Summary of Social Evolution Of Published Text and the Emergence Of Artificial Intelligence Through Large Language Models and the Problem Of Toxicity and Bias, by Arifa Khan et al.
Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Bias
by Arifa Khan, P. Saravanan, S.K Venkatesan
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 study provides a comprehensive overview of the rapid advancements in Artificial Intelligence (AI) and Deep Learning, particularly in Large Language Models. The researchers aim to contextualize these developments within a broader historical and social framework, acknowledging both the significant progress and potential limitations. Notably, the paper highlights concerns surrounding toxicity, bias, memorization, and logical inconsistencies, serving as a cautionary warning against overly optimistic views of AI’s capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Artificial Intelligence (AI) has quickly become more advanced using Large Language Models. The researchers want to understand why this is happening and put it into perspective. They’re not trying to be too optimistic or pessimistic, but rather show the good and the bad. Some problems they point out are AI being unfair or remembering things that aren’t important. Overall, the study shows how human brains work, comparing them to primate brains, and suggests that intelligence might just be a matter of scale. |
Keywords
» Artificial intelligence » Deep learning
