Aran Nayebi
@anayebi.bsky.social
Assistant Professor of Machine Learning, Carnegie Mellon University (CMU)
Building a Natural Science of Intelligence 🧠🤖
Prev: ICoN Postdoctoral Fellow @MIT, PhD @Stanford NeuroAILab
Personal Website: https://cs.cmu.edu/~anayebi
Building a Natural Science of Intelligence 🧠🤖
Prev: ICoN Postdoctoral Fellow @MIT, PhD @Stanford NeuroAILab
Personal Website: https://cs.cmu.edu/~anayebi
November 10, 2025 at 8:46 PM
Finally, we briefly discuss Querying Transformers for text-image alignment, as a hold-over from last lecture on multimodal foundation models!
October 23, 2025 at 1:44 PM
Finally, we briefly discuss Querying Transformers for text-image alignment, as a hold-over from last lecture on multimodal foundation models!
We also discuss data quality & amount (where you get great performance with a smaller model trained on lots of tokens), how to get good data depending on your application, and Moravec's paradox for robotics foundation models.
October 23, 2025 at 1:44 PM
We also discuss data quality & amount (where you get great performance with a smaller model trained on lots of tokens), how to get good data depending on your application, and Moravec's paradox for robotics foundation models.
Full paper (to appear in NeurIPS 2025!) here: arxiv.org/abs/2506.00138
Autonomous Behavior and Whole-Brain Dynamics Emerge in Embodied Zebrafish Agents with Model-based Intrinsic Motivation
Autonomy is a hallmark of animal intelligence, enabling adaptive and intelligent behavior in complex environments without relying on external reward or task structure. Existing reinforcement learning ...
arxiv.org
October 21, 2025 at 2:41 AM
Full paper (to appear in NeurIPS 2025!) here: arxiv.org/abs/2506.00138
Thanks @undo-hubris.bsky.social for the invite & for hosting!
Slides: anayebi.github.io/files/slides...
Paper 1 (alignment barriers): arxiv.org/abs/2502.05934
Paper 1 summary: bsky.app/profile/anay...
Paper 2 (corrigibility): arxiv.org/abs/2507.20964
Paper 2 summary: bsky.app/profile/anay...
Slides: anayebi.github.io/files/slides...
Paper 1 (alignment barriers): arxiv.org/abs/2502.05934
Paper 1 summary: bsky.app/profile/anay...
Paper 2 (corrigibility): arxiv.org/abs/2507.20964
Paper 2 summary: bsky.app/profile/anay...
October 10, 2025 at 3:16 PM
Thanks @undo-hubris.bsky.social for the invite & for hosting!
Slides: anayebi.github.io/files/slides...
Paper 1 (alignment barriers): arxiv.org/abs/2502.05934
Paper 1 summary: bsky.app/profile/anay...
Paper 2 (corrigibility): arxiv.org/abs/2507.20964
Paper 2 summary: bsky.app/profile/anay...
Slides: anayebi.github.io/files/slides...
Paper 1 (alignment barriers): arxiv.org/abs/2502.05934
Paper 1 summary: bsky.app/profile/anay...
Paper 2 (corrigibility): arxiv.org/abs/2507.20964
Paper 2 summary: bsky.app/profile/anay...
Academic paper: bsky.app/profile/anay...
Can a Universal Basic Income (UBI) become feasible—even if AI fully automates existing jobs and creates no new ones?
We derive a closed-form UBI threshold tied to AI capabilities that suggests it's potentially achievable by mid-century even under moderate AI growth assumptions:
We derive a closed-form UBI threshold tied to AI capabilities that suggests it's potentially achievable by mid-century even under moderate AI growth assumptions:
October 5, 2025 at 3:23 PM
Academic paper: bsky.app/profile/anay...
Next time we discuss how to optimize these reward models via DPO/policy gradients!
Slides: www.cs.cmu.edu/~mgormley/co...
Full course info: bsky.app/profile/anay...
Slides: www.cs.cmu.edu/~mgormley/co...
Full course info: bsky.app/profile/anay...
October 1, 2025 at 7:46 PM
Next time we discuss how to optimize these reward models via DPO/policy gradients!
Slides: www.cs.cmu.edu/~mgormley/co...
Full course info: bsky.app/profile/anay...
Slides: www.cs.cmu.edu/~mgormley/co...
Full course info: bsky.app/profile/anay...
Specifically, we cover methods which don't involve parameter-updating, e.g. In-Context Learning / Prompt-Engineering / Chain-of-Thought Prompting, to methods that do, such as Instruction Fine-Tuning & building on IFT to perform full-fledged Reinforcement Learning from Human Feedback (RLHF).
October 1, 2025 at 7:46 PM
Specifically, we cover methods which don't involve parameter-updating, e.g. In-Context Learning / Prompt-Engineering / Chain-of-Thought Prompting, to methods that do, such as Instruction Fine-Tuning & building on IFT to perform full-fledged Reinforcement Learning from Human Feedback (RLHF).
September 29, 2025 at 8:00 PM
6/6 I close with reflections on AI safety and alignment, and the Q&A explores open questions: from building physically accurate (not just photorealistic) world models to the role of autoregression and scale.
🎥Watch here: www.youtube.com/watch?v=5deM...
Slides: anayebi.github.io/files/slides...
🎥Watch here: www.youtube.com/watch?v=5deM...
Slides: anayebi.github.io/files/slides...
RI Seminar: Aran Nayebi : Using Embodied Agents to Reverse-Engineer Natural Intelligence
YouTube video by CMU Robotics Institute
www.youtube.com
September 29, 2025 at 2:02 PM
6/6 I close with reflections on AI safety and alignment, and the Q&A explores open questions: from building physically accurate (not just photorealistic) world models to the role of autoregression and scale.
🎥Watch here: www.youtube.com/watch?v=5deM...
Slides: anayebi.github.io/files/slides...
🎥Watch here: www.youtube.com/watch?v=5deM...
Slides: anayebi.github.io/files/slides...
5/6 I also touch on the Contravariance Principle/Platonic Representation Hypothesis, our proposed NeuroAI Turing Test, and why embodied agents are essential for building not just more capable, but also more reliable, autonomous systems.
September 29, 2025 at 2:02 PM
5/6 I also touch on the Contravariance Principle/Platonic Representation Hypothesis, our proposed NeuroAI Turing Test, and why embodied agents are essential for building not just more capable, but also more reliable, autonomous systems.
4/6 This journey culminates in our first task-optimized “NeuroAgent”, integrating advances in visual and tactile perception (including our NeurIPS ’25 oral), mental simulation, memory, and intrinsic curiosity.
September 29, 2025 at 2:02 PM
4/6 This journey culminates in our first task-optimized “NeuroAgent”, integrating advances in visual and tactile perception (including our NeurIPS ’25 oral), mental simulation, memory, and intrinsic curiosity.
3/6 By grounding agents in perception, prediction, planning, memory, and intrinsic motivation — and validating them against large-scale neural data from rodents, primates, and zebrafish — we show how neuroscience and machine learning can form a unified *science of intelligence*.
September 29, 2025 at 2:02 PM
3/6 By grounding agents in perception, prediction, planning, memory, and intrinsic motivation — and validating them against large-scale neural data from rodents, primates, and zebrafish — we show how neuroscience and machine learning can form a unified *science of intelligence*.
2/6 I present a cohesive framework that develops these notions further, grounded in both machine learning and experimental neuroscience.
In it, I outline our efforts over the past 4 years to set the capabilities of humans & animals as concrete engineering targets for AI.
In it, I outline our efforts over the past 4 years to set the capabilities of humans & animals as concrete engineering targets for AI.
September 29, 2025 at 2:02 PM
2/6 I present a cohesive framework that develops these notions further, grounded in both machine learning and experimental neuroscience.
In it, I outline our efforts over the past 4 years to set the capabilities of humans & animals as concrete engineering targets for AI.
In it, I outline our efforts over the past 4 years to set the capabilities of humans & animals as concrete engineering targets for AI.
Check out our accompanying open-source library!
bsky.app/profile/anay...
bsky.app/profile/anay...
🚀 New Open-Source Release! PyTorchTNN 🚀
A PyTorch library for biologically-inspired temporal neural nets: unrolling computation through time. Integrates with our recent Encoder-Attender-Decoder, which flexibly combines models (Transformer, SSM, RNN) since no single one fits all sequence tasks.
🧵👇
A PyTorch library for biologically-inspired temporal neural nets: unrolling computation through time. Integrates with our recent Encoder-Attender-Decoder, which flexibly combines models (Transformer, SSM, RNN) since no single one fits all sequence tasks.
🧵👇
September 18, 2025 at 9:33 PM
Check out our accompanying open-source library!
bsky.app/profile/anay...
bsky.app/profile/anay...