Empirical Computing: Unlocking Limitless Calculation Speed

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Discussion Overview

The discussion revolves around the concept of "empirical computing" and its potential to enhance calculation speed by utilizing physical examples, particularly in complex systems like turbulent flow. Participants explore the feasibility of using empirical observations as answers to computational problems and the implications of such an approach.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • Some participants propose that computers could utilize physical examples of turbulent flow as answers to calculations, potentially leading to limitless calculation speed.
  • Others argue that the relevance of results from one problem to another may be questionable, suggesting that similarities between situations might undermine the accuracy of calculations.
  • One participant describes the concept as akin to an "analog computer," referencing how electrical circuits can solve differential equations, indicating a technical basis for the idea.
  • Another viewpoint emphasizes the need for careful selection of systems and problems to ensure they are closely related, framing this as a practical challenge rather than a theoretical one.
  • There is a suggestion that all computing is inherently "empirical," as conventional computers rely on physical processes that mimic logical operations, but a distinction is made between conventional and "real" empirical computing.
  • One participant speculates that new AI technologies may be necessary to identify parallels between physical systems and computational problems, expressing doubt about human capability in this area without extensive research.

Areas of Agreement / Disagreement

Participants express differing views on the viability and implications of empirical computing, with no consensus reached on its effectiveness or the practicality of the proposed methods.

Contextual Notes

The discussion highlights uncertainties regarding the relationship between different problems and the accuracy of using empirical data in calculations. There are also assumptions about the capabilities of future AI technologies and the nature of physical systems relevant to computation.

Bartholomew
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We know the basic principles behind many systems which are nevertheless too complicated to calculate accurately--turbulent flow, for example. The number of calculations required to precisely model turbulent flow is enormous, but it happens in nature and can be empirically observed. What would prevent a computer from taking physical examples of turbulent flow as ANSWERS to calculations, and finding uses for those answers in other problems? If done properly there would hardly be any upper limit on how fast calculations could be performed.
 
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Bartholomew said:
We know the basic principles behind many systems which are nevertheless too complicated to calculate accurately--turbulent flow, for example. The number of calculations required to precisely model turbulent flow is enormous, but it happens in nature and can be empirically observed. What would prevent a computer from taking physical examples of turbulent flow as ANSWERS to calculations, and finding uses for those answers in other problems? If done properly there would hardly be any upper limit on how fast calculations could be performed.

I don't think this would work. What would the results from problem A have to do with problem B? If the situations are similar, then the answers will be close, but doesn't that destroy the point of "accurately" calculating?
 
Actually, what you are describing is an "analog computer". For example, one can show that an electrical circuit, with induction coil of strength L, resitance of strength R, and capacitor of strength C connected to a voltage source V(t), gives rise to the differential equation L y"+ Ry'+ (1/C)y= V(t). One can use an electrical circuit, with variable coil, resistance, and restistance, connected to a voltage generator, to solve differential equations of the form ay"+ by'+ c= f(t).
 
You would have to pick the system to observe and the problem to solve very cleverly, so that they are as close as possible. But since there are so many physical systems to pick from and so many ways of stating problems, this is only a practical obstacle.

Anyway, we do it already; all computing is actually "empirical computing." We choose the physical processes that go on in computer chips so that they parallel logical operators as closely as possible. When we punch in a calculation on a calculator, what we are doing is trusting that the processes inside the calculator mimic closely enough the abstract procedure of finding the answer.

The difference between that kind of empirical computing and REAL empirical computing--and it is a big one--is the trade-off between complete control of what is calculated (conventional computers) and complete speed of processing (real-world chaotic systems).

Probably it would require some new kind of AI to find the parallels. I doubt any group of humans would be smart enough unless thousands of years were devoted to the research. But... I say it's possible.
 

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