Summary of Multiple Realizability and the Rise Of Deep Learning, by Sam Whitman Mcgrath and Jacob Russin
Multiple Realizability and the Rise of Deep Learning
by Sam Whitman McGrath, Jacob Russin
First submitted to arxiv on: 21 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the implications of deep learning models on the multiple realizability thesis, which suggests that psychological states can be implemented in various physical systems. The study argues that the deep learning revolution brings this possibility to life, offering plausible examples of man-made implementations of sophisticated cognitive functions. The authors challenge the view that multiple realizability requires studying the mind independently of its implementation in the brain or artificial analogues. Instead, they suggest that deep neural networks can play a crucial role in formulating and evaluating hypotheses about cognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep learning models relate to the idea that mental states can be found in different physical systems. The study says that deep learning is making this idea come true by creating man-made versions of complex thinking processes. It argues that we should rethink the way we study the mind, and that artificial neural networks can help us understand how it works. |
Keywords
» Artificial intelligence » Deep learning
