# Yejin Choi: teaching AI common sense and morality

Page: https://stenobird.com/podcast/robot-brains-podcast/yejin-choi-teaching-ai-common-sense-and-morality
Text version: https://stenobird.com/podcast/robot-brains-podcast/yejin-choi-teaching-ai-common-sense-and-morality.md
Podcast: [The Robot Brains Podcast](https://stenobird.com/podcast/robot-brains-podcast)
Published: 2023-05-17T19:31:22+00:00
Episode link: https://www.therobotbrains.ai/who-is-yejin-choi
Audio file: https://sphinx.acast.com/p/open/s/6053a29a0d11b0148adcfc96/e/646415dbbe93c900116fc524/media.mp3
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JSON: https://stenobird.com/v1/public/podcasts/robot-brains-podcast/episodes/yejin-choi-teaching-ai-common-sense-and-morality
Duration seconds: 3398

## Resource

AI researcher Yejin Choi explores why large language models still struggle with fundamental common sense and social norms. She discusses the potential of using AI-generated data for training and the shift from symbolic logic to neural reasoning.

## Highlights
- Main idea: Large language models act as powerful unified engines for language-based reasoning and pattern recognition
- Failure mode: Models frequently fail at simple, intuitive tasks that even children can solve, revealing a lack of true common sense
- Practical takeaway: High-quality, curated data and iterative AI-to-AI criticism can be more effective than simply increasing training volume
- Research direction: Moving toward 'truth-seeking' pre-training to prioritize correctness over mere linguistic probability
- Future outlook: The evolution of LLMs opens new possibilities for personalized educational tools and intelligent tutoring systems

## Topics

Natural Language Processing, Large Language Models, Common Sense Reasoning, Artificial Intelligence, Machine Learning, Neural Networks, AI Ethics, Data Quality

## Chapters
- 5:05 — The Power of Unified Models: An exploration of how LLMs use massive internet datasets to achieve unprecedented capabilities in language and reasoning.
- 9:20 — The Quest for Truth-Seeking AI: Discussing the need to reformulate pre-training to focus on correctness and reliability rather than just pattern matching.
- 21:55 — Hidden Behaviors in Neural Networks: Insights into unexpected empirical results and degenerate behaviors discovered within large-scale language models.
- 26:30 — AI-Generated Data and Self-Correction: How using one AI to critique another can act as a form of reinforcement learning to improve data quality.
- 35:15 — The Common Sense Gap: Defining common sense as shared human knowledge and identifying why models still fail at basic social norms.
- 43:50 — New Frontiers in Education: The potential for neural language models to power transformative applications like intelligent tutoring and student assistants.
- 48:25 — Personal Journey in AI: Yejin Choi reflects on her path from studying math and science to becoming a leader in the NLP field.

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## Transcript

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