TezlaOil

> before the price hikes you weren't willing to pay a dollar more for name brand (or premium drugs etc), but after the price hike you are?

Imagine that 10 normal people and Bill Gates want to buy bottles of wine. Bill goes for a bottle of $6000 Richebourg Grand Cru, and the 10 normal people always go for Shitscreek Red, sold for $4. So about 9% of the buyers are willing to pay extra for the name brand.

After the Great Wine Tax Bill of 2021, the prices are both increased by a fixed per-bottle amount of $6000. So now the Richebourg costs $12000, and the Shitscreek costs $6004. The 10 normal people migrate to beer, and Bill Gates still buys Richebourg. So about 100% of the buyers are willing to pay extra for the name brand.

The computer program BACON (1987) of Nobel Prize winner Herbert Simon was given the distances of planets from the sun together with their period of revolution and it independently rediscovered Kepler's third law, illustrating how far the positivism at work in "AI" can go. But Kepler's achievement was not determining a - straightforward - relation between two rows of numbers: it was to figure out which numbers should be related, and Kepler's real achievement was actually finding the right question. Incidentally, Kepler stated a fourth law relating planets to perfect polyhedra, and one wonders why this fourth law has not been rediscovered by computer yet...

-- Jean-Yves Girard: Locus Solum

> The tactic system then basically build the actual proof by putting everything on a stack and applying in reverse: once you've proved both goals, it knows it can apply the forward-reasoning induction proof function to construct a proof of the original statements from the two proofs you gave.

In Agda, you would type something like `nat-ind ?x ?y` into a hole of type (forall m -> P m). Then ?x becomes a hole of type (P zero) and ?y becomes a hole of type (forall n -> P n -> P (suc n)), and you can fill these holes with proofs at your own leisure. It does not feel all that different from using `induction m` in Coq. In Agda, you'd normally implement your "reflective tactics" as functions, or you'd write macros if you really need them (e.g. for ring solvers), but there is no separate tactic language and proofs are never tactic scripts.

The cited price was $10/year, not $10/month. That's a huge difference.

8.90 EUR per month is about $130 a year, so still an order of magnitude more expensive than cheap PHP hosting.

> wouldn't pass even the most basic "is this poll statistically valid?" test

It totally would. Statistically, a sample size of 282 out of a population of 60000 is pretty good.

If this test was authentic, we would be 95% sure that the true percentage of the population who think that the memo is harmless is between 47% and 60%.

Gene swapping makes perfect sense when the success of a given solution is due to the various contributions of the solution's components (this is closely related to the Credit Assignment Problem).

For example, a 'random' tour of the traveling salesman might have a nice sensible path between Paris and Moscow, while another solution has a good path between San Francisco and Mexico City. Chromosomal crossover (the fundamental operation of genetic algorithms) can create "offspring" tours that contain both of these components.

This is not at all analogous to exploring the middle between two local optima - the solution space often has no meaningful geometric structure, so being located "between" two other solutions may not make sense at all.

Machine learning is automated classification, regression, and decision-making. Genetic algorithms don't tend to perform so well in these areas (just as ML is not so appropriate for combinatorial optimization).

Instead, genetic methods work well for various messy industrial engineering such as strip packaging design or antenna design. One of my favorite recent results solves a structural engineering problem using grammatical evolution [1].

[1] http://www.sciencedirect.com/science/article/pii/S0926580513...

Software Foundations book [1] covers many case studies. One of the authors has a lecture course based around the book as well.

[1] softwarefoundations.cis.upenn.edu/current/index.html