Get it on Google Play

105. Yannic Kilcher - A 10,000-foot view of AI

105. Yannic Kilcher - A 10,000-foot view of AI

Author: The TDS team December 1, 2021 Duration: 1:03:04
Towards Data Science
Technology

There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the case. The recent explosion of research activity in AI has turned keeping up to date with new developments into a full-time job.

Fortunately, people like YouTuber, ML PhD and sunglasses enthusiast Yannic Kilcher make it their business to distill ML news and papers into a digestible form for mortals like you and me to consume. I highly recommend his channel to any TDS podcast listeners who are interested in ML research — it’s a fantastic resource, and literally the way I finally managed to understand the Attention is All You Need paper back in the day.

Yannic is joined me to talk about what he’s learned from years of following, reporting and doing AI research, including the trends, the challenges and the opportunities that he expects are going to shape the course of AI history in coming years.

--- 

Intro music:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

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

--- 

Chapters:

- 0:00 Intro

- 1:20 Yannic’s path into ML

- 7:25 Selecting ML news

- 11:45 AI ethics → political discourse

- 17:30 AI alignment

- 24:15 Malicious uses

- 32:10 Impacts on persona

- 39:50 Bringing in human thought

- 46:45 Math with big numbers

- 51:05 Metrics for generalization

- 58:05 The future of AI

- 1:02:58 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

Official website RSS
youtubelinkedinmedium
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…

«12345»