BC DATA SCIENCE
Saved by @zmbnski

On Bayesianism, the Many Worlds Interpretation, and personal identity.

Some thoughts worked out in a letter to a friend, which is the kind of thing you do when off Twitter for a glorious week. (🧵)

 “Chance is ignorance”—the Bayesian story; all probabilities represent states of mind, not states of the world. One *could* put (some) chances “in the world”, but let’s take Occam’s Razor seriously...
That the probability of a fair coin coming up heads is 50% simply means that marginalizing (tracing, as the physicists say) over the hidden facts leaves you, nearly, maximally ignorant of the outcome.
Quantum uncertainty (access below!) poses an apparent challenge to this story. There seems to be nothing to be ignorant about when it comes to (say) electron spin—there is nothing “inside” the object.
https://t.co/DhHmN0ndjx
The electron is a simple object, in other words. So where does the uncertainty come from? One could follow David Wallace’s wonderful interpretation in terms of chaotic dynamics and decoherence, but let’s see if we can take another route...
It lies, finally, not in our lack of knowledge of what’s inside the electron (there is nothing there), but rather a lack of self-knowledge—we do not know which branch of the Universe we ended up in!
We lack knowledge, in other words, of the referent of a pronoun—which “I” I am. If you’re Saul Kripke, the rigid designator has detached—you must be re-baptized. For that reason, we should naturally refer to MWI as Born Again Quantum Theory (with the obvious pun).
The world is constantly forking, of course, but the vast majority of those forks are completely meaningless to us; they fall, for all intents and purposes, into the same equivalence class.
It’s only in rare cases (like those electrons at ANU) that we come directly into contact with these forks, where the equivalence class splits. Most classical uncertainty is simply further refining knowledge about the initial quantum state.
(I won’t tell you how my ANU quantum coin came up—ha ha! I know which branch we’re on, you don’t, but that’s just a classical, Bayesian matter.)
This is probably super-obvious to someone like @seanmcarroll, but there are two nice things about it.
First, it’s a nice way to get at the MWI from an unexpected direction. You don’t have to know about the Schrödinger Equation, decoherence, etc to realize the Universe *must* split.
That might help (depending on your philosophy of science) increase your confidence in the underlying metaphysical claims.
Second, it helps me understand why The Universe Splitter is a non-trivial device. It really does split universes... https://t.co/Uar00s1zmN
And it’s kind of cool to realize that it wasn’t until we learned to isolate and pay attention to quantum effects that the universes began to split!
Definitely a (classical) fact of the matter! The radioactive decay couples very quickly to the classical world—which means you’ve already split before you have time to wonder about his state. https://t.co/ffFcngLV3a
In any case, nearly all the forks that matter in human history happened long ago (at inflation, or perhaps reheating). I think. Can anyone think of a pre-twentieth century coupling to novel quantum uncertainty?
Most quantum effects are invisible to us—meaning that our ignorance of which universe we’re in is permanent, and therefore irrelevant. Perhaps when it comes to single-photon events—@DavidDeutschOxf’s frog in _Fabric of Reality_...
But did any decision, or memory, ever hang on the outcome on how a single photon interfered with itself? (Until today, when we basically, being humans, do it for the lulz!)
I’m a little worried I’m missing some massively obvious quantum influence, but one point in my favor is that this kind of world-splitting re-baptism took so long to discover. It’s not obvious at all.
And technically, we couldn’t know about it until Bell’s Inequality proved that there were no (relevant) hidden gears within the electron.
YES! I think this is meaningful. https://t.co/bKiU3y0cAX
It’s (nearly) all classical uncertainty. Those worlds exist, but are separated by a vast gulf—they split billions of years ago, well before any decision we might make.
Until someone uses the ANU device to hang some history on. There’s even a kind of odd utilitarian morality to it—if you are thinking about doing something bad, and hang it off ANU, you reduce the harm by a factor of two.
Nearly all personal identity splits happened (literal) eons ago. Laplace’s demon—we’re just discovering the consequences of the (decohered) initial conditions. https://t.co/pXX0YHIVJy
Those Universes separated so long ago that there’s no real sense in which Classical Simon1 and Classical Simon2 are comparable. You might as well line me up with Alpha Centauri Simon.
Nothing actually forks it. We’re emergent phenomena, our experiences equivalence classes over quantum states. ANU just happens (for the lulz) to split them, which I’m suggesting is actually super-rare in human history. https://t.co/hJ47tyaGIN
Definitely. My lamp tips over. That informs me about a sequence of classical events that goes back to the formation of the Earth, and much further. Now I know which path the atoms took after initial decoherence, but that was my ignorance. https://t.co/ig89rZCbcQ
Kripke semantics would say that a copy of the standard meter doesn’t have to “know” it’s not the standard meter for it to be distinct. https://t.co/Y6EzUTtgtP
This also helps me understand all those bits that ANU gathers but never serves to users. They (eventually) vanish (deleted for space), leaving no trace. Nobody will ever know how they turned out, and so our equivalence class can’t split.
Just realized that one source of universe-splitting started happening with (I think) cosmic ray errors in silicon. Where, in particular, the ray interacts is a quantum effect—causing one error over another, becoming visible to the operator.
You have to buy the Kripke story about how pronouns work. I, personally, think it’s intuitive and how I use them—but you may not! https://t.co/w5kHRVIc4K
I don’t know! I have Meillassoux on my desk (_After Finitude_) and would love any suggestions. https://t.co/JyY1yQsmzF
Oh, I think this works, yes. Sad, but true. Any happier examples? (Although in another sense, Max’s example *is* happy—there are yous that survive.) https://t.co/hErcyUtJ66

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Donna Strickland, one of this year's Nobel Laureates, was not considered "notable enough" for Wikipedia as late as this May—reported by a number of sites, including the Independent. I dug into this a little. Here's the data—


This is the first deletion, back in 2014. A bit hard to read between the lines, but the basic story that an admin had Stickland's page "speedy deleted"—i.e., deleted without debate. The method was something called Copyright Jujitsu.

In particular, the admin had the page deleted not because of notability, but because it included a photograph of Strickland that had ambiguous copyright status. This is a method that people developed to get rid of content they didn't want, but also didn't want to debate.

"Copyright Jujitsu" because it is usually used against spam from companies; a PR officer uploads promotional material to Wikipedia. Instead of debating whether it's neutral, the admin can say "we'd love to have it, but the material appears to violate your company's copyright".

Usually the PR office and the IP office are separate in a company, and the idea of releasing promotional material under public domain is such a legal nightmare that the PR person goes away.
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So—probability. Do we really need it? This has been on my mind since 2017, when I finally sat down to think through quantum theory. (/n)

One thing that’s always struck me is how *late* probability theory came in intellectual history. We had integral calculus before we had probability. And probability is insanely simple, mathematically!

I’m tempted to say that probability theory is not, in fact, Lindy. Frequentist probability is (for all the usual reasons) best understood as a heuristic. Bayesian interpretations, by contrast, take the remarkable step of tying it to mental states.

You have to work very hard to convince yourself that beliefs really are “degrees of belief in sets of events” (or whatever). It’s not natural—and I won’t rehearse the whole story about rational choice and decision theory...

So with those critiques in the back of my mind, when I read David Wallace’s decision-theoretic account of the Born Rule I was rather primed to say, hey, so what? Meaning...
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So, today, for the first time in 25 (!) years of Apple, I downgraded. From the 2016 MacBook Pro to my 2013, which I had kept in a drawer...


It was pretty simple to do—Apple Time Machine backups let me do it with one click.

That first tweet captures, in two pictures, how badly Apple has “lost the plot” (to quote @wylieprof). On the right is the Apple MagSafe adapter, from 2013. On the left, what I had “upgraded” to.

Thanks, Apple! I really was nostalgic for worrying about yanking my computer off the table.

Oh and I really appreciated not knowing if my computer was charging. What was great was the little whoop sound you used, so that the speaker before me could be informed I was charging my laptop.
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Simon DeDeo
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Someone tosses a coin ten times; it comes up heads every time. What's the probability it comes up heads on the next toss? (Pretty darn high—part of @nntaleb's work is unprogramming you from your high-school rules of thumb.) Now consider the (related) Gambler's fallacy...

In this case, it's a theory about compensation: the worse one's luck is, the more likely it is to see a reversal. On the surface, it's irrational. The more bad luck you have, the more you accumulate evidence that the system is rigged.

But there's also an anthropic component. If the luck is bad enough, it starts to become inconsistent with your survival. You've accumulated evidence for correlations in the environment, but these correlations (may be) inconsistent with (people like you) being in this environment.

An example. You're in a city where everyone takes public transport. You encounter a string of bad delays. It's reasonable to conclude they'll end—otherwise people wouldn't take public transport. It's unlikely that you happened to show up right when the network collapses.

Of course, that's a bad heuristic in a casino, which relies on a constant influx of losers. But in other environments, particularly with persistent populations and no evidence for sudden changes in the underlying laws, it makes sense.
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Simon DeDeo
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This is a pretty valiant attempt to defend the "Feminist Glaciology" article, which says conventional wisdom is wrong, and this is a solid piece of scholarship. I'll beg to differ, because I think Jeffery, here, is confusing scholarship with "saying things that seem right".


The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.


Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)


There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.


At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?
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We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: https://t.co/pRqXtFpYXq #ResearchSoftwareHour
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Tips for AI writers:

1. Spend 30% of your effort on skimming all student ML papers (e.g. Stanford NLP CS224n) the past 3 years and prototype your favorites

The idea is everything. Pick an area you are interested in and ideally something that has a visual aspect to it

Most of my 'on the top of my mind' ideas were bad in retrospect. Skimming 100s of student papers will give you an overview of what's interesting.

Student papers are overlooked, easy to understand, and have good compute constraints.

2. Spend 30% on your effort on coding

Create an edge to the project. Apply it to something new and use FastAI or Keras to improve the accuracy with 5-30%.

3. Spend 30% writing an in-depth article

Have a north star article in terms of structure and quality. Find something that stretches you to your utmost capability. I used @copingbear’s Style transfer article:

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I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

https://t.co/zSpp67kJSg

Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.


📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.


📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...


📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
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Cheat sheet that summarizes #DataScience in 10 pages
(Links in the comments below 👇)


2/ Link to the cheatsheet by Maverick
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1/ A ∞-wide NN of *any architecture* is a Gaussian process (GP) at init. The NN in fact evolves linearly in function space under SGD, so is a GP at *any time* during training. https://t.co/v1b6kndqCk With Tensor Programs, we can calculate this time-evolving GP w/o trainin any NN


2/ In this gif, narrow relu networks have high probability of initializing near the 0 function (because of relu) and getting stuck. This causes the function distribution to become multi-modal over time. However, for wide relu networks this is not an issue.

3/ This time-evolving GP depends on two kernels: the kernel describing the GP at init, and the kernel describing the linear evolution of this GP. The former is the NNGP kernel, and the latter is the Neural Tangent Kernel (NTK).

4/ Once we have these two kernels, we can derive the GP mean and covariance at any time t via straightforward linear algebra.


5/ So it remains to calculate the NNGP kernel and NT kernel for any given architecture. The first is described in https://t.co/cFWfNC5ALC and in this thread
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