Summary of Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework and Methods From Neuroscience, by Zhonghao He et al.
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
by Zhonghao He, Jascha Achterberg, Katie Collins, Kevin Nejad, Danyal Akarca, Yinzhu Yang, Wes Gurnee, Ilia Sucholutsky, Yuhan Tang, Rebeca Ianov, George Ogden, Chole Li, Kai Sandbrink, Stephen Casper, Anna Ivanova, Grace W. Lindsay
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A novel approach is proposed to interpret deep learning systems with billions of parameters. The authors draw parallels between analyzing brain function and artificial neural networks, suggesting that both require examining multiple levels of analysis using different tools. A grand challenge is identified for scientists studying brains and AI: understanding how distributed mechanisms give rise to complex cognition and behavior. To achieve this, the paper presents a framework for multilevel interpretability, organized according to Marr’s three levels: computation/behavior, algorithm/representation, and implementation. This framework aims to link structure, computation, and behavior, clarify assumptions and research priorities, and facilitate a unified understanding of intelligent systems, whether biological or artificial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is getting smarter! But how do we understand what makes it work? Imagine trying to figure out how your brain works – it’s complicated! This paper says that understanding artificial neural networks (like those used in AI) requires looking at different levels. It’s like studying a puzzle with many pieces. The authors suggest that scientists who study brains and those who study AI should work together to understand how these complex systems work. They provide tools to help us do this, organized into three levels: what the system does (computation), how it does it (algorithm), and how it’s put together (implementation). This framework will help us better understand how intelligent systems, whether human or artificial, work. |
Keywords
» Artificial intelligence » Deep learning
