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This topic clearly touches a nerve with the HN community, but I strongly agree with you. To be honest, I've been someone disappointed with the way AI/DL resear

This topic clearly touches a nerve with the HN community, but I strongly agree with you. To be honest, I've been someone disappointed with the way AI/DL research has proceeded in the last several years and none of this really surprises me. From the beginning, this whole enterprise has been detached from basic computational and statistical theory. At some level this is fine — you don't need to understand everything you create — but when you denigrate that underlying theory you end up in a situation where you don't understand what you're doing. So you end up with a lot of attention paid to things like "explainability" and "interpretability" and less so to "information-theoretic foundations of DL models", even though the latter probably leads to the former. If you have a community that considers itself above basic mathematical, statistical, and computational theory, is it really a surprise that you end up with rhetoric about it being beyond our understanding? In most endeavors I've been involved with, there would be a process of trying to understand the fundamentals before moving on to something else, and then using that to bootstrap into something more powerful. I probably come across as overly cynical but a lot of this seems sort of like a self-fulfilling prophecy: a community constituting individuals who have convinced themselves that if it is beyond their understanding, it must be beyond anyone's understanding. There are certainly risks to AI that should be discussed, but it seems these discussions and inquiries should be more open, probably involving other people outside the core community of Big Tech and associated academic researchers. Maybe it's not that AI is more capable than everyone, just that others are maybe more capable of solving certain problems — mathematicians, statisticians, and yes, philosophers and psychologists — than those who have been involved with it so far.

nologic01 3y ago
> mathematicians, statisticians, and yes, philosophers and psychologists — than those who have been involved with it so far. I think mathematicians and statisticians are hard to flummox but the risk with non-mathematically trained people such as philosophers and psychologists is that they can be sidetracked easily by vague and insinuating language that allows them to "fill-in" the gaps. They need an unbiased "interpreter" of what the tech actually does (or can do) and that might be hard to come by. I would add political scientists and economists to the list. Not that I have particular faith in their track record solving any problem, but conceptually this is also their responsibility and privilege: technology reshapes society and the economy and we need to have a mature and open discussion about it.
calf 3y ago
Do you have any stories of how AI/DL has ignored foundational scientific problems? I do know that my old EECS professors who have pivoted toward AI and coming from adjacent/tangential research areas, are specifically interested in theoretical and scientific clarification properties of neural networks. One of them basically has been trying to start Theoretical Machine Learning as a new discipline and approach that is sorely needed.

nologic01 3y ago

> mathematicians, statisticians, and yes, philosophers and psychologists — than those who have been involved with it so far. I think mathematicians and statisticians are hard to flummox but the risk with non-mathematically trained people such as philosophers and psychologists is that they can be sidetracked easily by vague and insinuating language that allows them to "fill-in" the gaps. They need an unbiased "interpreter" of what the tech actually does (or can do) and that might be hard to come by. I would add political scientists and economists to the list. Not that I have particular faith in their track record solving any problem, but conceptually this is also their responsibility and privilege: technology reshapes society and the economy and we need to have a mature and open discussion about it.

calf 3y ago

Do you have any stories of how AI/DL has ignored foundational scientific problems? I do know that my old EECS professors who have pivoted toward AI and coming from adjacent/tangential research areas, are specifically interested in theoretical and scientific clarification properties of neural networks. One of them basically has been trying to start Theoretical Machine Learning as a new discipline and approach that is sorely needed.