Summary of Towards Cognitive Ai Systems: a Survey and Prospective on Neuro-symbolic Ai, by Zishen Wan et al.
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
by Zishen Wan, Che-Kai Liu, Hanchen Yang, Chaojian Li, Haoran You, Yonggan Fu, Cheng Wan, Tushar Krishna, Yingyan Lin, Arijit Raychowdhury
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR)
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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 In this paper, researchers explore Neuro-Symbolic Artificial Intelligence (NSAI), a promising paradigm that combines neural networks, symbolic reasoning, and probabilistic approaches to create more interpretable, robust, and trustworthy AI systems. The authors review recent progress in NSAI, analyzing the performance characteristics and computational operators of NSAI models. They also discuss the challenges and potential future directions of NSAI from both system and architectural perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) has changed our lives a lot. But right now, we have some problems with how AI is used. To fix these problems, scientists are working on new kinds of AI that can think more like humans. This new kind of AI is called Neuro-Symbolic AI (NSAI). It combines different ways of thinking to make AI more understandable and reliable. The authors of this paper look at what’s been happening with NSAI recently and what we still need to figure out. |
