Summary of Xxai: Towards Explicitly Explainable Artificial Intelligence, by V. L. Kalmykov et al.
XXAI: Towards eXplicitly eXplainable Artificial Intelligence
by V. L. Kalmykov, L.V. Kalmykov
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Populations and Evolution (q-bio.PE)
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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 The proposed eXplicitly eXplainable AI (XXAI) framework addresses the black box problem in artificial intelligence by developing a fully transparent white-box AI based on deterministic logical cellular automata. This approach leverages the general theory of a domain as a knowledge base to derive inferences, enabling parallel multi-level logical inference at all levels of organization. XXAI can automatically verify the reliability, security, and ethics of sub-symbolic neural network solutions in both training and final phases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is like a mysterious box that makes decisions without telling us why. This raises concerns about its safety and reliability. To fix this problem, scientists propose a new way to build AI using logical rules based on the laws of nature. This approach lets us understand how the AI makes decisions, making it more reliable and safe. In this paper, the team shows that this method works by testing it with ecological theories. They also explain the ideas behind this new AI framework and discuss its potential applications. |
Keywords
» Artificial intelligence » Inference » Knowledge base » Neural network
