97. Anthony Habayeb - The present and future of AI regulation

Towards Data Science

10/06/21

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49m

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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 and more, they’re expected to develop and implement governance strategies to reduce the incidence of bias, and increase the transparency of their AI systems and development processes. Those expectations have historically come from consumers, but governments are starting impose hard requirements, too.

So for today’s episode, I spoke to Anthony Habayeb, founder and CEO of Monitaur, a startup focused on helping businesses anticipate and comply with new and upcoming AI regulations and governance requirements. Anthony’s been watching the world of AI regulation very closely over the last several years, and was kind enough to share his insights on the current state of play and future direction of the field.

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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

1:45 Anthony’s background

6:20 Philosophies surrounding regulation

14:50 The role of governments

17:30 Understanding fairness

25:35 AI’s PR problem

35:20 Governments’ regulation

42:25 Useful techniques for data science teams

46:10 Future of AI governance

49:20 Wrap-up

Previous Episode

96. Jan Leike - AI alignment at OpenAI

September 29, 2021

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65m

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 undesirable, or downright dangerous. Even a slight misalignment between the motives of a sufficiently advanced AI and human values could be hazardous.

That’s why leading AI labs like OpenAI are already investing significant resources into AI alignment research. Understanding that research is important if you want to understand where advanced AI systems might be headed, and what challenges we might encounter as AI capabilities continue to grow — and that’s what this episode of the podcast is all about. My guest today is Jan Leike, head of AI alignment at OpenAI, and an alumnus of DeepMind and the Future of Humanity Institute. As someone who works directly with some of the world’s largest AI systems (including OpenAI’s GPT-3) Jan has a unique and interesting perspective to offer both on the current challenges facing alignment researchers, and the most promising future directions the field might take.

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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

1:35 Jan’s background

7:10 Timing of scalable solutions

16:30 Recursive reward modeling

24:30 Amplification of misalignment

31:00 Community focus

32:55 Wireheading

41:30 Arguments against the democratization of AIs

49:30 Differences between capabilities and alignment

51:15 Research to focus on

1:01:45 Formalizing an understanding of personal experience

1:04:04 OpenAI hiring

1:05:02 Wrap-up

Next Episode

98. Mike Tung - Are knowledge graphs AI’s next big thing?

October 13, 2021

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48m

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 language models are trying to emulate an imperfect target.

Language models often babble, or make up answers to questions they don’t understand. And it can make them unreliable sources of truth. Which is why there’s been increased interest in alternative ways to retrieve information from large datasets — approaches that include knowledge graphs.

Knowledge graphs encode entities like people, places and objects into nodes, which are then connected to other entities via edges, which specify the nature of the relationship between the two. For example, a knowledge graph might contain a node for Mark Zuckerberg, linked to another node for Facebook, via an edge that indicates that Zuck is Facebook’s CEO. Both of these nodes might in turn be connected to dozens, or even thousands of others, depending on the scale of the graph.

Knowledge graphs are an exciting path ahead for AI capabilities, and the world’s largest knowledge graphs are trained by a company called Diffbot, whose CEO Mike Tung joined me for this episode of the podcast to discuss where knowledge graphs can improve on more standard techniques, and why they might be a big part of the future of AI.

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Intro music by:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

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

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0:00 Intro

1:30 The Diffbot dynamic

3:40 Knowledge graphs

7:50 Crawling the internet

17:15 What makes this time special?

24:40 Relation to neural networks