The Dilemma of AI: Copying Humans vs. True Intelligence #AIdebate

The author reflects on the limitations of language models in artificial intelligence, as discussed by Yann LeCun, a prominent figure in the field. LeCun argues that language is a human filter that distills reality into words, creating a limited projection of the world. Current large language models only process this linguistic projection, disconnected from direct experience of the real world. The author distinguishes between human-mimicking AI, which replicates human intelligence through text data processing, and true artificial general intelligence (AGI) that surpasses human capabilities. AGI would learn independently and problem-solve beyond human patterns. While creating human-mimicking AI seems feasible through supervised learning, achieving AGI remains a challenge. The author questions the data needed for AGI to tackle complex problems effectively. Language models have their place, but for high-stakes tasks, true AGI is required. The path to AGI involves a combination of supervised and reinforcement learning, but the type of data necessary for true intelligence remains uncertain. The author acknowledges the ongoing development of ideas and the need to explore beyond human-like intelligence towards AGI.

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Source link: https://medium.com/@FdForThought/the-ai-conundrum-human-mimicry-vs-true-general-intelligence-2eff0dfb181f?source=rss——ai-5