Mark J. Nelson

Comp. sci. prof. @ American University, Washington DC. AI & games researcher with miscellaneous other interests. https://www.kmjn.org/

Articles & links

Small-scale study: 16 law professors were asked to judge short-answer Q&A practice materials written by one of the other 15 professors, or generated by an LLM (Gemini 2.5 Pro). They preferred the LLM materials in 75% of cases, with fairly strong inter-rater agreement. Has some…

law.stanford.edu
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Mark J. Nelson reposted
Raphaël Millière @raphaelmilliere.com

Now published in open access! Your one-stop shop for the philosophy of language models. It's the spiritual descendant of our two-part preprint from 2024, fully updated. This should be particularly useful for anyone looking for an entry point into this rapidly growing field.

compass.onlinelibrary.wiley.com View on Bluesky →
Mark J. Nelson reposted
Ramon Astudillo @ramon-astudillo.bsky.social

if you are wondering about Mistral koenvangilst.nl/lab/mistral-...

Notes from the AI Now Summit by Mistral koenvangilst.nl
AI Weekly's analysis
  • Mistral owns data centers directly, backing its full-stack infrastructure claim with physical compute assets beyond model licensing.
  • Domain-specific smaller models outperformed general-purpose alternatives in speed and efficiency across Mistral's enterprise demonstrations.
  • Mistral signed anchor enterprise deals with ASML, BNP Paribas, and Amazon Alexa+ as evidence of European sovereign AI demand.
Read full analysis →
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Intriguing-looking postdoc on human-centric reinforcement learning, part of "an interdisciplinary project between artificial intelligence, social sciences, and societal partners". At TU Eindhoven with Hendrik Baier, deadline July 9. www.tue.nl/en/working-a...

Postdoc on human-centric, collaborative AI tue.nl
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Recent commentary

A reason I've pulled back from reviewing for big AI conferences is a feeling that I'm doing unpaid supervision of other people's PhD students. Too many ppl submitting 10+ papers to a single conference where I doubt the prof whose name is on the paper has done a thorough review & revision themselves.

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An interesting thing about LLMs in Python is that they seem to broadly push code towards some kind of conventional wisdom about best practices, as judged maybe by whoever is setting up the posttraining recipes (I say "in Python" mostly because I notice that more strongly in Python).

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A thing LLM coding sort of makes more feasible is making *smaller* personalized apps. Like instead of WMATA's big and annoying to use transit app, I'm trying out a custom little thing that just shows me the 2 bus lines I take. Lines hardcoded; stops hardcoded; no configuration; barely any interface.

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Asked Gemini something about DC apartment logistics, and it recommended I "pop down to the front desk" to verify. Takeover of Google AI by DeepMind Londoners confirmed.

View on Bluesky · ♥ 8 ↻ 0 ↩ 0 · 19d ago

Isabelle/Isar has a nicely named proof method, blast, that tries to brute force, letting you concisely prove simple but tedious lemmas by just writing 'thus "Q" by blast', which'll go through if in fact blast can prove it. In good cases, LLMs feel kind of like that for me now: pip install by blast.

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A pop AI book where the two people contributing blurbs were Al Gore and Sam Altman. A little microcosm of 2018.

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