The Fundamentals of Machine Learning

3min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

At the end of the day, these models, they're prediction models. They model the statistics of the world and the data we feed them to train them. The simplest model that people use a few hundred years ago is okay, you wanted to model your heights given your age or your weight given your age. It's not going to be perfect, but here's a bunch of data points. You draw the best line that fits it and you observe some uncertainty around it. Given X, you predict what likely Y is with some uncertainty. That's basically the foundation of machine learning.

David Ha is the Head of Strategy at Stability AI, and one of the top minds working in AI today. He previously worked as a research scientist in the Brain team at Google. David is particularly interested in evolution and complex systems, and his research explores how intelligence may emerge from limited resource constraints. He joins the show to discuss the advantages of open-source models, modelling AI as an emergent system, why large language models are bad at maths and MUCH more! Important Links:

Show Notes:

Why David joined Stability AI
The advantages of open-source models
We cannot predict the inventions of tomorrow
Making memes with generative AI
The centaur approach to AI
An introduction to large language models
The relationship between complex systems and resource constraints
Large language models are bad at maths
Modelling AI as an emergent system
Understanding different perspectives
MUCH more!

Books Mentioned:

The Beginning of Infinity: Explanations That Transform the World; by David Deutsch
Why Greatness Cannot Be Planned: The Myth of the Objective; by Kenneth Stanley and Joel Lehman