演講主題：AI數位時代，如何擴增能力?
演講者：國立陽明交通大學資訊工程學系/林一平終身講座教授
時間：2025.11.14(五)下午16:20-18:10
地點：東海大學工學院 E108教室
演講摘要與講者簡歷請參閱附件
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聯絡人：工工系謝小姐 04-23594319 #112
Abstract
In 1880, New York City had at least 150,000 horses, producing over 100,000 tons of manure and 10 million gallons of horse urine annually. Crossing sweepers, who cleared walkable paths for pedestrians, were in short supply. However, within a few years, automobile technology rendered horses obsolete. By 1912, the number of cars in New York had surpassed that of horses, and the manure problem vanished. The crossing sweepers all lost their jobs. Are we the crossing sweepers of the AI era?
Everyone knows the basic principles behind LLMs (Large Language Models), but unlike teams like DeepSeek, we can’t apply them as cleverly by harnessing data to significantly reduce the cost of reasoning. The DeepSeek team is both smart and diligent. They also have access to more computing power than Taiwan does. Can Taiwan afford not to catch up?
You shouldn’t fear being replaced by AI itself—but rather by those who know how to wield it effectively. As artificial intelligence becomes more embedded in our lives, it’s not just a technological shift; it’s an evolutionary one. Humans are not simply being replaced—we’re being reshaped. Our minds and bodies are adapting to collaborate with machines, to the point where the line between human and machine is increasingly blurred.
The real transformation lies in who adapts. Those who learn to work with AI will evolve, being pushed to focus on creative, strategic, and emotional labor that machines cannot replicate. Ironically, this might mean humans work even harder, not less, as described by the Jevons Paradox: greater efficiency leads to more, not less, effort. Meanwhile, those who fail to adapt, regardless of their raw intelligence, risk obsolescence, will become, in Keynesian terms, modern Neanderthals.
Instead of the dystopian fear that AI will eliminate all jobs, the likelier scenario is a divided world: one group thrives through close AI integration, while another becomes dependent, idle, or governed by algorithms they can’t control. The challenge isn't to outcompete AI, but to evolve alongside it.
