We're not evaluating "models" (whatever that means), we're evaluating arbitrary AI systems. They can include whatever harnesses or tools they want. But they cannot have been handcrafted or trained for ARC-AGI-3 specifically, because then we wouldn't testing AGI, we would be
AGI
-
AI Testing Performance: Achieving Near-Perfect Scores
By
–
Having played all of these games, I feel strongly that I would have scored >95% in a real testing session. Even the #1 human tester replay tends to be very, very far from optimal, and we're using #2 as baseline, so it's easy to score 100% on an given environment. You don't need
-
AGI Must Build Its Own Harness for True Generality
By
–
AGI will make its own harness (or whatever else it needs to solve a new problem). As long as you need a human engineer to handcraft a task-specific harness/system for each new problem, AI isn't general. It's an automation tool to be wielded by software engineers. Harness-related
-
Governing the Agentic AI Ecosystem: Future of Autonomous Intelligence
By
–
The Era Of The 'Agentic' Ecosystem: How To Govern A World Run By #AI
by @gregoriopatino @Forbes Learn more: https://
bit.ly/4d4k68G #GenAI #ArtificialIntelligence #MachineLearning #ML -
ARC-AGI-3: a different test with different rules and measurements
By
–
It helps to think of ARC-AGI-3 as a different test entirely than the previous ARC-AGIs. It measures different things (though, as in the previous tests, precisely what it measures isn’t clear) and has different rules. That doesn’t mean it isn’t good, but it is its own thing.
-
New AGI Eval Focuses Research Efforts on Critical Gaps
By
–
If you care about the rate of AGI progress, you should be excited about a new eval that focuses research efforts by pointing out important gaps & providing a way to measure progress towards fixing them If instead you only care about having your preconceptions confirmed, too bad
-
ARC-AGI-3 Environments Mirror Scientific Method for Breakthrough AI
By
–
Many people expect that current AI is ready to cure cancer and do breakthrough new science. ARC-AGI-3 envs are like a microcosm of the scientific method: you must observe a tiny world, form a theory of how it works, test it, iterate until correct. Over the course of a few
-
AI Systems Fall Short of Human Job Performance Standards
By
–
Virtually every human job on earth has a higher bar. These are not very high expectations for AI systems that claim to be able to do everything humans can.
-
Defining ASI: Super Intelligence Beyond Human Performance
By
–
"2+ people can do it out of an unfiltered pool of 10 people that might well be a below-average sample" is not the sign of a insurmountable challenge. It's not certainly where I would set the bar for "super intelligence". ASI is when AI is better than *every single human* — for
-
Power Centralization in AI Attracts Those Seeking Control
By
–
Such a centralization of power inevitably attracts those that wish to yield it.