3. THE SERIES OF l APPROXIMATIONS TO ENGLISHTo give a visual idea of how this series of processes approaches a language, typical sequences in the approximations to English have been constructed and are given below. In all cases we have assumed a 27-symbol “alphabet,” the 26 letters and a space.1. Zero-order approximation (symbols independent and equiprobable).XFOML RXKHRJFFJUJ ZLPWCFWKCYJ FFJEYVKCQSGHYD QPAAMKBZAACIBZL HJQD.2. First-order approximation (symbols independent but with frequencies of English text).OCRO HLI RGWR NMIELWIS EU LL NBNESEBYA TH EEI ALHENHTTPA OOBTTVA NAH BRL.3. Second-order approximation (digram structure as in English).ON IE ANTSOUTINYS ARE T INCTORE ST BE S DEAMY ACHIN D ILONASIVE TUCOOWE AT TEASONARE FUSO TIZIN ANDY TOBE SEACE CTISBE.4. Third-order approximation (trigram structure as in English).IN NO IST LAT WHEY CRATICT FROURE BIRS GROCID PONDENOME OF DEMONSTURES OF THE REPTAGIN IS REGOACTIONA OF CRE.5. First-order word approximation. Rather than continue with tetragram, ::: , n-gram structure it is easierand better to jump at this point to word units. Here words are chosen independently but with theirappropriate frequencies.REPRESENTING AND SPEEDILY IS AN GOOD APT OR COME CAN DIFFERENT NATURAL HERE HE THE A IN CAME THE TO OF TO EXPERT GRAY COME TO FURNISHES THE LINE MESSAGE HAD BE THESE.6. Second-order word approximation. The word transition probabilities are correct but no further struc-ture is included.THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THATTHE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.
read that manga thanks to the shilling these 2 past daysits pretty good
And this is to go...EVENFURTHERBEYOND
>>89942171Honestly I just have a folder with tangentially unrelated images for when I don't have one good fit for the OP.
The resemblance to ordinary English text increases quite noticeably at each of the above steps. Note that these samples have reasonably good structure out to about twice the range that is taken into account in their construction. Thus in (3) the statistical process insures reasonable text for two-letter sequences, but four-letter sequences from the sample can usually be fitted into good sentences. In (6) sequences of four or more words can easily be placed in sentences without unusual or strained constructions. The particular sequence of ten words “attack on an English writer that the character of this” is not at all unreasonable. It appears then that a sufficiently complex stochastic process will give a satisfactory representation of a discrete source. The first two samples were constructed by the use of a book of random numbers in conjunction with (for example 2) a table of letter frequencies. This method might have been continued for (3), (4) and (5), since digram, trigram and word frequency tables are available, but a simpler equivalent method was used. To construct (3) for example, one opens a book at random and selects a letter at random on the page. This letter is recorded. The book is then opened to another page and one reads until this letter is encountered. The succeeding letter is then recorded. Turning to another page this second letter is searched for and the succeeding letter recorded, etc. A similar process was used for (4), (5) and (6). It would be interesting if further approximations could be constructed, but the labor involved becomes enormous at the next stage.>It would be interesting if further approximations could be constructed, but the labor involved becomes enormous at the next stage.Oh the irony
>>89941828So it's all Markov chains?
ITT OP thinks he understands anything but doesn't
>>89947641The underlying theory is the same, what's new are the vast datasets and the probabilistic analysis engines.
>>89947575Always has been. People thinking general AI can develop from "nerual networks" are literally retarded.
>>89947770it's quite differentit's not fair to say the underlying theory is the same. they are (both methods predict), but the methods are very different and you can't say that one is the other. they aren't. they're not even similar in mechanism.
should this thread be posted on >>>/a/mods?
>>89947844Op is correct in that gpt 3 and similar llms predict the next token from a probability distribution conditional on previous tokens. It should be obvious that this is not how humans generate language, even if llms can produce very sophisticated imitation of human language.