Vertaling en analyse van woorden door kunstmatige intelligentie ChatGPT
Op deze pagina kunt u een gedetailleerde analyse krijgen van een woord of zin, geproduceerd met behulp van de beste kunstmatige intelligentietechnologie tot nu toe:
- hoe het woord wordt gebruikt
- gebruiksfrequentie
- het wordt vaker gebruikt in mondelinge of schriftelijke toespraken
- opties voor woordvertaling
- Gebruiksvoorbeelden (meerdere zinnen met vertaling)
- etymologie
number generator - vertaling naar arabisch
.jpg?width=200 "This Van de Graaff generator of the first Hungarian linear particle accelerator achieved 700 kV in 1951 and 1000 kV in 1952.")
![The Museum of Science in Boston]], Massachusetts](https://commons.wikimedia.org/wiki/Special:FilePath/Spark by Van de Graaff generator.jpg?width=200 "The Museum of Science in Boston]], Massachusetts")
![A Van de Graaff particle accelerator in a pressurized tank at [[Pierre and Marie Curie University]], Paris](https://commons.wikimedia.org/wiki/Special:FilePath/Van den Graaff DSC09091.JPG?width=200 "A Van de Graaff particle accelerator in a pressurized tank at [[Pierre and Marie Curie University]], Paris")
Definitie
Wikipedia
Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs), which generate numbers that only look random but are in fact pre-determined—these generations can be reproduced simply by knowing the state of the PRNG.
Various applications of randomness have led to the development of different methods for generating random data. Some of these have existed since ancient times, including well-known examples like the rolling of dice, coin flipping, the shuffling of playing cards, the use of yarrow stalks (for divination) in the I Ching, as well as countless other techniques. Because of the mechanical nature of these techniques, generating large quantities of sufficiently random numbers (important in statistics) required much work and time. Thus, results would sometimes be collected and distributed as random number tables.
Several computational methods for pseudorandom number generation exist. All fall short of the goal of true randomness, although they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible). This generally makes them unusable for applications such as cryptography. However, carefully designed cryptographically secure pseudorandom number generators (CSPRNGS) also exist, with special features specifically designed for use in cryptography.
