121. Alexei Baevski - data2vec and the future of multimodal learning

Towards Data Science

04/27/22

•

49m

About

Comments

Featured In

If the name data2vec sounds familiar, that’s probably because it made quite a splash on social and even traditional media when it came out, about two months ago. It’s an important entry in what is now a growing list of strategies that are focused on creating individual machine learning architectures that handle many different data types, like text, image and speech.

Most self-supervised learning techniques involve getting a model to take some input data (say, an image or a piece of text) and mask out certain components of those inputs (say by blacking out pixels or words) in order to get the models to predict those masked out components.

That “filling in the blanks” task is hard enough to force AIs to learn facts about their data that generalize well, but it also means training models to perform tasks that are very different depending on the input data type. Filling in blacked out pixels is quite different from filling in blanks in a sentence, for example.

So what if there was a way to come up with one task that we could use to train machine learning models on any kind of data? That’s where data2vec comes in.

For this episode of the podcast, I’m joined by Alexei Baevski, a researcher at Meta AI one of the creators of data2vec. In addition to data2vec, Alexei has been involved in quite a bit of pioneering work on text and speech models, including wav2vec, Facebook’s widely publicized unsupervised speech model. Alexei joined me to talk about how data2vec works and what’s next for that research direction, as well as the future of multi-modal learning.

***

Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

***

Chapters:

2:00 Alexei’s background
10:00 Software engineering knowledge
14:10 Role of data2vec in progression
30:00 Delta between student and teacher
38:30 Losing interpreting ability
41:45 Influence of greater abilities
49:15 Wrap-up

Previous Episode

120. Liam Fedus and Barrett Zoph - AI scaling with mixture of expert models

April 20, 2022

•

40m

AI scaling has really taken off. Ever since GPT-3 came out, it’s become clear that one of the things we’ll need to do to move beyond narrow AI and towards more generally intelligent systems is going to be to massively scale up the size of our models, the amount of processing power they consume and the amount of data they’re trained on, all at the same time.

That’s led to a huge wave of highly scaled models that are incredibly expensive to train, largely because of their enormous compute budgets. But what if there was a more flexible way to scale AI — one that allowed us to decouple model size from compute budgets, so that we can track a more compute-efficient course to scale?

That’s the promise of so-called mixture of experts models, or MoEs. Unlike more traditional transformers, MoEs don’t update all of their parameters on every training pass. Instead, they route inputs intelligently to sub-models called experts, which can each specialize in different tasks. On a given training pass, only those experts have their parameters updated. The result is a sparse model, a more compute-efficient training process, and a new potential path to scale.

Google has been pushing the frontier of research on MoEs, and my two guests today in particular have been involved in pioneering work on that strategy (among many others!). Liam Fedus and Barrett Zoph are research scientists at Google Brain, and they joined me to talk about AI scaling, sparsity and the present and future of MoE models on this episode of the TDS podcast.

***

Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

***

Chapters:

2:15 Guests’ backgrounds
8:00 Understanding specialization
13:45 Speculations for the future
21:45 Switch transformer versus dense net
27:30 More interpretable models
33:30 Assumptions and biology
39:15 Wrap-up

Next Episode

122. Sadie St. Lawrence - Trends in data science

May 4, 2022

•

43m

As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks and other challenges that the future of AI might bring. But I thought that for today’s episode, it would be fun to change things up a bit and talk about the applied side of data science, and how the field has evolved over the last year or two.

And I found the perfect guest to do that with: her name is Sadie St. Lawrence, and among other things, she’s the founder of Women in Data — a community that helps women enter the field of data and advance throughout their careers — and she’s also the host of the Data Bytes podcast, a seasoned data scientist and a community builder extraordinaire. Sadie joined me to talk about her founder’s journey, what data science looks like today, and even the possibilities that blockchains introduce for data science on this episode of the towards data science podcast.

***

Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

***

Chapters: