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Discussing computer designed organisms (AKA "Xenobots") with CNN anchor @FWhitfield.
Joint work with @drmichaellevin, Douglas Blackiston and @Kriegmerica.
youtube.com/watch?v=Q_pFkP…
YouTube
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Josh Bongard
3.7K posts
Josh Bongard
@DoctorJosh
Robotics professor at the University of Vermont. Also into xenobots, crowdsourcing, evolution, machine learning, artificial intelligence.
Katılım Nisan 2008
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Josh Bongard retweetledi
New #preprint, @PigozziFederico:
arxiv.org/abs/2605.06746
"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."
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Lectures: youtube.com/playlist?list=…
Assignments: reddit.com/r/ludobots/
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The final lecture of evolutionary robotics course:
Self-replicating Xenobots.
So long, and thanks for all the fish!
youtu.be/_V9XFNvw3a4
YouTube
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* Lectures 25 and 26 are unavailable to the public as we discussed some unpublished data in lecture. Apologies.
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@mooreth42 Sounds great Tom. But, with respect, I think we've already done that: krorgs.github.io
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@DoctorJosh Would you be interested in The Living Machine Challenge? It's an X-prize inspired contest to create an Autonomous Self-Replicating Machine.
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The Golgafrinchans built Ark Ship B because they never figured out how to build AI. 😬
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Lecture 24 of evolutionary robotics course:
Differentiable robots.
youtu.be/B56x986YQDw
YouTube
English
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
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