I’ve seen a lot of versions of this “tact knowledge” argument against AI and I think it’s a bad argument. It would hold against “logist” or “symbolic AI” which is constructed from axioms and inferences from axioms. But I don’t see how it holds against the “connectionist” AI that is behind the current AI boom. The whole point of machine learning is that it is able to pick up patterns from a data set without having to be explicitly taught. It’s a lot more flexible and able to pick up context-dependent cues. It’s not taught on explicit rules like traditional (GOFAI) AI, rather it picks up patterns from large amounts of data. Think of how LLM learned how to use language: again, not by learning all the rules of language, but by getting exposed to a vast data base of language-use and observing statistical regularities. This isn’t exactly like human tact knowledge, but it’s similar enough to make the argument unconvincing. Granted, we are still talking about disembodied AIs—they aren’t learning in an embodied way like human beings. However, a lot of the arguments that were valid against Symbolic AI, don’t hold with the Connectionist AI. One argument that I think is still valid is the fact that human intelligence is intentional and conscious. AIs are following a mechanical and mathematical process and are not aware of what they are doing. This is why they keep hallucinating.
Well, the context-based knowledge here isn't just missing from digital records; it's inherently beyond reach because it's non-reflexive and never explicitly stated. It certainly can't be ruled out—even if it's currently a long way off technically—that eventually real-time recording will be possible through brain-computer interfaces. However, that only solves the challenge of capturing knowledge, not the problem of applying it. Because areas of operation are always evolving, the appropriate expertise must be selected for every specific situation and moment, which calls for critical discernment.
But don’t you think that say, facial recognition isn’t already something like tact knowledge? It’s impossible to create a list of rules to identify a given face, but the AI manages to pick up regularities in the data.
I accept that facial recognition is capable of detecting nuances that point toward embodied knowledge; however, I don't believe there is a straight line from there to capturing tacit knowledge in its entirety or achieving context-aware discernment.
Yes this is exactly the conversation and set of refinements I hoped would blossom after "last mile." Human knowledge is rich varied, and outside what LLMs will reach (though I expect they'll get closer and closer).
Thanks, Hollis. I agree: there will be further progress in the digital capture and processing of pre-digital knowledge. However, given that the world is both highly complex and highly dynamic, and since every new situation calls for curation, I’m quite confident that human expertise will continue to be in demand.
I’ve seen a lot of versions of this “tact knowledge” argument against AI and I think it’s a bad argument. It would hold against “logist” or “symbolic AI” which is constructed from axioms and inferences from axioms. But I don’t see how it holds against the “connectionist” AI that is behind the current AI boom. The whole point of machine learning is that it is able to pick up patterns from a data set without having to be explicitly taught. It’s a lot more flexible and able to pick up context-dependent cues. It’s not taught on explicit rules like traditional (GOFAI) AI, rather it picks up patterns from large amounts of data. Think of how LLM learned how to use language: again, not by learning all the rules of language, but by getting exposed to a vast data base of language-use and observing statistical regularities. This isn’t exactly like human tact knowledge, but it’s similar enough to make the argument unconvincing. Granted, we are still talking about disembodied AIs—they aren’t learning in an embodied way like human beings. However, a lot of the arguments that were valid against Symbolic AI, don’t hold with the Connectionist AI. One argument that I think is still valid is the fact that human intelligence is intentional and conscious. AIs are following a mechanical and mathematical process and are not aware of what they are doing. This is why they keep hallucinating.
Well, the context-based knowledge here isn't just missing from digital records; it's inherently beyond reach because it's non-reflexive and never explicitly stated. It certainly can't be ruled out—even if it's currently a long way off technically—that eventually real-time recording will be possible through brain-computer interfaces. However, that only solves the challenge of capturing knowledge, not the problem of applying it. Because areas of operation are always evolving, the appropriate expertise must be selected for every specific situation and moment, which calls for critical discernment.
But don’t you think that say, facial recognition isn’t already something like tact knowledge? It’s impossible to create a list of rules to identify a given face, but the AI manages to pick up regularities in the data.
I accept that facial recognition is capable of detecting nuances that point toward embodied knowledge; however, I don't believe there is a straight line from there to capturing tacit knowledge in its entirety or achieving context-aware discernment.
Yes this is exactly the conversation and set of refinements I hoped would blossom after "last mile." Human knowledge is rich varied, and outside what LLMs will reach (though I expect they'll get closer and closer).
Thanks, Hollis. I agree: there will be further progress in the digital capture and processing of pre-digital knowledge. However, given that the world is both highly complex and highly dynamic, and since every new situation calls for curation, I’m quite confident that human expertise will continue to be in demand.