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Friston

Two of my favorite blogs — Slate Star Codex (topics: psychiatry, social commentary) and Marginal Revolution (topics: economics, everything else) — have both linked to Karl Friston papers in the last 24 hours. Since one of my bosses is a Friston enthusiast, and he's the only Friston devotee I've ever met, and neither of these blogs has anything to do with what I work on, this gave me a Worlds-Are-Colliding feeling.

I haven't read either paper yet ("An aberrant precision account of autism" and "Predicting green: really radical (plant) predictive processing") but I do want to respond to SSC's commentary. Here's what he had to say:

A while ago I quoted a paper by Lawson, Rees & Friston about predictive-processing-based hypotheses of autism. They said:

This provides a simple explanation for the pronounced social-communication difficulties in autism; given that other agents are arguably the most difficult things to predict. In the complex world of social interactions, the many-to-one mappings between causes and sensory input are dramatically increased and difficult to learn; especially if one cannot contextualize the prediction errors that drive that learning.

And I was really struck by the phrase “arguably the most difficult thing to predict”. Really? People are harder to predict than, I don’t know, the weather? Weird little flying bugs? Political trends? M. Night Shyamalan movies? And of all the things about people that should be hard to predict, ordinary conversations?

I totally endorse the rest of his post, but here I need to disagree. Other people being the hardest thing to predict seems perfectly reasonable to me. The weather isn't that hard to predict decently well: just guess that the weather tomorrow will be like it is today and you'll be pretty damn accurate. Add in some basic seasonal trends — it's early summer, so tomorrow will be like today but a little warmer — and you'll get closer yet. This is obviously not perfect, but it's also not that much worse than what you can do with sophisticated meteorological modeling. Importantly, the space between the naive approach and the sophisticated approach doesn't leave a lot of room to evolve or learn better predictive ability.

Weird flying bugs aren't that hard to predict either; even dumb frogs manage to catch them enough to stay alive. I'm not trying to be mean to amphibians here, but on any scale of inter-species intelligence they're pretty stupid. The space between how well a frog can predict the flight of a mosquito and how well some advanced avionics system could do so is potentially large, but there's very little to be gained by closing that predictive gap.

Political trends are hard to predict, but only because you're predicting other human agents aggregated on a much larger scale. A scale that was completely unnecessary for us to predict, I might add, until the evolutionary eye-blink of ten thousand years or so ago.

Predicting movies is easier than predicting other human agents, because dramatic entertainments — produced by humans, depicting humans — are just a subset of interacting with other human agents. If you have a good model of how other people will behave, then you also have a good model of how other people will behave when they are acting as story tellers, or when they are characters. (If characters don't conform to the audience's model of human agents at least roughly, they aren't good characters.)

Maybe a better restatement of Friston et al. would be "people are are arguably the most difficult things to predict from the domain of things we have needed to predict precisely and have any hope of predicting precisely."

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"The disposable academic"

The Economist :: The disposable academic

You know you are a graduate student, goes one quip, when your office is better decorated than your home and you have a favourite flavour of instant noodle.

True. And true.

Although the first has more to do with my wife and I having diverging opinons about contemporary art. I think Jared Tarbell prints and John Maeda quotes are great things to put on the wall. My wife... feels otherwise.

As to the second, my preference from among the widely-distributed brands is Maruchan Roast Chicken, but most varieties are good with a little extra curry powder, some sriracha, a bit of cilantro or spring onion, and a squeeze of lime.

(Side note: If you want to branch out on your ramen choices, check out Ramenbox.)

Even graduates who find work outside universities may not fare all that well. PhD courses are so specialised that university careers offices struggle to assist graduates looking for jobs, and supervisors tend to have little interest in students who are leaving academia.

That part is true sans caveats. My advisor is supportive of me leaving academia, but neither he now anyone else knows how to help me look for non-academic jobs. There's plenty of support if I wanted to stay in academia, and a fair amount if I wanted to be at a place like Sandia or MSR. But for the types of positions I want, I'm on my own.

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Reading List for 2 Apr 2013

Alan Winfield's Web Log :: Extreme debugging — a tale of microcode and an oven

"Components on the CPU circuit board were melting, but still it didn't crash. So that's how I debugged code with an oven."

If that's not a closing line that gets you to click through, I don't know what is.

Forbes :: Tomio Geron :: Quantopian Brings Algorithmic Trading To The Masses

Why didn't this exist five years ago? I would have had *so* much fun. But no, it can't get invented until I'm up to my ears in dissertation and have already adopted half a dozen new hobbies in the last two years. (Via the Lab49 Blog)

Marginal Revolution :: Alex Tabarrok :: Cognitive Democracy: Condorcet with Competence

More generally, if the voter competences levels are $\{p_1, p_2, p_3\}$ then the cognitively most efficient voting scheme gives each voter a weight of $\log \left(p_i/(1-p_i)\right)$ the result is remarkable for a being such a simple formula of the voter’s own competence level.

There are a ton of links between voting, structured finance, and machine learning ensembles. For example, the logit equation Tabarrok gives is also used to weight members of Bayesian Model Combination ensembles, and is closely related to the weighting scheme used in AdaBoost.

I have every intention of writing about the overlaps between these topics one day, but until that day...

Leonard's critique is brilliant in its simplicity. RTWT.

Very briefly: if it is in fact so simple to "nudge" people between sets of preferences, how can you even claim they have real preferences? If people's preferences for apples or cookies is all an artifact of which comes first in the cafeteria line then central planners aren't allowing people to act on their low discount rate preferences instead of their high discount rate preferences, they're creating those preferences. David Henderson has a more in depth summary, but do read the original.

chrmoe :: LED Cube 8x8x8 running on an Arduino

Now that I've got a Raspberry Pi up and running I need to dive into Arduino.

I'm going to build one of these to cut my teeth, then before you know it I'll be giving Leo Villareal a run for his money 😉

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Roman Mars

The latest episode of Bullseye includes a great interview with radio producer and podcaster Roman Mars.

Mars is the man behind the wonderful podcast 99% Invisible. (No relation to OWS.) 99% Invisible is about the design and architecture of both the extremely weird (Kowloon Walled City, Razzle Camoflage) to the entirely prosaic (broken Metro escalators, check cashing stores, culs-de-sac) to the outright awesome (The Feltron Annual Report, Trappist ales).

I had always thought Mars had a background in architecture. Turns out he actually went to grad school to study genetics. A lot of what he said about studying and learning and why he went to grad school really resonated with me, which is why I'm writing this post. (Besides wanting to evangelize 99% Invisible, which I couldn't recommend more.)

The only complaint I have with the interview came when Mars said that if you simply read a list of his podcast topics without listening to the show you'd think they were the most boring things in the world.

I couldn't disagree more. The topics he chooses are exactly the sort of thing that lead people like me to descend into hour-long Wikipedia spelunking expeditions. (Except his investigations of them have way higher production value and are told much more artfully than the Wikipedia writing-by-committee process produces.) Don't you want to learn about how Gallaudet University designed buildings suitable for the deaf? Or how audio engineers sound-design the Olympics? Yes. Yes you do.

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Hello world!

This is my new blog. I noticed that the book reviews and other notes on my personal webpage were getting longer and unwieldy, so I've decided to migrate some of that content over here.

I plan on posting notes on what I've been reading, as well as thoughts about science, computation, business, art, and... whatever else. You know, blog stuff.

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