Sign up to save your podcasts
Or
Share Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT
If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it.
In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that focus on memorization or task completion, ARC is designed to measure generalization—and expose where today’s top models fall short.
They discuss:
🧠 Why we still lack a shared definition of intelligence
🧪 How ARC tasks force models to learn novel skills at test time
📉 Why GPT-4-class models still underperform on ARC
🔎 The limits of traditional benchmarks like MMLU and Big-Bench
⚙️ What the OpenAI O₃ results reveal—and what they don’t
💡 Why generalization and efficiency, not raw capability, are key to AGI
Greg also shares what he’s seeing in the wild: how startups and independent researchers are using ARC as a North Star, how benchmarks shape the frontier, and why the ARC team believes we’ll know we’ve reached AGI when humans can no longer write tasks that models can’t solve.
This conversation is about evaluation—not hype. If you care about where AI is really headed, this one’s worth your time.
LINKS
🎓 Want to go deeper?
Check out Hugo's course: Building LLM Applications for Data Scientists and Software Engineers.
Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.
This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.
Includes over $800 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.
Cohort starts July 8 — Use this link for a 10% discount
View all episodes
By Hugo Bowne-Anderson
5
1111 ratings
If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it.
In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that focus on memorization or task completion, ARC is designed to measure generalization—and expose where today’s top models fall short.
They discuss:
🧠 Why we still lack a shared definition of intelligence
🧪 How ARC tasks force models to learn novel skills at test time
📉 Why GPT-4-class models still underperform on ARC
🔎 The limits of traditional benchmarks like MMLU and Big-Bench
⚙️ What the OpenAI O₃ results reveal—and what they don’t
💡 Why generalization and efficiency, not raw capability, are key to AGI
Greg also shares what he’s seeing in the wild: how startups and independent researchers are using ARC as a North Star, how benchmarks shape the frontier, and why the ARC team believes we’ll know we’ve reached AGI when humans can no longer write tasks that models can’t solve.
This conversation is about evaluation—not hype. If you care about where AI is really headed, this one’s worth your time.
LINKS
🎓 Want to go deeper?
Check out Hugo's course: Building LLM Applications for Data Scientists and Software Engineers.
Learn how to design, test, and deploy production-grade LLM systems — with observability, feedback loops, and structure built in.
This isn’t about vibes or fragile agents. It’s about making LLMs reliable, testable, and actually useful.
Includes over $800 in compute credits and guest lectures from experts at DeepMind, Moderna, and more.
Cohort starts July 8 — Use this link for a 10% discount
More shows like Vanishing Gradients
View all
Data Skeptic
476 Listeners
a16z Podcast
1,083 Listeners
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
434 Listeners
Super Data Science: ML & AI Podcast with Jon Krohn
302 Listeners
NVIDIA AI Podcast
340 Listeners
DataFramed
268 Listeners
Practical AI
210 Listeners
Google DeepMind: The Podcast
194 Listeners
Machine Learning Street Talk (MLST)
89 Listeners
Dwarkesh Podcast
489 Listeners
No Priors: Artificial Intelligence | Technology | Startups
133 Listeners
Latent Space: The AI Engineer Podcast
97 Listeners
AI + a16z
33 Listeners
High Signal: Data Science | Career | AI
18 Listeners
OpenAI Podcast
52 Listeners