Summary of Generative Ai Meets Semantic Communication: Evolution and Revolution Of Communication Tasks, by Eleonora Grassucci et al.
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
by Eleonora Grassucci, Jihong Park, Sergio Barbarossa, Seong-Lyun Kim, Jinho Choi, Danilo Comminiello
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper explores the potential of deep generative models in communication frameworks, particularly in semantic communication. Currently, these models are being utilized in computer vision and natural language processing, but their adoption in communication is still underutilized. The authors demonstrate that generative models can solve classic communication problems like denoising, restoration, or compression. However, they argue that the real potential lies in semantic communication frameworks where receivers regenerate content consistent with transmitted messages. This shift paves the way for reduced data traffic and increased versatility in novel applications. The paper presents a unified perspective on deep generative models in semantic communication and highlights their role in future frameworks, enabling emerging tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models are really good at making things look like they were made by computers! They can help with big problems in how we send information, like getting rid of noise or fixing broken messages. But what’s even cooler is that these models can be used to make new kinds of communication systems. Instead of just sending a bunch of ones and zeros (like usual computer talk), these systems can let the person on the other end create their own content that makes sense with what was sent. This could help reduce how much data we send and open up new ways for us to communicate. |
Keywords
* Artificial intelligence * Natural language processing
