Vanishing Gradients

Episode 6: Bullshit Jobs in Data Science (and what to do about them)

April 5th, 2022  |  Season 1  |  1 hr 27 mins

Hugo speaks with Jacqueline Nolis, Chief Product Officer at Saturn Cloud (formerly Head of Data Science), about all types of failure modes in data science, ML, and AI, and they delve into bullshit jobs in data science (yes, that’s a technical term, as you’ll find out) –they discuss the elements that are bullshit, the elements that aren’t, and how to increase the ratio of the latter to the former.

Episode 5: Executive Data Science

March 23rd, 2022  |  Season 1  |  1 hr 48 mins

ai, data science, machine learning

Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior.

Episode 4: Machine Learning at T-Mobile

March 10th, 2022  |  Season 1  |  1 hr 44 mins

Hugo speaks with Heather Nolis, Principal Machine Learning engineer at T-mobile, about what data science, machine learning, and AI look like at T-mobile, along with Heather’s path from a software development intern there to principal ML engineer running a team of 15.

Episode 3: Language Tech For All

March 1st, 2022  |  Season 1  |  1 hr 32 mins

Hugo speaks with Rachael Tatman about the democratization of natural language processing, conversational AI, and chatbots, including, among other things, the data scientist’s responsibility to end-users and stakeholders.

Episode 2: Making Data Science Uncool Again

February 21st, 2022  |  Season 1  |  1 hr 45 mins

Hugo talks with Jeremy Howard about the past, present, and future of data science, machine learning, and AI, with a focus on the democratization of deep learning.

Episode 1: Introducing Vanishing Gradients

February 16th, 2022  |  Season 1  |  5 mins 29 secs

In this episode, Hugo introduces the new data science podcast Vanishing Gradients.