한국관련자료
The myth of the monolith: AI is not one thing
With the intense attention focused on large language models (LLMs) like ChatGPT, AI today is often discussed as a unitary force. But this framing obscures more than it reveals. AI is not one thing―it is a sprawling field of interlocking techniques, tools, and capabilities, developing and deploying across domains and specific applications as diverse as molecular property prediction, video super-resolution, and multilingual speech generation.
This research examines and illustrates these domains and applications. The exercise underscores the key reality that AI is neither singular, nor static. Rather, it comprises dynamic capabilities of growing complexity, including numerous domains like medical diagnostics, physical robotics, game-playing agents, graph reasoning, and more. This landscape is evolving rapidly, and the result is not a single revolution or form of intelligence, but instead a layered and incrementally expanding ecosystem of capabilities.
This diversity matters, not just to the marketplace but for all aspects of AI strategies and governance. It shapes what kind of testing is needed, how risk is assessed, and how or why countries choose to regulate or adopt specific forms of AI. Thinking about AI as one, monolithic thing in this diverse ecosystem can end up seeking to boil the ocean. The rest of this piece explores how to avoid that trap: first, by showing the breadth of AI domains and tasks; second, by unpacking how these capabilities map onto real systems and risks; and finally, by discussing the governance and sovereignty strategies that emerge once AI is treated as plural rather than singular.
This research examines and illustrates these domains and applications. The exercise underscores the key reality that AI is neither singular, nor static. Rather, it comprises dynamic capabilities of growing complexity, including numerous domains like medical diagnostics, physical robotics, game-playing agents, graph reasoning, and more. This landscape is evolving rapidly, and the result is not a single revolution or form of intelligence, but instead a layered and incrementally expanding ecosystem of capabilities.
This diversity matters, not just to the marketplace but for all aspects of AI strategies and governance. It shapes what kind of testing is needed, how risk is assessed, and how or why countries choose to regulate or adopt specific forms of AI. Thinking about AI as one, monolithic thing in this diverse ecosystem can end up seeking to boil the ocean. The rest of this piece explores how to avoid that trap: first, by showing the breadth of AI domains and tasks; second, by unpacking how these capabilities map onto real systems and risks; and finally, by discussing the governance and sovereignty strategies that emerge once AI is treated as plural rather than singular.
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