Summary of Position: Stop Making Unscientific Agi Performance Claims, by Patrick Altmeyer et al.
Position: Stop Making Unscientific AGI Performance Claims
by Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 whether large language models (LLMs) exhibit sparks of Artificial General Intelligence (AGI). While LLMs do distill meaningful representations in their latent embeddings that correlate with external variables, the authors argue that this does not necessarily imply AGI. They demonstrate that various model types, including random projections and transformers, can predict latent or external variables without being linked to AGI. The study highlights the importance of exercising caution when interpreting AI research outcomes, as humans are prone to seek patterns and anthropomorphize. Key findings and methodologies include matrix decompositions, deep autoencoders, and transformer models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether big language models can think like humans. While these models do understand some things about our world, the researchers show that this doesn’t mean they’re actually thinking like us. They tested different kinds of models and found that all of them can make predictions without being super smart. The authors warn that people are often quick to find patterns and assume things about how AI works, when in reality it’s just a machine doing its thing. |
Keywords
» Artificial intelligence » Transformer
