7 Vintage Books Science-Minded Readers Will Love Paired With A 2025 Book Recommendation A collection of seven book reviews from our archives, each paired with a recently published book we recommend

7 Vintage Books Science-Minded Readers Will Love Paired With A 2025 Book Recommendation #Science #Other #VintageBooks #ScienceReading #BookRecommendations

Reading list incoming! 📚
Dr. Zhian Chen curated some fantastic reviews covering gasdermins, ferroptosis, cell death, and BCR signaling. Perfect for your weekend science reading (because we all do that, right? 😅)
#CytokineSociety #ScienceReading #ResearchReviews #WeekendReading

The @physicsworld.bsky.social Quantum Briefing 2025 is out now — your essential guide to the latest breakthroughs, big ideas, and bold questions shaping the quantum frontier.

🔗 physicsworld.com/p/magazine/a...

#QuantumPhysics #QuantumTech #PhysicsWorld #ScienceReading

Abstract of the paper, reads: "Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems."

Finally reading "Is ChatGPT Bullshit?", which argues the falsehoods in the outputs of language models are not "hallucinations" but "bullshit", and explains why the distinction is useful.

link.springer.com/article/10.1...

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Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning - Tal Yarkoni, Jacob Westfall, 2017 Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled...

Can a focus on prediction (as opposed to explanation) ultimately lead us to greater understanding of behavior? At first seems paradoxical to me. Let's see what @talyarkoni.com and Westfall have to say and consider potential lessons for #sociology

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