Jeremy Howard of fast.ai— The Simple but Profound Insight Behind Diffusion

Technological Spike

In this section, Jeremy Howard talks about the significant spike in technological capability and how it is changing what humans are able to do.

The Significance of Technological Capability

• Jeremy believes that we are in the middle of a significant spike in technological capability.

• This spike is substantially changing what humans are able to do.

• Those who are not at the forefront of this change will miss out on opportunities for startups, scientific progress, and societal improvement.

Introduction to Jeremy Howard

In this section, Lukas Biewald introduces Jeremy Howard and his work at fast.ai.

About Jeremy Howard

• Jeremy Howard is the founding researcher at fast.ai.

• Fast.ai is a research institute dedicated to making deep learning more accessible.

• They provide an incredible Python repository for deep learning projects and classes that many people love.

Learning Process

In this section, Lukas Biewald asks about Jeremy's learning process and what he has been learning lately.

Learning New Things

• The most memorable part of their previous interview was when Jeremy talked about setting aside time every day for undirected learning.

• Recently, he has been spending all his spare time on generative modeling around diffusion modeling space.

• He believes that if you're not doing something related to this technological shift, you're missing out on being at the forefront of something substantial.

Diffusion Models

In this section, Lukas Biewald asks about diffusion models specifically and how they relate to machine learning more broadly.

Boosted Models vs. Diffusion Models

• When Jeremy talks about the technological spike, he specifically means diffusion models.

• Diffusion models are based on the insight that it's difficult for a model to generate something creative and aesthetic from nothing.

• The idea is to train a model to do something slightly better than nothing and then run it multiple times to improve the output each time.

• Boosted models are similar in that they train a model to fix a previous model's errors, but diffusion models have not been used in generative models before.

Creating a Function

In this section, Jeremy Howard talks more about creating a function that can be applied to an input.

Broadly Speaking

• The goal is to create a function that can be applied to an input and generate something creative, aesthetic, and correct.

• At the moment, we're not close at all to doing this optimally.

• The fantastic results we're seeing now are based on what will be considered extremely primitive approaches in a year's time.

Creating a Cute Teddy Bear

In this section, the speaker discusses how to create a cute teddy bear using machine learning models.

Approaches to Creating a Cute Teddy Bear

• The model needs to recognize the output of the previous run as valid input for running again.

• Current approaches use "crap-ification" by adding Gaussian noise all over it.

• One step of inference is making it slightly more like a cute teddy bear and then sprinkling less noise back onto the pixels than before.

• Gradually moving away from theoretically convenient stuff to more flexible approaches with fiddly hyperparameters.

Using Machine Learning Models with Humans in the Loop

In this section, the speaker discusses how machine learning models can be used effectively with humans in the loop.

Conditioning and Prompts

• The models are trained with conditioning where they're conditioned on captions that are known to be wrong.

• The prompts themselves are a bit of an accident, and so is conditioning.

• There's whole books of prompts tried and outputs look like. Customizing requires interaction between human and machine.

Future Developments in Machine Learning Models

In this section, the speaker discusses future developments in machine learning models.

Human-Machine Interaction

• There's still much we don't understand about how to use these models effectively with humans in the loop.

• It will turn into a powerful tool for computer-assisted creation.

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