Solve this puzzle for AGI — The million dollar AGI challenge
ARC-AGI the only forma benchmark for AGI
Here’s what makes ARC-AGI unique and important
Over the last week, ARC-AGI has gained attention as a challenging benchmark for Large Language Models (LLMs).
In this post, let’s break down
- what it is,
- what’s the hype about,
- how to start solving and
- video that also cover a promising solution approach.
What is ARC?
Abstraction and Reasoning Corpus to measure the LLM’s ability to learn new skills and solve open-ended tasks without explicit instructions.
Why the Hype?
The hype is primarily because of the “ARC Prize” which is a public competition with a $1,000,000+ reward to beat the ARC-AGI benchmark, hosted by Francois Chollet and Mike Knoop.
What’s Different About This Benchmark?
Despite being trained on vast data, LLMsstruggle with simple, unfamiliar problems. Francois Chollet suggests AGI progress has stalled due to outdated evaluation methods.
Problems with Current Benchmarks
Most AI benchmarks measure skill, not intelligence.
Examples include:
- HellaSwag: Tests commonsense natural language inference.
- HumanEval: Assesses code-writing skills.
- MMLU: Tests understanding across diverse subjects.
General intelligence is the ability to efficiently acquire new skills
ARC-AGI is unique as it tests both skill and skill acquisition, making it a true benchmark for AGI.
To learn more on how you can start solving and which approach has returned SOTA 50%, you should check out my video on the approach that gets 50% and is the best so far.
Watch here:
