JOPRO

JOPRO supports interdisciplinary scholarship, cultivates emerging researchers, and builds the networks that connect them.

[JOPRO @JOPRO_org]

Summer Internships have launched:

https://t.co/O08DsNmkrp

[Michael Levin @drmichaellevin]

Awesome new theme issue of Philosophical Transactions:

‘World models in natural and artificial intelligence’

Thank you @adamsafron!

https://t.co/Tzu1OH9Hib

[Jes Parent 🧭 @JesParent]

Let's go! now live https://t.co/hGREvvxzhm

cc @JOPRO_org @Orthogonal_Lab @CognitionFuture

[JOPRO @JOPRO_org]

Summer internship: Human Flourishing, Agency, Cognitive Sovereignty

https://t.co/r2T7jnwZPD

[JOPRO @JOPRO_org]

Happening now:

[AHA at MIT Media Lab @AHA_MediaLab]

From early algorithms to generative AI to autonomous agents — each shapes us differently. Under what conditions does media actually support human flourishing?
THIS THURSDAY: AHA Seminar Series featuring Sunny Xun Liu (Stanford Social Media Lab) Link in comments!

[JOPRO @JOPRO_org]

Launching the DigiNEST program blog today. The first post asks: what happens to human agency when AI systems begin to mediate how we interpret our own experiences? Shorter answer: we need better design language, and better questions.

[JOPRO @JOPRO_org]

This Saturday:

https://t.co/1h20d7YD6y

[JOPRO @JOPRO_org]

Happening now:

[AHA at MIT Media Lab @AHA_MediaLab]

🌐 AI is now more persuasive than political ads — for controversial sociopolitical questions. So what does that mean for our shared reality?
THIS THURSDAY: AHA Seminar Series with Thomas Costello on AI & the future of belief. April 2 | 4–5pm ET | Join us: https://t.co/e0YEvfzwlf

[JOPRO @JOPRO_org]

@medialab Full post via the JOPRO Substack:

https://t.co/8OuLeVbyHk

[JOPRO @JOPRO_org]

Launching the DigiNEST program blog today. The first post asks: what happens to human agency when AI systems begin to mediate how we interpret our own experiences? Shorter answer: we need better design language, and better questions.

[JOPRO @JOPRO_org]

@medialab The concern is no longer theoretical. Recent work from MIT, Oxford Internet Institute, and others shows that AI sycophancy, emotional dependency, and eroded autonomy are already measurable in deployed systems.

[JOPRO @JOPRO_org]

Full announcement of internships in the coming week:

[JOPRO @JOPRO_org]

The @JOPRO_org 2025: Year in Review, with @JesParent

https://t.co/csK42klLpW

[Jes Parent 🧭 @JesParent]

If you or someone you know has done research in this space, we'd love to hear from you.

Soft tease -> @JOPRO_org: Forthcoming internship & RA opportunities ahead on these topics!

Paper: "Slow convergence: Career impediments to interdisciplinary biomedical research"

https://t.co/RowAgIyh2E

Please DM / reach out, or share with folks who may be up for a discussion around these topics.

[Andy Masley @AndyMasley]

This is a work in progress but I made a visual that compares how much water AI and data centers use in total to other industries. Relies on a lot of botecs I justify in the methodology. https://t.co/dY1GYYLFNG

[Jes Parent 🧭 @JesParent]

Good reading ahead of Embodied Intelligence '26, and our work in @CognitionFuture @JOPRO_org @Orthogonal_Lab

[Jes Parent 🧭 @JesParent]

Happening now: @AHA_MediaLab @medialab with @valdemardanry

[Elan Barenholtz @ebarenholtz]

(1/2) Just dropped a new paper: "World Properties without World Models: Recovering Spatial and Temporal Structure from Co-occurrence Statistics in Static Word Embeddings".

A key line of evidence for LLM "world models": linear probes recover city coordinates and historical dates from hidden states. @wesg52 & @tegmark did this with Llama-2 and got R²=0.91 for city locations. Very cool result.

But is it world models or word statistics? An alternative: maybe the structure isn't emerging inside the LLM. Maybe it was already latent in training text itself, inherited from the systematic differences in how language describes different places and eras.

I ran the same probes on GloVe and Word2Vec, static word embeddings from 2013-2014, trained purely on distributional statistics; no layers, no attention, no contextual processing. R²=0.71–0.87 for city coordinates (see map).

And the signal is selective, not a probe artifact. Latitude, longitude, temperature: all recoverable. Elevation, GDP, population: R² goes negative. The probe finds real distributional structure, not noise.

So: deflationary for world models. But inflationary for language. Co-occurrence statistics alone preserve a richer imprint of the physical world than anyone assumed. The words that surround "Nairobi" and the words that surround "Oslo" are systematically different, and that difference is enough to localize them. On a map.

The structure was never in the model. It was in the corpus. 👇

[augmentation lab @auglab]

Congratulations to Aida, who started working on @be_inaudible with us at the Augmentation Lab. We’re so excited to see more privacy-restoring, human-centric technology! 🦾

[Jes Parent 🧭 @JesParent]

Join us this weekend at Envision @ Princeton! I'll be a part of the Future of AI in Education Panel alongside excellent squad of @tammykwan, Amber Berry, and Morgan Mifflin!

Don't miss the rest of the lineup including: @random_walker, Pallavi Nuka, Nathan C. Walker, Steven Kelts, Liat Krawczyk, Jeffrey Oakman, Edward You, Vikram Ramaswamy, Alexander Kriebitz, Benedikt Lehnert, Michelle Ma, Noah Broestl

Beyond education at large, there's already been in lots of Envision-related conversation about how technology is impacting knowledge generation, AI & careers, and being agile in a world where traditional structures and businesses are being disrupted.

Looking forward to returning to @ptonenvision after a wonderful set of events form 2017-2019 IRL (and a few PandemicEra virtual events)!