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…
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Mark J. Nelson
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Researcher with public evidence across AI research.
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Comp. sci. prof. @ American University, Washington DC. AI & games researcher with miscellaneous other interests. https://www.kmjn.org/
What they're sharing
law.stanford.edu
Who gets to understand AI? | Ai2 allenai.org
CrankGPT — fully offline, human-powered local AI squeezlabs.github.io
China’s AI Boyfriend Business Is Taking On a Life of Its Own wired.com
compass.onlinelibrary.wiley.com
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science.org
AIIDE 2026 - Sponsors sites.google.com
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How Many Submissions May an Author Make? A Harmonic Quota for Submissions under Coauthorship arxiv.org
Assistant Professors in Computer Science (5 positions) vacancies.maastrichtuniversity.nl
Notes from the AI Now Summit by Mistral koenvangilst.nl
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We Don't Understand Neural Networks At The Algorithmic Level kripken.github.io
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Shape the Impact of Games research: Become an Assistant or Associate Professor at the SDU Metaverse Lab fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com
Articles & links
Mark J. Nelson reposted
Our fully open releases give researchers the data, code, checkpoints, and methods they need to inspect claims, reproduce findings, and advance new science. Read more about why that’s so important to us. ⬇️ allenai.org/blog/who-get...
AI Weekly's analysis
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- Ai2 argues meaningful AI transparency requires not just open weights but training data, code, methods, checkpoints, evaluations, and documentation.
- Post cites three studies enabled by open Olmo releases, covering clinical demographic bias, benchmark inflation, and how models reason about drug names.
- Without that access, Ai2 warns, technical direction of the field risks becoming concentrated inside a small number of companies.
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finally some off-the-grid local AI
AI Weekly's analysis
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- CrankGPT runs a full voice-interactive AI pipeline on a Raspberry Pi 5 with 8GB RAM, powered solely by a 20W hand-crank generator.
- Cold-start to functional conversation takes roughly 30 seconds; time to first token ranges from 0.8 to 2.9 seconds depending on model size.
- Memory bandwidth, not raw compute, is the primary bottleneck for on-device LLM inference, with DDR5 hardware achieving 29-58% faster token generation than DDR4.
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did you see this one a few months ago?
Mark J. Nelson reposted
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.
Kind of reassuring to read that one of the biggest current problems in ML for drug discovery isn't any kind of exotic new AI/ML problem but just, still, the difficulty of preventing data leakage from the test set.
Mark J. Nelson reposted
Consider sponsoring the AAAI AIIDE conference in Belo Horizonte, Brazil! Your support will help motivate cutting-edge advancements in Game AI and creative technologies while giving you access to the best global talent in the field. Get involved: sites.google.com/view/aiide20...
Dealing with too many submissions to TMLR by deriving "a Generalized Harmonic Quota Rule, a framework that subsumes the Harmonic Quota Rule and other natural quota rules" in a 12-page paper is a very The Machine Learning Community solution to this particular problem.
Many openings for Assistant Professors in Computer Science at Maastricht University! Areas of interest include Programming Languages and AI+PL/SE (among others). Deadline August 16.
Mark J. Nelson reposted
if you are wondering about Mistral koenvangilst.nl/lab/mistral-...
Mark J. Nelson reposted
New blogpost: We Don't Understand Neural Networks At The Algorithmic Level kripken.github.io/blog/neurosc... So, why is this even a question? Don't scientists agree on whether we understand LLMs?
Assistant/Associate Professor opening at the University of Southern Denmark in games & interactive technologies; focus areas Human-Computer/AI Interaction, data science, and AI. Deadline August 1.
Their own posts
Recent commentary
Underreported thing about Gemini (more than other LLMs I think) is that it's an ok replacement for Google Books, and fluidly multilingual. Like I can ask a question and request answers be based only on books by a specific academic (which are in Greek) and it will dig up relevant passages.
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.
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).
Not a strongly held opinion, but I'm a little skeptical of the recent LLM math results not really being compared against baseline search methods with similar compute budgets. Some of them are using pretty huge compute budgets!
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.
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.
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.
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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