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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Simon DeDeo
Simon DeDeo
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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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Simon DeDeo
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@SimonDeDeo
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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Simon DeDeo
Simon DeDeo
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"I lied about my basic beliefs in order to keep a prestigious job. Now that it will be zero-cost to me, I have a few things to say."


We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.

Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)

It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.

Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
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Simon DeDeo
Simon DeDeo
@SimonDeDeo
One thing that goes unremarked about Wikipedia: the vast majority of the content was written over 12 years ago—which means (given turnover) that the editors dominant today were not directly involved in that creation.

Put another way, the editors who built the dominant nodes in this network...


...have little overlap with the ones who made this much more recent managerial flowchart.


Internet time runs at hundred-fold speed—the difference between the people who painted what's in the Uffizi, and the people in charge of keeping those paintings from deteriorating. Very different tasks, and (one presumes) very personalities as well. @PaulSkallas?
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Simon DeDeo
Simon DeDeo
@SimonDeDeo
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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This is a more wonky thread about how I made this visualization in #Rstats using the awesome visNetwork

First step is to create the underlying network data. We need one file of "nodes" - i.e. the people and organizations. And one file of "edges" - i.e. the connections between them.

I created these by hand, based on excellent investigate journalism:


Now we can pull these together to create a network visualization!

You'll notice that I included a column for "type" in the nodes file. This allows me to use different icons for people vs firms vs political organizations.


All the icons are taken from @fontawesome. I *think* the visNetwork 📦 currently only works with fontawesome version 4.7, which is a bit limited – e.g. I decided to use a book icon to represent the fringe Evangelical Christian sect "Exclusive Brethren"! 😂


I very much enjoyed getting to use the "incognito" icon to represent all the unknown donors that have funded Tory MP Owen Paterson's overseas jaunts!
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Tired of word clouds? Want to do better sentiment analysis? Not sure how to look at the words underneath your measures?

Our long overdue paper on generalized word shift graphs is finally here!
https://t.co/lIBXvbMJWX
https://t.co/vSL1REYT8V

So what are they?

1/n


If we have two texts, there are many ways we can compare them. Weighted averages are a particularly useful measure because they're flexible and interpretable

Proportions, Shannon entropy, the KLD, the JSD, and dictionary methods can all be written as weighted averages

2/n

But weighted avgs are also slippery. When we try to compress complex phenomena like happiness, surprise, divergence, or diversity into a single number, it can be unclear what we're measuring

If the measure goes up, what does that mean? Why did it do that? Can we trust it?

3/n

Very often, that's the end of the line and we're left with an uneasy feeling in the pit of our stomach that our weighted avg is actually picking up a data artifact or some other unintended peculiarity

Word shift graphs help us address those concerns

4/n

First, word shifts look under the hood of weighted averages to see what's going on

All weighted averages are a sum of contributions from individual words. We can pull out those words, and rank which ones contribute the most to the difference between two texts

5/n
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To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇

https://t.co/EwwOzgfDca : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks.

https://t.co/J4qMzPAZ6u Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs).

https://t.co/9IgKkSxPCq a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.

https://t.co/EAqn2YngIE : TensorFlow Java API (experimental)
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Research Engineering at the Turing
Research Engineering a...
@turinghut23
✨✨ BIG NEWS: We are hiring!! ✨✨
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩

👇 Here below a thread on who we are and what we

We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 https://t.co/KcSVMb89yx #RSEng

We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
https://t.co/zjoQDGxKHq
/ @DavidBeavan @LivingwMachines

In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩‍💻 presentation for some examples:

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:

4. Spend 10% marketing your project

Invest a week in studying the strategies to rank on sites like HN and Reddit, then use them. If you have an interesting result and a great article, you've done the hard work.
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1. The major problem most startups face is marketing and the ability to carve out a niche, find and retain paying customers.

Even with a stellar developer team and a stellar product, your startup may not grow or survive without a great marketing and sales teams.

2. Start growing your audience before you’ve a product or an idea.

You should start promoting yourself and building your audience before you have a finished product or even before you have a great idea that you want to build.

3. Don’t wait to start building your audience after you’ve launched a product.

Most first-time developers actually ignore marketing.

They’re oblivious to the challenge of attracting people to take a look at something they’ve created.

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There are so many times that I have seen developers spend countless hours building something, releasing it with no fanfare...

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We should stop confusing what someone was doing when we became aware of them with all they are capable of, interested in, or ever hope to be

Let folks have their many talents, interests and gifts. Life is far more fun with variety, loves.

A lot of folks have come to know me as an activist & I’m grateful that folks care to know me at all.

But I wasn’t born in 2014. I was a whole teacher, executive, policy person, speaker, arts and culture lover, reader, writer, woman of faith, fashion and more before 4 yrs ago 🤷🏾‍♀️

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Can I live?

And don’t go reading anything personal into this-this isn’t about me necessarily and it’s no subtweet (I try hard not to do that.)

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Evolution should be our aspiration.

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I have been working on pandemic outbreaks for 15 years.

There is a misunderstanding of the difference between the response in much of the West, versus successful countries (including New Zealand and Australia).

Summarizing:

1.Reactive versus proactive and goal oriented.

1/-

2.Mitigation (slowing transmission) versus elimination (stopping transmission)

3.Gradually responding to increasing levels of infection by imposing greater restrictions which enables the infection rate to grow (red zone strategy), …

2/

versus starting with high restrictions to arrest transmission and relaxing restrictions only when the number of new cases is so low that contact tracing or localized short term action can stop community transmission (green zone strategy, including localized "fire fighting").

3/

4.Trying to keep economic activity and travel as open as possible but perpetuating the economic harm and imposing yoyo restrictions, versus making an initial sacrifice of economic activity and travel in order to benefit from the rapid restoration of normal economic activity.

4/

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