Panel 1: Text: “Imagine an alternate universe in which people don’t have words for different forms of transportation, only the collective noun ‘vehicle’.” Illustration: a stick figure stands next to a much more detailed motorcycle, which a speech bubble saying “Woah! Sweet vehicle!” Panel 2: Text: “They use that word to refer to: cars, buses, bikes, spacecraft, and all other ways of getting from place A to place B.” Illustration: A car, a school bus, a bicycle, and a space shuttle, all with a stamp that says “vehicle” on them.

Panel 1: Text: “Conversations in this world are confusing.” Illustration: A speech bubble coming from the left: “Can you drive a vehicle?”, A speech bubble coming from the right: “Definitely!”, A illustration of a car crashed into a tree. Left speech bubble: “I thought you said you could drive!” Right speech bubble: “I can! I’m just used to ones with two wheels!” Panel 2: Text: “There are furious debates about whether or not vehicles are environmentally friendly… even though no one realizes that one side is talking about bikes and the other is talking about trucks. Illustration: Left speech bubble: “Vehicles produce so much pollution!” Right speech bubble: “That’s an exaggeration! They are actually very green!”

Panel 1: Text: “There is a breakthrough in rocketry, but the media focuses on how vehicles have gotten faster so people call there car (‘car’ is crossed out and replaced with ‘vehicle’) dealer to ask when faster models will be available.” Illustration: A TV news report with a picture of a rocket ship and a chyron saying “Breaking: Vehicles reach 1000 mph!”. Below that is a drawing of two stick figures talking at a car dealership. One says, “So I can take this to space, right?” Panel 2: Text: “Meanwhile, fraudsters have capitalize on the fact that consumers don’t know what to believe when it comes to vehicle technology, so scams are rampant in the vehicle sector.” Illustration: A stick figure with a mean smile and a sparkle next to his eye pats a car that has plane wings taped to it. A speech bubble says, “Oh yeah! You can fly this baby across the ocean!”

Panel 1: Text: “Now replace the word “vehicle” with “artificial intelligence” and we have a pretty good descriptor of the world we live in.” Illustration: One crowd of people say “AI is bad for the environment!” Underneath them is a large box that is labeled “Size of AI people are concerned about”, another crowd of people says “AI is used for climate research!” Underneath them is a much smaller box saying “Size of AI used for climate research”. In the foreground there is a person watching the debate with several question marks above it. Panel 2: Credits. “Text from AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference by Arvin’s Narayanan and Sayash Kapoor. Art by Ayla Taylor. www.aylataylor.com”

A silly little comic based on the opening section of AI Snake Oil by @randomwalker.bsky.social and @sayash.bsky.social.

Let me know when the T-shirt is available, I will order five pieces right away.

Zen Browser: The One You’ve Always Wanted YouTube video by SuperUser Tech

youtu.be/lT6WU-1S96U?...

zen-browser.app

😅 Switching to Zen browser (vertical tabs+different contexts) was a life changing experience for me and reduced my open tab count by at least 80%

Everyone should watch this

In this sense, "putting on the evolutionary pair of glasses" is nothing else but to account for the inevitable theory-ladenness of observation. There seems in fact no good reason to assume why machine learning should be exempt from theory-ladenness, which is one way to read the NFL theorems.

A #philscy perspective on our work: As machine learning is underdetermined (following the no-free-lunch theorems), the training process has little chance to infer an internal representation that is phylogenetically plausible - unless we introduce explicit inductive bias.

...in particular, we can consider the splits diagrams of all quartets of taxa and train the neural network so that the splits diagrams for all quartets degenerate to trees.

If we pass a (knwon and well-established) phylogenetic tree to the training process of the neural network, we can utilize mathematical structures behind evolutionary distances...

As has been observed by other authors before, the internal representations of neural networks are usually incompatible with known phylogenies. So, how could a neural network learn to infer features that are evolutionarily plausible?

A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data Neural networks are widely used in bioinformatics to extract features from morphological, structural, and sequence data of different taxa. A key quest…

Link to the full paper: www.sciencedirect.com/science/arti...

Equipping Artificial Intelligence with the Lense of Evolution Random patterns have so far obscured AI’s view of evolutionary relationships. A team in Bochum is teaching them how to go about this.

We found AI to be a bit short-sighted towards evolution, so we went out to grind some computational lenses. The resulting pair of evolutionary glasses is finally available in the latest issue of CSBJ

news.rub.de/english/pres...

Sieht sehr böhmisch aus!

Bhattacharya is the New Lysenko Starving the NIH through ineptitude, bitterness, and thirst for revenge

New post about Lysenko and Bhattacharya from @angierasmussen.bsky.social

open.substack.com/pub/rasmusse...

In Henk de Regt’s book on Understanding Scientific Understanding, skill takes center stage for understanding as a primary goal of science.

We are witnessing the destruction of science in America | Paul Darren Bieniasz If we stay on this administration’s course, future life-saving medicines may never be invented

"Science in the US is entering a phase resembling the Lysenko era in the Soviet Union."
www.theguardian.com/commentisfre...

Charlie Chaplin - Final Speech from The Great Dictator YouTube video by Charlie Chaplin

Almost like it was written for the world these days... the "Final Speech" by Charly Chaplin's hairdresser and dictator-by-coincidence Hynkel. youtu.be/J7GY1Xg6X20?...

Large Language Model is Secretly a Protein Sequence Optimizer

Figure 1

Figure 2

Figure 3

Large Language Model is Secretly a Protein Sequence Optimizer [new]
LLMs, surprisingly, can optimize protein sequences via directed evolution, even without explicit protein training data, finding high fitness variants.

I donated. It would be great to have a social-media protocol that wasn't subject to capture, and it does seem possible.

freeourfeeds.com

…and most importantly: Calculators could actually *do* arithmetic, and did not imitate the process of doing arithmetic.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study Eric Oermann and colleagues ask whether a DL-based model for pneumonia detection performs well in external validation and consider the effects of hospital system–specific biases.

There is also the "classical" paper by Zech et al. which identifies potential shortcuts in pneumonia detection (see e.g. Fig. 2)

journals.plos.org/plosmedicine...

The risk of shortcutting in deep learning algorithms for medical imaging research - Scientific Reports Scientific Reports - The risk of shortcutting in deep learning algorithms for medical imaging research

AI predicts beer consumption from knee x-rays www.nature.com/articles/s41...

"Researchers trained medical AI models on over 25,000 knee X-rays. They then essentially worked backwards, tasking the deep learning programs to find commonalities that predicted nonsensical traits, such as which knee-owners clearly drank beer or ate refried beans—which is patently absurd."

cdn.bsky.app/img/feed_thu...

Here's what we've been missing in the meantime. Better tell the owner of the "Hammer of Justice" not to hit his own thumb.

Meanwhile, in the less free corners of the internet…