96. Jan Leike - AI alignment at OpenAI

96. Jan Leike - AI alignment at OpenAI

Author: The TDS team September 29, 2021 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 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


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
87. Evan Hubinger - The Inner Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:09:32
How can you know that a super-intelligent AI is trying to do what you asked it to do? The answer, it turns out, is: not easily. And unfortunately, an increasing number of AI safety researchers are warning that this is a…
86. Andy Jones - AI Safety and the Scaling Hypothesis [not-audio_url] [/not-audio_url]

Duration: 1:25:44
When OpenAI announced the release of their GPT-3 API last year, the tech world was shocked. Here was a language model, trained only to perform a simple autocomplete task, which turned out to be capable of language transl…
85. Brian Christian - The Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:06:19
In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they describe how an AI that was trained to maximize its score in a boat racing game ended up discovering a strange ha…
83. Rosie Campbell - Should all AI research be published? [not-audio_url] [/not-audio_url]

Duration: 52:37
When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for the AI industry to experime…
82. Jakob Foerster - The high cost of automated weapons [not-audio_url] [/not-audio_url]

Duration: 54:07
Automated weapons mean fewer casualties, faster reaction times, and more precise strikes. They’re a clear win for any country that deploys them. You can see the appeal. But they’re also a classic prisoner’s dilemma. Once…
81. Nicolas Miailhe - AI risk is a global problem [not-audio_url] [/not-audio_url]

Duration: 56:03
In December 1938, a frustrated nuclear physicist named Leo Szilard wrote a letter to the British Admiralty telling them that he had given up on his greatest invention — the nuclear chain reaction. "The idea of a nuclear…