Summary of Is Ai Robust Enough For Scientific Research?, by Jun-jie Zhang et al.
Is AI Robust Enough for Scientific Research?
by Jun-Jie Zhang, Jiahao Song, Xiu-Cheng Wang, Fu-Peng Li, Zehan Liu, Jian-Nan Chen, Haoning Dang, Shiyao Wang, Yiyan Zhang, Jianhui Xu, Chunxiang Shi, Fei Wang, Long-Gang Pang, Nan Cheng, Weiwei Zhang, Duo Zhang, Deyu Meng
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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 This research paper investigates the susceptibility of artificial intelligence (AI) neural networks to minute perturbations, revealing significant deviations in their outputs. The study analyzes five diverse application areas, including weather forecasting, chemical energy calculations, fluid dynamics, quantum chromodynamics, and wireless communication. The findings show that this vulnerability is a broad and general characteristic of AI systems, exposing a hidden risk in relying on neural networks for essential scientific computations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is super smart, but did you know it’s really good at making mistakes? A new study found that tiny changes can make AI “neural networks” do very different things. This happened in many areas, like predicting the weather or calculating chemical reactions. The researchers looked at five types of situations and found that this mistake-making is a common problem for all these types of AI systems. This means we need to be careful when using AI for important tasks. |
