I'd say this article seems to conflate *tacit knowledge* (which can become accessible to AI only through a process of legibilisation, recording, or rediscovery of grey literature and humanly-latently-carried tacit knowledge) and *the need for exploration, experimentation, and trial and error*. Those are quite distinct in my view!
Exploration is crucial to uncovering new insight (and the practice of good experimentation lies in taking steps to make the most 'informative mistakes' you can). Tacit knowledge is one form that insights, new or old, can take. They're not really the same thing, though both can be bottlenecks to AI-driven scientific breakthroughs.
In the spirit of "What could be problematic is that a frontier large language model trained on the technoscientific literature will only “know” the “what” that worked, but not the full story of “why” and “how” " ... perhaps it's not the lack of tacit knowledge that is limiting AI's ability to "make scientific breakthroughs" ... perhaps it its the dogmatic training biases we have instilled in it with "wrong" knowledge, or the inability to think as nature has intended which is mankind's innate strength ... see https://tinyurl.com/startwiththeanswer
Interesting, though I think it describes where we are today more than where we’re headed.
AI has already solved math problems in novel ways. That’s arguably a signal that interpretation may not remain an exclusively human domain for long.
Models, and especially world (3D) models, are becoming increasingly capable, self-improving, and fast. They’ll soon ask questions we never thought to ask, and generate hypotheses we wouldn’t have imagined.
The idea that AI will always need humans to interpret reality is probably an illusion.
I wrote last year with some of this in mind: You Can't Skip Exploration (https://www.oliversourbut.net/p/you-cant-skip-exploration)
I'd say this article seems to conflate *tacit knowledge* (which can become accessible to AI only through a process of legibilisation, recording, or rediscovery of grey literature and humanly-latently-carried tacit knowledge) and *the need for exploration, experimentation, and trial and error*. Those are quite distinct in my view!
Exploration is crucial to uncovering new insight (and the practice of good experimentation lies in taking steps to make the most 'informative mistakes' you can). Tacit knowledge is one form that insights, new or old, can take. They're not really the same thing, though both can be bottlenecks to AI-driven scientific breakthroughs.
In the spirit of "What could be problematic is that a frontier large language model trained on the technoscientific literature will only “know” the “what” that worked, but not the full story of “why” and “how” " ... perhaps it's not the lack of tacit knowledge that is limiting AI's ability to "make scientific breakthroughs" ... perhaps it its the dogmatic training biases we have instilled in it with "wrong" knowledge, or the inability to think as nature has intended which is mankind's innate strength ... see https://tinyurl.com/startwiththeanswer
We just finished a massive review of Tacit Knowledge, I invite you to take a look: https://curriculumredesign.org/wp-content/uploads/Tacit-Knowledge-CCR.pdf
Contact me if you wish, Charles.Fadel@CurriculumRedesign.org
Interesting, though I think it describes where we are today more than where we’re headed.
AI has already solved math problems in novel ways. That’s arguably a signal that interpretation may not remain an exclusively human domain for long.
Models, and especially world (3D) models, are becoming increasingly capable, self-improving, and fast. They’ll soon ask questions we never thought to ask, and generate hypotheses we wouldn’t have imagined.
The idea that AI will always need humans to interpret reality is probably an illusion.