Today, I wanted to talk about a little tool that I came across about a month ago that has

really changed the way that I do some of my work. And that was a tool released by Andre

Carpathie. Now, what did Carpathie release? He released this thing called Auto Research. It's a

piece of open source software. It's only 600 lines of Python code. It's already had 57,000 stars on GitHub.

And what auto research does is it enables AI systems and you running an AI system to automatically

conduct research. I said, look, this could probably work outside of the machine learning domain. Could we

find some other way of defining the objective, right? Why does it have to be the loss function in an

ML product? Couldn't we define a business objective? So my new setup is this new version of auto research.

And what that allows me to do is run this iterative loop, this scientific loop,

hypothesis-driven testing automatically on all sorts of commercial, intellectual,

academic, theoretical, theoretical problems that are relevant for us.

This is a reduction in the cost of the scientific method.

You know, I'm applying this scientific method now to questions that benefit from it

where it would have been too expensive previously.

And science is the best method we have found for producing knowledge.

And we have given that method now to

LLMs at very, very low cost, and I get to choose what they investigate.

Well, I mean, for now.

I want you to zoom back and understand what this is about.

This is about addressing the best strategy that we as humanity have for producing knowledge.

That strategy is called science.

And science is the act of coming up with some kind of hypothesis

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