Data Science Weekly - Issue 655

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Data Science Weekly - Issue 655<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>Jun 11, 2026

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Issue #655<br>June 11, 2026

Hello!<br>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

An Overview of Modern AI Robotics from First Principles<br>There is a deceptively simple way to describe what physical AI is all about, a way in which anyone with a STEM background will intuitively understand. Like all other AI models, a model which controls a robot is also a function. It takes in observations (camera pixels, joint angles, the felt resistance of a gripper, etc) and it outputs actions, the next set of positions and torques for its motors…If you’ve ever trained a model that maps inputs to outputs, you can already grasp the shape of the problem. The interesting part is what happens when you take this familiar shape and drop it into a moving, active world…This sounds like ordinary machine learning, and for a while you can pretend it is. But robotics introduces a third axis that classic ML never had to respect: inference time…

My unvarnished guide to solution engineering<br>Nowadays I feel more or less comfortable interacting with customers. But I was awful at first. I know because one of the cofounders gave me harsh feedback after a call with our first serious customer. I still remember slamming the lid of my computer when we debriefed. What I perceived as harsh feedback at the time turned out to help me grow quickly…I used to be a regular data scientist assigned to internal projects. Talking to prospects and customers got me out of my comfort zone. You owe them a service, and they expect you to deliver something. If something goes wrong they’ll go above your head to your founders, at which point you start feeling the heat. It can be quite harsh. But it can also be rewarding when things go well…

Navier-Stokes fluid simulation explained with Godot game engine<br>Let me start with the mathematical description of what we will do in this blog post. This description might sound daunting, but don’t worry - we’ll explain everything as we go. Here goes: we will simulate fluid flow by moving a scalar density field through a vector velocity field. We’ll simulate velocity diffusion and advection as well as density diffusion and advection. Then we will add velocity projection with the goal of making the fluid obey the law of mass conservation - which will happen by balancing divergence with a pressure field. We will use bilinear interpolation and Gauss-Seidel relaxation for approximating values where needed…

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Data Science Articles & Videos

The Anti-Scaling Law in Biology, and Why AI Could Make Crowding Worse Before Making Drug Development Better<br>One of the main reasons for the tech community’s optimism is the scaling-law. Once you demonstrated 0-1, you can do 1-100 much quicker. The internet, social media, and so on…In biology and drug development, I think there is a mirror image, the anti-scaling law. Because of that, here’s my contrarian view: AI could make crowding in drug development worse, before making it better. And that’s my perspective as a genuine believer in the transformative power of AI, and an AI practitioner who used $14,000 worths of AI tokens in the past 2 months…

What is there besides Frequentist and Bayesian stats? [Reddit]<br>I am wondering whether there are lesser known statistical paradigms. like most people, I was first acquainted with the Frequentist framework, and later got introduced to Bayesian stats. I really like the way this made me reconsider some of what I thought were basic assumptions, so now I’m wondering what the next thing could be? Are there any other branches/frameworks which are not as well known?…

Forecasting: Principles and Practice, the Pythonic Way<br>This textbook is based on Forecasting: Principles and Practice (3rd ed) and is intended to provide a comprehensive introduction to forecasting methods and to present just enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will hopefully fill in many of those details…

The Simplest Learning Machine, Pt.2<br>In the previous article I outlined the concept of the Simplest Learning Machine. It’s an imaginary algorithm that uses one byte of persistent memory and learns to predict something about a stream of binary events…Can we actually write something like that? How would it work?…One semi-obvious thing we can learn is the rate of positive events in the stream. This would give us some...