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1. This search space is too large to hit anything by firing shots in the dark. 2. Results will either be completely redundant or ethically questionable. 3. Th

1. This search space is too large to hit anything by firing shots in the dark. 2. Results will either be completely redundant or ethically questionable. 3. The resources used for optimization should not exceed what performance may be gained. There is no route around full comprehension of this problem before you can solve it. It is a philosophical event horizon.

KRAKRISMOTT 3y ago
> 1. This search space is too large to hit anything by firing shots in the dark. 2. Results will either be completely redundant or ethically questionable. 3. The resources used for optimization should not exceed what performance may be gained. There is no route around full comprehension of this problem before you can solve it. It is a philosophical event horizon. My sweet summer child http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- ganzuul 3y ago
  That links to a spiritual quest described in the language of information theory. It is looking for what makes us different from machines instead of what makes us similar. It is an ego talking about predictions that did not pan out. Rookie mistake.
  - KRAKRISMOTT 3y ago
    That's not a spiritual quest, that's an empirical observation of what worked and what failed. Expert systems without the guidance of statistical learning has gone nowhere. The 90s Japan is calling and they want you back to wage war against the AI winter and make their fifth generation computing project great again.
inasio 3y ago
People routinely solve quadratic assignment problems (perhaps not provably optimal, but finding good solutions within a small bound of optimality) where the search space is greater than 10^3000 [0], using relatively simple heuristics like simulated annealing. [0] e.g. any of the QAPLIB instances in Goh et al. Proceedings of the 2022 Genetic and Evolutionary Computation Conference

KRAKRISMOTT 3y ago

> 1. This search space is too large to hit anything by firing shots in the dark. 2. Results will either be completely redundant or ethically questionable. 3. The resources used for optimization should not exceed what performance may be gained. There is no route around full comprehension of this problem before you can solve it. It is a philosophical event horizon. My sweet summer child http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html

ganzuul 3y ago
That links to a spiritual quest described in the language of information theory. It is looking for what makes us different from machines instead of what makes us similar. It is an ego talking about predictions that did not pan out. Rookie mistake.
- KRAKRISMOTT 3y ago
  That's not a spiritual quest, that's an empirical observation of what worked and what failed. Expert systems without the guidance of statistical learning has gone nowhere. The 90s Japan is calling and they want you back to wage war against the AI winter and make their fifth generation computing project great again.

inasio 3y ago

People routinely solve quadratic assignment problems (perhaps not provably optimal, but finding good solutions within a small bound of optimality) where the search space is greater than 10^3000 [0], using relatively simple heuristics like simulated annealing. [0] e.g. any of the QAPLIB instances in Goh et al. Proceedings of the 2022 Genetic and Evolutionary Computation Conference