not even a month ago
Who's Who of AI
David Marx
Why they matter
Directory member with public evidence across Culture, work & education.
- AI signals
- 12 past 30d
- Sources
- 8 distinct domains
- Discusiones
- 74 past 30d
- Latest signal
- 6h ago
I read a lot of research. Mostly ML.
Currently reading: https://dmarx.github.io/papers-feed/
Statistical Learning
Information Theory
Ontic Structural Realism
Morality As Cooperation
Free Culture, Open Access
YIMBY, UBI
Research MLE
Frmr FireFighter
What they're sharing
Statement on the US government directive to suspend access to Fable 5 and Mythos 5 anthropic.com
Smart cellular bricks for decentralized shape classification and damage recovery | Nature Communications nature.com
Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry arxiv.org
Beyond expert users: agents should help users construct preferences, not just elicit them arxiv.org
politico.com
Simple contagion drives population-scale platform migration arxiv.org
The Lattice Representation Hypothesis of Large Language Models arxiv.org
An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models arxiv.org
The Geometry of Reasoning: Flowing Logics in Representation Space arxiv.org
From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning arxiv.org
Frontiers | Shaping integrity: why generative artificial intelligence does not have to undermine education frontiersin.org
GitHub - dmarx/anthology-of-modern-ml: Collection of important articles to be treated as a textbook github.com
Articles & links
David Marx reposted
We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.” Blog: sakana.ai/smart-cellul... Paper: www.nature.com/articles/s41... Thread 🧵
AI Weekly's analysis
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- Cubic bricks running identical neural cellular automata policies classified four 3D shapes with 98.97% accuracy in simulation and 100% on physical hardware.
- Physical builds ranged from 26 bricks for a guitar to 197 for a round table, converging on a shape label in fewer than 60 update cycles.
- The same decentralized framework detects structural damage with over 90% accuracy and guides regrowth by predicting one of six axis directions.
Read full analysis →
View on Bluesky →
David Marx reposted
Our new paper sets the stage for the biggest practical use case of model interpretability: stress testing and dataset development. All you need is interpretable linear features and simple geometry.
AI Weekly's analysis
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- A Compositional Interference metric derived from feature geometry predicts LLM failures without evaluating specific inputs.
- On multihop question answering, correlation between the CI metric and model accuracy reached r = -0.855.
- The method predicts cross-lingual transfer failures across 10+ languages using only English fact representations.
Read full analysis →
View on Bluesky →
2026 - agents should help users construct preferences, not just elicit them - Irena Saracay, Ludwig Schmidt, Carlos Guestrin 4/n
AI Weekly's analysis
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- New arxiv paper argues AI agents should help non-expert users construct preferences, not assume users already know what they want.
- The authors introduce CoShop, an interactive benchmark where no tested agent exceeded 56% accuracy after five turns of dialogue.
- Failures came from agents' limited knowledge expansion, not from difficulty finding items once preferences were specified.
Read full analysis →
apparently this nonsense is still going on
It's on you to get your friends and colleagues off of twitter. Pass it along.
AI Weekly's analysis
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- Researchers linked 276,431 Twitter/X scholars to their profiles among 16.7 million Bluesky accounts, tracked January 2023 through December 2024.
- Brazil's court-ordered suspension of Twitter/X served as the natural experiment, with treatment effects on migration that were short-lived and dose-dependent.
- Adoption was driven by simple contagion rather than complex contagion, with early reconnection to prior contacts predicting longer tenure on Bluesky.
Read full analysis →
If you're coming at this from the background of a traumatized logician, you'll probably enjoy this paper demonstrating how linear representations (i.e. "embeddings" of the kind learned by DNNs) are connected to boolean logic and formal concept analysis.
2025 - An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models - Kenneth Payne 10/n
2026 - The Geometry of Reasoning: Flowing Logics in Representation Space - Yufa Zhou, Yixiao Wang, Xunjian Yin, Shuyan Zhou, Anru R. Zhang 8/n
2025 - From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning - Chen Shani, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv 7/n
2025 - generative artificial intelligence does not have to undermine education - Myles Tan, Nicholle Maravilla 6/n
I haven't updated it in a minute, but I have a solid collection of important ML milestones which might include a few you'd want to add: github.com/dmarx/anthol...
Their own posts
Recent commentary
"Thank god we're finally making progress, it took forever for the LLM to understand what I was trying to- NOOOOOOOOO!"
AI is good at answering questions. Having access to AI doesn't magically make you better at asking them.
Biggest ChatGPT failure so far: couldn't connect the dots that the Knicks were taking the NBA Finals, attributed city-wide cheering to a Haiti-Scottland WCS game watch party instead.
A hilarious window into the real world business consequences of chasing AI hype culture: contractors who know they could be delivering a cheaper-to-operate solution, but aren't even proposing it because they know their customer wants toys that hoover tokens.
@Anthropic people: for the love of god, can you please teach Claude how to use test-driven development instead of YOLO-implementing fixes based on incorrect assumptions about what the underlying problem was?
claude hallucinating all sorts of nonexistent postgres features over here
I bet anthropic has interesting metrics on how people talk/interact differently with different models
LLM assisted coding is especially powerful when you're inebriated and can't write coherently, but it understands what you're asking for anyway.
AI ad for some snake oil scam on youtube: > "You don't have constipation: you have POOP PARALYSIS!"
Their network
In David Marx's orbit
Center = David Marx. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.