Arc V1
Extracting target signal
Arc V1
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“merging deep learning and program synthesis together towards ARK1 and AR2. And hopefully sometime soon we'll start looking at V3. This question that Greg asked, I think is a really common one that we...”
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Arcmira tracks where ARC-V1 is discussed across indexed YouTube videos, transcripts, channels, and related entities.
merging deep learning and program synthesis together towards ARK1 and AR2. And hopefully sometime soon we'll start looking at V3. This question that Greg asked, I think is a really common one that we got with V1 and V2 a lot. I'm curious to get your take here with V3, which is that, okay, you guys have now three versions of the benchmark. They all have AGI in the name. if we can make progress and we can beat V1, V2 and V3 here, do we have AGI or and if not if that's not a binary what does it mean exactly? Can you help us kind of like map out what what does it mean to make progress against ARK 1 2 and 3? Right. So first of all, you know, as Greg stated, uh, with V3, just like with V1 and V2, we're not making the claim that this is an asset test for whether we have a GI or not. Like solving V1, solving V2, solving V3 does not necessarily mean it's not sufficient condition to say that we have a GI. That's not the purpose of the benchmark. Now, if you look at what it would take uh to solve V3, like especially compared to V1 and V2, we adding a few uh really important abilities. uh we're adding the ability to uh discover goals like acquire your own goals from your own experience do temporal planning and of course you know interactive learning uh with v1 v2 you are just doing passive uh model feeding you are looking at the data trying to come up with model to explain it here you have to collect your own data by interacting with environment so what it would mean to create a system that could do these things at human level uh information efficiency human level action efficiency where it means you're really good at, you know, agentic interactive learning in novel environments. You're really efficient at it. To me, that's basically a microagg. These are the properties you would want to see in an AGI system, but on a very small scale. So, why very small scale? Because these games are really simple. They're really easy. Like any one of you in this room could just go and play them and you would do really well, rightCombined with ARC-V2 and ARC-V3 in summary. ARC-V1 and ARC-V2 mentioned as older versions without interactive learning. Mentions of 'V1' and 'ARC V1' are consolidated.
Arcmira tracks 1 indexed media appearances or mentions for ARC-V1, tied to source videos, channels, and transcript-derived context.
Arcmira uses indexed YouTube videos and transcripts. Representative source evidence on this page includes "Francois Chollet + Mike Knoop | ARC Prize @ MIT" with transcript-derived context and links when available.
ARC-V1 is connected to visual benchmark, universal intelligence, uniform distribution in Arcmira's media graph.