Vincent Carchidi
Imagine an artificial intelligence (AI) application that you can meaningfully communicate with during moments of careful deliberation. I do not mean the mimicry of communication popularized by chatbots powered by Large Language Models (LLMs), most recently embodied in OpenAI’s GPT-4o. I envision an AI model that can productively engage with specialized literature, extract and re-formulate key ideas, and engage in a meaningful back-and-forth with a human expert. One easily imagines the applicability of such a model in domains like medical research. Yet, the machine learning systems that have captured the world’s attention—generative AIs like ChatGPT, Gemini, and Claude—lack the intellectual resources and autonomy necessary to support such applications. Our lofty AI vision remains a matter of science fiction—for now.
The drive to master AI in geopolitics is undeterred by this reality. Indeed, the geopolitical “scramble” for AI triggered in 2023—represented by states as diverse as Britain, France, Germany, India, Saudi Arabia, the United Arab Emirates, the United States, and China—was undoubtedly sparked by generative AI and machine learning more broadly. However, some corners of the AI world conceive of machine learning as merely the current stage of state-of-the-art AI—but not its final stage.
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