Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research
Authors: Riley Simmons-Edler, Ryan Paul Badman, Shayne Longpre, Kanaka Rajan
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here. |
| Researcher Affiliation | Academia | 1Department of Neurobiology, Harvard Medical School, Boston, USA 2Kempner Institute, Harvard University, Cambridge, USA 3Massachusetts Institute of Technology, Cambridge, USA. |
| Pseudocode | No | This paper is a position paper and does not propose a new algorithm or method that would require pseudocode. |
| Open Source Code | No | The paper is a position paper and does not describe a methodology that would have associated open-source code for release. |
| Open Datasets | No | The paper is a position paper and does not conduct experiments using datasets, thus it does not refer to public or open datasets for training. |
| Dataset Splits | No | The paper is a position paper and does not conduct experiments that would involve training, validation, and test dataset splits. |
| Hardware Specification | No | The paper discusses various autonomous weapons systems and their components (e.g., 'Nvidia Jetson TX2 ML compute module', 'Nvidia Jetson Orin NX'), but these are hardware components of the *systems being discussed*, not hardware used by the authors to run their own experiments or computations. |
| Software Dependencies | No | The paper discusses various autonomous weapons systems and general software concepts (e.g., 'LLMs', 'ML systems') relevant to them, but it does not specify software dependencies with version numbers used for any research or experimental work conducted by the authors. |
| Experiment Setup | No | The paper is a position paper and does not describe an experimental setup with hyperparameters or training settings. |