Martin Wattenberg
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wattenberg.bsky.social
Martin Wattenberg
@wattenberg.bsky.social

Human/AI interaction. ML interpretability. Visualization as design, science, art. Professor at Harvard, and part-time at Google DeepMind.

Computer science 44%
Political science 34%

Reposted by Martin Wattenberg

Charts and graphs help people analyze data, but can they also help AI?

In a new paper, we provide initial evidence that it does! GPT 4.1 and Claude 3.5 describe three synthetic datasets more precisely and accurately when raw data is accompanied by a scatter plot. Read more in🧵!

Reposted by Martin Wattenberg

The 2024 Name of the Year sounds goofy, until it doesn't.
The 2024 Name of the Year is Shaboozey : Namerology
The name behind this year's biggest song isn't as simple as it seems.
bit.ly

Reposted by Martin Wattenberg

i'm building an experimental tool for exploring 25 years of my old sketchbooks, with image and text recognition powered by gemini

Reposted by Martin Wattenberg

i asked Claude to write a Barthelme-esque short story with the aesthetic sensibilities of "The School", and it gave me this. i mean. i mean.

Reposted by Martin Wattenberg

For the 4th year in a row, my all-sky camera has been taking an image of the sky above the Netherlands every 15 seconds. Combining these images reveal the length of the night changing throughout the year, the passage of clouds and the motion of the Moon and the Sun through the sky. #astrophotography

Reposted by Martin Wattenberg

In 1897, Alfred G. Mayer created his butterfly wing projections, an attempt to gain new insights into natural patterns and laws. Vertical blocks denote individual wings, distorted and stretched mathematically to fill a tidy rectangular space. More here: publicdomainreview.org/collection/m...

Reposted by Martin Wattenberg

ARBOR aims to accelerate the internal investigation of the new class of AI "reasoning" models.

See the ARBOR discussion board for a thread for each project underway.

github.com/ArborProjec...

Reposted by Martin Wattenberg

Can we understand the mechanisms of a frontier AI model?

📝 Blog post: www.anthropic.com/research/tra...
🧪 "Biology" paper: transformer-circuits.pub/2025/attribu...
⚙️ Methods paper: transformer-circuits.pub/2025/attribu...

Featuring basic multi-step reasoning, planning, introspection and more!
On the Biology of a Large Language Model
transformer-circuits.pub

Reposted by Martin Wattenberg

This map shows the hour of sunrise globally through the year. It reveals time zones following national and, sometimes, regional boundaries, and slicing through the oceans.

Reposted by Martin Wattenberg

AI is often thought of as a black box -- no way to know what's going on inside. That's changing in eye-opening ways. Researchers are finding "beliefs" models are forming as they converse, and how those beliefs correlate to what the models say and how they say it.

www.theatlantic.com/technology/a...
What AI Thinks It Knows About You
What happens when people can see what assumptions a large language model is making about them?
www.theatlantic.com

Reposted by Martin Wattenberg

The interactive NameGrapher is updated with 2024 baby name popularity stats! Come explore--and marvel that Oliver and Olivia have converged namerology.com/baby-name-gr...

A wonderful visualization for those of us obsessed by sunlight and geography!
This map shows the hour of sunrise globally through the year. It reveals time zones following national and, sometimes, regional boundaries, and slicing through the oceans.

An incredibly rich, detailed view of neural net internals! There are so many insights in these papers. And the visualizations of "addition circuit" features are just plain cool!
Can we understand the mechanisms of a frontier AI model?

📝 Blog post: www.anthropic.com/research/tra...
🧪 "Biology" paper: transformer-circuits.pub/2025/attribu...
⚙️ Methods paper: transformer-circuits.pub/2025/attribu...

Featuring basic multi-step reasoning, planning, introspection and more!
On the Biology of a Large Language Model
transformer-circuits.pub

Great news, congrats! And glad you’ll still be in the neighborhood!

I'd be curious about advice on teaching non-coders how to test programs they've written with AI. I'm not thinking unit tests so much as things like making sure you can drill down for verifiable details in a visualization—basic practices that are good on their own, but also help catch errors.

Now that we have vibe coding, we need vibe testing!

Oh, that looks super relevant and fascinating, reading through it now...

Ha! I think (!) that for me, the word "calculate" connotes narrow precision and correctness, whereas "think" is more expansive but also implies more fuzziness and the possibility of being wrong. That said, your observation does give me pause!

We're following the terminology of the DeepSeek-R1 paper that introduced this model: arxiv.org/abs/2501.12948 Whether it's really the best metaphor is certainly worth asking! I can see pros and cons for both "thinking" and "calculating"
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT)...
arxiv.org

These are great questions! I believe there's at least one graph of p(correct answer) on the main Arbor discussion page, and generally there are a lot more details: github.com/ARBORproject...
Reasoning or Performing: locating "breakthrough" in the model's reasoning · ARBORproject arborproject.github.io · Discussion #11
Research Question When asked the DeepSeek models a challenging abstract algebra question, they often generated hundreds of tokens of reasoning before providing the final answer. Yet, on some questi...
github.com

Interesting question! I haven't calculated this, but @yidachen.bsky.social might know

This is a common pattern, but we're also seeing some others! Here are similar views for multiple-choice abstract algebra questions (green is the correct answer; other colors are incorrect answers) You can see many more at yc015.github.io/reasoning-pr... cc @yidachen.bsky.social

Very cool! You're definitely not alone in finding this fascinating. If you're looking for other people interested in this kind of thing, drop by the Arbor Project page, if you haven't already. github.com/ArborProject...
GitHub - ARBORproject/arborproject.github.io
Contribute to ARBORproject/arborproject.github.io development by creating an account on GitHub.
github.com

The wind map at hint.fm/wind/ has been running since 2012, relying on weather data from NOAA. We added a notice like this today. Thanks to @cambecc.bsky.social for the inspiration.

It's based on a data set of multiple-choice questions that have a known right answer, so this visualization only works when you have labeled ground truth. Definitely wouldn't shock me if those answers were labeled by grad students, though!

Great questions! Maybe it would be faster... or maybe it's doing something important under the hood that we can't see? I genuinely have no idea.

We also see cases where it starts out with the right answer, but eventually "convinces itself" of the wrong answer! I would love to understand the dynamics better.

You can see the model go down the wrong path, "realize" it's not right, then find the correct answer! To see more visualizations, or if you have related ideas, join the discussion here!
github.com/ARBORproject... (vis by @yidachen.bsky.social in conversation with @diatkinson.bsky.social )
Reasoning or Performing · ARBORproject arborproject.github.io · Discussion #11
Research Question When asked the DeepSeek Distilled R1 models a challenging abstract algebra question, they often generated hundreds of tokens of CoT before providing the final answer. Yet, on some...
github.com

Neat visualization that came up in the ARBOR project: this shows DeepSeek "thinking" about a question, and color is the probability that, if it exited thinking, it would give the right answer. (Here yellow means correct.)

Thank you! That's a great write-up, and this is definitely an interesting experiment. The distinction between how the model might do parsing vs. solving is very much worth thinking about. I added a few thoughts on the wiki page. github.com/ARBORproject...
Chain of Thought for Tsumego (Go Life or Death) Problems
Contribute to ARBORproject/arborproject.github.io development by creating an account on GitHub.
github.com