Summary of Ai Predicts Agi: Leveraging Agi Forecasting and Peer Review to Explore Llms’ Complex Reasoning Capabilities, by Fabrizio Davide et al.
AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore LLMs’ Complex Reasoning Capabilities
by Fabrizio Davide, Pietro Torre, Andrea Gaggioli
First submitted to arxiv on: 12 Dec 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 The paper investigates the ability of 16 state-of-the-art large language models (LLMs) to predict the likelihood of Artificial General Intelligence (AGI) emerging by 2030. The LLMs’ estimates vary widely, ranging from 3% to 47.6%, with a median of 12.5%. An automated peer review process (LLM-PR) is implemented to assess the quality of these forecasts. The results show that the LLMs’ estimates align closely with a recent expert survey projecting a 10% likelihood of AGI by 2027. The top-performing model is Pplx-70b-online, while Gemini-1.5-pro-api ranks the lowest. The paper also explores the use of weighting schemes based on external benchmarks to optimize the alignment of LLMs’ predictions with human expert forecasts. A new ‘AGI benchmark’ is developed to highlight performance differences in AGI-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computers called large language models (LLMs) to try to predict when super smart AI, called Artificial General Intelligence (AGI), might happen. The LLMs think differently and give different answers – some say it’s very likely, while others say it’s unlikely. The researchers looked at how good the LLMs are at making these predictions by using a special way of checking their work. They also compared the LLMs’ answers to what experts in the field think will happen. Some models did better than others, and one model, Pplx-70b-online, was really good. The researchers wanted to see how well the models could do at predicting AGI, and they came up with a new way of testing them. |
Keywords
» Artificial intelligence » Alignment » Gemini » Likelihood
