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The efficiency of application to long algorithms and large datasets is a function of available computing power. Certain problems in coding theory can be reduce
by williamjennings 11y ago
The efficiency of application to long algorithms and large datasets is a function of available computing power. Certain problems in coding theory can be reduced to homomorphic calculation, but it takes petabytes of data to represent the solution space.
I would recommend Moore's Law as an approximate formula which is useful for guessing what year you will be able to run programs at home on your personal computer. These days, conservative estimates for the number of transistors on a chip should double every 2 years.
- darkmighty 11y agoCan you elaborate on those coding problem reducible to homomorphic calculations? I'm very interested. I thought I heard the converse, homomorphic calculations using coding (my rough understanding of Learning With Errors).
- williamjennings 11y agoHow well do you understand the term "homomorphism"? Search engines are the most common example, in general.
- darkmighty 11y agoJust to be clear, you're referring to coding theory (https://en.wikipedia.org/wiki/Coding_theory https://en.wikipedia.org/wiki/Coding_theory) in the sense of geometric codes from information theory, right? I fail to see an obvious way search engines fit in.
- williamjennings 11y agoCoding theory is the matrix algebra of information theory. You should understand the meaning of the word "homomorphism" as it is used in Multilinear Algebra before you read about how search engines score results in an unbiased manner. This is not the type of thing you can read by haphazardly browsing wikipedia. Believe you me that I have tried inasmuch. You need to buy books, read journal articles, and then learn through a graduate level math course. The best advice I can give you is to read about how search engines calculate "hits", and to search through scholar.google.com before you check wikipedia.org!
- yshalabi 11y agoThat was true in an age where single threaded performance also saw similiar improvements. But those days a gone. The power wall and transister scaling also ate factors here. Today, increases in compute capabilities enabling new technology requires new architectures that match the problems. Similarly to how GPUs enabled deep learning.
- williamjennings 11y agoYour writing is grammatically incoherent. Please make more of an effort to express yourself correctly. GPUs did not enable deep learning, they brought it to desktop scale.