Thanks! I imagine we'd see similar results in the Novelty Challenge that when experts are reliable we can fine-tune LLMs to be reliable but experts may only be reliable in some disciplines/settings and less reliable in others.

Very cool challenge!!

Thank you for sharing your brilliance, quirks, and wisdom. I started reading your work after coming across your Aeon article on Awe many years ago, and I feel inspired everytime I read what you write.

And follow negrain.bsky.social who just joined Blue Sky today!

If you're curious about learning more, say hi to
Negar Kamali at #CHI2025 and see video

Awesome collaboration with
Karyn, @aakriti1kumar.bsky.social, Angelos, @jessicahullman.bsky.social

Video: youtu.be/PL_ggNzMd-o?...
Preprint: arxiv.org/pdf/2502.11989
CHI: dl.acm.org/doi/10.1145/...

This taxonomy offers a shared language (and see our how to guide on arXiv for many examples) to help people better communicate what looks or feels off.

It's also a framework that can generalize to multimedia.

Consider this, what do you notice at the 16s mark about her legs?

Based on generating thousands of images, reading the AI-generated images and digital forensics literatures (and social media and journalistic commentary), analyzing 30k+ participant comments, we propose a taxonomy for characterizing diffusion model artifacts in images

Scene complexity, artifact types, display time, and human curation of AI-generated images all play significant roles in how accurately people distinguish real and AI-generated images.

We examine photorealism in generative AI by measuring people's accuracy at distinguishing 450 AI-generated and 150 real images

Photorealism varies from image to image and person to person

83% of AI-generated images are identified as AI better than random chance would predict

Agreed with your observation that disciplinary perspectives can be too narrow minded on this problem and forget the big picture of both sides

www.nature.com/articles/s41... does a really nice job systematically reviewing the Human-AI collaboration literature across a bunch of different domains

At a high level, it depends on:

- human expertise
- human understanding for what the AI system is capable of
- quality of AI explanations
- task-specific potential for cognitive biases and satisficing constraints to influence humans
- instance-specific potential for OOD data to influence AI

NICO Intake form: kellogg.qualtrics.com/jfe/form/SV_...

You're welcome!! Def makes makers who move between both worlds feel very seen

Impressive on the 20 minute bits approach!

I definitely need 4 hour windows for productive, creative work.

Paul Graham's essay on the Maker/Manager schedule (paulgraham.com/makersschedu...) offers some tips for how to create schedules that address roles where one is both a Maker and Manager

and present evidence that perception is more than simply transforming light into representations of objects and their features, perception also automatically extracts relations between objects!

well said!

PDF with links here: mattgroh.com/pdfs/annual_...