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I think there's a related problem that would have been understandable at the time: if you don't understand how the world works, you are potentially missing out
by Retra 8y ago
I think there's a related problem that would have been understandable at the time: if you don't understand how the world works, you are potentially missing out on patterned information which allows one to form more accurate models of the world across domains. The assumption that's made seems to be that different problem domains are all independent, but that's not completely true. That's probably the main reason that mathematics and language are so useful.
- vanderZwan 8y ago> The assumption that's made seems to be that different problem domains are all independent, but that's not completely true. That's probably the main reason that mathematics and language are so useful. To be fair, the effectiveness of multi-disciplinary research seems to only have been acknowledged in recent decades, even though by and large the biggest breakthroughs have almost always been thanks to people looking outside of the silos of their field. The sciences are really "tribal", and it used to be much worse. For example, when I started studying in the early 2000s I remember the ML bachelor (then: AI) having recently split off from CompSci and being super-territorial. I also heard stories of CompSci having split off from mathematics a few decades before that, and during my Interaction Design (IxD) master I learned that it separated from HCI in the nineties (short version: HCI was idolising the methods of the quantitative sciences too much for its own design-oriented good, IxD decided to bring in methods and insights from the qualitative fields). I wonder if this was better or worse in Doyle's time.