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. (🧵)
Is there a fact of the matter as to whether the cat is alive before you open the box?— Avraham Eisenberg (@avi_eisen) November 8, 2020
I would say not, and all your references to how the world "is" are similarly incoherent.
Wait so you disagree with 'quantum splitting means that that there are futures where you become the next US president and futures where you murder your family and futures where you spontaneously combust' takes?— Peli Grietzer (@peligrietzer) November 8, 2020
Can you defend this distinction between past and future splits?— Avraham Eisenberg (@avi_eisen) November 8, 2020
You mentioned personal identity, are you going to argue that personal identity splits even if we're unaware of any differences?
My issue is what forks \u201cspace\u201d itself? Obv we need a QG theory, but MWI assumes some background independence or metaphysical substrate in which alternative quantum states can resolve.— U.S.O.U.S. (@hyperauxetic) November 8, 2020
You think there's a fact of the matter about whether you are Classical Simon1 or Classical Simon2? My instinct is that there isn't, if they are qualitatively identical to each other— Peli Grietzer (@peligrietzer) November 8, 2020
If both have the exact same memories and you can't tell which one "you" are, then from your perspective there shouldn't be a fact of the matter as to which one you are. At least, that's my view on personal identity. What's the argument against?— Avraham Eisenberg (@avi_eisen) November 8, 2020
Not sure how that's relevant to personal identity.— Avraham Eisenberg (@avi_eisen) November 8, 2020
Simon, I don't mean to distract you from your brilliant thread, here, but what would you say to a Meillassouxian-type committed to an arche-fossil as the basis of absolute contingency?— NAF Loves Meillassoux (@LovesNaf) November 8, 2020
Not sure this is what you\u2019re looking for, but Tegmark uses cosmic rays causing cancerous mutations as one example of quantum splitting have observable macro effects.— Matt Clancy (@mattsclancy) November 8, 2020
More from Simon DeDeo
As a dean of a major academic institution, I could not have said this. But I will now. Requiring such statements in applications for appointments and promotions is an affront to academic freedom, and diminishes the true value of diversity, equity of inclusion by trivializing it. https://t.co/NfcI5VLODi— Jeffrey Flier (@jflier) November 10, 2018
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".
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.
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.
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.
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...
More from Data science
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
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.
Our long overdue paper on generalized word shift graphs is finally here!
So what are they?
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
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?
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
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
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 🧬📖🧪📈🗺️⚕️🪐
/ @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
When are you doing pie charts?— #BlackLivesMatter (@surt_lab) October 13, 2020
Here's the code to generate the data frame. You can get the "raw" data from https://t.co/jcTE5t0uBT
Obligatory stacked bar chart that hides any sense of variation in the data
Obligatory stacked bar chart that shows all the things and yet shows absolutely nothing at the same time
STACKED Donut plot. Who doesn't want a donut? Who wouldn't want a stack of them!?! This took forever to render and looked worse than it should because coord_polar doesn't do scales="free_x".
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2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.
3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.
4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.
5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.
Arts are best form to Express thoughts & devotion. In All civilization Arts played very important role to express sprituality. Tibbeten Tangkhas are prime example .
List of My Paintings & Sketches
प्रलय पयोधि-जले धृतवान् असि वेदम्
विहित वहित्र-चरित्रम् अखेदम्
केशव धृत-मीन-शरीर, जय जगदीश हरे!!
छलयसि विक्रमणे बलिम् अद्भुत-वामन
केशव धृत-वामन रूप जय जगदीश हरे
नंदसि यज्ञ- विधेर् अहः श्रुति जातम्
केशव धृत-बुद्ध-शरीर जय जगदीश हरे
क्षत्रिय-रुधिर-मये जगद् -अपगत-पापम्
स्नपयसि पयसि शमित-भव-तापम्
केशव धृत-भृगुपति रूप जय जगदीश हरे
What happens when you have:
Verizon All Access (after they buy CBS/Viacom)
Answer? Internet Slows To A Craw and Dies.
Netflix's gets 35% of all internet traffic.
Now we all know Apple Coming to Netflix Corner.
We know that WarnerMedia Planning One
We Know about Disney+
Now how will the net handle 8 Streaming Platforms all at once?
Answer - IT CANT.
But Novid, the speed, the 4K the all everything?
Even if you could do it and even if AWS ran six million clouds, The Net Will still slow to a crawl. 35%, goes to nearly 90% if any of the 8 or all of the 8 eat at netflix's numbers.
Oh, they wouldn't be running at once.
FOOL. You forget how bad things were when game of thrones season premieres came around. HBO SERVERS FALL DOWN GO BOOM!
Now see if a season like 2021 come around and they air shows on a same day. It gets crazy. AT&T and others gonna realize they cant build out forever. Something will give and it might be your entertainment consumption big time.
There are three factors that suggest that the recent stability could evaporate and that equities are "about to enter a sustained bear market", Oppenheimer says.
First is the fact that growth, inflation and interest rate outlook is unfriendly for equities.
Second is that greater volatility, such as multiple corrections and new peaks (which we've seen in 2018) tend to presage a full bear market.
Here is Goldman's data showing how falls and bounces tend to come ahead of "the dramatic final fall".