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

Towards Data Science

01/05/22

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50m

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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

Previous Episode

107. Kevin Hu - Data observability and why it matters

December 15, 2021

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49m

Imagine for a minute that you’re running a profitable business, and that part of your sales strategy is to send the occasional mass email to people who’ve signed up to be on your mailing list. For a while, this approach leads to a reliable flow of new sales, but then one day, that abruptly stops. What happened?

You pour over logs, looking for an explanation, but it turns out that the problem wasn’t with your software; it was with your data. Maybe the new intern accidentally added a character to every email address in your dataset, or shuffled the names on your mailing list so that Christina got a message addressed to “John”, or vice-versa. Versions of this story happen surprisingly often, and when they happen, the cost can be significant: lost revenue, disappointed customers, or worse — an irreversible loss of trust.

Today, entire products are being built on top of datasets that aren’t monitored properly for critical failures — and an increasing number of those products are operating in high-stakes situations. That’s why data observability is so important: the ability to track the origin, transformations and characteristics of mission-critical data to detect problems before they lead to downstream harm.

And it’s also why we’ll be talking to Kevin Hu, the co-founder and CEO of Metaplane, one of the world’s first data observability startups. Kevin has a deep understanding of data pipelines, and the problems that cap pop up if you they aren’t properly monitored. He joined me to talk about data observability, why it matters, and how it might be connected to responsible AI 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 0:00

Chapters:

0:00 Intro
2:00 What is data observability?
8:20 Difference between a dataset’s internal and external characteristics
12:20 Why is data so difficult to log?
17:15 Tracing back models
22:00 Algorithmic analyzation of a date
26:30 Data ops in five years
33:20 Relation to cutting-edge AI work
39:25 Software engineering and startup funding
42:05 Problems on a smaller scale
46:40 Future data ops problems to solve
48:45 Wrap-up

Next Episode

109. Danijar Hafner - Gaming our way to AGI

January 12, 2022

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50m

Until recently, AI systems have been narrow — they’ve only been able to perform the specific tasks that they were explicitly trained for. And while narrow systems are clearly useful, the holy grain of AI is to build more flexible, general systems.

But that can’t be done without good performance metrics that we can optimize for — or that we can at least use to measure generalization ability. Somehow, we need to figure out what number needs to go up in order to bring us closer to generally-capable agents. That’s the question we’ll be exploring on this episode of the podcast, with Danijar Hafner. Danijar is a PhD student in artificial intelligence at the University of Toronto with Jimmy Ba and Geoffrey Hinton and researcher at Google Brain and the Vector Institute.

Danijar has been studying the problem of performance measurement and benchmarking for RL agents with generalization abilities. As part of that work, he recently released Crafter, a tool that can procedurally generate complex environments that are a lot like Minecraft, featuring resources that need to be collected, tools that can be developed, and enemies who need to be avoided or defeated. In order to succeed in a Crafter environment, agents need to robustly plan, explore and test different strategies, which allow them to unlock certain in-game achievements.

Crafter is part of a growing set of strategies that researchers are exploring to figure out how we can benchmark and measure the performance of general-purpose AIs, and it also tells us something interesting about the state of AI: increasingly, our ability to define tasks that require the right kind of generalization abilities is becoming just as important as innovating on AI model architectures. Danijar joined me to talk about Crafter, reinforcement learning, and the big challenges facing AI researchers as they work towards general intelligence on this episode of the TDS podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

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

***

Chapters: