How I Define AGI
A follow-up essay: a working definition of AGI, how it will come about, and the precautions it needs.
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Studying the path to AGI.
I'm a student studying the path to AGI, working toward original research in NeuroAI.
Writing
Notes and essays about intelligence, AI, and the questions I keep coming back to.
My Perception of AGI
Why the brain's efficiency may be an important clue for building more general AI.
Projects
What I'm building to test the ideas I write about.
AGI Development Plan Concept stage
A plan for how I think AGI actually gets built: stop feeding models the internet, and start feeding them the way a baby learns — small amounts of real, embodied, sensory experience gathered over time, instead of scraped text by the billions. Laid out in full, including the hardware gap it still has to solve and the safety precautions it needs from day one, in "How I Define AGI."
"If sample-efficiency is part of what makes intelligence general, simply scaling today's architectures may never cross the threshold into AGI — no matter how good the benchmark scores get."
Predictive-Coding Efficiency Study Early stage
Testing whether a brain-inspired network design needs less labeled data than a standard baseline to reach the same accuracy — a direct extension of the brain-inspired thesis in my essay.
"Does a small predictive-coding network need less labeled data than a standard baseline network to reach the same accuracy on a given task?"
Research interests
Areas I want to understand more deeply and contribute to over time.
- 01NeuroAI
How the brain learns, and what neuroscience can teach us about artificial intelligence. - 02AI Ethics
What it takes to make a system this powerful safe — precautions built in from the first design choice, not bolted on after. - 03AGI
What it will take to close the gap between narrow models and broadly capable intelligence.
Current question
Can a deeper understanding of how the brain learns help us build more capable, more efficient AI? This is the question I keep returning to.
About
I'm Ethan Ganz, a student building a public record of ideas and work on the path to artificial general intelligence, working toward original research in NeuroAI. I lean toward believing that studying the brain's architecture and efficiency — not just scaling existing systems further — is the more promising path, though I take the scaling, neuro-symbolic, and neuromorphic camps seriously too.