0 Members and 4 Guests are viewing this topic.
But surely the principle of doing experimentation is not well understood by anyone.
You can not demonstrate reliably any further phenomenon behaviour if your experimental devices itself use the phenomenon you want to study.
OK, say you want to do an experiment to find out about tigers.According to you, you can't include a tiger in my experiment because that would be "the phenomenon you want to study."
You seem to think that nobody understands how to do experiments.That's plainly wrong.
I say that it is not well understood "by anyone".This mean that not anyone well understand... not that everyone not understand.
Something that is not well understood by anyone means that nobody understands it well.
You are wrong.
There are two possibilities to understand it
english langage is not well suited for science.
english langage is not well suited for science... but i known it already
Thats why you do the confusion.I am not doing the confusion
(i am aware of the subtility).
In diplomacy, it's very helpful to say 1 thing, but mean 2.
DeepL translator (surely a better english translator than some Bored Chimist) :
What you should have said (if that was what you meant) was "But surely the principle of doing experimentation is not well understood by everyone."
No it is very awfull.You probably do the confusion between diplomacy and trade.Diplomacy and trade are 2 separate words (just to say...).
So what ?Anyone has changed to everyone.I suppose everyone can now see that there is 2 possibilities to understand the first sentence.But you surely are not part of everyone.
Diplomacy and trade are 2 separate words (just to say...).I know what diplomacy is:https://www.quotery.com/quotes/ambassador-honest-man-sent-lie
Online translators rarely pick up these difference in meaning.
When you translate it from English, to French and then back, you change the meaning.
Thats because there is some ambiguity in english you dont even know
but i already explained it to you.
You are now trying to argue that you speak better English than two native English speakers- even though your writing is riddled with errors- because an electronic translator agrees with you- even though it disagrees with itself.
How does DeepL work?November 1, 2021We are frequently asked how it is that DeepL Translator often works better than competing systems from major tech companies. There are several reasons for this. Like most translation systems, DeepL Translator translates texts using artificial neural networks. These networks are trained on many millions of translated texts. However, our researchers have been able to make many improvements to the overall neural network methodology, mainly in four areas.Network architectureIt is well known that most publicly available translation systems are direct modifications of the Transformer architecture. Of course, the neural networks of DeepL also contain parts of this architecture, such as attention mechanisms. However, there are also significant differences in the topology of the networks that lead to an overall significant improvement in translation quality over the public research state of the art. We see these differences in network architecture quality clearly when we internally train and compare our architectures and the best known Transformer architectures on the same data.Training dataMost of our direct competitors are major tech companies, which have a history of many years developing web crawlers. They therefore have a distinct advantage in the amount of training data available. We, on the other hand, place great emphasis on the targeted acquisition of special training data that helps our network to achieve higher translation quality. For this purpose, we have developed, among other things, special crawlers that automatically find translations on the internet and assess their quality.Training methodologyIn public research, training networks are usually trained using the “supervised learning” method. The network is shown different examples over and over again. The network repeatedly compares its own translations with the translations from the training data. If there are discrepancies, the weights of the network are adjusted accordingly. We also use other techniques from other areas of machine learning when training the neural networks. This also allows us to achieve significant improvements.Network sizeMeanwhile, we (like our largest competitors) train translation networks with many billions of parameters. These networks are so large that they can only be trained in a distributed fashion on very large dedicated compute clusters. However, in our research we attach great importance to the fact that the parameters of the network are used very efficiently. This is how we have managed to achieve a similar translation quality even with our smaller and faster networks. We can therefore also offer very high translation quality to users of our free service.Of course, we are always on the lookout for very good mathematicians and computer scientists who would like to help drive development, further improve DeepL Translator, and break down language barriers around the world. If you also have experience with mathematics and neural network training, and if it fulfills you to work on a product that is used worldwide for free, then please apply to DeepL!
If you think it is defect, feel free to send the conceptor a mail to explain them that "you" the great bored chemist, have found some problem they have never seen before.
You would be very kind of course to inform us how loud they have laught when you have announced them your expertise.
Unfortunately, you are not bright enough to recognise that "someone" is not the same as "everyone" (nor that "quelqu'un" and "tout le monde" are different).
Change "body" by "one" if your intellect dont permit you to understand the translation.
Nobody talk about "someone" and "everyone", here the polemic involve "anyone" and "everyone".translated :Personne ne parle de "quelqu'un" et de "tout le monde", ici la polémique implique "tout le monde" et "tout le monde".Retranslated :
https://www.deepl.com/en/blog/how-does-deepl-work
translating something from English, to French and back again