Summary of Concept Alignment, by Sunayana Rane et al.
Concept Alignment
by Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky, Christopher Kello, Thomas L. Griffiths
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper argues that prior to aligning values between humans and AI systems, it is crucial to align the concepts used by both parties to understand the world. The authors integrate ideas from philosophy, cognitive science, and deep learning to demonstrate the need for concept alignment. They summarize existing research on how humans and machines learn concepts and outline opportunities and challenges in achieving shared concepts. Finally, they propose leveraging tools developed in cognitive science and AI research to accelerate progress towards concept alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how we can make sure that humans and AI systems understand the world in the same way. Right now, humans and machines use different ways of thinking, which can lead to misunderstandings. The authors think that before we try to make AI systems share human values, we need to find a common language. They explain how humans and machines currently learn concepts and highlight the challenges and opportunities in making them align. Overall, the paper is about finding a way for humans and machines to communicate effectively. |
Keywords
* Artificial intelligence * Alignment * Deep learning
