108. Last Week In AI — 2021: The (full) year in review

108. Last Week In AI — 2021: The (full) year in review

Author: The TDS team January 5, 2022 Duration: 50:21

2021 has been a wild ride in many ways, but its wildest features might actually be AI-related. We’ve seen major advances in everything from language modeling to multi-modal learning, open-ended learning and even AI alignment.

So, we thought, what better way to take stock of the big AI-related milestones we’ve reached in 2021 than a cross-over episode with our friends over at the Last Week In AI 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:

  • 0:00 Intro
  • 2:15 Rise of multi-modal models
  • 7:40 Growth of hardware and compute
  • 13:20 Reinforcement learning
  • 20:45 Open-ended learning
  • 26:15 Power seeking paper
  • 32:30 Safety and assumptions
  • 35:20 Intrinsic vs. extrinsic motivation
  • 42:00 Mapping natural language
  • 46:20 Timnit Gebru’s research institute
  • 49:20 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
99. Margaret Mitchell - (Practical) AI ethics [not-audio_url] [/not-audio_url]

Duration: 45:43
Bias gets a bad rap in machine learning. And yet, the whole point of a machine learning model is that it biases certain inputs to certain outputs — a picture of a cat to a label that says “cat”, for example. Machine lear…
98. Mike Tung - Are knowledge graphs AI’s next big thing? [not-audio_url] [/not-audio_url]

Duration: 48:56
As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human writing. And human writing is often wrong, biased, or both, which means…
97. Anthony Habayeb - The present and future of AI regulation [not-audio_url] [/not-audio_url]

Duration: 49:31
Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely on machine learning models to deliver value for their customers. More…
96. Jan Leike - AI alignment at OpenAI [not-audio_url] [/not-audio_url]

Duration: 1:05:17
The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use dangerously creative solutions that have side-effects that could be unde…
95. Francesca Rossi - Thinking, fast and slow: AI edition [not-audio_url] [/not-audio_url]

Duration: 46:42
The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniques to get us all the way…
94. Divya Siddarth - Are we thinking about AI wrong? [not-audio_url] [/not-audio_url]

Duration: 1:02:44
AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat — and even wholesale replacement of — human beings by artificial superintelligences that have their own sense…
92. Daniel Filan - Peering into neural nets for AI safety [not-audio_url] [/not-audio_url]

Duration: 1:06:02
Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on the podcast that the field of AI alignment has emerged to tackle this p…
91. Peter Gao - Self-driving cars: Past, present and future [not-audio_url] [/not-audio_url]

Duration: 1:01:21
Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And yet, just three years later, the company was acquired by GM for over a b…
90. Jeffrey Ding - China’s AI ambitions and why they matter [not-audio_url] [/not-audio_url]

Duration: 49:07
There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more importan…