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

More from Simon DeDeo

Simon DeDeo
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.
CRYPTO
Simon DeDeo
Simon DeDeo
@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
@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...
PROBABILITY
Simon DeDeo
Simon DeDeo
@SimonDeDeo
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
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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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.
DATA SCIENCE
Goku Mohandas
Goku Mohandas
@GokuMohandas
🔥 Putting ML in Production! We're going to publicly develop @madewithml's first ML service. Here is the broad curriculum:

- 📦 Product
- 🔢 Data
- 🤖 Modeling
- 📝 Scripting
- 🛠 API
- 🚀 Production

More details (lessons, task, etc.) here: https://t.co/xmMm9XGK9j

Thread 👇


Questions that this thread will answer:

- What is it?
- Who is this course for?
- What is the format?
- What makes this course unique?
- Why constrain to open source tools?
- What are my qualifications?
- Why is this free?
- What are the

What is it?

Putting ML in Production: a guide and code-driven case study on MLOps. We will be developing and deploying Made With ML's first ML service, from Product → ML → Production, with open source tools.

This ML service will act as a foundation for all future ML features and subsequent iterations. The first feature is tagifai - multilabel classification of tags for a project. We'll discuss the need and utility of this feature in the first lesson.

Who is this course for?

- ML developers looking to become end-to-end ML developers.
- Software engineers looking to learn how to responsibly deploy and monitor ML systems.
- Product managers who want to have a comprehensive understanding of the different stages of ML dev.
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John Myles White
John Myles White
@johnmyleswhite
This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
DATA SCIENCE
Pat Schloss
Pat Schloss
@PatSchloss
Wellll... A few weeks back I started working on a tutorial for our lab's Code Club on how to make shitty graphs. It was too dispiriting and I balked. A twitter workshop with figures and code:


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".
DATA SCIENCE
Sophie Hill
Sophie Hill
@sophie_e_hill
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!
DATA SCIENCE

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Trading view scanner process -

1 - open trading view in your browser and select stock scanner in left corner down side .

2 - touch the percentage% gain change ( and u can see higest gainer of today)


3. Then, start with 6% gainer to 20% gainer and look charts of everyone in daily Timeframe . (For fno selection u can choose 1% to 4% )

4. Then manually select the stocks which are going to give all time high BO or 52 high BO or already given.

5. U can also select those stocks which are going to give range breakout or already given range BO

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7 . Now next day if any stock show momentum u can take trade in it with RM

This looks very easy & simple but,

U will amazed to see it's result if you follow proper risk management.

I did 4x my capital by trading in only momentum stocks.

I will keep sharing such learning thread 🧵 for you 🙏💞🙏

Keep learning / keep sharing 🙏
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一个北大毕业的殡葬工自述武汉封城期间的经历，凄惨。这应该才是武汉的真实情景，跟很多人的叙述是吻合的。


https://t.co/QYTO1SYedG
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As per Skanda Puran, Kartikeya had narrated to Muni Agastya about the signs of the coming death. This is as per this ancient scripture.


If one experiences the air coming out of right nostril continuously then it is observed that the person dies within three years. If the breathing is continuous towards upper side from both the nostrils, then the person can die within two three days.

If the breathing is through the mouth instead of the nostrils then the person may live only for two days. The person should take into coignance these signs of the occurance of any sudden death.

As per astrology when the Surya is in the 7th house and Chandrama is in its own house and the breathing is only through the right nostril then the danger exists. If you see a yellow black coloured figure who becomes invisible immediately then the life span is limited to 2 years.

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