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Effective Large Models Despite Lack of Explicit Concepts
The scaling hypothesis has led to surprisingly effective large models that exhibit properties like concepts and abstractions without the need for explicit algorithmic breakthroughs. While it was initially thought that an additional algorithmic breakthrough akin to how the brain works would be necessary to achieve this, the current systems can implicitly learn abstract concepts without explicit guidance.