iiinova — where your digital self keeps evolving

Gaurav Kamath @grvkamath.bsky.social · 05/06/2026

Super cool project that I really enjoyed being part of! tl;dr - when a human or model encounters new visual stimuli, how closely is it mapped to other, previously encountered concepts? (Come for weird dog-monster, stay for the science 🙂 )

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Gaurav Kamath @grvkamath.bsky.social · 04/03/2026

🚨New Paper!🚨 How do reasoning LLMs handle inferences that have no deterministic answer? We find that they diverge from humans in some significant ways, and fail to reflect human uncertainty… 🧵(1/10)

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Gaurav Kamath @grvkamath.bsky.social · 11/02/2026

Super cool interpretability work from @bennokrojer.bsky.social , that I think is also relevant to anyone interested in how word meanings are represented in LLMs!

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Reposted by Gaurav Kamath

Tom McCoy @rtommccoy.bsky.social · 15/08/2025

🤖 🧠 NEW PAPER ON COGSCI & AI 🧠 🤖 Recent neural networks capture properties long thought to require symbols: compositionality, productivity, rapid learning So what role should symbols play in theories of the mind? For our answer...read on! Paper: arxiv.org/abs/2508.05776 1/n

The top shows the title and authors of the paper: "Whither symbols in the era of advanced neural networks?" by Tom Griffiths, Brenden Lake, Tom McCoy, Ellie Pavlick, and Taylor Webb.

At the bottom is text saying "Modern neural networks display capacities traditionally believed to require symbolic systems. This motivates a re-assessment of the role of symbols in cognitive theories."

In the middle is a graphic illustrating this text by showing three capacities: compositionality, productivity, and inductive biases. For each one, there is an illustration of a neural network displaying it. For compositionality, the illustration is DALL-E 3 creating an image of a teddy bear skateboarding in Times Square. For productivity, the illustration is novel words produced by GPT-2: "IKEA-ness", "nonneotropical", "Brazilianisms", "quackdom", "Smurfverse". For inductive biases, the illustration is a graph showing that a meta-learned neural network can learn formal languages from a small number of examples.

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Reposted by Gaurav Kamath

Proceedings of the National Academy of Sciences @pnas.org · 11/08/2025

Using congressional speeches as a corpus, researchers quantify how younger and older adults adopt new meanings for words as language changes. Older people may be a bit slower to change, but can show considerable linguistic flexibility. In PNAS: www.pnas.org/doi/10.1073/...

Examples of word sense probability over the time range of the corpus.

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Reposted by Gaurav Kamath

Philip Ball @philipcball.bsky.social · 30/07/2025

My latest column for @thenewworldmag.bsky.social looks at the question of how new meanings for words spread in the population. www.thenewworld.co.uk/philip-ball-...

thenewworld.co.uk

Why we need to be more chill about language change

It appears that our vocabulary is entrained with the Zeitgeist, whether we like it or not

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Gaurav Kamath @grvkamath.bsky.social · 29/07/2025

Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor. w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)

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