J Bostock

• J Bostock 6 May 2024 10:55 UTC

  1 point

  0

  on: Biorisk is an Un­helpful Anal­ogy for AI Risk

  I agree with this point when it comes to technical discussions. I would like to add the caveat that when talking to a total amateur, the sentence:

  AI is like biorisk more than it is like than ordinary tech, therefore we need stricter safety regulations and limits on what people can create at all.

  Is the fastest way I’ve found to transmit information. Maybe 30% of the entire AI risk case can be delivered in the first four words.

Con­served Quan­tities (Stat Mech Part 2)

• J Bostock 28 Apr 2024 19:22 UTC

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  in reply to: EGI’s comment on: So What’s Up With PUFAs Chem­i­cally?

  I’d be most interested in detecting hydroperoxides, which is easier than detecting trans fats. I don’t know how soluble a lipid hydroperoxide is in hexane, but isopropanol-hexane mixtures are often used for lipid extracts and would probably work better.

  Evaporation could probably be done relatively safely by just leaving the extract at room temperature (I would definitely not advise heating the mixture at all) but you’d need good ventilation, preferably an outdoor space.

  I think commercial LCMS/​GCMS services are generally available to people in the USA/​UK, and these would probably be the gold standard for detecting various hydroperoxides. I wouldn’t trust IR spectroscopy to distinguish the hydroperoxides from other OH-group containing contaminants when you’re working with a system as complicated as a box of french fries.

• J Bostock 28 Apr 2024 12:16 UTC

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  on: So What’s Up With PUFAs Chem­i­cally?

  As far as I’m aware nobody claims trans fats aren’t bad.

  See comment by Gilch, allegedly Vaccenic acid isn’t harmful. The particular trans-fats produced by isomerization of oleic and linoleic acid, however, probably are harmful. Elaidic acid for example is a major trans-fat component in margarines, which were banned.

• J Bostock 28 Apr 2024 12:14 UTC

  3 points

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  in reply to: gilch’s comment on: So What’s Up With PUFAs Chem­i­cally?

  Yeah i was unaware of vaccenic acid. I’ve edited the post to clarify.

• J Bostock 27 Apr 2024 20:07 UTC

  9 points

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  in reply to: Joel Burget’s comment on: So What’s Up With PUFAs Chem­i­cally?

  I’ve also realized that it might explain the anomalous (i.e. after adjusting for confounders) effects of living at higher altitude. The lower the atmospheric pressure, the less oxygen available to oxidize the PUFAs. Of course some foods will be imported already full of oxidized FAs and that will be too late, but presumably a McDonalds deep fryer in Colorado Springs is producing less PUFAs/​hour than a correspondingly-hot one in San Francisco.

  This feels too crazy to put in the original post but it’s certainly interesting.

• J Bostock 27 Apr 2024 15:40 UTC

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  in reply to: MondSemmel’s comment on: So What’s Up With PUFAs Chem­i­cally?

  That post is part of what spurred this one

So What’s Up With PUFAs Chem­i­cally?

• J Bostock 22 Apr 2024 15:11 UTC

  3 points

  2

  in reply to: Gunnar_Zarncke’s comment on: For­get Every­thing (Statis­ti­cal Me­chan­ics Part 1)

  I uhh, didn’t see that. Odd coincidence! I’ve added a link and will consider what added value I can bring from my perspective.

For­get Every­thing (Statis­ti­cal Me­chan­ics Part 1)

Mea­sur­ing Learned Op­ti­miza­tion in Small Trans­former Models

Briefly Ex­tend­ing Differ­en­tial Op­ti­miza­tion to Distributions

• J Bostock 7 Mar 2024 12:57 UTC

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  in reply to: Magdalena Wache’s comment on: From Finite Fac­tors to Bayes Nets

  Thanks for the feedback. There’s a condition which I assumed when writing this which I have realized is much stronger than I originally thought, and I think I should’ve devoted more time to thinking about its implications.

  When I mentioned “no information being lost”, what I meant is that in the interaction $A\to B$, each value $b\in B$ (where $B$ is the domain of $P_B$) corresponds to only one value of $a\in A$. In terms of FFS, this means that each variable must be the maximally fine partition of the base set which is possible with that variable’s set of factors.

  Under these conditions, I am pretty sure that $A\perp C⟹A\perp C|B$

• J Bostock 3 Feb 2024 19:04 UTC

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  in reply to: tailcalled’s comment on: From Finite Fac­tors to Bayes Nets

  I was thinking about causality in terms of forced directional arrows in Bayes nets, rather than in terms of d-separation. I don’t think your example as written is helpful because Bayes nets rely on the independence of variables to do causal inference: $X\to Y\to Z$ is equivalent to $X\leftarrow Y\leftarrow Z$.

  It’s more important to think about cases like $X\to Y\leftarrow Z$ where causality can be inferred. If we change this to $\hat{X},\hat{Y},\hat{Z}$ by adding noise then we still get a distribution satisfying $\hat{X}\to \hat{Y}\leftarrow \hat{Z}$ (as $\hat{X}$ and $\hat{Z}$ are still independent).

  Even if we did have other nodes forcing $X\to Y\to Z$ (such as a node $U$ which is parent to $Y$, and another node $V$ which is parent to $Z$), then I still don’t think adding noise lets us swap the orders round.

  On the other hand, there are certainly issues in Bayes nets of more elements, particularly the “diamond-shaped” net with arrows $W\to X,W\to Y,X\to Z,Y\to Z$. Here adding noise does prevent effective temporal inference, since, if $\hat{X}$ and $\hat{Y}$ are no longer d-separated by $\hat{W}$, we cannot prove from correlations alone that no information goes between them through $\hat{Z}$.

Finite Fac­tored Sets to Bayes Nets Part 2

• J Bostock 25 Jan 2024 19:20 UTC

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  in reply to: Morpheus’s comment on: From Finite Fac­tors to Bayes Nets

  I had forgotten about OEIS! Anyway Ithink the actual number might be 1577 rather than 1617 (this also gives no answers). I was only assuming agnosticism over factors in the overlap region $ABCD$ if all pairs $AB,AC,...,CD$ had factors, but I think that is missing some examples. My current guess is that any overlap region like $ABCD$ should be agnostic iff all of the overlap regions “surrounding” it in the Venn diagram ($ABC$, $ABD$, $ACD$, $BCD$) in this situation either have a factor present or agnostic. This gives the series 1, 2, 15, 1577, 3397521 (my computer has not spat out the next element). This also gives nothing on the OEIS.

  My reasoning for this condition is that we should be able to “remove” an observable from the system without trouble. If we have an agnosticism, in the intersection $ABCD$, then we can only remove observable $B$ if this doesn’t cause trouble for the new intersection $ABD$, which is only true if we already have an factor in $ABD$ (or are agnostic about it).

From Finite Fac­tors to Bayes Nets

• J Bostock 16 Jan 2024 22:38 UTC

  5 points

  3

  on: Nat­u­ral La­tents: The Math

  I know very, very little about category theory, but some of this work regarding natural latents seem to absolutely smack of it. There seems to be a fairly important three-way relationship between causal models, finite factored sets, and Bayes nets.

  To be precise, any causal model consisting of root sets $B$, downstream sets $X$, and functions mapping sets to downstream sets like $f_4:(B_1\otimes B_3\otimes X_2)\to X_4$ must, when equipped with a set of independent probability distributions over B, create a joint probability distribution compatible with the Bayes net that’s isomorphic to the causal model in the obvious way. (So in the previous example, there would be arrows from only $B_1$, $B_3$, and $X_2$ to $X_4$) The proof of this seems almost trivial but I don’t trust myself not to balls it up somehow when working with probability theory notation.

  In the resulting Bayes net, one “minimal” natural latent which conditionally separates $X_i$ and $X_j$ is just the probabilities over just the root elements from $B$ which both $X_i$ and $X_j$ depend on. It might be possible to show that this “minimal” construction of $\Lambda$ satisfies a universal property, and so other ${\Lambda }^{\prime }$ which is also “minimal” in this way must be isomorphic to $\Lambda$.

• J Bostock 27 Dec 2023 15:11 UTC

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  in reply to: Charlie Steiner’s comment on: Differ­en­tial Op­ti­miza­tion Reframes and Gen­er­al­izes Utility-Maximization

  I think the position of the ball is in V, since the players are responding to the position of the ball by forcing it towards the goal. It’s difficult to predict the long-term position of the ball based on where it is now. The position of the opponent’s goal would be an example of something in U for both teams. In this case both team’s utility-functions contain a robust pointer to the goal’s position.

Differ­en­tial Op­ti­miza­tion Reframes and Gen­er­al­izes Utility-Maximization