Chasing Real AGI: Inside ARC Prize 2025 with Chollet & Knoop
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What is the main topic discussed in this episode?
Welcome back to the Mad Podcast. We have a particularly fascinating episode today with Francois Chalet and Mike Noop. Now, Francois is one of the legends of the space and rose to industry fame as a senior staff engineer at Google, where, amongst other things, he created the ubiquitous Keras Deep Learning Library. Mike was previously the co-founder and then head of AI at Zapier, and together they partnered both on the Arc Prize. And on creating a new A Gi Research Lab called Endia. In the first part of the conversation, we went deep with Francois into the early limitations and evolution of LLMs.
Current models are really good at uh absorbing mountains of knowledge and and specialized skills, but they're not very good at making sense of novelty on the fly.
The definition of intelligence and edge.
You have a GI when it's no longer possible to easily come up with tasks that you and I can do naturally but no AI system. and promising approaches to AGI, including programme synthesis. We really believe that the frontier is test time adaptation and that the best approach to solve test time adaptation is a deep learning guided or intuition guided program such.
In the second part of the conversation, we discuss with Mike and Francois their big recent news, the announcement of ARC AGI2 and ARC Price 2025, which is often known as an IQ test for machines, which historically has tripped up LLMs from big research labs.
We got a bunch of like test time adaptation methods that came out of ArcPrice twenty twenty four. Then we got this big set of evidence from O three going into ArcPrice twenty twenty five. And we're excited to see people bring and apply some of those ideas.
We close the conversation with a few words on NDA and their incredibly ambitious vision. We have a vision about what
we want to build, and it's a vision that's very different from what the other Frontier Labs are actually looking into.
I think in our long term, we'd love to see India become basically the most innovative company in the history of the world, producing the most number of new technologies, the most number of new knowledge using the technology we directly built.
Please enjoy this wonderful conversation with Francois and Mike. Francois, the big news is the announcement of uh Arc Prize two thousand twenty five and the concurrent launch of uh ARC AGI two. But maybe to start from the top, what is uh ARC AGI as a benchmark and why did you create it?
ARC is an AI benchmark that tries to measure AI fluid intelligence as opposed to skill on specific tasks. And that's a very different approach from uh basically any any other AI benchmark. Most benchmarks they're they're looking at uh the ability to answer specific questions, perform specific tasks, that's gonna be very reliant on knowledge uh or or skills uh that can be memorized in advance uh of trying to pass the benchmark. And ARC is not like this at all. ARC is a set of tasks that you cannot prepare for. Uh so it's not trying to measure what you know, it's trying to measure how well you can adapt to something you've never seen before. on the fly. Uh as as it turns out, this is actually something that's extremely challenging for AI.
Uh current models are really good at uh absorbing uh uh mountains of of knowledge and and specialized skills, but they're not very good at making sense uh of novelty on the fly, at adapting. This is something that uh AI is is not good at and that's what makes art really interesting. Um and one uh one thing to note is you know, last year uh there was this big uh shift uh in in the AI research world where uh the A research community started to move away from uh this uh idea of uh just scaling up pre training. And then using uh the pre trained models uh in a in a static fashion at inference time, just you know, running them, they're they're they're not changing, they're not learning anything new, and they're not adapting to novelty.
Um that's that's models like you know, GPT three, GPT four, GPT four point five. uh uh Gemini that that that kind of thing.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–11:17
2
What is the ARC Prize 2025 and why was it created?
11:17–13:07
3
How does ARC differ from traditional AI benchmarks?
13:07–17:30
4
Why do current LLMs struggle with fluid intelligence and test‑time adaptation?
17:30–21:52
5
What is test‑time adaptation and how did models like O3 achieve a breakthrough?
21:52–39:27
6
What are the structure, tracks, and open‑source requirements of the ARC Prize?
39:27–39:41
7
How does ARC‑AGI 2 improve on ARC 1 and what capabilities does it test?
39:41–46:04
8
How does program synthesis work and why is it crucial for AGI?
46:04–1:00:23
Speakers
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