Exceptional framing of the optimization vs explanation tradeoff. The Pearl/Wiener lineage you trace really underscore how this isn't new—its just that modern compute makes statistical control scalable enough to displace causal reasoning entirely. In my experience building ML pipelines, the moment you optimize for prediction accuracy over interpretability, you're essentially trading debuggability for performance dunno if thats always the right call tho.
Thank you Neural Foundry! That tradeoff shows up fast once you’re actually building things. As soon as prediction becomes the main goal, explanation quietly drops out, and you feel it the first time something breaks and you can’t tell why. Performance looks great until reality pushes back. I don’t think it’s always the wrong call, but it’s rarely a free one.
I enjoyed this post very much. A real chicken and egg conundrum, except in the eyes of AI they both have equal potential.
I thought the comment regarding “asking why is disruptive” was thought provoking. Science, which is built around specific human understanding and observations, claims to continually as “why”. However because humans are involved it seems tougher to crack a biased ego than to challenge concrete evidence by asking a contradictory questions. Those individuals are looked upon as crackpots and uninformed.
I believe the closer we get to allowing AI to cracking the “undeniable truths” the sooner we’ll see initiatives from scientists wanting to shut these experiments down in an effort to “regain control”, which will be code for “we’re not ready to have our construct destroyed and replaced”.
Thank you very much for the insightful comment Scott! I’m with you on this. The “why” really is the disruptive part, especially when it points back at human assumptions instead of the data. Challenging evidence is one thing, but challenging ego and long held frameworks is where people dig in.
I also think you’re right that as AI starts pushing on things we treat as settled or unquestionable, there will be a strong impulse to slow it down or shut it down. Not because it’s wrong, but because it threatens the sense of control we’ve built around our models. That tension is very much what this piece was circling.
Exceptional framing of the optimization vs explanation tradeoff. The Pearl/Wiener lineage you trace really underscore how this isn't new—its just that modern compute makes statistical control scalable enough to displace causal reasoning entirely. In my experience building ML pipelines, the moment you optimize for prediction accuracy over interpretability, you're essentially trading debuggability for performance dunno if thats always the right call tho.
Thank you Neural Foundry! That tradeoff shows up fast once you’re actually building things. As soon as prediction becomes the main goal, explanation quietly drops out, and you feel it the first time something breaks and you can’t tell why. Performance looks great until reality pushes back. I don’t think it’s always the wrong call, but it’s rarely a free one.
I enjoyed this post very much. A real chicken and egg conundrum, except in the eyes of AI they both have equal potential.
I thought the comment regarding “asking why is disruptive” was thought provoking. Science, which is built around specific human understanding and observations, claims to continually as “why”. However because humans are involved it seems tougher to crack a biased ego than to challenge concrete evidence by asking a contradictory questions. Those individuals are looked upon as crackpots and uninformed.
I believe the closer we get to allowing AI to cracking the “undeniable truths” the sooner we’ll see initiatives from scientists wanting to shut these experiments down in an effort to “regain control”, which will be code for “we’re not ready to have our construct destroyed and replaced”.
Thank you very much for the insightful comment Scott! I’m with you on this. The “why” really is the disruptive part, especially when it points back at human assumptions instead of the data. Challenging evidence is one thing, but challenging ego and long held frameworks is where people dig in.
I also think you’re right that as AI starts pushing on things we treat as settled or unquestionable, there will be a strong impulse to slow it down or shut it down. Not because it’s wrong, but because it threatens the sense of control we’ve built around our models. That tension is very much what this piece was circling.