Issue #575
November 28, 2024

Hello!

Once a week, we write this email to share the links we thought were worth sharing in the Data Science, ML, AI, Data Visualization, and ML/Data Engineering worlds.

And now…let's dive into some interesting links from this week.

Editor's Picks

• Announcing the NeurIPS 2024 Test of Time Paper Awards
  We are honored to announce the Test of Time Paper Awards for NeurIPS 2024. This award is intended to recognize papers published 10 years ago at NeurIPS 2014 that have significantly shaped the research field since then, standing the test of time…This year, we are making an exception to award two Test of Time papers given the undeniable influence of these two papers on the entire field. The awarded papers are:

  • Generative Adversarial Nets
    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio

  • Sequence to Sequence Learning with Neural Networks
    Ilya Sutskever, Oriol Vinyals, Quoc V. Le…
    

• Seven Sins of Quantitative Investing
  In this paper, we discuss the seven common mistakes investors tend to make when they perform backtesting and build quant models. Some of these may be familiar to our readers, but nonetheless, you may be surprised to see the impact of these biases. The other sins are so commonplace in both academia and practitioner’s research that we usually take them for granted…
  

• An Intuitive Explanation of Sparse Autoencoders for LLM Interpretability
  Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models…Machine learning models and LLMs are becoming more powerful and useful, but they are still black boxes, and we don’t understand how they do the things that they are capable of. It seems like it would be useful if we could understand how they work…Using SAEs, we can begin to break down a model’s computation into understandable components. There are several existing explanations of SAEs, and I wanted to create a brief writeup from a different angle with an intuitive explanation of how they work…
  

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Data Science Articles & Videos

• Evaluating Bayesian Mixed Models in R/Python
  In this article, my goal guide is you through some useful model checking and evaluation VISUAL METHODS for Bayesian models (not your typical RMSE) in both R and Python. I will build upon an example and set of models covered in my previous post so I recommend you take a quick look before moving forward….

• A Knight’s Tour
  The “knight’s tour” is a classic problem in graph theory, first posed over 1,000 years ago and pondered by legendary mathematicians including Leonhard Euler before finally being solved in 1823. We will use the knight’s tour problem to illustrate a common graph algorithm called depth first search…
  

• Ask, and it shall be given: Turing completeness of prompting
  In this work, we show that prompting is in fact Turing-complete: there exists a finite-size Transformer such that for any computable function, there exists a corresponding prompt following which the Transformer computes the function. Furthermore, we show that even though we use only a single finite-size Transformer, it can still achieve nearly the same complexity bounds as that of the class of all unbounded-size Transformers…
  

• The Problem with Reasoners
  o1 reasoners are the most exciting models since the original GPT-4. They prove what I predicted earlier this year in my AI Search: The Bitter-er Lesson paper: we can get models to think for longer instead of building bigger models. It's too bad they suck on problems you should care about…
  

• Model Context Protocol
  The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need…


• 🎨 Diagram-as-Code: Creating Dynamic and Interactive Documentation for Visual Content
  Diagrams is a 🐍Python library that implements the Diagram as Code approach, enabling you to create architectural infrastructure diagrams and other types of diagrams through code. With Diagrams, you can easily define cloud infrastructure components (such as AWS, Azure, and GCP), network elements, software services, and more, all with just a few lines of code…
  

• Unlocking the power of time-series data with multimodal models
  We compare the performance of multimodal models on the understanding of time-series data when presented visually as plots compared to numerical values. We find significant performance improvements when presented with plots on tasks like fall detection…
  

• The optimisers curse
  When looking for the best hyperparameters you can spend a lot of compute. So much so, that you can also spend *too much*. It is a sutble thing, but if you're not careful you can become a victim to something that's known as "the optimisers curse". This video explains the warning in full detail…
  

• Hazard and Survival

  Here’s a question from the Reddit statistics forum: “If I have a tumor that I’ve been told has a malignancy rate of 2% per year, does that compound? So after 5 years there’s a 10% chance it will turn malignant?”…This turns out to be an interesting question, because the answer depends on what that 2% means. If we know that it’s the same for everyone, and it doesn’t vary over time, computing the compounded probability after 5 years is a relatively simple…
  

• You’ve Been Waiting for Native Mobile Apps with R? The Wait Is Over.
  For the past couple of months, I’ve been sharing how webR will transform the way we build apps with R inside. If you’re unfamiliar, webR is a WebAssembly compilation of R. In simpler terms, it enables R to run within JavaScript environments. If you are familiar, you know it’s a bit more nuanced—but let’s keep it straightforward for now. I’m convinced that webR will redefine how we create apps that harness the power of R. And today, we’re taking the next big step: native mobile apps…
  

• Dismantling ELT: The Case for Graphs, Not Silos
  I generally advocate for breaking down silos between software development and data analytics teams by improving collaboration, aligning team incentives, and adopting engineering practices like data contracts and data products. In a nutshell, "Shift Left" applied to data analytics. I also wrote about how the rise of incremental processing (across all data platforms) only makes it more important to do this. With all that in mind, let’s now look at ELT (and its cousins ETL and Reverse ETL) where the letters stand for “Extract”, “Load”, “Transform”…
  

• Unveiling DeepSeek: A Story of Even More Radical Chinese Technological Idealism [PDF]
  Among the seven prominent large model startups in China, DeepSeek (深度求索) is the quietest yet often remembered for its unexpected moves. A year ago, its surprising presence stemmed from being backed by High-Flyer Quant (High-Flyer), a private quantitative hedge fund powerhouse. It was the only non-tech giant to stockpile tens of thousands of A100 chips…In May, a period saturated with AI developments, DeepSeek shot to fame by releasing an open-source model called DeepSeek V2, which offered unprecedented cost-effectiveness: inference costs dropped to just 1 RMB (0.14 USD) per million tokens—approximately one-seventh the cost of Llama3 70B and one-seventieth that of GPT-4 Turbo…
  

• ChatGPT for Data Analysis: A Beginner’s Guide
  ChatGPT is a comprehensive data analysis tool that can handle various data file formats, including Excel spreadsheets, CVS files, PDFs, and even JSON files…In this guide, I am going to show you how to use ChatGPT to perform several data analysis tasks in minutes without coding experience or expensive statistical software. After uploading your data, you can use simple conversational prompts to clean, transform, and visualize your data…

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Last Week's Newsletter's 3 Most Clicked Links

• What’s the most surprising or counterintuitive insight you’ve learned about machine learning recently?

• What’s a machine learning paper or research breakthrough from the last year that everyone should know about?

• Large Language Models, explained briefly, by 3Blue1Brown

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* Based on unique clicks.
** Find last week's issue #574 here.

Cutting Room Floor

• A Visual Dive into Conditional Flow Matching

• Understanding Polars data types

• Competitive Programmer's Handbook

• Variations on Müller's method of Polar Decomposition

• The Law of Large Numbers: A Foundation for Statistical Modeling in Distributed Systems

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Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian