decision-making like in the umbrella example. The scientific method too can be described in Bayesian terms, as we inspect new evidence and deduce how much it discriminates between conflicting theories, and how much information is contained in it.
So, if Bayesian-like processes in our brains are responsible for rationality, and if you have 100% faith in something, then mathematically, no evidence will be of consequence. You will still believe your pet theory with 100% probability. Maybe that's how unquestioned beliefs operate: they aren't necessarily functionally unquestioned (the brain machinery is constantly at work) but the resulting belief is predictably stable.
But where could those crazily strong prior beliefs come from? That's a very important question, but one for another post.
Sunday, August 3, 2008
Saturday, July 26, 2008
Randy Pausch 1960-2008
RIP Randy Pausch, CS professor at CMU who achieved his childhood dreams and delivered a poignant "Last Lecture" when he was diagnosed with terminal cancer.
His main legacy is Alice, a free software tool that pretends to teach kids how to tell stories in 3D graphics, but really teaches them serious programming. Programming, in the words of Don Slater from the Alice promotional video, is telling the machine in front of you how to do what you need it to do. But instead of focusing on the minutia of a programming language syntax, it gives students the ability to easily create and animate 3D virtual inhabited worlds. The characters are objects with members and methods, and the environment supports variables, loops, conditionals, recursion, -- most things necessary for a thorough introduction to programming, which can be done entirely through a drag-and-drop interface where no mistakes can be made.
The trick is that the students' attention gets engaged from day one with a storytelling/world-building experience and without the frustration of compiler errors and segmentation faults. The Alice team cite statistics for at-risk students getting better grades and a lower drop-out rate in computer science. "At-risk" of course means students who don't fit the traditional CS mold, precisely the students CS departments would like to recruit and keep in the name of diversity, creativity, and a richer talent pool.
Basically, Alice is like an all-virtual Lego MindStorms on crack. For some reason, I think this sort of inappropriate language for an obit (which this isn't) would be appreciated by the late Prof. Pausch. Alice takes to university level what the Lego programming interface gave to younger kids: the same wonderment at just having created something with your own drag-and-dropping hands, at at watching it do its stuff. This is how many an engineer gets her calling.
Randy Pausch and the Alice team say: look, kiddo, you can take these abstract things and stack them any which way, and depending on how you do it, your machine will make you a world full of action and character, joy and meaning. This is so close to magic. And by the way, you have now learned Java. I wish I was learning to program all over again.
His main legacy is Alice, a free software tool that pretends to teach kids how to tell stories in 3D graphics, but really teaches them serious programming. Programming, in the words of Don Slater from the Alice promotional video, is telling the machine in front of you how to do what you need it to do. But instead of focusing on the minutia of a programming language syntax, it gives students the ability to easily create and animate 3D virtual inhabited worlds. The characters are objects with members and methods, and the environment supports variables, loops, conditionals, recursion, -- most things necessary for a thorough introduction to programming, which can be done entirely through a drag-and-drop interface where no mistakes can be made.
The trick is that the students' attention gets engaged from day one with a storytelling/world-building experience and without the frustration of compiler errors and segmentation faults. The Alice team cite statistics for at-risk students getting better grades and a lower drop-out rate in computer science. "At-risk" of course means students who don't fit the traditional CS mold, precisely the students CS departments would like to recruit and keep in the name of diversity, creativity, and a richer talent pool.
Basically, Alice is like an all-virtual Lego MindStorms on crack. For some reason, I think this sort of inappropriate language for an obit (which this isn't) would be appreciated by the late Prof. Pausch. Alice takes to university level what the Lego programming interface gave to younger kids: the same wonderment at just having created something with your own drag-and-dropping hands, at at watching it do its stuff. This is how many an engineer gets her calling.
Randy Pausch and the Alice team say: look, kiddo, you can take these abstract things and stack them any which way, and depending on how you do it, your machine will make you a world full of action and character, joy and meaning. This is so close to magic. And by the way, you have now learned Java. I wish I was learning to program all over again.
Tuesday, July 1, 2008
The Boston Phoenix weighs in on the eternal question
Will robots take over the world?
There's endless speculation, and I promise to post some links and actual commentary shortly, but meanwhile, I'm a little late in on this: http://thephoenix.com//Boston/Life/61912-Rage-against-the-machines/ (published May 28, 2008), where I sound like a total ass.
There's endless speculation, and I promise to post some links and actual commentary shortly, but meanwhile, I'm a little late in on this: http://thephoenix.com//Boston/Life/61912-Rage-against-the-machines/ (published May 28, 2008), where I sound like a total ass.
Monday, June 30, 2008
A tournament of social computers
It is where they don their armor and charge, in the name of 10,000 euros.
"They" are pseudo-code strategies for a strange game that the organizers claim will shed some light on the computational properties of social behavior.
The rules of Project Cultaptation's Social Learning Strategies Tournament are complex, but the game is simple. Your strategy has to invade a population of 100 agents over thousands of simulation runs, based on its accumulated score. You get points by pulling levers of an n-armed bandit machine. But you can only pull a lever that you know about. And that knowledge can come from two sources: either you try your random luck, or you look at someone pulling levers next to you. Seems like a no-brainer, but the devil is in the details of this clever setup. Observing someone may be error-prone. The scoring may change over time, maybe faster than the expected lifetime of the agents.
I'm sorry to say that the deadline for entries to the tournament is over; it was today. Playing with the simulation has been truly fun. Of course, I really doubt that my entry will win -- I hope it gets past the round-robin selection phase! But I'd like to make a prediction about the winner.
I think the winning strategy will be very simple -- 10 lines of elementary pseudo-code maximum -- and stochastic -- with in-built randomness. I might have my reasons for that, but the chief ones come from history (the strategy that won the Prisoner's Dilemma tournament back in the 1980s was a very simple tit-for-tat) and perhaps misplaced reasoning-by-analogy (there's randomness in the environment, therefore a good strategy should include some of that too).
And I think the results won't tell us much about the fundamental nature of social learning, that is, gleaming truths about the way the world works from observing the behavior of others.
Why not? For instance, because in the tournament, my actions don't influence anything about the world beyond my own ability to score and therefore reproduce (ha ha). Or because there is no competition for resources -- all agents can choose the same action and they will all receive the same scores, same as it would be if only one agent chose it. Or even because there is no timeline, no sequences in this game, no planning a few steps ahead even.
Still, I can't wait to see the results, and they will be fascinating for all the simplifications of the game. And if I'm wrong, if the winning strategy is complex, or deterministic, or biologically plausible, it'll be pretty exciting to reason why that should be the case, and how the tournament actually captured some fundamental property of learning in groups.
"They" are pseudo-code strategies for a strange game that the organizers claim will shed some light on the computational properties of social behavior.
The rules of Project Cultaptation's Social Learning Strategies Tournament are complex, but the game is simple. Your strategy has to invade a population of 100 agents over thousands of simulation runs, based on its accumulated score. You get points by pulling levers of an n-armed bandit machine. But you can only pull a lever that you know about. And that knowledge can come from two sources: either you try your random luck, or you look at someone pulling levers next to you. Seems like a no-brainer, but the devil is in the details of this clever setup. Observing someone may be error-prone. The scoring may change over time, maybe faster than the expected lifetime of the agents.
I'm sorry to say that the deadline for entries to the tournament is over; it was today. Playing with the simulation has been truly fun. Of course, I really doubt that my entry will win -- I hope it gets past the round-robin selection phase! But I'd like to make a prediction about the winner.
I think the winning strategy will be very simple -- 10 lines of elementary pseudo-code maximum -- and stochastic -- with in-built randomness. I might have my reasons for that, but the chief ones come from history (the strategy that won the Prisoner's Dilemma tournament back in the 1980s was a very simple tit-for-tat) and perhaps misplaced reasoning-by-analogy (there's randomness in the environment, therefore a good strategy should include some of that too).
And I think the results won't tell us much about the fundamental nature of social learning, that is, gleaming truths about the way the world works from observing the behavior of others.
Why not? For instance, because in the tournament, my actions don't influence anything about the world beyond my own ability to score and therefore reproduce (ha ha). Or because there is no competition for resources -- all agents can choose the same action and they will all receive the same scores, same as it would be if only one agent chose it. Or even because there is no timeline, no sequences in this game, no planning a few steps ahead even.
Still, I can't wait to see the results, and they will be fascinating for all the simplifications of the game. And if I'm wrong, if the winning strategy is complex, or deterministic, or biologically plausible, it'll be pretty exciting to reason why that should be the case, and how the tournament actually captured some fundamental property of learning in groups.
Friday, May 9, 2008
I thought I was joking
But actually, today and tomorrow, the Neukom Institute for Computational Science at Dartmouth College is hosting a conference on The Human Algorithm. I wish I knew about it in time to go there! Speakers include Daniel Dennett, Patricia Churchland, Marc Hauser and others for an impressive line-up who for sure will reveal the exact steps to be taken by any machine longing to be functionally equivalent to a human being. Free will and legal responsibility included.
I'm only half-joking. Maybe they will publish the proceedings.
I'm only half-joking. Maybe they will publish the proceedings.
Thursday, May 8, 2008
PhD
This is the obligatory tribute to Jorge Cham, the creator of phdcomics (Piled Higher and Deeper). A fact about Dr (of course!) Cham that I hadn't been aware of until yesterday: he's the same person who made the robotic cockroaches I am so fond of and who have such pretty names (the Sprawlettes): http://www-cdr.stanford.edu/biomimetics/.
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