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Solving such problems will forever require a marriage of ingenious human algorithms and computer brute force, no matter how fast computers will get.
I realized then that computer brute force is limited, and sat down and used heuristic methods combining various partial chains, finally coming up with a chain that was over 400 words long!
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Readers who do not have experience with the phenomenal numerical complexity of these problems may wonder why we can't just use computer "brute-force" algorithms to examine all possible cases.
Using a computer's brute force, Cutler figured out by trial and error that there were only 536 solutions to the puzzle, excluding rotating and reflecting the final assembled square.
Computers use "brute force" methods.
In total, the number of different possible arrangements of stones stretches beyond 10100, rendering it impossible for a computer to play by brute force computation of all possible outcomes.
Both the man and the computer presumably do massive amounts of "brute force" computation on their very different architectures.
A computer can win through brute force by taking opening moves that lead to a game it has already played out and won.
We have already looked at problems that resist exact brute force computer solutions, where human intuition does quite well, when we examined the traveling salesman and related problems in Open Science, Numberplay Style!
It was once believed that computers could never beat the best human at chess, because computers perform very inefficient brute force attacks on problems, instead of relying on intuition and hierarchical structures like our brains do.
AI is, today, nothing more than brute force computing, with superfast computers crunching massive amounts of data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com