Daily Digest: Essentials for AGI

PLUS: Microsoft ditches GPTs

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Hello folks, AGI needs two essential elements: core research and funds to bring to everyone. That’s what we have today;

PICKS
  1. Mistral AI has raised €600M in Series B funding. General Catalyst led this funding (their thesis)—valuing the company at $6B. Quick reminder: Mistral announced their seed funding (whopping $113M) just a year ago and they’re one of the top LLM makers in the market now. We’ve done a 🍿case study on Mistral’s journey before.

  2. “LLMs are not the way to AGI.” There’s a large camp of AI researchers who believe this to be true. But what to do about it? François Chollet, the creator of Keras and Mike Knoop, co-founder of Zapier are launching a $1M challenge to find alternative ways to get to “general intelligence”. Hear ’em talk on Dwarkesh’s Podcast and the No Priors Pod or… you can read 🍿Our Summary (also below).

  3. Microsoft is retiring the GPT Builder for its consumer customers. Shocker!! GPTs are useful, but Microsoft and OpenAI's commitment to them as a product has been shaky. Microsoft’s GPT Builder will still be available for enterprise customers and this current news doesn’t concern ChatGPT’s GPT store in any way.

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QUICK BITES

“LLMs are not the way to AGI.” There’s a large camp of AI researchers who believe this to be true. But what to do about it? François Chollet, the creator of Keras and Mike Knopp, co-founder of Zapier are launching a challenge to find alternative ways to get to “general intelligence”.

What is going on here?

ARC Prize is offering $1M to improve the reasoning of AI models (not just LLMs).

What does this mean?

ARC stands for Abstraction and Reasoning Corpus. It’s a collection of tasks where you have to observe a few input-output pairs and predict the output for a similar input. We humans are pretty good at it.

Just try this example. You can easily guess the output for the Test Input.

SPOILER: Just get rid of the smaller piece. Play it here and guess the colour.

But what if AI systems try it? Well, the current state-of-the-art performance is 34% accuracy (humans are at 85%). So, the ARC-AGI challenge thinks this is a good challenge to test general reasoning.

It defines AGI as a system that can efficiently acquire new skills outside of its training data. And to top it all, there’s a million-dollar prize pool for beating humans at this challenge.

François and Mike talk about the challenge more on Dwarkesh Patel’s podcast and the No Priors Pod. 

Why should I care?

The explanation by the organizers makes sense. LLMs don’t reason, instead, they copy-paste reasoning patterns in the vast amount of data they consume while training.

The organizers also argue that the progress in AI has turned closed source and the narrative of “scale is all you need” is hurting the direction of global research. New ideas are needed and I’m up for it (except that I spent way too much time playing the puzzles last night).

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