Towards Data Science – Details, episodes & analysis

Podcast details

Technical and general information from the podcast's RSS feed.

Towards Data Science

The TDS team

Technology

Frequency: 1 episode/9d. Total Eps: 130

Note: The TDS podcast's current run has ended. Researchers and business leaders at the forefront of the field unpack the most pressing questions around data science and AI.

Site

RSS

Apple

Recent rankings

Latest chart positions across Apple Podcasts and Spotify rankings.

Apple Podcasts

🇫🇷 France - technology
21/07/2026
#100

Spotify

No recent rankings available

Shared links between episodes and podcasts

Links found in episode descriptions and other podcasts that share them.

See all

http://sharpestminds.com/
3 shares
https://towardsdatascience.com/data-science-is-boring-1d43473e353e
2 shares
https://carrcenter.hks.harvard.edu/
1 share

RSS feed quality and score

Technical evaluation of the podcast's RSS feed quality and structure.

See all

RSS feed quality

To improve

Score global : 58%

Publication history

Monthly episode publishing history over the past years.

Year

Latest published episodes

Recent episodes with titles, durations, and descriptions.

See all

130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

Season 3 · Episode 130

Wednesday, October 12, 2022 • Duration 58:22

Progress in AI has been accelerating dramatically in recent years, and even months. It seems like every other day, there’s a new, previously-believed-to-be-impossible feat of AI that’s achieved by a world-leading lab. And increasingly, these breakthroughs have been driven by the same, simple idea: AI scaling.

For those who haven’t been following the AI scaling sage, scaling means training AI systems with larger models, using increasingly absurd quantities of data and processing power. So far, empirical studies by the world’s top AI labs seem to suggest that scaling is an open-ended process that can lead to more and more capable and intelligent systems, with no clear limit.

And that’s led many people to speculate that scaling might usher in a new era of broadly human-level or even superhuman AI — the holy grail AI researchers have been after for decades.

And while that might sound cool, an AI that can solve general reasoning problems as well as or better than a human might actually be an intrinsically dangerous thing to build.

At least, that’s the conclusion that many AI safety researchers have come to following the publication of a new line of research that explores how modern AI systems tend to solve problems, and whether we should expect more advanced versions of them to perform dangerous behaviours like seeking power.

This line of research in AI safety is called “power-seeking”, and although it’s currently not well understood outside the frontier of AI safety and AI alignment research, it’s starting to draw a lot of attention. The first major theoretical study of power seeking was led by Alex Turner, who’s appeared on the podcast before, and was published in NeurIPS (the world’s top AI conference), for example.

And today, we’ll be hearing from Edouard Harris, an AI alignment researcher and one of my co-founders in the AI safety company (Gladstone AI). Ed’s just completed a significant piece of AI safety research that extends Alex Turner’s original power-seeking work, and that shows what seems to be the first experimental evidence suggesting that we should expect highly advanced AI systems to seek power by default.

What does power seeking really mean though? And does all this imply for the safety of future, general-purpose reasoning systems? That’s what this episode will be all about.

***

Intro music:

- Artist: Ron Gelinas

- Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters:

- 0:00 Intro

- 4:00 Alex Turner's research

- 7:45 What technology wants

- 11:30 Universal goals

- 17:30 Connecting observations

- 24:00 Micro power seeking behaviour

- 28:15 Ed's research

- 38:00 The human as the environment

- 42:30 What leads to power seeking

- 48:00 Competition as a default outcome

- 52:45 General concern

- 57:30 Wrap-up

Read moreCollapse

129. Amber Teng - Building apps with a new generation of language models

Season 3 · Episode 129

Wednesday, October 5, 2022 • Duration 51:21

It’s no secret that a new generation of powerful and highly scaled language models is taking the world by storm. Companies like OpenAI, AI21Labs, and Cohere have built models so versatile that they’re powering hundreds of new applications, and unlocking entire new markets for AI-generated text.

In light of that, I thought it would be worth exploring the applied side of language modelling — to dive deep into one specific language model-powered tool, to understand what it means to build apps on top of scaled AI systems. How easily can these models be used in the wild? What bottlenecks and challenges do people run into when they try to build apps powered by large language models? That’s what I wanted to find out.

My guest today is Amber Teng, and she’s a data scientist who recently published a blog that got quite a bit of attention, about a resume cover letter generator that she created using GPT-3, OpenAI’s powerful and now-famous language model. I thought her project would be make for a great episode, because it exposes so many of the challenges and opportunities that come with the new era of powerful language models that we’ve just entered.

So today we’ll be exploring exactly that: looking at the applied side of language modelling and prompt engineering, understanding how large language models have made new apps not only possible but also much easier to build, and the likely future of AI-powered products.

***

Intro music:

- Artist: Ron Gelinas

- Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters:

- 0:00 Intro

- 2:30 Amber’s background

- 5:30 Using GPT-3

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

Season 3 · Episode 120

Wednesday, April 20, 2022 • Duration 40:47

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

30. Interviewing the Medium data science team

Season 1 · Episode 30

Wednesday, April 22, 2020 • Duration 43:09

Revenues drop unexpectedly, and management pulls aside the data science team into a room. The team is given its marching orders: “your job,” they’re told, “is to find out what the hell is going on with our purchase orders.”

That’s a very open-ended question, of course, because revenues and signups could drop for any number of reasons. Prices may have increased. A new user interface might be confusing potential customers. Seasonality effects might have to be considered. The source of the problem could be, well, anything.

That’s often the position data scientists find themselves in: rather than having a clear A/B test to analyze, they frequently are in the business of combing through user funnels to ensure that each stage is working as expected.

It takes a very detail-oriented and business-savvy team to pull off an investigation with that broad a scope, but that’s exactly what Medium has: a group of product-minded data scientists dedicated to investigating anomalies and identifying growth opportunities hidden in heaps of user data. They were kind enough to chat with me and talk about how Medium does data science for this episode of the Towards Data Science podcast.

Read moreCollapse

29. Cameron Davidson-Pillon - Data science at Shopify

Season 1 · Episode 29

Wednesday, April 15, 2020 • Duration 40:13

If you want to know where data science is heading, it helps to know where it’s been. Very few people have that kind of historical perspective, and even fewer combine it with an understanding of cutting-edge tooling that hints at the direction the field might be taking in the future.

Luckily for us, one of them is Cameron Davidson-Pillon, the former Director of Data Science at Shopify. Cameron has been knee-deep in data science and estimation theory since 2012, when the space was still coming into its own. He’s got a great high-level perspective not only on technical issues but also on hiring and team-building, and he was kind enough to join us for today’s episode of the Towards Data Science podcast.

Read moreCollapse

28. Emily Robinson - Building a Career in Data Science

Season 1 · Episode 28

Tuesday, April 7, 2020 • Duration 43:49

It’s easy to think of data science as a purely technical discipline: after all, it exists at the intersection of a number of genuinely technical topics, from statistics to programming to machine learning.

But there’s much more to data science and analytics than solving technical problems — and there’s much more to the data science job search than coding challenges and Kaggle competitions as well. Landing a job or a promotion as a data scientist calls on a ton of career skills and soft skills that many people don’t spend nearly enough time honing.

On this episode of the podcast, I spoke with Emily Robinson, an experienced data scientist and blogger with a pedigree that includes Etsy and DataCamp, about career-building strategies. Emily’s got a lot to say about the topic, particularly since she just finished authoring a book entitled “Build a Career in Data Science” with her co-author Jacqueline Nolis. The book explores a lot of great, practical strategies for moving data science careers forward, many of which we discussed during our conversation.

Read moreCollapse

27. Alayna Kennedy - AI safety, AI ethics and the AGI debate

Season 1 · Episode 27

Monday, March 30, 2020 • Duration 45:54

Most of us believe that decisions that affect us should be made rationally: they should be reached by following a reasoning process that combines data we trust with a logic that we find acceptable.

As long as human beings are making these decisions, we can probe at that reasoning to find out whether we agree with it. We can ask why we were denied that bank loan, or why a judge handed down a particular sentence, for example.

Today however, machine learning is automating away more and more of these important decisions, and as a result, our lives are increasingly governed by decision-making processes that we can’t interrogate or understand. Worse, machine learning algorithms can exhibit bias or make serious mistakes, so a black-box-ocracy risks becoming more like a dystopia than even the most imperfect human-designed systems we have today.

That’s why AI ethics and AI safety have drawn so much attention in recent years, and why I was so excited to talk to Alayna Kennedy, a data scientist at IBM whose work is focused on the ethics of machine learning, and the risks associated with ML-based decision-making. Alayna has consulted with key players in the US government’s AI effort, and has expertise applying machine learning in industry as well, through previous work on neural network modelling and fraud detection.

Read moreCollapse

26. Jeremy Howard - Coronavirus: the data behind the disease

Season 1 · Episode 26

Friday, March 20, 2020 • Duration 43:25

In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pandemic, with hundreds of thousands — perhaps even millions — of infections worldwide, with the potential to unleash a wave of economic damage not seen since the 1918 Spanish influenza or the Great Depression.

The exponential growth that led us from a few isolated infections to where we are today is profoundly counterintuitive. And it poses many challenges for the epidemiologists who need to pin down the transmission characteristics of the coronavirus, and for the policy makers who must act on their recommendations, and convince a generally complacent public to implement life-saving social distancing measures.

With the coronas in full bloom, I thought now would be a great time to reach out to Jeremy Howard, co-founder of the incredibly popular Fast.ai machine learning education site. Along with his co-founder Rachel Thomas, Jeremy authored a now-viral report outlining a data-driven case for concern regarding the coronavirus.

Read moreCollapse

25. Chris Parmer - Plotly founder on what data science is, and where it's going

Season 1 · Episode 25

Wednesday, March 18, 2020 • Duration 41:34

It’s easy to think of data scientists as “people who explore and model data”. Bur in reality, the job description is much more flexible: your job as a data scientist is to solve problems that people actually have with data.

You’ll notice that I wrote “problems that people actually have” rather than “build models”. It’s relatively rare that the problems people have actually need to be solved using a predictive model. Instead, a good visualization or interactive chart is almost always the first step of the problem-solving process, and can often be the last as well.

And you know who understands visualization strategy really, really well? Plotly, that’s who. Plotly is a company that builds a ton of great open-source visualization, exploration and data infrastructure tools (and some proprietary commercial ones, too). Today, their tooling is being used by over 50 million people worldwide, and they’ve developed a number of tools and libraries that are now industry standard. So you can imagine how excited I was to speak with Plotly co-founder and Chief Product Officer Chris Parmer.

Chris had some great insights to share about data science and analytics tooling, including the future direction he sees the space moving in. But as his job title suggests, he’s also focused on another key characteristic that all great data scientists develop early on: product instinct (AKA: “knowing what to build next”).

Read moreCollapse

24. Xander Steenbrugge - Machine learning as a creative tool, and the quest for artificial general intelligence

Season 1 · Episode 24

Tuesday, March 10, 2020 • Duration 39:42

Most machine learning models are used in roughly the same way: they take a complex, high-dimensional input (like a data table, an image, or a body of text) and return something very simple (a classification or regression output, or a set of cluster centroids). That makes machine learning ideal for automating repetitive tasks that might historically have been carried out only by humans.

But this strategy may not be the most exciting application of machine learning in the future: increasingly, researchers and even industry players are experimenting with generative models, that produce much more complex outputs like images and text from scratch. These models are effectively carrying out a creative process — and mastering that process hugely widens the scope of what can be accomplished by machines.

My guest today is Xander Steenbrugge, and his focus is on the creative side of machine learning. In addition to consulting with large companies to help them put state-of-the-art machine learning models into production, he’s focused a lot of his work on more philosophical and interdisciplinary questions — including the interaction between art and machine learning. For that reason, our conversation went in an unusually philosophical direction, covering everything from the structure of language, to what makes natural language comprehension more challenging than computer vision, to the emergence of artificial general intelligence, and how all these things connect to the current state of the art in machine learning.

Read moreCollapse