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Recent discussions in the AI community focus on the potential of Large Language Models (LLMs) to achieve superintelligence and their role as a cognitive interface between humans and machines. The debate includes whether LLMs can reason or learn new skills, with some emphasizing the importance of new capabilities emerging. The ARC-AGI test has shown a significant jump in accuracy, raising questions about the development of generalization capabilities in LLMs.
Is LLM-based #AGI here? 👊ARC-AGI is one of the only tests for general intelligence. Suddenly accuracy went from 34% -> 50%, but how? Did #LLMs develop new generalization capabilities? #AI #ArtificialIntelligence https://t.co/jPRqCUuyUr
In the last few weeks, there has been a lot of noise about ARC-AGI. #LLM & Symbolic #AI people taking bouts at each other. Let's take a look at how there was such a massive jump in accuracy on the ARC, something that has been very slow to progress. https://t.co/jPRqCUuyUr
Intelligence Explosion does NOT require AGI I think the whole "LLMs can't reason" debate sidesteps the most relevant fact that, even in the most underwhelming scenario - one where new capabilities stop emerging and we never build AI systems capable of reasoning - what we have…
