Summary of Generalisation Of Total Uncertainty in Ai: a Theoretical Study, by Keivan Shariatmadar
Generalisation of Total Uncertainty in AI: A Theoretical Study
by Keivan Shariatmadar
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
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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 investigates the role of uncertainty in artificial intelligence (AI) and its impact on decision-making, forecasting, and learning mechanisms. The authors argue that uncertainty is a critical factor in achieving highly accurate results, particularly with small or varying data sets. By drawing from established works, latest developments, and practical applications, the study proposes a novel definition of total uncertainty in AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI has trouble dealing with uncertainty, making it hard to get accurate results, especially when working with small amounts of data that might be different each time. This affects how we make decisions, predict what will happen, and learn new things. The researchers behind this study want to understand the kind of uncertainty that happens in AI by looking at existing ideas, recent advancements, and real-world uses. They hope to come up with a new way to define total uncertainty in AI. |
