Summary of Gen-ai For User Safety: a Survey, by Akshar Prabhu Desai et al.
Gen-AI for User Safety: A Survey
by Akshar Prabhu Desai, Tejasvi Ravi, Mohammad Luqman, Mohit Sharma, Nithya Kota, Pranjul Yadav
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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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 proposes a novel approach to detect user safety violations using machine learning and data mining techniques. Specifically, it develops classifiers that can understand natural language context and nuances, overcoming the limitations of existing ML/DM approaches. The authors leverage Gen-AI techniques, which enable translation between languages, fine-tuning across tasks and domains, and improved performance in detecting spam emails and fraudulent web-pages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to keep people safe online. Right now, AI can already detect some bad things, like spam emails or fake websites that ask for your password. But there’s a problem – these AIs don’t really understand what words mean in different contexts. For example, if someone writes “Hello” and then asks you to buy them a million dollars worth of gift cards, the AI might not catch on because it doesn’t get that “Hello” is just a friendly greeting. This paper shows how new kinds of AI can fix this problem by understanding language better, which helps keep people safe from online threats. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Translation
