Summary of Inductive Models For Artificial Intelligence Systems Are Insufficient Without Good Explanations, by Udesh Habaraduwa
Inductive Models for Artificial Intelligence Systems are Insufficient without Good Explanations
by Udesh Habaraduwa
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper sheds light on the limitations of machine learning, particularly deep artificial neural networks, in approximating complex functions while lacking transparency and explanatory power. The authors highlight the “problem of induction,” a philosophical issue where past observations may not predict future events, which ML models face when encountering new data. To overcome this challenge, the study emphasizes the importance of providing good explanations alongside predictions, rather than solely relying on predictive accuracy. The paper argues that AI’s progress relies on developing models that offer insights and explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how machine learning can be improved by focusing on explanation power, not just prediction. Right now, AI models are great at making guesses, but they don’t always tell us why they made those guesses. The authors think this is a big problem because it means we don’t really understand what’s going on inside these powerful machines. To fix this, the study suggests that AI should prioritize explaining its decisions, not just making accurate predictions. |
Keywords
* Artificial intelligence * Machine learning
