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Guest Stevan Harnad, professor of psychology and cognitive science, discusses the Symbol Grounding Problem in AI. They explore category formation, non-linguistic category learning, and the limitations of current AI models. The episode also touches on Alan Turing's motivation, the easy and hard problems of AI, multimodal systems, and categorical perception in machine learning models and humans.
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Quick takeaways
- Language models like GPT-3 lack true cognitive abilities and cannot pass the Turing test beyond the T2 level.
- True symbol grounding in AI requires sensory-motor capacity and is absent in pure symbol crunchers like GPT-3.
Deep dives
Grounding and Meaning in Language Models
Grounding is a crucial concept in AI, specifically in relation to language models. The debate revolves around whether language models have intrinsic meaning in their representations or if they simply manipulate symbols. The Chinese room argument by John Searle challenges the idea of computationalism, which posits that mental states are independent of their physical implementation. Stevon Harnad, an expert in cognition, argues that direct grounding must be sensory-motor in nature, meaning that interaction with the physical world is necessary to give meaning to representations. The Symbol Grounding Problem, proposed by Harnad, explores how certain words and concepts gain intrinsic meaning through sensory-motor experiences. While language can help learn new concepts, these concepts are still grounded in direct sensory-motor experiences. Harnad differentiates between artificial intelligence and reverse engineering cognition, emphasizing the need to understand the building blocks of cognition rather than solely focusing on computational models. Harnad's perspective sheds light on the limitations and challenges of language models achieving grounding.
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