Aran Nayebi

A nice application of our NeuroAI Turing Test! Check out
@ithobani.bsky.social's thread for more details on comparing brains to machines!

Academic paper: bsky.app/profile/anay...

AI Is taking jobs: Could universal basic income become a reality? Forecasts that AI could erase tens of millions of jobs by the end of the decade appear to be making the notion of a guaranteed income less radical.

Honored to be quoted in this @newsweek.com article discussing how AI could accelerate the need for UBI.

Read more here: www.newsweek.com/ai-taking-jo...

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...

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).

In today's Generative AI lecture, we talk about all the different ways to take a giant auto-complete engine like an LLM and turn it into a useful chat assistant.

Slides: www.cs.cmu.edu/~mgormley/co...

Full course info: bsky.app/profile/anay...

In today's Generative AI lecture, we discuss the 4 primary approaches to Parameter-Efficient Fine-Tuning (PEFT): subset, adapters, Prefix/Prompt Tuning, and Low-Rank Adaptation (LoRA).

We show each of these amounts to finetuning a different aspect of the Transformer.

YouTube video by CMU Robotics Institute RI Seminar: Aran Nayebi : Using Embodied Agents to Reverse-Engineer Natural Intelligence

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...

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.

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.

1/6 Recent discussions (e.g. Rich Sutton on @dwarkesh.bsky.social’s podcast) have highlighted why animals are a better target for intelligence — and why scaling alone isn’t enough.
In my recent @cmurobotics.bsky.social seminar talk, “Using Embodied Agents to Reverse-Engineer Natural Intelligence”,

Check out our accompanying open-source library!
bsky.app/profile/anay...

Excited to have this work accepted as an *oral* to NeurIPS 2025!

Excited to have this work accepted to NeurIPS 2025! See you all in San Diego!

In today's Generative AI lecture, we discuss how to implement Diffusion Models and go through their derivation. Next time, we discuss their deeper relationships with variational inference :)

Slides: www.cs.cmu.edu/~mgormley/co...

Full course info: bsky.app/profile/anay...

In today's Generative AI lecture, we discuss Generative Adversarial Networks (GANs) & review probabilistic graphical models (PGMs) as a prelude to Diffusion models and VAEs, which we will discuss next time!

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...

In today's Generative AI lecture, we cover Vision Transformers (as well as the broader notion of Encoder-Only Transformers).

We also explain the historical throughline to some of these ideas, inspired by Nobel-prize-winning observations in neuroscience!

Actually the point of the present work isn't the % that's automated (though that's certainly a factor that can affect the UBI threshold), but more about the pressure part, that an AI with increasing capability lowers the societal "barrier-to-entry" because you don't have to increase the automation %

Agreed, but AI might finally create both the surplus and the pressure to make it happen, even if I’m cautious about how human nature and politics play out.

Totally agree. UBI here isn't meant to solve the meaning/purpose problem, but just to identify what societal levers there are to minimally cover the basics.

Slides: www.cs.cmu.edu/~mgormley/co...

Full course info: bsky.app/profile/anay...

In today's Generative AI lecture, we give an overview of the pre-training/post-training pipeline, and discuss modern Transformer implementations, from Rotary Position Embeddings (RoPE) to Grouped Query Attention (GQA) to Sliding Window Attention.

Cool UBI simulator made by the AI+Wellbeing Institute based on our paper! www.ai-well-being.com/building-our...

Slides: www.cs.cmu.edu/~mgormley/co...
Full course info: bsky.app/profile/anay...

In today's Generative AI lecture, we cover how to train a Transformer Language Model, as well as what makes it efficient at learning in order to scale to GPT levels—covering key-value caching & tokenizers, among other things:

If you’re attending ILIAD, I’ll be presenting this work online tomorrow from 11 am - 12 pm PT!
www.iliadconference.com