Summary of Generative Artificial Intelligence: a Systematic Review and Applications, by Sandeep Singh Sengar et al.
Generative Artificial Intelligence: A Systematic Review and Applications
by Sandeep Singh Sengar, Affan Bin Hasan, Sanjay Kumar, Fiona Carroll
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 systematic review and analysis of recent advancements in Generative AI highlights its groundbreaking capabilities in supervised and unsupervised learning scenarios, achieving state-of-the-art performance in real-world applications such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and more. The paper focuses on the development of large language models and their interdisciplinary applications, including generative models for specific tasks like image generation. Technically accurate and faithful to the original abstract, this summary covers model names, methods, datasets, tasks, and relevant subfields, with a focus on evaluation metrics, benchmarks, and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative AI has changed the game! This technology can translate images, diagnose medical issues, combine text and images, process languages, and more. It’s already making a big impact in many areas. This paper looks at what’s been happening in Generative AI recently, how it works, and what the future might hold. It also talks about being responsible with this powerful technology. |
Keywords
» Artificial intelligence » Image generation » Natural language processing » Supervised » Translation » Unsupervised
