3 ms·

I think this is a very simplistic view that only holds for a very few areas of study. The idea that computer simulation is an adequate substitute for scientifi

I think this is a very simplistic view that only holds for a very few areas of study. The idea that computer simulation is an adequate substitute for scientific experimentation in the majority of the sciences is puzzling to me. Not that models don't help, but there's still far too much we either don't know how to model, or don't have the computational power to model (or we have both, but it would be more expensive than just testing it in a lab). I also don't understand why your fourth point means that specialization is not necessary. Isn't it the opposite--that generalists with shallow knowledge of many fields no longer suffice? People must specialize because the level of complexity in newer technology is extremely high, and to get anywhere it is necessary to focus on a small but very detailed slice of the pie. The only really valid point is that information formerly available in journals is now available online. But I cannot say that I have seen a noticeable uptick in research being done by non-Ph.D.s despite this widespread availability, so I think it's at least premature to conclude that you can develop the same level of expertise as an autodidact.

raverbashing 12y ago
"The idea that computer simulation is an adequate substitute for scientific experimentation in the majority of the sciences is puzzling to me" Depends on the field. Even in natural sciences, there are several research areas not related directly with experimentation. I would say that, for a lot of areas, we have the bases covered, but what's needed are better tools to understand it. Protein folding is one area. The physics are ok, what we don't know is how to make it work for several atoms at once. Data transmission is another. The real changes behind 4G, 802.11N, etc, is not a better radio transmitter only, but better channel estimation, error correction and use of diversity. "I also don't understand why your fourth point means that specialization is not necessary" It is necessary, but not sufficient. If you only focus on one area but don't know how to interface with the necessary surroundings you won't get anywhere. " so I think it's at least premature to conclude that you can develop the same level of expertise as an autodidact." The application of new discoveries and techniques is important as well, and several companies and individuals are doing important work in this area. (Go, Rust, several machine learning libraries, etc)
- erkkie 12y ago
  I think saying specialization is necessary but not sufficient sums up the trends in many fields. The barriers of entry to having information available are the lowest they've ever been and thus traditional models are being eaten by ad-hoc methods. And that is fine, requires reorientation though.

raverbashing 12y ago

"The idea that computer simulation is an adequate substitute for scientific experimentation in the majority of the sciences is puzzling to me" Depends on the field. Even in natural sciences, there are several research areas not related directly with experimentation. I would say that, for a lot of areas, we have the bases covered, but what's needed are better tools to understand it. Protein folding is one area. The physics are ok, what we don't know is how to make it work for several atoms at once. Data transmission is another. The real changes behind 4G, 802.11N, etc, is not a better radio transmitter only, but better channel estimation, error correction and use of diversity. "I also don't understand why your fourth point means that specialization is not necessary" It is necessary, but not sufficient. If you only focus on one area but don't know how to interface with the necessary surroundings you won't get anywhere. " so I think it's at least premature to conclude that you can develop the same level of expertise as an autodidact." The application of new discoveries and techniques is important as well, and several companies and individuals are doing important work in this area. (Go, Rust, several machine learning libraries, etc)

erkkie 12y ago
I think saying specialization is necessary but not sufficient sums up the trends in many fields. The barriers of entry to having information available are the lowest they've ever been and thus traditional models are being eaten by ad-hoc methods. And that is fine, requires reorientation though.