101. Ayanna Howard - AI and the trust problem

Towards Data Science

11/03/21

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53m

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Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the reach of AI to new problem classes. There’s a lot to be excited about.

But as we’ve seen in other episodes of the podcast, there’s a lot more to getting value from an AI system than jacking up its capabilities. And increasingly, one of these additional missing factors is becoming trust. You can make all the powerful AIs you want, but if no one trusts their output — or if people trust it when they shouldn’t — you can end up doing more harm than good.

That’s why we invited Ayanna Howard on the podcast. Ayanna is a roboticist, entrepreneur and Dean of the College of Engineering at Ohio State University, where she focuses her research on human-machine interactions and the factors that go into building human trust in AI systems. She joined me to talk about her research, its applications in medicine and education, and the future of human-machine trust.

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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

1:30 Ayanna’s background

6:10 The interpretability of neural networks

12:40 Domain of machine-human interaction

17:00 The issue of preference

20:50 Gelman/newspaper amnesia

26:35 Assessing a person’s persuadability

31:40 Doctors and new technology

36:00 Responsibility and accountability

43:15 The social pressure aspect

47:15 Is Ayanna optimistic?

53:00 Wrap-up

Previous Episode

100. Max Jaderberg - Open-ended learning at DeepMind

October 27, 2021

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45m

On the face of it, there’s no obvious limit to the reinforcement learning paradigm: you put an agent in an environment and reward it for taking good actions until it masters a task.

And by last year, RL had achieved some amazing things, including mastering Go, various Atari games, Starcraft II and so on. But the holy grail of AI isn’t to master specific games, but rather to generalize — to make agents that can perform well on new games that they haven’t been trained on before.

Fast forward to July of this year though and a team of DeepMind published a paper called “Open-Ended Learning Leads to Generally Capable Agents”, which takes a big step in the direction of general RL agents. Joining me for this episode of the podcast is one of the co-authors of that paper, Max Jaderberg. Max came into the Google ecosystem in 2014 when they acquired his computer vision company, and more recently, he started DeepMind’s open-ended learning team, which is focused on pushing machine learning further into the territory of cross-task generalization ability. I spoke to Max about open-ended learning, the path ahead for generalization and 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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Chapters:

0:00 Intro

1:30 Max’s background

6:40 Differences in procedural generations

12:20 The qualitative side

17:40 Agents’ mistakes

20:00 Measuring generalization

27:10 Environments and loss functions

32:50 The potential of symbolic logic

36:45 Two distinct learning processes

42:35 Forecasting research

45:00 Wrap-up

Next Episode

102. Wendy Foster - AI ethics as a user experience challenge

November 10, 2021

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44m

AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect from a quantitative discipline like computer science or physics.

But somehow, the ethics rubber has to meet the AI road, and where that happens — where real developers have to deal with real users and apply concrete ethical principles — is where you find some of the most interesting, practical thinking on the topic.

That’s why I wanted to speak with Wendy Foster, the Director of Engineering and Data Science at Shopify. Wendy’s approach to AI ethics is refreshingly concrete and actionable. And unlike more abstract approaches, it’s based on clear principles like user empowerment: the idea that you should avoid forcing users to make particular decisions, and instead design user interfaces that frame AI-recommended actions as suggestions that can be ignored or acted on.

Wendy joined me to discuss her practical perspective on AI ethics, the importance of user experience design for AI products, and how responsible AI gets baked into product at Shopify 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:

0:00 Intro

1:40 Wendy’s background

4:40 What does practice mean?

14:00 Different levels of explanation

19:05 Trusting the system