About Languages

Spelling doesn't matter? Oh! Really?
So, decode this word then: "yeiaiiiltnmcrsdn"? Come on! You don't know? :) Why is that? Yet, the following is quite easy! Mmm! I will explain!
Aoccdrnig to rscheearch by the Lngiusiitc Dptanmeret at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a total mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.
Actually, the following was ... dispelled (sorry for the bad pun)! I know, I know it is so sad, but the truth is that spelling does matter and, in the case of English, we wish it did matter because with all of its misspellings, it takes a long time to learn to read and spell it.

A careful analysis of the words chosen reveal that many (half) are words that have 4 letters or less. How difficult is it to switch 2 letters around? Notice that there are many 3 letter words or less that are not misspelled as well! Furthermore, all words start with the letter the word is supposed to start with. Once you have figured out a few words and all of those easy words, one can easily rely also on context. A harder test would be with ONLY 5 letter words (or more) where ALL the letters would be scrambled yeiaiiiltnmcrsdn! How easy is that last word? And I gave you a lot of clues! :) Furthermore, there are many words (usually longer words) where the changes are minor changes, where only 2 letters are switched around, side by side:

people: p ... oe ... lp ... e
phenomenal: ph ... (not switched) ... a  ...on ...mne ... al (not switched)
university: U ... in ... erv ... t ... is ... y
problem: p ...or ... b ... el ... m
amazing: Amza ... nig
always: awl ... ya ... s
spelling: slpe ... ling (not switched)
important: i ... prmo (messed up) ... at (switched) nt (not switched)

Here is a video explaining things better than me!

video

The text below shows that ANYONE (smart or not) can read the following. The way the letters are put together, it is not hard at all.




The above idea (that one can read anything where letters of words are mixed up, was dispelled. See the explanation above. And, while you are it, go to "breaking the spell" page to see why we could save so much money if we were to regularize English spelling.

Here is the rebuttal!

Baby robot learns first words from human teacher

AT FIRST it's just noise: a stream of incoherent sounds, burbling away. But, after a few minutes, a fully formed word suddenly emerges: red. Then another: box. In this way, a babbling robot has learned to speak its first real words, just by chatting with a human.
Seeing this developmental leap in a machine may lead to robots that speak in a more natural, human-like way, and help uncover how children first start to make sense of language. Between the ages of 6 and 14 months childrenmove from babbling strings of syllables to uttering actual words. It is a necessary step en route to acquiring full language. Once a few "anchor" words have been established, they provide clues as to where words may start and finish and so it becomes easier for a child to learn to speak.
Inspired by this process, a team led by computer scientist Caroline Lyon at the University of Hertfordshire, UK, programmed their humanoid robot iCub – called DeeChee – with almost all the syllables that exist in English – around 40,000 in total. This allowed it to babble rather like a baby, by arbitrarily stringing syllables together.
The researchers also enlisted 34 people to act as teachers, who were told to treat DeeChee as if it were a child. DeeChee took part in an 8-minute dialogue with each teacher but, between each session, its memory was saved, and then wiped and reset, so that with each teacher, the experiment started anew. At the outset of the dialogue, each of the syllables in DeeChee's lexicon had an identical score.

Lexicon score

All that started to change once the lesson began. Programmed to take turns listening and then speaking, DeeChee turned the teacher's speech into syllables, totting up the number of instances of each one. It then used this information to update the scores in its own lexicon, giving extra points to syllables the teacher had used.
When it next spoke, it would be more likely to repeat the syllables the teacher had uttered because these now had higher scores.
Lyon says this is reminiscent of human infants. "When they hear frequent sounds, they become sensitive to them," says Lyon. "They prefer what's familiar."
This learning by imitation was then reinforced, as teachers made encouraging comments when DeeChee spoke a recognisable word. DeeChee was programmed to detect these comments and give extra points to the syllables that preceded the teacher's approval. Inevitably some nonsense syllables would get extra points too. But as this process was repeated, only those syllables that made up words would keep showing up in strings that gained approval.
Though the robot was still uttering nonsense streams of syllables, towards the end of the 8 minutes, real words kept popping up more often than if DeeChee were still selecting syllables at random.
That words can emerge from babble using a statistical learning process not specific to language demonstrates that this stage of language acquisition does not require hard-wired grammar faculties, says Lyon.
Paul Vogt, a cognitive scientist at Tilburg University in the Netherlands is impressed: "It's a very interesting first step towards having robots that can help us study language acquisition."
Right now, DeeChee's speech is a far cry from full-blown language, but starting with babbling could be the best way to create robots that speak naturally. "If you want the robot to work with natural speech, then you might need to teach it from the very beginning," says Lyon.
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