Link to a relevant folk song about parasites that has a verse about this sort of wasp

That is some shit straight out of sci-fi horror like Alien.

Machine Learning enthusiasts: Why settle for linear regression when you can deploy a Gradient-Boosted Random Deep Neural Net Surface Vector Cluster that consumes the entire power of Iceland to trace a perfect ∞-dimensional hypersphere around those blue points? Overparameterization is the future!

Best Linear Unbiased Estimator

Partially true

He was under a lot of pressure

Colourful mohawk bird on the right is way cooler

35-year old Heidegger slid into the DMs of 18-year old Ahrendt as her professor. This was way before you were cancelled for ‘sleeping with students’ or for ‘being a fascist’ was a thing.

Twister his arms wide open

To be very German, here is my favourite Max Planck quote: Scientific Progress marches on one funeral at a time.

Perhaps it was meant like that. And the old guard that disliked his papers is now dead and progress has been made.

So what. I’m also fascinated when I see Humans without a gag reflex.

Why? Did the Eastern Hummingbird fuck a couch?

Damn nature, you scary!

Have you heard that there are great serialised file formats like .parquet from appache arrow, that can easily be used in typical data science packages like duckdb or polars. Perhaps it even works with pandas (although do not know it that well. I avoid pandas as much as possible as someone who comes from the R tidyverse and try to use polars more when I work in python, because it often feels more intuitive to work with for me.)

No idea how Fortnite works. But can you (realistically) use the Cybertruck to cut off your enemies fingers with it’s automatic doors.

Well. The folk punks always knew that we all are just compost in training.

This is what CRISPR was invented for

This is actually why I find a lot of arguments about AI’s limitations as stochastic parrots very shortsighted. Language, picture or video models are indeed good at memorizing some reasonable features from their respective domains and building a simplistic (but often inaccurate) world model where some features of the world are generalized. They don’t reason per se but have really good ways to look up how typical reasoning would look like.

To get actual reasoning, you need to do what all AI labs are currently working on and add a neuro-symbolic spin to model outputs. In these approaches, a model generates ideas for what to do next, and the solution space is searched with more traditional methods. This introduces a dynamic element that’s more akin to human problem-solving, where the system can adapt and learn within the context of a specific task, even if it doesn’t permanently update the knowledge base of the idea-generating model.

A notable example is AlphaGeometry, a system that solves complex geometry problems without human demonstrations and insufficient training data that is based on an LLM and structured search. Similar approaches are also used for coding or for a recent strong improvement in reasoning to solve example from the ARC challenge..

Now that sounds dystopian as fuck. Because at scale this will involve human workers being tracked all the time and limited in their freedom. Ironically an AI might be used to track what workers do in such a scenario.