Incoming links and notes

Notes

I need to reclassify the bio computing links; that section has become confusing and there are too many nice ideas there not clearly distinguished.

To subscribe to

All done

To post to team chat

• OpenAI: How Do They Do It? Lessons from Jiu-Jitsu Innovation and for S&T Policy

• Experiment metascience grant

• Via Louise Ord, virtuosic algorithmic line art in MetaPost

• FAQ, Frequently Asked Questions about HandWiki Encyclopedia of science and computing

• Superhuman: What can AI do in 30 minutes?

• Matthew Feeney, Markets in fact-checking

• Étienne Fortier-Dubois, The elements of scientific style

• Saloni Dattani, Real peer review has never been tried

• Secret Cyborgs: The Present Disruption in Three Papers

• andrewcb/cope: Continuous Processing Engine, a small Python framework for turning input files into output files on an ongoing basis

• pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch

• Eight Graphs That Explain Software Engineering Salaries in 2023

• Transformers.js

• Typst: Compose papers faster reimagines LaTeX with modern tech and workflow. I think they are doomed because while they solve the horrible, awful, nastybad misfeatures of most of the LaTeX workflow, they also do not support the part of LaTeX which people (rather than journals) need, which is to say, the mathematical markup.

• Mitchell Hashimoto on the disarming ease of ChatGPT plugins

• The Future - by Rohit Krishnan

• Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

• Avid Research - An Australian STEM Podcast

• Azimuth Project News

• LASER-UMASS/Themis: Themis™ is a software fairness tester.git

• Brittany Johnson-Matthews: Causal testing: understanding the root causes of defects

• Tianyi Zhang: Interactive Debugging and Testing Support for Deep Learning

• DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction / momentum-lab-workspace/DeepSeer

• Eugenio Culurciello, The fall of RNN / LSTM. We fell for Recurrent neural networks…

• Trick Facial Recognition Software into Thinking You're a Zebra or Giraffe with These Psychedelic Garments

• by Eric Topol, When M.D. is a Machine Doctor.

• Women in AI awards

  • WAI Awards
  • WAI Awards 2023 APAC
  • ANZ Awards 2022

Links

• LAMP

• Ecosystem Graphs for Foundation Models

• Dignity of risk - Wikipedia

• Flyte: An Open Source Orchestrator for ML/AI Workflows - The New Stack

• Build production-grade data and ML workflows, hassle-free with Flyte* Cloud-Native Geospatial Foundation

  The Cloud-Native Geospatial Foundation is a forthcoming initiative from Radiant Earth created to increase adoption of highly efficient approaches to working with geospatial data in public cloud environments.

• Radar Trends to Watch: May 2023 – O’Reilly

• Stochastic Differential Equations

• Mesa-Optimization - AI Alignment Forum

• WhaleGPT

• fast.ai - Mojo may be the biggest programming language advance in decades

• Nostr, a simple protocol for decentralizing social media that has a chance of working

• Bluesky Social/ Bluesky

• Lilian Weng’s updated The Transformer Family Version 2.0

• How does in-context learning work? A framework for understanding the differences from traditional supervised learning | SAIL Blog

• Sam Kriss, in All the nerds are dead, conflates geeks and nerds, but is funny anyway

• The General Theory of Employment, Interest and Money by John Maynard Keynes

• The Practical Guides for Large Language Models

• The reasonable(?) effectiveness of data analysis

  Why is it that we can be thrown into the work of other people, in a field we have zero experience in, and have any expectation of making any useful impact at all? When stated objectively, it sounds utterly ridiculous. But in my experience, a data team can find something to make an improvement on, even if the impact can sometimes be small.

• Tackling Collaboration Challenges in the Development of ML-Enabled Systems “I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto).The study sought to identify collaboration challenges common to the development of ML-enabled systems. Through interviews conducted with numerous individuals engaged in the development of ML-enabled systems, we sought to answer our primary research question: What are the collaboration points and corresponding challenges between data scientists and engineers? We also examined the effect of various development environments on these projects. Based on this analysis, we developed preliminary recommendations for addressing the collaboration challenges reported by our interviewees.”

• Software²: A new generation of AIs that become increasingly general by producing their own training data

• Probability Is Not A Substitute For Reasoning – Ben Landau-Taylor

• Self-Healing Concrete: What Ancient Roman Concrete Can Teach Us

• Differentiating the discrete: Automatic Differentiation meets Integer Optimization | μβ

• Information Transfer Economics: Organization of information equilibrium concepts

• Underrated ideas in psychology - The Seeds of Science

• How to train your own ChatGPT Alpaca style, part one

• What does peer review know? - by Matt Clancy

• Serge Zaitsev, World’s smallest office suite

• Annie Lowrey, We Haven’t Been Measuring How the Economy Really Works

• Why Your Polyamorous Friend's Relationship Sucks - by Aella

• Alex Komoroske, On Schelling Points in Organizations

• Alex Komoroske, Coordination Headwind - How Organizations Are Like Slime Molds

• Alternative to the tedious openhub workflow: analyzemyrepo.com | about

• TIL Apophenia vs Pareidolia

• How to stylize images using Stable Diffusion AI

• Matthew Feeney, Markets in fact-checking

• Étienne Fortier-Dubois, The elements of scientific style

• Saloni Dattani, Real peer review has never been tried

• Jason Collins, We don’t have a hundred biases, we have the wrong model

• Schimmack on Psychological Science and Real World Racism

• If there are already smarter people around, how can I find good ideas?

• Fear, Rage, and Anguish on America’s Happiest Campus

• The Giving Tree Alternate Ending

• ICML 2023 Workshop

• Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.

• The ‘Enshittification’ of TikTok

• ICML 2023 Workshops

• CSCI 601.771: Self-supervised Models

• Colossal-AI Overview

  Colossal-AI is designed to be a unified system to provide an integrated set of training skills and utilities to the user. You can find the common training utilities such as mixed precision training and gradient accumulation. Besides, we provide an array of parallelism including data, tensor and pipeline parallelism. We optimize tensor parallelism with different multi-dimensional distributed matrix-matrix multiplication algorithm. We also provided different pipeline parallelism methods to allow the user to scale their model across nodes efficiently. More advanced features such as offloading can be found in this tutorial documentation in detail as well.

• dynamicslab/pykoopman: A package for computing data-driven approximations to the Koopman operator.

• 89luca89/distrobox: Use any linux distribution inside your terminal. Enable both backward and forward compatibility with software and freedom to use whatever distribution you’re more comfortable with. Mirror available at: https://gitlab.com/89luca89/distrobox

• Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation

• Geometry in Text-to-Image Diffusion Models

• Diffusion Probabilistic Fields

• Stable Diffusion with Core ML on Apple Silicon

• Building Resilient Organizations: Toward Joy and Durable Power in a Time of Crisis

• Talking to a Person

• Humata - GPT for your files

• Is Anything Worth Maximizing? How metrics shape markets, how we’re… | by Joe Edelman

• Values-Based Social Design

• factorization_machine Something something kernels, something regression something interaction effects.

• Journey Mapping 101

• Facebook, Google Give Police Data to Prosecute Abortion Seekers

• Rest of World - Reporting Global Tech Stories

• Cleanlab: “We publish research, develop open source tools, and design interfaces to help you improve the quality of your datasets and diagnose various issues in them.” See their blog e.g. ActiveLab: Active Learning with Data Re-Labeling

• How does in-context learning work? A framework for understanding the differences from traditional supervised learning

  TL; DR—In-context learning is a mysterious emergent behavior in large language models (LMs) where the LM performs a task just by conditioning on input-output examples, without optimizing any parameters. In this post, we provide a Bayesian inference framework for understanding in-context learning as “locating” latent concepts the LM has acquired from pretraining data. This suggests that all components of the prompt (inputs, outputs, formatting, and the input-output mapping) can provide information for inferring the latent concept. We connect this framework to empirical evidence where in-context learning still works when provided training examples with random outputs. While output randomization cripples traditional supervised learning algorithms, it only removes one source of information for Bayesian inference (the input-output mapping).

• Bayesian Neural Networks by Duvenaud’s team

• Rohit, People always put their money in futures they predict

  What have we seen so far? People didn’t use to have much disposable income to invest a century ago. When they did, or rather those who did, invested their savings mostly in land or (if they were rich enough) businesses, or commodities.

  Where should I invest my money is a relatively old question, but until recently it wasn’t a very interesting question. This is because until recently the answers were understood, but not that actionable. The futures would get better, things would get built, and you could ride optimism as a thesis if you could find a way how. The avenues available were extremely limited, and the optionality you had was minimal.

• What Are the Different Approaches for Detecting Content Generated by LLMs Such As ChatGPT? And How Do They Work and Differ?

• Olúfẹ́mi O. Táíwò, Identity Politics and Elite Capture

• [2302.00237] Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)

• Tiangang Cui

• Don’t guess what’s true: choose what’s optimal. A probability transducer for machine-learning classifiers

• How to wrap up research projects gracefully

• What’s the difference between a tutorial and how-to guide? - Diátaxis

• Career Sponsorship Is a Two-Way Street

• Making Friends with Machine Learning

• Bing: “I will not harm you unless you harm me first”

• From Bing to Sydney

• Instagram, TikTok, and the Three Trends

  the company correctly intuited a significant gap between its users stated preference — no News Feed — and their revealed preference, which was that they liked News Feed quite a bit. The next fifteen years would prove the company right.

• Kedro | A Python framework for creating data science code /Kedro Frequently asked questions. Kedro rationale by Joel Schwarzmann: The importance of layered thinking in data engineering

• Darts:

  • Transfer Learning for Time Series Forecasting with Darts
  • Time Series Made Easy in Python

• Prof Steve Keen | Creating realistic economics for the post-crash world

• Welcome to LangChain — 🦜🔗

  Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge.

  This library is aimed at assisting in the development of those types of applications.

• How did places like Bell Labs know how to ask the right questions?

• Color Oracle simulates color blindness for accessibility of visualisations and plots etc

• darrenjw/fp-ssc-course: An introduction to functional programming for scalable statistical computing

• White Collar Crime Risk Zones

• Do organizations have to get slower as they grow? (with Alex Komoroske)

• Clara in Blunderland. With forty illus. by S.R : Lewis, Caroline, pseud : Free Download, Borrow, and Streaming : Internet Archive

• Kolibri User Guide

  Kolibri is an open-source educational platform specially designed to provide offline access to a wide range of quality, openly licensed educational resources in low-resource contexts like rural schools, refugee camps, orphanages, and also in non-formal school programs.

• What even are GFlownets?

• Team Silverblue — About packaged apps for Fedora

• The Adaptable Linux Platform Guide PAckaed apps for SUSE.

• Trick Facial Recognition Software into Thinking You're a Zebra or Giraffe with These Pyschedelic Garments

• The Machines of Mastery - by Ethan Mollick

• OY, A.I.

• MusicLM

• Carr–Madan Formula

  The Carr–Madan formula is really just a special case of a Taylor expansion. For completeness, let’s rederive the Taylor expansion with an integral remainder.

• Center for New Liberalism

• When explaining becomes a sin—by Tom Stafford file under taboos and Tetlock and compassion/comprehension

• Riot psychology—Mind Hacks

• Jordan Peterson’s zombie climate ideas

• Karloo Pools and the hidden alternative swimming spots nearby—Walk My World

• An Uncontroversial Guide to Being Controversial

• Markets Are Kinda Fake—by Judah—Be Wrong

• How TikTok changed the world in 2020 - BBC Culture

• A Brief History Of The Burpee—HuffPost Life

• Cult Classic ’Fight Club’ Gets a Very Different Ending in China

• A Turkish Farmer Tests Out VR Goggles on Cows To Get More Milk

• How to buy a social network, with Tumblr CEO Matt Mullenweg—The Verge

• Fake Feelings—ai emo. When post-hardcore emo band Silverstein… | by Dadabots—Medium

• Why the super rich are inevitable

  Meanwhile, the richer player will gain money. That’s because, from their perspective, every game they lose means they have an opportunity to win it back—and then some—in the next coin flip. Every game they win means, no matter what happens in the next coin flip, they’ll still be at a net-plus.

  Repeat this process millions of times with millions of people, and you’re left with one very rich person.

• Pluralistic: Tiktok’s enshittification (21 Jan 2023) – Pluralistic: Daily links from Cory Doctorow

• Stop Talking to Each Other and Start Buying Things: Three Decades of Survival in the Desert of Social Media

• Adjacent Possible—Steven Johnson

• Pluralistic: EU to Facebook, ’Drop Dead’ (07 Dec 2022) – Pluralistic: Daily links from Cory Doctorow

• In Which Long-Time Netizen & Programmer-at-Arms Dave Winer Records a Podcast for Me, Personally

• Þe Forlorn Hope Þt Was Vox.com, & BRIEFLY NOTED

• Against Cop Shit—Jeffrey Moro

• DRMacIver’s Notebook: Three key problems with Von-Neumann Morgenstern Utility Theory

  The first part is about physical difficulties with measurement—you can only know the probabilities up to some finite precision. VNM theory handwaves this away by saying that the probabilities are perfectly known, but this doesn’t help you because that just moves the problem to be a computational one, and requires you to be able to solve the halting problem. e.g. choose between \(L_1=p B+(1-p) W\) and \(L_2=q B+(1-q) W\) where \(p=0.0 \ldots\) until machine \(M 1\) halts and 1 after and \(q\) is the same but for machine \(M 2\).

  The second demonstrates that what you get out of the VNM theorem is not a utility function. It is an algorithm that produces a sequence converging to a utility function, and you cannot recreate even the original decision procedure from that sequence without being able to take the limit (which requires running an infinite computation, again giving you the ability to solve the halting problem) near the boundary.

• Supervised Training of Conditional Monge Maps—Apple Machine Learning Research

• How To Be an Academic Hyper-Producer—Economics from the Top Down

• Password Generator—Strong & Random Password Generator

• A global analysis of matches and mismatches between human genetic and linguistic histories—PNAS

• Movetodon: Find your Twitter Friends on Mastodon

• Building A Virtual Machine inside ChatGPT

• Desmos—Let’s learn together. graphing calculator online

• Leaving Your Mark on The World—a free interactive tool to help you do more good! | ClearerThinking.org

• LIFTOFF: Couch to Barbell

• The Cause of Depression Is Probably Not What You Think—Quanta Magazine

• What Monks Can Teach Us About Paying Attention—The New Yorker

• Archive—The Common Reader

• In the Stacks (Maisie’s Tune), by Robin Sloan

• Postcard

• nkanaev/yarr: yet another rss reader

• Actually, Japan has changed a lot—by Noah Smith — japanese real estate is surprsising

• Make Work Better—Bruce Daisley—Substack

• One Useful Thing (And Also Some Other Things) | Ethan Mollick—Substack

• How to… use ChatGPT to boost your writing

• Nintil—Bryan Johnson’s Blueprint

• How America Lost the Atomic Age

• Democracy: forking the project—Nicholas Gruen

• Casey Johnston on Turning Weight-lifting Into a Business

• The radical idea that people aren’t stupid paired with How to achieve self-control without “self-control”

• Colonialism did not cause the Indian famines—History Reclaimed

• Orwell Was Right

• Erik van Zwet, Shrinkage Trilogy Explainer on modelling the publication process

• Mathematics of the impossible: Computational Complexity—Thoughts

• Inclusive Scientific Meetings — 500 Women Scientists

• Download the Atkinson Hyperlegible Font—Braille Institute What makes it different from traditional typography design is that it focuses on letterform distinction to increase character recognition, ultimately improving readability. We are making it free for anyone to use!

• Low-Rank Approximation Toolbox: Nyström Approximation—Ethan Epperly

• -ise or-ize? Is-ize American? (1/3) – Jeremy Butterfield Editorial

• Introducing Massively Open Online Papers (MOOPs) | KULA: Knowledge Creation, Dissemination, and Preservation Studies

• Iron deficiencies are very bad and you should treat them—Aceso Under Glass

• The Australian academic STEMM workplace post-COVID: a picture of disarray

• They Rule

• torchgeo—torchgeo 0.3.1 documentation/microsoft/torchgeo: TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

• Why Everything at Walgreens Is Suddenly Behind Plastic

• Does Metformin Work as an Anti-Aging Drug?

• Merve Emre, Has Academia Ruined Literary Criticism?

• Matt Clancy, Age and the Nature of Innovation “Are there some kinds of discoveries that are easier to make when young, and some that are easier to make when older”?

• Tom Stafford, Microarguments and macrodecisions

• Kevin Munger, Why I am (Still) a Conservative (For Now)

• Kevin Munger, Facebook is Other People

• Randy Au, in Data science has a tool obsession talks about Gear Acquisition Syndrome for data scientists.

• Clive Thompson, The Power of Indulging Your Weird, Offbeat Obsessions

• Niche Museums: Find tiny museums near you

• You Don’t Know How Bad the Pizza Box Is

• Cities Really Can Be Both Denser and Greener

• Would a bust be all bad for San Francisco?

• omg.lol - A lovable web page and email address, just for you

• A Writer Used AI To Plagiarize Me. Now What?

• Donate to a highly effective charity - Effective Altruism Australia. Focussed on poverty and health interventions.

• What are the best charities to donate to in 2023? · Giving What We Can

• Climate Change Fund | Founders Pledge

• The Global Catastrophic Risks (GCR) Fund | Founders Pledge

• karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.

• What is the “forward-forward” algorithm, Geoffrey Hinton’s new AI technique?

• Simon Willison, AI assisted learning: Learning Rust with ChatGPT, Copilot and Advent of Code

• Fission: Build the future of web apps at the edge incubates several decentralized protocols

• A debugging manifesto

• RIP Passwords? Passkey support rolls out to Chrome stable

• danah boyd, What if failure is the plan?. I’ve been thinking a lot about failure…

• Lina Yao’s Homepage

• The Architecture of Open Source Applications

• aristocratos/bpytop: Linux/OSX/FreeBSD resource monitor

• The Expanding Dark Forest and Generative AI

• The Dark Forest and the Cozy Web

• Mastodon—and the pros and cons of moving beyond Big Tech gatekeepers

• Why Not Mars (Idle Words)

• how and why to be ladylike (for women with autism)

• Michael Nielsen on science online

  • Exploratory notes: Community as the unit of scientific contribution
  • Doing science online

• Great bloggers are rare, weird, and not team players – Kevin Drum

• Swayable: RCTs for marketing campaigns via ingenious audience recruiting network

• How Dobbs Triggered a ‘Vasectomy Revolution’

• Zoomers Co-Working Community (co-working for accountability)

• Denoising Diffusion Restoration Models

• Lattice Boltzmann methods

• Normconf Lightning Talks/Normconf: The Normcore Tech Conference — a conference on the stuff that we actually need to do in ML, as opp. the stuff we would like to pretend is what we do.

• Jean Gallier and Jocelyn Quaintance , Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning, 2188 pages as of 2022/10/30, and growing.

• MINDMELT - Music Visualizer

• Terence Eden, You can have user accounts without needing to manage user accounts

• Adam Mastroianni, Ludwin-Peery, EJ, Things could be better

• Adam Mastroianni, The great myths of political hatred

• AI & Music Deep Dive Pt 2: Is It Game Over For Musicians?

• Big correlations and big interactions ([2105.13445] The piranha problem: Large effects swimming in a small pond)

• Blower Door Test - What's the Cost & Who Does Them?

• causalscience.org aims to bring academia and industry together to advance causal inference in practice.

• Coasean Floor and Ceiling

• How to keep cakes moist and cause the greatest tragedies of the 20th century

• Distribution testing

  • Taming big probability distributions
  • Topics and Techniques in Distribution Testing (Survey)

• Denoising Diffusion Restoration Models

• Electronic Frontiers Australia

• Focus Is Saying No To Good Ideas

• Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

• GPflow/GeometricKernels: Geometric kernels on manifolds, meshes and graphs

• How Nicky Case learns

• George Ho, How to Improve Your Static Site's Typography (for code formatting)

• ICML 2022 Tutorial on causality and deep learning

• Interpretable Machine Learning Book

• Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model

• Lagrange dual, weak duality and strong duality

• Quasi-Newton methods: L-BFGS

• Microsoft CSR’s Law Enforcement Request Report is disconcerting transparency

• Marc ten Bosch, Let's remove Quaternions from every 3D Engine (An Interactive Introduction to Rotors from Geometric Algebra)

• Michele Coscia, Meritocracy vs Topocracy

• Named Tensors

• oxcsml/riemannian-score-sde: Score-based generative models for compact manifolds

• Public-facing Censorship Is Safety Theater, Causing Reputational Damage

• Ti John’s Publications

• Reverse engineering the NTK: towards first-principles architecture design—The Berkeley Artificial Intelligence Research Blog

• Riemannian Score-Based Generative Modelling

• SNStatComp/awesome-official-statistics-software: An awesome list of statistical software for creating and accessing official statistics

• Starboard, a shareable in-browser notebook that runs pyton (!)

• Students Are Using AI to Write Their Papers, Because Of Course They Are

• Supervised Training of Conditional Monge Maps

• Symmetry and Physics—Not Even Wrong

• Technical Report: Exploring Climate Risk in Australia

• Tom Pendergast’s workplace surveillance novel

• Treehugger Introduces a Modern Pyramid of Energy Conservation

• Unbiased MCMC with couplings

• A Visit to the Idea Machine Fair

• Community Values

• Interintellect’s Upcoming Salons

• Vast.ai “Rent Cloud GPU Servers for Deep Learning and AI”

• Vibecamp Community Values

• What is a feed? (a.k.a. RSS)

• You Aren't Learning If You Don't Close the Loops

• Adam Mastroianni, Things could be better

• Co-Ops Vs DAOs with Austin Robey and David Spinks

• Michael Burnam-Fink, What is Scientific about Data Science?

• You Aren't Learning If You Don't Close the Loops

• Christian Lawson-Perfect’s Interesting Esoterica is a collection of weird papers in maths.

• Erik Hoel, Why do most popular science books suck?

• Étienne Fortier-Dubois, The Vibes Are Off

• George Ho, Understanding NUTS and HMC

• Gordon Brander, Coevolution creates living complexity

• Gordon Brander, Thinking together, on egregores, Dunbar numbers and information-processing thresholds in Holocene social evolution, all to motivate

  • subconsciousnetwork/noosphere: Noosphere is a protocol for thought; let's discover it together!
  • noosphere/explainer.md at main · subconsciousnetwork/noosphere

• Kate Mannell, Eden T. Smith Alternative Social Media and the Complexities of a More Participatory Culture: A View From Scuttlebutt

• Peter Woit, Symmetry and Physics

• Rob J Hyndman, We need more open data in Australia

• Vicki Boykis, How I learn machine learning

• 24: Monasteries - Monomythical

• Oshan Jarow, Markets Underinvest In Vitality

• Judah, A Speech I would Like To Give Undergrads

• google/evojax

• Spark the Mood You Want

• Spirals of Delusion: How AI Distorts Decision-Making and Makes Dictators More Dangerous (not convinced tbh)

• The Good Research Code Handbook

• Erik Hoel, The gossip trap

• Schelling fences on slippery slopes

• The Developer Certificate of Origin is a great alternative to a CLA

• Scaling Factors for Hidden Markov Models

• roboticcam/machine-learning-notes: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接

• The Social Capital Atlas

• TPU Research Cloud

• Computational Research Platform on Code Ocean

• wesselb/varz: Painless optimisation of constrained variables in AutoGrad, TensorFlow, PyTorch, and JAX

• I. Risk Management Foundations - Machine Learning for Financial Risk Management with Python [Book]

• jkbren/einet: Uncertainty and causal emergence in complex networks

• [2206.13637] Utility Theory for Sequential Decision Making

• [2207.06544] Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

• Darren Wilkinson’s Bayesian inference for a logistic regression model 1, 2, 3, 4, 5

• https://www.patreon.com/posts/taylor-modeling-70131808

• Against Facebook

• Book Review: Public Choice Theory And The Illusion Of Grand Strategy

• Douglas Murray’s War on the West—A Review

• Stephen Malina — Deriving the front-door criterion with the do-calculus

• Cat and Girl on Distraction

• Your Daily AI Research tl;dr

• https://downforeveryoneorjustme.com/

• Census is a tool which links all the weird different data storage systems and CRM stuff

• Michael Lewis podcast on illegible experts

• The Political Is Personal

• Anonymous GitHub

• Nemanja Rakicevic, NeurIPS Conference: Historical Data Analysis

• Yanir Seroussi, The mission matters: Moving to climate tech as a data scientist

• Keir Bradwell, #1: In-group Cheems

• AAAI2022: Presidential Address: The State of AI

• Samuel Moore, Why open science is primarily a labour issue

• Adam Mastroianni, Against All Applications

• Have The Effective Altruists And Rationalists Brainwashed Me?

• Predicting Generalization using GANs

• Anthony Lee Zhang, The War for Eyeballs

• Field Guide to the Curve Wars: DeFi’s Fight for Liquidity

• The text-to-image revolution, explained

• Digital artists’ post-bubble hopes for NFTs don’t need a blockchain

• Generative Flow Networks - Yoshua Bengio

• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

• GFlowNet Tutorial

• https://melbourne.nerdnite.com/

• ReproducibilityChecklist

• I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale

• When to Stop Exploring | The explore-exploit trade-off

• Reformation in the Church of Science

• ClearerThinking.org’s courses, e.g.

  • Introduction to Decision Academy: The Science of Better Decisions
  • Rhetorical Fallacies: Dodging Argument Traps
  • Learning from Mistakes: A Systematic Approach
  • Probabilistic Fallacies: Gauging the Strength of Evidence
  • Explanation Freeze: Interpreting Uncertain Events
  • Aspire: A Tool to Help You Improve Your Life
  • The Sunk Cost Fallacy: Focusing on the Future

• Nelson’s Weblog: culture / generative-art-and-open-source

• Reddit for AI-generated and manipulated content

• Bayesian Neural Nets and how to effectively train them with Stochastic Gradient Markov Chain Monte Carlo | Simen Eide

• Cautionary Tales from Cryptoland

• PJ Vogt, Selling Drugs to Buy Crypto

• Crypto, NFTs, Web3: People Hate the Future of the Internet

• TODOs from Paper Systems

• Michele Coscia, Pearson Correlations for Networks

• External Music Services - ListenBrainz

• The DAIR Institute “The Distributed AI Research Institute is a space for independent, community-rooted AI research, free from Big Tech’s pervasive influence.”

• Dongkong track breakdown

• Machine Learning Trick of the Day (1): Replica Trick— Shakir Mohammed

• Machine Learning Trick of the Day (7): Density Ratio Trick— Shakir Mohammed

• The Paradox of Choice in Computing-Research Conferences

• ApplyingML - Papers, Guides, and Interviews with ML practitioners

• Learning About the Long Run

• Dive in

• Milan Cvitkovic — Things you are allowed to do

• Random-Feature Matching

• Information loss and entropy

• Useful inequalities cheat sheet

• [2111.00110] FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics

• The local minima of suckiness · Vicki Boykis

• Brave Wallet

• A from-scratch tour of Bitcoin in Python

• The Art of Documentation – Chelsea Troy

• Ryan Broderick, We were the unpaid janitors of a bloated tech monopoly

• Mark Zuckerberg Is TNR’s 2021 Scoundrel of the Year

• Facebook Is an Authoritarian State

• Why Facebook won’t let you turn off its news feed algorithm

• How to incorporate observation weights into an estimator

• Convex hull of zeros

• How should we compare neural network representations?

• Marden’s theorem on complex roots

• Rumble in the Ensemble

• SimSWE 4: Wants, needs, and chasm-crossing - apenwarr

• Willingness to look stupid

• The two sides of envy at work

• microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.

• Maxwell’s Relations (Part 1)

• The Analytical Edge

• Weekly Research — Verdad

• Sinkhorn and circular law

• ARiMA is not Sufficient

• fastdownload: the magic behind one of the famous 4 lines of code · fast.ai

• Steven Buss, Politics for Software Engineers, Part 1, Part 3

• Schneier, When AIs Start Hacking

• Conceptual Foundations of Statistical Learning, Spring 2021

• Big Tech’s guide to talking about AI ethics

• Blackwell Approachability - Que-sais je?

• What Is a Companion Matrix? – Nick Higham

• Multimodal Neurons in Artificial Neural Networks/ Distill version of Multimodal Neurons in Artificial Neural Networks

• https://cbergmeir.com/

• http://i.giwebb.com/

• Francis Bach Going beyond least-squares – II : Self-concordant analysis for logistic regression

• The Cauchy residue trick: spectral analysis made “easy”

• On the Generalization Ability of Online Strongly Convex Programming Algorithms

• Thomas Lumley visualises data pooling simply and well.

Illustrations

Homeless links

Bookmarked but where will they ever go?

• Causal vs Normative Explanations — Malcolm Ocean

• Dispel your justification-monkey with a “HWA!” - Malcolm Ocean

• How To Actually Reduce Administrative Burden on Research

• Roger’s Bacon, Living and Dying with a Mad God

• washable & breathable flexiOH cast adapts to the patient’s skin

• Owncast - Selfhosted Livestreaming

• Why Do Rich People Love Quiet?

• Kilogram 📸 1KB image gallery

• Tripitaka Koreana - carved on 81258 woodblocks in the 13th century - is the most successful large data transfer over time yet achieved by humankind. 52 million characters of information, transmitted over nearly 8 centuries with zero data loss - an unequalled achievement.

• ‘We can continue Pratchett’s efforts’: the gamers keeping Discworld alive

• AO3’s 15-year journey from blog post to fanfiction powerhouse - The Verge

• Ambigrams

• Making Light

  today I took a desk lamp whose Halogen light had burned out, whose crappy transformer always made those bulbs sputter, and whose mildly art-deco appearance I’d always liked, and swapped it out to run an LED bulb off USB power. It took about an hour’s work to replace the light with an LED, the switch with a nice heavy clicky one and now the whole thing runs off USB-C instead of wall voltage. It emits no appreciable heat, and if these calculations are to be believed, will run for decades for a few cents per year, assuming I leave it on all the time.

  I hadn’t really appreciated how big a deal USB-PD voltage negotiation was until I found out that the little chips that handle that negotiation are about the size of the end of a pencil, that if you include the USB-C port you can replace basically any low-voltage transformer with something smaller than a quarter.

  The magic search string, if you want to try this yourself, is “usb-pd trigger module”,

• Chip in pressure HSBC to cut off Adani

• vscode-paste-image/README.md at master · mushanshitiancai/vscode-paste-image

• mhoye/awesome-falsehood: 😱 Falsehoods Programmers Believe in

• Gary Brecher, The War Nerd: Taiwan — The Thucydides Trapper Who Cried Woof

• 1DollarScan - FAQ

• Penzil — 3d sketching for the web

• 10 years of… whatever this has been

• How to Copy a Song: A Handy Primer

• Modulo | Secular Homeschooling Made Easy

• My family’s unlikely homeschooling journey

• The Sordid Saga of Hunter Biden’s Laptop.

• Tutorial on using LLVM to JIT PyTorch fx graphs to native code (x86/arm/risc-v/wasm) (Part I – Scalars)

• Toolfinder - Earth Defenders Toolkit

• OK, WTF Are Wordcels and Shape Rotators?

• Evidence of Fraud in an Influential Field Experiment About Dishonesty. Looks bad for Dan Ariely. Damn.

• on programming humans (Amir’s work)

• Communications' digital initiative and its first digital event

• I Should Be Able to Mute America

• Playable Half Earth Socialism simulator

• Michelle Phan, Dan Howell, and why YouTubers never log off

• flatmax/vector-synth: Old 2002 era vector synth code based on XFig

• Nick Chater, Would you Stand Up to An Oppressive Regime.

• On “Geek” Versus “Nerd”

• A Perfect Storm: The Chocolate, Coffee, and Climate Crises

• The Iraqi who saved Norway from oil

• Lambda School’s Job Placement Rate May Be Far Worse Than Advertised

• A fulsome aquifer swells imperfectly over the little songs of the mutilated. | Little Songs Of The Mutilated

• I would like to read the diaries of Usama ibn Munqidh

• The latest target of China’s tech regulation blitz: algorithms

• State Power and the Power Law, State Power and the Power Law 2

• Interview Questions Database (IQDb)

• DSHR’s Blog: The Data Isn’t Yours

• The Economics of Pseudonymity

• Yuling Yao, The likelihood principle in model check and model evaluation “We are (only) interested in estimating an unknown parameter \(\theta\), and there are two data generating experiments both involving \(\theta\) with observable outcomes \(y_1\) and \(y_2\) and likelihoods \(p_1\left(y_1 \mid \theta\right)\) and \(p_2\left(y_2 \mid \theta\right)\). If the outcome-experiment pair satisfies \(p_1\left(y_1 \mid \theta\right) \propto p_2\left(y_2 \mid \theta\right)\), (viewed as a function of \(\theta\) ) then these two experiments and two observations will provide the same amount of information about \(\theta\).”

• Chowdhury-DSP/ChowMatrix: Matrix delay effect

• Chowdhury DSP is creating audio software

• Algorithmic Trap

• Liquid Information Flow Control, a confidential computing DSL

• Jag Bhalla, Vaccine Greed: Capitalism Without Competition Isn’t Capitalism, It’s Exploitation

• Kostas Kiriakakis, A Day At The Park

• Nietzsche Faces the Modern Age

• Home | Absci

• Xyla

  By analyzing medical text and extracting biomedical entities and relations from the entire history of published medical science, Xyla can facilitate better real-world evidence-based clinical decision support and help make clinical research—such as research into new treatments, including de novo drug design as well as the repurposing of existing drugs—smarter and faster. In so doing, Xyla is fulfilling its mission of organizing the world’s medical knowledge and making it more useful.

• Why Americans Are Dying from Despair

• pyribs

• My2050 calculator - create your pathway for the UK to be net zero by 2050

• Museum of Imaginary Musical Instruments

• Software is no longer sold; it’s adopted - Orbit

• Is Pandemic Stress to Blame for the Rise in Traffic Deaths? Nope apparently it is decreased congestion making drivers drive faster on shit roads.

• Global AI talent tracker

• Deleting unethical data sets isn’t good enough

• Protocol and plan for the development of the automated algorithm for choosing the best systematic review

• Marisa Abrajano has a provoking list of research topics. I would like to read the work to see her methodology.

• Do normal people need to know or care about “the metaverse”?

• doing - BrettTerpstra.com

• Apple acquires song-shifting startup AI Music, here’s what it could mean for users

• Are coevolutionary “Free Lunches” really free lunches?

• Autonomous Work Pod

• Butterfly Ideas

• hckr news - Hacker News sorted by time

• Black Americans are pessimistic about their position in U.S. society

• 24: Monasteries - Monomythical

• Against Waldenponding

• Smart technologies | Internet Policy Review

  Speaking of ‘smart’ technologies we may avoid the mysticism of terms like ‘artificial intelligence’ (AI). To situate ‘smartness’ I nevertheless explore the origins of smart technologies in the research domains of AI and cybernetics. Based in postphenomenological philosophy of technology and embodied cognition rather than media studies and science and technology studies (STS), the article entails a relational and ecological understanding of the constitutive relationship between humans and technologies, requiring us to take seriously their affordances as well as the research domain of computer science. To this end I distinguish three levels of smartness, depending on the extent to which they can respond to their environment without human intervention: logic-based, grounded in machine learning or in multi-agent systems. I discuss these levels of smartness in terms of machine agency to distinguish the nature of their behaviour from both human agency and from technologies considered dumb. Finally, I discuss the political economy of smart technologies in light of the manipulation they enable when those targeted cannot foresee how they are being profiled.

• Concurrent programming, with examples

  Mention concurrency and you’re bound to get two kinds of unsolicited advice: first that it’s a nightmarish problem which will melt your brain, and second that there’s a magical programming language or niche paradigm which will make all your problems disappear.

  We won’t run to either extreme here. Instead we’ll cover the production workhorses for concurrent software – threading and locking – and learn about them through a series of interesting programs. By the end of this article you’ll know the terminology and patterns used by POSIX threads (pthreads).

• Climate-Smart Mining: Minerals for Climate Action

• A study of lights at night suggests dictators lie about economic growth

• The dangerous side of Munchausen’s - UnHerd

• DIY Collective Embeds Abortion Pill Onto Business Cards, Distributes Them At Hacker Conference

• Hapgood | Mike Caulfield’s latest web incarnation. Networked Learning, Open Education, and Online Digital Literacy

• Cryptography, hydrodynamics, and celestial mechanics

• Penny Wyatt, Developer Innovation and the Free Puppy

• Elizabeth Van Nostrand, A Quick Look At 20% Time

• Pocket Science Lab

• Ax · Adaptive Experimentation Platform

• Paint With Music — Google Arts & Culture

• Chalk is a non-terrible calculator for macos, incorporating useful things like matrices and bitwise ops

• Sustainable building: The hottest new material is, uh, wood