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. (🧵)
https://t.co/DhHmN0ndjx
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".
More from Data science
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
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.
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)