Josh Bongard

Robotics professor at the University of Vermont. Also into xenobots, crowdsourcing, evolution, machine learning, artificial intelligence.

[Josh Bongard @DoctorJosh]

Discussing computer designed organisms (AKA "Xenobots") with CNN anchor @FWhitfield.

Joint work with @drmichaellevin, Douglas Blackiston and @Kriegmerica.

https://t.co/nr6YSDokkS

[Josh Bongard @DoctorJosh]

Congratulations to **Dr.** @KamBielawski for successfully defending his PhD thesis today!

[Michael Levin @drmichaellevin]

A lot of people have suggested over the centuries that ecosystems might have some degree of cognition. What would that look like? Could there be recognizable memory phenomena on the scale of population dynamics? Here's a #preprint where amazing high-school student @asamanta42, @HananelHazan, and I use a model system - in silico predator-prey dynamics - and analyze the possibility of several kinds of learning:
https://t.co/JqmiakAJPM

(the basics are kind of like https://t.co/PH1ZKUxXqS, but some very cool new stuff here, including the interesting and unique pattern of learning-compatible values in the parameter space).

[Michael Levin @drmichaellevin]

More #preprints:

Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control
https://t.co/xVpyelyWKA
@namlehai @DrPatrickE @YanboZhang3 @DoctorJosh

ZapGPT: Free-form Language Prompting for Simulated Cellular Control
https://t.co/4QdOzWpgEq
@namlehai @DrPatrickE @YanboZhang3 @DoctorJosh

Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery
https://t.co/RbhXq7S1VZ
@YanboZhang3 @hectorzenil and many others!

[Michael Levin @drmichaellevin]

New #preprint, @PigozziFederico:

https://t.co/hJe7b14hVm

"The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents"

"A hallmark of life on Earth is the ability of agents to exert causal power and be drivers of subsequent events. This is key to cognition at all scales. Causal emergence, measuring the degree to which an agent exerts unique predictive power on its future, is one consequence of causal power. Indeed, recent discoveries have shown that biological agents, even minimal ones, increase their causal emergence after learning new memories. However, there is a major knowledge gap regarding how causally emergent artificial agents are. We focused on Reinforcement Learning (RL) of neural-network agents across an array of environmental conditions, encompassing different algorithms, agent architectures, and six environments arranged on a complexity spectrum. For consistency, we computed the causal emergence of their latent-space representations over their lifetimes. We used the recently proposed {\Phi}ID to estimate causal emergence and tested how it related to learning performance. Our results suggested a Causally Emergent Alignment Hypothesis: successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks. This idea suggests that causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents, with the potential to establish causal relationships and interventions that will lead to better RL agents. Our work also highlights the alignment between causal emergence and learning as another way biological and artificial creatures compare."

[Josh Bongard @DoctorJosh]

Lectures: https://t.co/UK6UCLr20o

Assignments: https://t.co/t4GMU3Q8xD

[Josh Bongard @DoctorJosh]

Evolutionary robotics course,

student project highlight reel.

https://t.co/efn8dRKeGd

[Josh Bongard @DoctorJosh]

The final lecture of evolutionary robotics course:

Self-replicating Xenobots.

So long, and thanks for all the fish!

https://t.co/QQpZj5YZx8

[Josh Bongard @DoctorJosh]

All assignments: https://t.co/t4GMU3Q8xD

[Josh Bongard @DoctorJosh]

Lecture 28 of evolutionary robotics course:

Xenobots. Part II.

https://t.co/Zj5R7bYgVQ

[Josh Bongard @DoctorJosh]

All lectures: https://t.co/UK6UCLr20o

[Josh Bongard @DoctorJosh]

(Almost) all lectures: https://t.co/UK6UCLr20o

[Josh Bongard @DoctorJosh]

Lecture 27 of evolutionary robotics course*:

Xenobots, Part I.

https://t.co/oVIJ1WYTif

[Josh Bongard @DoctorJosh]

All assignments: https://t.co/eACInbGnOc

[Josh Bongard @DoctorJosh]

* Lectures 25 and 26 are unavailable to the public as we discussed some unpublished data in lecture. Apologies.

[Josh Bongard @DoctorJosh]

@mooreth42 Sounds great Tom. But, with respect, I think we've already done that: https://t.co/ZjRinvvu2L

[Josh Bongard @DoctorJosh]

Lecture 23 of evolutionary robotics course:

Soft robots.

https://t.co/Ce7gLP7Axm

[Josh Bongard @DoctorJosh]

The Golgafrinchans built Ark Ship B because they never figured out how to build AI. 😬

[Josh Bongard @DoctorJosh]

All assignments: https://t.co/t4GMU3Q8xD

[Josh Bongard @DoctorJosh]

Lecture 24 of evolutionary robotics course:

Differentiable robots.

https://t.co/QrMPjN11X9

[Josh Bongard @DoctorJosh]

All lectures: https://t.co/UK6UCLr20o

[Josh Bongard @DoctorJosh]

Do writers dream of ontological leaps?

New work by @AlexanderGRoss explores how sci-fi has been quietly mapping the terrain of embodied AI all along.

Worth a read for anyone thinking seriously about cognition, bodies, and machines.

doi:10.1088/1757-899X/1343/1/012012