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We will never run out of problems to solve. When we have cured cancer and made fully autonomous sex dolls there are still stars to explores and deep problems ab

by Ma8ee 10y ago

We will never run out of problems to solve. When we have cured cancer and made fully autonomous sex dolls there are still stars to explores and deep problems about nature to solve. The problem is that a huge part of the population won't have neither the skills or the inclination to work on hard problems.

cousin_it 10y ago
Tell that to 10 postdocs fighting for 1 tenured position.
- Ma8ee 10y ago
  That is not a lack of problems, that is a lack of funding.
  - cousin_it 10y ago
    My point was that it's not a lack of skills and inclination.
    - antisthenes 10y ago
      For every postdoc out of those 10, there's a 100 graduates fighting for a position and 1000 undergrads fighting for an internship. At any given time slice, skills in a given field are a pyramid. If we assume that the unsolved problems require the highest level of skill to be approached and solved, then there will _always_ be a shortage of problem-solvers (as the problems left become increasingly more difficult and complex) So yes, it is a lack of skills.
      - cousin_it 10y ago
        There's tons of very simple problems in the world, and tons of people who would be willing to solve them, if only they could get paid for that. That's the limiting factor, not skills.
geebee 10y ago
This is an interesting question. I'm actually not sure that there is an unlimited amount of R&D or interesting engineering work to go around. Just to be clear, I'm not convinced there isn't, either, I just think it's an interesting enough thesis that I'm willing to consider it (sorry for no cite, I have read interesting discussions of this point, may google around for them later). I think the basic thesis boils down to this - there are, at any given moment, an "unlimited" number of scientific and technical tasks required to solve those deep problems you mentioned. However, there are (this is a thesis, not a conclusion) a limited number of those problems that can be worked on right now. Those problems need to be solved before the next set can be solved. Now, if you have 10 research teams working on these problems, you have a better chance of solving them in a reasonable amount of time than if only 1 research team is working on them. 100 is better than 10. 1000 is better than 100... but at some point, you're maxed out on the number of research positions that can contribute effectively. Out of 1000, 100 will take a promising approach, 10 will get it right, and... 1 will solve the problem a bit faster than the other 9. There's a point at which you hit diminishing returns. Is this true? (and btw, I'm constructing some of this thesis myself, as you can probably tell, I don't think I've read it in quite this form before). Is it true-ish? I'm not really sure. I'm not sure it matters for the moment, because I don't think our budget for R&D and high value engineering work, or our educational systems to develop talent, are anywhere close to a point where we should be discussing whether we're reaching diminishing returns to scale. But is there a theoretical point at which throwing more genius at problems won't make a difference in how quickly we progress? The question interests me, in an abstract way.

by Ma8ee 10y ago

cousin_it 10y ago

Tell that to 10 postdocs fighting for 1 tenured position.

Ma8ee 10y ago
That is not a lack of problems, that is a lack of funding.
- cousin_it 10y ago
  My point was that it's not a lack of skills and inclination.
  - antisthenes 10y ago
    For every postdoc out of those 10, there's a 100 graduates fighting for a position and 1000 undergrads fighting for an internship. At any given time slice, skills in a given field are a pyramid. If we assume that the unsolved problems require the highest level of skill to be approached and solved, then there will _always_ be a shortage of problem-solvers (as the problems left become increasingly more difficult and complex) So yes, it is a lack of skills.
    - cousin_it 10y ago
      There's tons of very simple problems in the world, and tons of people who would be willing to solve them, if only they could get paid for that. That's the limiting factor, not skills.

geebee 10y ago

This is an interesting question. I'm actually not sure that there is an unlimited amount of R&D or interesting engineering work to go around. Just to be clear, I'm not convinced there isn't, either, I just think it's an interesting enough thesis that I'm willing to consider it (sorry for no cite, I have read interesting discussions of this point, may google around for them later). I think the basic thesis boils down to this - there are, at any given moment, an "unlimited" number of scientific and technical tasks required to solve those deep problems you mentioned. However, there are (this is a thesis, not a conclusion) a limited number of those problems that can be worked on right now. Those problems need to be solved before the next set can be solved. Now, if you have 10 research teams working on these problems, you have a better chance of solving them in a reasonable amount of time than if only 1 research team is working on them. 100 is better than 10. 1000 is better than 100... but at some point, you're maxed out on the number of research positions that can contribute effectively. Out of 1000, 100 will take a promising approach, 10 will get it right, and... 1 will solve the problem a bit faster than the other 9. There's a point at which you hit diminishing returns. Is this true? (and btw, I'm constructing some of this thesis myself, as you can probably tell, I don't think I've read it in quite this form before). Is it true-ish? I'm not really sure. I'm not sure it matters for the moment, because I don't think our budget for R&D and high value engineering work, or our educational systems to develop talent, are anywhere close to a point where we should be discussing whether we're reaching diminishing returns to scale. But is there a theoretical point at which throwing more genius at problems won't make a difference in how quickly we progress? The question interests me, in an abstract way.