123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

Towards Data Science

05/12/22

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54m

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Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning entry in some kind of startup buzzword bingo. But what I discovered talking to Jacob and Ala was that they actually have good reasons to combine those two ingredients together.

At a high level, doing AI on a blockchain allows you to decentralize AI research and reward labs for building better models, and not for publishing papers in flashy journals with often biased reviewers.

And that’s not all — as we’ll see, Ala and Jacob are taking on some of the thorniest current problems in AI with their decentralized approach to machine learning. Everything from the problem of designing robust benchmarks to rewarding good AI research and even the centralization of power in the hands of a few large companies building powerful AI systems — these problems are all in their sights as they build out Bittensor, their AI-on-the-blockchain-startup.

Ala and Jacob joined me to talk about all those things and more on this episode of the TDS podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

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

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Chapters:

2:40 Ala and Jacob’s backgrounds
4:00 The basics of AI on the blockchain
11:30 Generating human value
17:00 Who sees the benefit? 22:00 Use of GPUs
28:00 Models learning from each other
37:30 The size of the network
45:30 The alignment of these systems
51:00 Buying into a system
54:00 Wrap-up

Previous Episode

122. Sadie St. Lawrence - Trends in data science

May 4, 2022

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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.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters:

2:00 Founding Women in Data
6:30 Having gendered conversations
11:00 The cultural aspect
16:45 Opportunities in blockchain
22:00 The blockchain database
32:30 Data science education
37:00 GPT-3 and unstructured data
39:30 Data science as a career
42:50 Wrap-up

Next Episode

124. Alex Watson - Synthetic data could change everything

May 18, 2022

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51m

There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, photorealistic AI-generated picture of a human face. As the website’s name suggests, there’s no human being on the face of the earth who looks quite like the person staring back at you on the page.

Each of those generated pictures are a piece of data that captures so much of the essence of what it means to look like a human being. And yet they do so without telling you anything whatsoever about any particular person. In that sense, it’s fully anonymous human face data.

That’s impressive enough, and it speaks to how far generative image models have come over the last decade. But what if we could do the same for any kind of data?

What if I could generate an anonymized set of medical records or financial transaction data that captures all of the latent relationships buried in a private dataset, without the risk of leaking sensitive information about real people? That’s the mission of Alex Watson, the Chief Product Officer and co-founder of Gretel AI, where he works on unlocking value hidden in sensitive datasets in ways that preserve privacy.

What I realized talking to Alex was that synthetic data is about much more than ensuring privacy. As you’ll see over the course of the conversation, we may well be heading for a world where most data can benefit from augmentation via data synthesis — where synthetic data brings privacy value almost as a side-effect of enriching ground truth data with context imported from the wider world.

Alex joined me to talk about data privacy, data synthesis, and what could be the very strange future of the data lifecycle on this episode of the TDS podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters: