andreas

Karma: 295

andreas 2 Feb 2013 5:42 UTC
47 points
0
on: Rationality Quotes February 2013

“I design a cell to not fail and then assume it will and then ask the next ‘what-if’ questions,” Sinnett said. “And then I design the batteries that if there is a failure of one cell it won’t propagate to another. And then I assume that I am wrong and that it will propagate to another and then I design the enclosure and the redundancy of the equipment to assume that all the cells are involved and the airplane needs to be able to play through that.”

—Mike Sinnett, Boeing’s 787 chief project engineer

andreas 17 Jan 2012 3:51 UTC
5 points
0
on: Random thought: What is the optimal PD strategy under imperfect information?
The game theory textbook “A Course in Microeconomic Theory” (Kreps) addresses this situation. Quoting from page 516:

We will give an exact analysis of this problem momentarily (in smaller type), but you should have no difficulty seeing the basic trade-off; too little punishment, triggered only rarely, will give your opponent the incentive to try to get away with the noncooperative strategy. You have to punish often enough and harshly enough so that your opponent is motivated to play [cooperate] instead of [defect]. But the more often/more harsh is the punishment, the less are the gains from cooperation. And even if you punish in a fashion that leads you to know that your opponent is (in her own interests) choosing [cooperate] every time (except when she is punishing), you will have to “punish” in some instances to keep your opponent honest.

andreas 24 Apr 2011 1:31 UTC
7 points
0
in reply to: lukeprog’s comment on: Heading Toward: No-Nonsense Metaethics
I am more motivated to read the rest of your sequence if the summary sounds silly than if I can easily see the arguments myself.

andreas 24 Apr 2011 1:13 UTC
14 points
0
on: Heading Toward: No-Nonsense Metaethics
Back when Eliezer was writing his metaethics sequence, it would have been great to know where he was going, i.e., if he had posted ahead of time a one-paragraph technical summary of the position he set out to explain. Can you post such a summary of your position now?

andreas 12 Mar 2011 21:29 UTC
3 points
0
on: Making Reasoning Obviously Locally Correct

Now, citing axioms and theorems to justify a step in a proof is not a mere social convention to make mathematicians happy. It is a useful constraint on your cognition, allowing you to make only inferences that are actually valid.

When you are trying to build up a new argument, temporarily accepting steps of uncertain correctness can be helpful (if mentally tagged as such). This strategy can move you out of local optima by prompting you to think about what further assumptions would be required to make the steps correct.

Techniques based on this kind of reasoning are used in the simulation of physical systems and in machine inference more generally (tempering). Instead of exploring the state space of a system using the temperature you are actually interested in, which permits only very particular moves between states (“provably correct reasoning steps”), you explore using a higher temperature that makes it easier to move between different states (“arguments”). Afterwards, you check how probable the state is that you moved to when evaluated using the original temperature.

andreas 24 Jan 2011 3:14 UTC
6 points
0
in reply to: lukeprog’s comment on: Hyperlinks and Less Wrong
As you wish: Drag the link on this page to your browser’s bookmark bar. Clicking it on any page will turn all links black and remove the underlines, making links distinguishable from black plain text only through changes in mouse pointer style. Click again to get the original style back.

andreas 30 Nov 2010 9:47 UTC
2 points
0
in reply to: cousin_it’s comment on: Unsolved Problems in Philosophy Part 1: The Liar’s Paradox
See also: A Universal Approach to Self-Referential Paradoxes, Incompleteness and Fixed Points, which treats the Liar’s paradox as an instance of a generalization of Cantor’s theorem (no onto mapping from N->2^N).

The best part of this unified scheme is that it shows that there are really no paradoxes. There are limitations. Paradoxes are ways of showing that if you permit one to violate a limitation, then you will get an inconsistent systems. The Liar paradox shows that if you permit natural language to talk about its own truthfulness (as it—of course—does) then we will have inconsistencies in natural languages.

andreas 27 Nov 2010 6:33 UTC
2 points
0
in reply to: XiXiDu’s comment on: Rationality is Not an Attractive Tribe
Do you think that your beliefs regarding what you care about could be mistaken? That you might tell yourself that you care more about being lazy than about getting cryonics done, but that in fact, under reflection, you would prefer to get the contract?

andreas 8 Nov 2010 3:43 UTC
10 points
0
in reply to: Perplexed’s comment on: An Xtranormal Intelligence Explosion
Please stop commenting on this topic until you have understood more of what has been written about it on LW and elsewhere. Unsubstantiated proposals harm LW as a community. LW deals with some topics that look crazy on surface examination; you don’t want people who dig deeper to stumble on comments like this and find actual crazy.

andreas 7 Nov 2010 0:07 UTC
2 points
0
in reply to: fiddlemath’s comment on: The Curve of Capability
Similarly, inference (conditioning) is incomputable in general, even if your prior is computable. However, if you assume that observations are corrupted by independent, absolutely continuous noise, conditioning becomes computable.

andreas 30 Oct 2010 19:53 UTC
5 points
0
in reply to: [deleted]’s comment on: Ben Goertzel: The Singularity Institute’s Scary Idea (and Why I Don’t Buy It)
Consider marginal utility. Many people are working on AI, machine learning, computational psychology, and related fields. Nobody is working on preference theory, formal understanding of our goals under reflection. If you want to do interesting research and if you have the background to advance either of those fields, do you think the world will be better off with you on the one side or on the other?

andreas 25 Oct 2010 4:33 UTC
3 points
0
on: A Paradox in Timeless Decision Theory

Now suppose you are playing against another timeless decision theory agent. Clearly, the best strategy is to be that actor which defects no matter what. If both agents do this, the worst possible result for both of them occurs.

Which shows that defection was not the best strategy in this situation.

andreas 24 Oct 2010 23:03 UTC
0 points
0
in reply to: DanB’s comment on: Church: a language for probabilistic modeling
Yes, deriving mechanisms that take complex models and turn them into something tractable is mostly an open problem.

andreas 24 Oct 2010 2:10 UTC
1 point
0
in reply to: jsalvatier’s comment on: Church: a language for probabilistic modeling
They don’t work without continuous parameters. If you have a probabilistic program that includes both discrete and continuous parameters, you can use gradient methods to generate MH proposals for your continuous parameters. I don’t think there are any publications that discuss this yet.

andreas 23 Oct 2010 21:42 UTC
0 points
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in reply to: [deleted]’s comment on: Three kinds of political similarity
A pdf of the nature intro is here.

andreas 23 Oct 2010 21:06 UTC
1 point
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in reply to: Richard_Kennaway’s comment on: Church: a language for probabilistic modeling
I was comparing the two choices people face who want to do inference in nontrivial models. You can either write the model in an existing probabilistic programming language and get inefficient inference for free or you can write model+inference in something like Matlab. Here, you may be able to use libraries if your model is similar enough to existing models, but for many interesting models, this is not the case.

andreas 23 Oct 2010 20:44 UTC
2 points
0
in reply to: jsalvatier’s comment on: Church: a language for probabilistic modeling
Current universal inference methods are very limited, so the main advantages of using probabilistic programming languages are (1) the conceptual clarity you get by separating generative model and inference and (2) the ability to write down complex nonparametric models and immediately be able to do inference, even if it’s inefficient. Writing a full model+inference implementation in Matlab, say, takes you much longer, is more confusing and less flexible.

That said, some techniques that were developed for particular classes of problems have a useful analog in the setting of programs. The gradient-based methods you mention have been generalized to work on any probabilistic program with continuous parameters.

andreas 23 Oct 2010 20:20 UTC
4 points
0
in reply to: JoshuaZ’s comment on: Church: a language for probabilistic modeling
Probabilistic inference in general is NP-hard, but it is not clear that (1) this property holds for the kinds of problems people are interested in and, even if it does, that (2) approximate probabilistic inference is hard for this class of problems. For example, if you believe this paper, probabilistic inference without extreme conditional probabilities is easy.

andreas 23 Oct 2010 5:13 UTC
3 points
0
in reply to: pdf23ds’s comment on: Lifelogging: the recording device
Combine this with speech-to-text transcription software and you get a searchable archive of your recorded interactions!

ETA: In theory. In practice, dictation software algorithms are probably not up to the task of turning noisy speech from different people into text with any reasonable accuracy.

andreas 23 Oct 2010 0:32 UTC
20 points
0
on: Church: a language for probabilistic modeling
The key idea behind Church and similar languages is that they allow us to express and formally reason about a large class of probabilistic models, many of which cannot be formalized in any concise way as Bayes nets.

Bayes nets express generative models, i.e. processes that generate data. To infer the states of hidden variables from observations, you condition the Bayes net and compute a distribution on the hidden variable settings using Bayesian inference or some approximation thereof. A particularly popular class of approximations is the class of sampling algorithms, e.g. Markov Chain Monte Carlo methods (MCMC) and importance sampling.

Probabilistic programs express a larger class of models, but very similar approximate inference algorithms can be used to condition a program on observations and to infer the states of hidden variables. In both machine learning and cognitive science, when you are doing Bayesian inference with some model that expresses your prior belief, you usually code both model and inference algorithm and make use of problem-specific approximations to Bayesian inference. Probabilistic programs separate model from inference by using universal inference algorithms.

If you are interested in this set of ideas in the context of cognitive science, I recommend this interactive tutorial.

Church is based on Lisp. At the lowest level, it replaces Boolean gates with stochastic digital circuits. These circuits are wired together to form Markov chains (the probabilistic counterpart of finite state machines.) At the top, it’s possible to define probabilistic procedures for generating samples from recursively defined distributions.

This confuses Church as a language for expressing generative models with ideas on how to implement such a language in hardware. There are three different ideas here:
- Church as a language for generative models with sampling-based semantics
- MCMC as an approximate inference method for such models (that can be implemented on traditional von Neumann architectures)
- Machine architectures that are well-suited for MCMC
So this is an open call for volunteers—any brave Bayesians want to blog about a brand new computer language?

I’ll write an exposition within the next weeks if people are interested.