Summary of Opening the Ai Black Box: Program Synthesis Via Mechanistic Interpretability, by Eric J. Michaud et al.
Opening the AI black box: program synthesis via mechanistic interpretability
by Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 MIPS, a novel program synthesis method, uses automated mechanistic interpretability of neural networks to learn Python code. The approach, which doesn’t rely on human training data, is highly complementary to GPT-4, solving 32 out of 62 algorithmic tasks and 13 not solved by GPT-4. MIPS works by converting an RNN into a finite state machine using an integer autoencoder and then applying Boolean or integer symbolic regression to capture the learned algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MIPS is a new way to learn code from neural networks. It’s like a translator that turns complex computer programs into simple, understandable Python code. This approach doesn’t need help from humans to work, which makes it different from other language models. MIPS can solve tricky math problems and even some tasks that GPT-4, another powerful model, cannot do. |
Keywords
* Artificial intelligence * Autoencoder * Gpt * Regression * Rnn
