Summary of A Measure For Level Of Autonomy Based on Observable System Behavior, by Jason M. Pittman
A Measure for Level of Autonomy Based on Observable System Behavior
by Jason M. Pittman
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 paper addresses the challenge of measuring the autonomy level of artificial intelligence (AI) systems, particularly in autonomous systems used in automotive and defense domains. The current lack of clear understanding hinders human-machine interaction and interdiction. To overcome this, researchers propose a measure to predict autonomy level based on observable actions. The algorithm incorporates this proposed measure, enabling blind comparison of autonomous systems at runtime, which is crucial for defense-based implementations that require robust identification of autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we can better understand and compare artificial intelligence (AI) systems that are meant to work independently, like self-driving cars. Right now, it’s hard to figure out just how much these AI systems can do on their own, which makes it difficult for humans to interact with them or stop them if needed. To fix this problem, the researchers suggest a new way to measure how autonomous an AI system is based on what actions it takes. This could be useful for people who want to compare different self-driving cars and make sure they’re safe. |
