122. Sadie St. Lawrence - Trends in data science

122. Sadie St. Lawrence - Trends in data science

Author: The TDS team May 4, 2022 Duration: 43:02

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:

  • 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

While the active production of Towards Data Science has concluded, its archive remains a vital resource. Created by The TDS team, this collection captures a specific moment in the rapid evolution of data science and artificial intelligence. Each conversation pulls you directly into the room with leading researchers and practitioners who were shaping the tools and theories of their time. The discussions are not abstract lectures; they are grounded explorations of real-world problems, ethical dilemmas, and technical challenges that defined the field's trajectory. You'll hear experts dissect the implications of their work, from algorithmic fairness to the practicalities of deploying models at scale. This podcast served as a forum for nuanced debate, where complex ideas were unpacked with clarity and depth. Listening now offers a unique historical perspective, a chance to understand the foundational conversations that continue to influence where technology is headed next. The archive of Towards Data Science stands as a substantive record of insight, preserving the voices and questions from the forefront of a digital revolution.
Author: Language: en-us Episodes: 50

Towards Data Science
Podcast Episodes
119. Jaime Sevilla - Projecting AI progress from compute trends [not-audio_url] [/not-audio_url]

Duration: 48:34
There’s an idea in machine learning that most of the progress we see in AI doesn’t come from new algorithms of model architectures. instead, some argue, progress almost entirely comes from scaling up compute power, datas…
118. Angela Fan - Generating Wikipedia articles with AI [not-audio_url] [/not-audio_url]

Duration: 51:44
Generating well-referenced and accurate Wikipedia articles has always been an important problem: Wikipedia has essentially become the Internet's encyclopedia of record, and hundreds of millions of people use it do unders…
117. Beena Ammanath - Defining trustworthy AI [not-audio_url] [/not-audio_url]

Duration: 46:46
Trustworthy AI is one of today’s most popular buzzwords. But although everyone seems to agree that we want AI to be trustworthy, definitions of trustworthiness are often fuzzy or inadequate. Maybe that shouldn’t be surpr…
116. Katya Sedova - AI-powered disinformation, present and future [not-audio_url] [/not-audio_url]

Duration: 54:24
Until recently, very few people were paying attention to the potential malicious applications of AI. And that made some sense: in an era where AIs were narrow and had to be purpose-built for every application, you’d need…
115. Irina Rish - Out-of-distribution generalization [not-audio_url] [/not-audio_url]

Duration: 50:12
Imagine, for example, an AI that’s trained to identify cows in images. Ideally, we’d want it to learn to detect cows based on their shape and colour. But what if the cow pictures we put in the training dataset always sho…
114. Sam Bowman - Are we *under-hyping* AI? [not-audio_url] [/not-audio_url]

Duration: 47:48
Google the phrase “AI over-hyped”, and you’ll find literally dozens of articles from the likes of Forbes, Wired, and Scientific American, all arguing that “AI isn’t really as impressive at it seems from the outside,” and…
113. Yaron Singer - Catching edge cases in AI [not-audio_url] [/not-audio_url]

Duration: 35:20
It’s no secret that AI systems are being used in more and more high-stakes applications. As AI eats the world, it’s becoming critical to ensure that AI systems behave robustly — that they don’t get thrown off by unusual…
110. Alex Turner - Will powerful AIs tend to seek power? [not-audio_url] [/not-audio_url]

Duration: 46:57
Today’s episode is somewhat special, because we’re going to be talking about what might be the first solid quantitative study of the power-seeking tendencies that we can expect advanced AI systems to have in the future.…