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
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.
Imagine for a moment the most obscurantist, jargon-filled, po-mo article the politically correct academy might produce. Pure SJW nonsense. Got it? Chances are you're imagining something like the infamous "Feminist Glaciology" article from a few years back.https://t.co/NRaWNREBvR pic.twitter.com/qtSFBYY80S— Jeffrey Sachs (@JeffreyASachs) October 13, 2018
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?
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".
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?
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
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!
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
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)
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
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. Lin Wood shares the password
2. Website has an article where the first letter of each sentence matches password
3. Title of article is an anagram for issac kappy
4. Somehow the file is stored in tor because of the reference to torsocks
5. Nobody has done an in depth analysis of the source code to see if there’s any hints there
6 search engine searches for slack, tor, and website returned nothing
https://t.co/lCajyM4TWp @sistronk @Crazy_German17 @boy17_tommy @105artillery @thecoffeebarons @Mareq16 @MKEBRAWLER @RealMaciejHelak @C8red8r @FabianBlondel @LaureenZapf
Silicon Valley is modelled after Crassus
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.
4. At least until they see their first project crash and burn within hours of the launch.
There are so many times that I have seen developers spend countless hours building something, releasing it with no fanfare...
5. and only then trying to figure out how to promote it by asking marketing questions on Indie Hackers, Quora or Hacker News. Don’t make that mistake.
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 🤷🏾♀️
We rightfully complain that marginalized people are not allowed to be fully human.
But we internalize and transfer our oppression daily. It’s a smog. We all breathe it in & act it out.
And then tell WoC “girl ain’t you supposed to be a _______? Why you doing ____?”
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.)
I’ve just been observing that behavior more and more lately. Especially when it comes to marginalized folks.
Evolution should be our aspiration.
“Can’t knock the hustle” should be our anthem.
As long as someone isn’t bringing active and continual harm, why can’t they explore their many sides?
There is a misunderstanding of the difference between the response in much of the West, versus successful countries (including New Zealand and Australia).
1.Reactive versus proactive and goal oriented.
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), …
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").
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.
5.Focusing attention on few individuals resistant to social action because of shortsightedness or selfishness, versus recognizing the vast majority do the right thing if given clear guidance and support, which is what matters for success, as elimination is a robust strategy.