In recent years, AI’s evolution has captivated even former skeptics like Douglas Hofstadter. Initially critical of AI systems, he expressed a newfound admiration for GPT-4, released in 2023. He noted its advanced capabilities, which mirror human-like thinking, emphasizing that these models exhibit a form of understanding, albeit in an alien way. At the core of large language models (LLMs) lies a sophisticated “vector” system that encodes words in high-dimensional spaces, capturing nuanced meanings. This geometric representation enables models to generate analogies and contextual responses, as demonstrated in practical tasks. Insights from researchers, such as Trenton Bricken at Anthropic, reveal the intricate workings of LLMs, including feature controls akin to brain function. Notably, the Transformer architecture, foundational to models like ChatGPT, echoes concepts introduced by Pentti Kanerva. As neuroscience increasingly intersects with AI, researchers leverage LLMs to deepen our understanding of human cognition, transforming perspectives on intelligence and memory models.
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