ARC Prize v2 Launch! (Francois Chollet and Mike Knoop)

ARC Prize v2 Launch! (Francois Chollet and Mike Knoop)

We are joined by Francois Chollet and Mike Knoop, to launch the new version of the ARC prize! In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adversarially selected so that frontier reasoning models can't solve them. The best LLMs today get negligible performance on this challenge.


https://arcprize.org/


SPONSOR MESSAGES:

***

Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.


Goto https://tufalabs.ai/

***


TRANSCRIPT:

https://www.dropbox.com/scl/fi/0v9o8xcpppdwnkntj59oi/ARCv2.pdf?rlkey=luqb6f141976vra6zdtptv5uj&dl=0


TOC:

1. ARC v2 Core Design & Objectives

[00:00:00] 1.1 ARC v2 Launch and Benchmark Architecture

[00:03:16] 1.2 Test-Time Optimization and AGI Assessment

[00:06:24] 1.3 Human-AI Capability Analysis

[00:13:02] 1.4 OpenAI o3 Initial Performance Results


2. ARC Technical Evolution

[00:17:20] 2.1 ARC-v1 to ARC-v2 Design Improvements

[00:21:12] 2.2 Human Validation Methodology

[00:26:05] 2.3 Task Design and Gaming Prevention

[00:29:11] 2.4 Intelligence Measurement Framework


3. O3 Performance & Future Challenges

[00:38:50] 3.1 O3 Comprehensive Performance Analysis

[00:43:40] 3.2 System Limitations and Failure Modes

[00:49:30] 3.3 Program Synthesis Applications

[00:53:00] 3.4 Future Development Roadmap


REFS:

[00:00:15] On the Measure of Intelligence, François Chollet

https://arxiv.org/abs/1911.01547

[00:06:45] ARC Prize Foundation, François Chollet, Mike Knoop

https://arcprize.org/

[00:12:50] OpenAI o3 model performance on ARC v1, ARC Prize Team

https://arcprize.org/blog/oai-o3-pub-breakthrough

[00:18:30] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei et al.

https://arxiv.org/abs/2201.11903

[00:21:45] ARC-v2 benchmark tasks, Mike Knoop

https://arcprize.org/blog/introducing-arc-agi-public-leaderboard

[00:26:05] ARC Prize 2024: Technical Report, Francois Chollet et al.

https://arxiv.org/html/2412.04604v2

[00:32:45] ARC Prize 2024 Technical Report, Francois Chollet, Mike Knoop, Gregory Kamradt

https://arxiv.org/abs/2412.04604

[00:48:55] The Bitter Lesson, Rich Sutton

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

[00:53:30] Decoding strategies in neural text generation, Sina Zarrieß

https://www.mdpi.com/2078-2489/12/9/355/pdf

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(271)

What Most People Get Wrong About Evolution | Akarsh Kumar

What Most People Get Wrong About Evolution | Akarsh Kumar

A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mut...

10 Loka 47min

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Tsung-Hsien (Shawn) Wen, CTO of PolyAI, tells Tim Scarfe why voice agents are harder than text agents. Voice adds time, and a good conversation depends on adapting to the person on the line, not just ...

1 Loka 1h 10min

Who Checks a Proof No Human Can Read? — Leo de Moura

Who Checks a Proof No Human Can Read? — Leo de Moura

Leonardo de Moura created Lean and co-created Z3. ---This episode is sponsored by Parallel.Parallel, where agents find answers: web search, extraction and deep research APIs built for AI agents.Start ...

30 Syys 1h 14min

When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhen...

26 Syys 43min

How Deep Learning Finally Cracked Messy Tables - Frank Hutter

How Deep Learning Finally Cracked Messy Tables - Frank Hutter

Frank Hutter, co-founder of Prior Labs, talks about TabPFN, a tabular foundation model that makes predictions in a single forward pass, and the research behind it.TabPFN is pre-trained on synthetic da...

23 Syys 1h 53min

Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

Alexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, afte...

21 Syys 2h 14min

How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

The car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, gene...

15 Syys 25min

Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

Pavan Kumar Reddy leads audio research at Mistral AI. He joins Tim Scarfe for a deep technical tour of Voxtral — and explains why the frontier of deployed voice is still a cascade of specialised model...

14 Syys 1h 42min