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No Priors Ep. 68 | With Zapier Co-Founder and Head of AI Mike Knoop

The first step in achieving AGI is nailing down a concise definition and Mike Knoop, the co-founder and Head of AI at Zapier, believes François Chollet got it right when he defined general intelligence as a system that can efficiently acquire new skills. This week on No Priors, Mike joins Elad to discuss Arc Prize which is a multi-million dollar non-profit public challenge that is looking for someone to beat the Abstraction and Reasoning Corpus (ARC) evaluation. In this episode, they also get into why Mike thinks LLMs will not get us to AGI, how Zapier is incorporating AI into their products and the power of agents, and why it’s dangerous to regulate AGI before discovering its full potential. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mikeknoop Show Notes: 0:00 Introduction 1:10 Redefining AGI 2:16 Introducing ARC Prize 3:08 Definition of AGI 5:14 LLMs and AGI 8:20 Promising techniques to developing AGI 11:0 Sentience and intelligence 13:51 Prize model vs investing 16:28 Zapier AI innovations 19:08 Economic value of agents 21:48 Open source to achieve AGI 24:20 Regulating AI and AGI

Elad GilhostMike Knoopguest
Jun 11, 202426mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Zapier’s Mike Knoop Challenges LLM Dominance With New AGI Prize

  1. Mike Knoop, Zapier co-founder and Head of AI, discusses why he believes progress toward AGI has stalled despite rapid advancements in large language models and economic applications.
  2. He contrasts the prevailing “economically useful work” definition of AGI with François Chollet’s definition centered on efficiently acquiring new skills, arguing that current LLMs are powerful memorization systems but not generally intelligent.
  3. Knoop outlines the ARC Prize, a $1M+ nonprofit challenge to beat Chollet’s ARC-AGI benchmark with open-source solutions, intentionally designed to attract outsiders and new paradigms like program synthesis and neural architecture search.
  4. He also explains how Zapier is productizing AI through tools and agents, advocates for open-source and open research, and cautions against prescriptive AI/AGI regulation without empirical evidence of capabilities.

IDEAS WORTH REMEMBERING

5 ideas

Redefine AGI around skill acquisition, not just economic usefulness.

Knoop favors François Chollet’s definition of general intelligence as the efficient acquisition of new skills, arguing that the popular “can do most economically useful work” framing overestimates our proximity to true AGI.

LLMs are powerful memorizers, not yet true general reasoners.

He characterizes current language models as high-dimensional memorization systems that can recombine existing patterns but struggle with open-ended problems whose solution patterns don’t appear in training data.

Beating ARC-AGI likely requires new paradigms, not just scale.

State-of-the-art performance on the ARC-AGI benchmark has only moved from ~20% to ~34% in four years, and has resisted LLM and scale-based approaches, suggesting we need fundamentally different techniques.

Program synthesis and relaxed neural architecture search are promising paths.

Knoop highlights approaches that search over program or architecture space (rather than just gradient descent on fixed models) as promising ways to discover more general reasoning systems.

Outsiders and small teams may drive key AGI breakthroughs.

The ARC competitions have attracted many one- and two-person teams outside major labs, and Knoop believes the winning approach may come from someone not entrenched in current LLM orthodoxy.

WORDS WORTH SAVING

5 quotes

My belief is that AGI’s progress has really stalled out over the last four or five years.

— Mike Knoop

General intelligence is a system that can effectively, efficiently acquire new skill.

— Mike Knoop (describing François Chollet’s definition)

Effectively, what large language models do today is they are high-dimensional memorization systems.

— Mike Knoop

Language models do not work to beat ARC. And people have tried.

— Mike Knoop

If you care about actually discovering AGI in our lifetime, then I think it’s sort of incumbent to try and promote things that increase the likelihood that we’re generating new ideas.

— Mike Knoop

Current definitions of AGI and why they may be flawedThe ARC-AGI benchmark and the new ARC Prize competitionLimitations of LLMs and the need for new architectures beyond scalingPromising research directions: program synthesis and neural architecture searchZapier’s AI strategy, products, and agent-like workflowsRole of open source and open research in AI/AGI progressRegulation, risk, and empirical approaches to AI and future AGI

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