I work with children with autism. Specifically, I make technologies to help them communicate.
¶ Radim s autističnom djecom. Točnije, stvaram tehnologije koje im pomažu komunicirati.
Now, many of the problems that children with autism face, they have a common source, and that source is that they find it difficult to understand abstraction, symbolism. And because of this, they have a lot of difficulty with language.
Mnogi problemi s kojima se autistična djeca suočavaju imaju zajednički izvor, a taj izvor jest da im je teško razumjeti apstrakciju, simbolizam. Zbog toga imaju mnogo problema s jezikom.
Let me tell you a little bit about why this is. You see that this is a picture of a bowl of soup. All of us can see it. All of us understand this. These are two other pictures of soup, but you can see that these are more abstract These are not quite as concrete. And when you get to language, you see that it becomes a word whose look, the way it looks and the way it sounds, has absolutely nothing to do with what it started with, or what it represents, which is the bowl of soup. So it's essentially a completely abstract, a completely arbitrary representation of something which is in the real world, and this is something that children with autism have an incredible amount of difficulty with. Now that's why most of the people that work with children with autism -- speech therapists, educators -- what they do is, they try to help children with autism communicate not with words, but with pictures. So if a child with autism wanted to say, "I want soup," that child would pick three different pictures, "I," "want," and "soup," and they would put these together, and then the therapist or the parent would understand that this is what the kid wants to say. And this has been incredibly effective; for the last 30, 40 years people have been doing this. In fact, a few years back, I developed an app for the iPad which does exactly this. It's called Avaz, and the way it works is that kids select different pictures. These pictures are sequenced together to form sentences, and these sentences are spoken out. So Avaz is essentially converting pictures, it's a translator, it converts pictures into speech.
Sada ću vam reći nešto o razlogu zašto. Vidite, ovo je slika zdjele s juhom. Svi možemo to vidjeti. Svi možemo to razumjeti. Ovo su druge dvije slike juhe, ali možete vidjeti da su više apstraktne. Ove nisu jednako konkretne. A kada stignete do jezika, vidite da to postaje riječ čija pojava, način na koji izgleda i način na koji zvuči nema apsolutno ništa s onim čime je počela ili s onim što predstavlja, a to je zdjela juhe. To je zapravo potpuno apstraktna, potpuno proizvoljna predodžba nečega što je u stvarnom svijetu, a to je nešto s čime autistična djeca imaju velikih problema. Zbog toga većina ljudi koji rade s autističnom djecom - logopedi, nastavnici - ono što rade je da pokušavaju pomoći autističnoj djeci komunicirati, ne s riječima, već sa slikama. Tako da ako bi autistično dijete htjelo reći, „Hoću juhu, “ to dijete bi izabralo tri različite slike, „Ja, “ „htjeti, “ i „juha, “ i stavili bi ih zajedno, i tada bi terapeut ili roditelj razumio da je to ono što dijete želi reći. I to je bilo nevjerojatno učinkovito; zadnjih 30, 40 godina ljudi su to radili. Zapravo, prije nekoliko godina, razvio sam aplikaciju za iPad koja radi upravo to. Zove se Avaz, i način na koji radi je da djeca izaberu različite slike. Te slike se poredaju zajedno kako bi formirale rečenice, i te rečenice se izgovaraju. Tako Avaz zapravo pretvara slike, to je prevoditelj, pretvara slike u govor.
Now, this was very effective. There are thousands of children using this, you know, all over the world, and I started thinking about what it does and what it doesn't do. And I realized something interesting: Avaz helps children with autism learn words. What it doesn't help them do is to learn word patterns. Let me explain this in a little more detail. Take this sentence: "I want soup tonight." Now it's not just the words here that convey the meaning. It's also the way in which these words are arranged, the way these words are modified and arranged. And that's why a sentence like "I want soup tonight" is different from a sentence like "Soup want I tonight," which is completely meaningless. So there is another hidden abstraction here which children with autism find a lot of difficulty coping with, and that's the fact that you can modify words and you can arrange them to have different meanings, to convey different ideas. Now, this is what we call grammar. And grammar is incredibly powerful, because grammar is this one component of language which takes this finite vocabulary that all of us have and allows us to convey an infinite amount of information, an infinite amount of ideas. It's the way in which you can put things together in order to convey anything you want to.
I sad, to je bilo jako učinkovito. Na tisuće djece koriste ovo, znate, u cijelom svijetu, i počeo sam razmišljati o tome što radi, a što ne radi. I shvatio sam nešto zanimljivo: Avaz pomaže autističnoj djeci naučiti riječi. Ono što im ne pomaže naučiti jesu obrasci riječi. Dopustite mi da ovo objasnim malo detaljnije. Pogledajte ovu rečenicu: „Večeras hoću juhu.“ Nisu samo riječi te koje ovdje prenose značenje. To je također i način na koji su ove riječi poslagane, način na koji su ove riječi promijenjene i poslagane. I zbog toga rečenica kao što je „Hoću juhu večeras“ je drukčija od rečenice kao što je „Juha htjeti ja večeras, “ koja je potpuno beznačajna. Tako je ovdje još jedna skrivena apstrakcija s kojom se autistična djeca teško nose, a to je činjenica da možete mijenjati riječi i poslagati ih tako da imaju različita značenja, da prenose različite ideje. I sad, to je ono što nazivamo gramatika. A gramatika je nevjerojatno snažna, jer je gramatika ta jedna komponenta jezika koja uzima ograničeni vokabular riječi koji svi imamo i dopušta nam prenositi beskonačno mnogo informacija, beskonačno mnogo ideja. To je način na koji možete složiti stvari kako bi prenijeli sve što želite.
And so after I developed Avaz, I worried for a very long time about how I could give grammar to children with autism. The solution came to me from a very interesting perspective. I happened to chance upon a child with autism conversing with her mom, and this is what happened. Completely out of the blue, very spontaneously, the child got up and said, "Eat." Now what was interesting was the way in which the mom was trying to tease out the meaning of what the child wanted to say by talking to her in questions. So she asked, "Eat what? Do you want to eat ice cream? You want to eat? Somebody else wants to eat? You want to eat cream now? You want to eat ice cream in the evening?" And then it struck me that what the mother had done was something incredible. She had been able to get that child to communicate an idea to her without grammar. And it struck me that maybe this is what I was looking for. Instead of arranging words in an order, in sequence, as a sentence, you arrange them in this map, where they're all linked together not by placing them one after the other but in questions, in question-answer pairs. And so if you do this, then what you're conveying is not a sentence in English, but what you're conveying is really a meaning, the meaning of a sentence in English. Now, meaning is really the underbelly, in some sense, of language. It's what comes after thought but before language. And the idea was that this particular representation might convey meaning in its raw form.
I tako nakon što sam razvio Avaz, brinuo sam se dugo vremena o tome kako mogu dati gramatiku autističnoj djeci. Rješenje mi je došlo iz jedne jako zanimljive perspektive. Igrom slučaja naišao sam na autistično dijete koje je razgovaralo sa svojom mamom, i dogodilo se ovo. Potpuno iznenadno, vrlo spontano, dijete se ustalo i reklo, „Jesti. “ I ono što je bilo zanimljivo jest način na koji je mama pokušala izvući značenje onoga što je dijete htjelo reći tako što joj je pričala u pitanjima. Tako ju je pitala, „Jesti što? Želiš li jesti sladoled? Ti želiš jesti? Netko drugi želi jesti? Želiš sad jesti sladoled? Želiš jesti sladoled navečer?“ Sinulo mi je da to što je majka napravila je bilo nešto nevjerojatno. Uspjela je navesti to dijete da joj prenese neku ideju bez gramatike. I sinulo mi je da možda je to ono što sam tražio. Umjesto slaganja riječ u poredak, u slijed, kao rečenicu, slažete ih u ovu mapu, gdje su zajedno povezane ne sa redanjem jedne iza druge nego sa pitanjima, u parovima pitanje-odgovor. I ako to tako napravite, ono što prenosite nije rečenica na engleskom, već ono što prenosite je zapravo značenje, značenje rečenice na engleskom. Značenje je na neki način zapravo podzemlje jezika. Ono dolazi poslije misli ali prije jezika. I ideja je bila da ovaj osobit prikaz bi mogao prenijeti značenje u svome sirovom obliku.
So I was very excited by this, you know, hopping around all over the place, trying to figure out if I can convert all possible sentences that I hear into this. And I found that this is not enough. Why is this not enough? This is not enough because if you wanted to convey something like negation, you want to say, "I don't want soup," then you can't do that by asking a question. You do that by changing the word "want." Again, if you wanted to say, "I wanted soup yesterday," you do that by converting the word "want" into "wanted." It's a past tense. So this is a flourish which I added to make the system complete. This is a map of words joined together as questions and answers, and with these filters applied on top of them in order to modify them to represent certain nuances. Let me show you this with a different example.
Zato sam bio jako uzbuđen zbog toga, znate, skakutajući okolo i naokolo pokušavajući odgonetnuti mogu li pretvoriti sve moguće rečenice koje čujem u ovo. I shvatio sam da to nije dovoljno. Zašto to nije dovoljno? Nije dovoljno zato što ako biste htjeli prenijeti nešto kao što je negacija, želite reći, „Neću juhu, “ tada to ne možete napraviti postavljanjem pitanja. To ćete napraviti mijenjanjem riječi „htjeti“. Ili ako biste htjeli reći, „Htio sam juhu jučer, “ to ćete napraviti pretvaranjem riječi „htjeti“ u „htio“. To je prošlo vrijeme. To je ukras koji sam dodao kako bih upotpunio sustav. Ovo je mapa riječi pridruženih zajedno kao pitanja i odgovori, i sa ovim filtrima nadodanim na njih kako bi ih promijenili da predstavljaju određene nijanse. Sada ću vam to pokazati na drukčijem primjeru.
Let's take this sentence: "I told the carpenter I could not pay him." It's a fairly complicated sentence. The way that this particular system works, you can start with any part of this sentence. I'm going to start with the word "tell." So this is the word "tell." Now this happened in the past, so I'm going to make that "told." Now, what I'm going to do is, I'm going to ask questions. So, who told? I told. I told whom? I told the carpenter. Now we start with a different part of the sentence. We start with the word "pay," and we add the ability filter to it to make it "can pay." Then we make it "can't pay," and we can make it "couldn't pay" by making it the past tense. So who couldn't pay? I couldn't pay. Couldn't pay whom? I couldn't pay the carpenter. And then you join these two together by asking this question: What did I tell the carpenter? I told the carpenter I could not pay him.
Uzmimo ovu rečenicu: „Rekao sam stolaru da mu nisam mogao platiti.“ To je poprilično složena rečenica. Način na koji ovaj radi jest da možete početi s bilo kojim dijelom ove rečenice. Počet ću sa riječju „govoriti“. Dakle ovo je riječ „govoriti“. Budući da se ovo dogodilo u prošlosti, pa ću promijeniti to u „govorio“. Sad, ono što ću napraviti jest da ću postavljati pitanja. Dakle, tko je rekao? Ja sam rekao. Rekao sam kome? Rekao sam stolaru. Sada počinjemo s drukčijim dijelom rečenice. Počinjemo s riječju „platiti“ i dodamo filtar mogućnosti kako bismo ju promijenili u „mogu platiti“. Potom ju promijenimo u „ne mogu platiti“, i možemo ju promijeniti u „nisam mogao platiti“ stavljajući ju u prošlo vrijeme. Tko nije mogao platiti? Ja nisam mogao platiti. Kome nisam mogao platiti? Nisam mogao platiti stolaru. I potom spojite to dvoje zajedno postavljanjem ovog pitanja: Što sam rekao stolaru? Rekao sam stolaru da mu nisam mogao platiti.
Now think about this. This is —(Applause)— this is a representation of this sentence without language. And there are two or three interesting things about this. First of all, I could have started anywhere. I didn't have to start with the word "tell." I could have started anywhere in the sentence, and I could have made this entire thing. The second thing is, if I wasn't an English speaker, if I was speaking in some other language, this map would actually hold true in any language. So long as the questions are standardized, the map is actually independent of language. So I call this FreeSpeech, and I was playing with this for many, many months. I was trying out so many different combinations of this.
Sada razmislite o tome. To je —(Pljesak)— to je prikaz ove rečenice bez jezika. I postoje dvije ili tri zanimljive stvari oko toga. Prvo, mogao sam početi bilo gdje. Nisam morao početi s riječju „govoriti“ . Mogao sam početi bilo gdje u rečenici I napraviti cijelu tu stvar. Druga stvar je da, da nisam govornik engleskog jezika, da govorim neki drugi jezik, ova bi mapa zapravo bila točna u bilo kojem jeziku. Dokle god su pitanja standardizirana, mapa je zapravo neovisna o jeziku. Zato ovo nazivam FreeSpeech (SlobodniGovor) i igrao sam se s time mnogo, mnogo mjeseci. Isprobavao sam toliko mnogo različitih kombinacija toga.
And then I noticed something very interesting about FreeSpeech. I was trying to convert language, convert sentences in English into sentences in FreeSpeech, and vice versa, and back and forth. And I realized that this particular configuration, this particular way of representing language, it allowed me to actually create very concise rules that go between FreeSpeech on one side and English on the other. So I could actually write this set of rules that translates from this particular representation into English. And so I developed this thing. I developed this thing called the FreeSpeech Engine which takes any FreeSpeech sentence as the input and gives out perfectly grammatical English text. And by putting these two pieces together, the representation and the engine, I was able to create an app, a technology for children with autism, that not only gives them words but also gives them grammar.
I tada sam primijetio nešto vrlo zanimljivo o FreeSpeechu. Pokušavao sam pretvoriti jezik, pretvoriti rečenice na engleskom u FreeSpeech rečenice, i obrnuto. I shvatio sam da mi je ova osobita konfiguracija, ovaj osobit način prikazivanja jezika, dopustio da zapravo stvorim vrlo jezgrovita pravila koja idu između FreeSpeecha s jedne strane i engleskog jezika s druge. Tako da sam zapravo mogao napisati skup pravila koji prevodi ovaj osobit prikaz na engleski. I tako sam razvio ovu stvar. Razvio sam stvar zvanu FreeSpeech Motor koja uzima bilo koju FreeSpeech rečenicu kao unos i izbacuje savršeno gramatički engleski tekst. I spajajući ta dva dijela zajedno, taj prikaz i taj motor, Mogao sam stvoriti aplikaciju, tehnologiju za autističnu djecu, koja im ne daje samo riječi već i gramatiku.
So I tried this out with kids with autism, and I found that there was an incredible amount of identification. They were able to create sentences in FreeSpeech which were much more complicated but much more effective than equivalent sentences in English, and I started thinking about why that might be the case. And I had an idea, and I want to talk to you about this idea next. In about 1997, about 15 years back, there were a group of scientists that were trying to understand how the brain processes language, and they found something very interesting. They found that when you learn a language as a child, as a two-year-old, you learn it with a certain part of your brain, and when you learn a language as an adult -- for example, if I wanted to learn Japanese right now — a completely different part of my brain is used. Now I don't know why that's the case, but my guess is that that's because when you learn a language as an adult, you almost invariably learn it through your native language, or through your first language. So what's interesting about FreeSpeech is that when you create a sentence or when you create language, a child with autism creates language with FreeSpeech, they're not using this support language, they're not using this bridge language. They're directly constructing the sentence.
Tako sam isprobao ovo s autističnom djecom, i vidio sam da je tu postojala velika količina poistovjećenosti Mogli su stvoriti rečenice u FreeSpeechu koje su bile mnogo složenije i mnogo djelotvornije nego odgovarajuće rečenice na engleskom, i počeo sam razmišljati o tome zašto je to tako. I dobio sam ideju, i sada želim s vama razgovarati o njoj. Negdje oko 1997. , oko prije 15 godina, bila je jedna grupa znanstvenika koji su pokušavali razumjeti kako mozak obrađuje jezik, i otkrili su nešto vrlo zanimljivo. Otkrili su da kad učite jezik kao dijete, kao jedan dvogodišnjak, učite ga sa određenim dijelom svoga mozga, a kad učite jezik kao odrastao čovjek – naprimjer, ako bih sada htio naučiti japanski – koristit će se potpuno drugi dio moga mozga. I sad, ne znam zašto je to slučaj, ali moja pretpostavka je da je to zbog toga što kada učite jezik kao odrastao čovjek, gotovo neizbježno ga učite kroz svoj materinji jezik, kroz svoj prvi jezik. Pa ono što je zanimljivo za FreeSpeech jest da kada stvarate neku rečenicu ili kada stvarate jezik, autistično dijete stvara jezik sa FreeSpeechom, ono ne koristi ovaj potporni jezik, ne koriste ovaj premosni jezik. Direktno sastavljaju rečenicu.
And so this gave me this idea. Is it possible to use FreeSpeech not for children with autism but to teach language to people without disabilities? And so I tried a number of experiments. The first thing I did was I built a jigsaw puzzle in which these questions and answers are coded in the form of shapes, in the form of colors, and you have people putting these together and trying to understand how this works. And I built an app out of it, a game out of it, in which children can play with words and with a reinforcement, a sound reinforcement of visual structures, they're able to learn language. And this, this has a lot of potential, a lot of promise, and the government of India recently licensed this technology from us, and they're going to try it out with millions of different children trying to teach them English. And the dream, the hope, the vision, really, is that when they learn English this way, they learn it with the same proficiency as their mother tongue.
I to mi je dalo ovu ideju Je li moguće koristiti FreeSpeech ne za autističnu djecu već za učenje jezika ljudi bez poteškoća u razvoju? I tako sam isprobao nekoliko pokusa. Prvu stvar koju sam napravio je bila da sam napravio slagalicu u kojoj su ova pitanja i odgovori kodirana u obliku oblika, u obliku boja, i imate ljude koji ih spajaju i pokušavaju razumjeti kako to radi. I napravio sam aplikaciju od toga, igricu od toga, u kojoj se djeca mogu igrati riječima i sa potkrjepljenjem, zvučnim potkrjepljenjem slikovnih struktura mogu naučiti jezik. I ovo, ovo ima mnogo mogućnosti, mnogo obećava. nedavno je indijska vlada licencirala ovu tehnologiju od nas, i isprobat će je sa milijunima različite djece pokušavajući naučiti ih engleski. I san, nada, vizija, zapravo, je da kada uče engleski na ovaj način, uče ga sa jednakom spretnošću kao i svoj materinji jezik.
All right, let's talk about something else. Let's talk about speech. This is speech. So speech is the primary mode of communication delivered between all of us. Now what's interesting about speech is that speech is one-dimensional. Why is it one-dimensional? It's one-dimensional because it's sound. It's also one-dimensional because our mouths are built that way. Our mouths are built to create one-dimensional sound. But if you think about the brain, the thoughts that we have in our heads are not one-dimensional. I mean, we have these rich, complicated, multi-dimensional ideas. Now, it seems to me that language is really the brain's invention to convert this rich, multi-dimensional thought on one hand into speech on the other hand. Now what's interesting is that we do a lot of work in information nowadays, and almost all of that is done in the language domain. Take Google, for example. Google trawls all these countless billions of websites, all of which are in English, and when you want to use Google, you go into Google search, and you type in English, and it matches the English with the English. What if we could do this in FreeSpeech instead? I have a suspicion that if we did this, we'd find that algorithms like searching, like retrieval, all of these things, are much simpler and also more effective, because they don't process the data structure of speech. Instead they're processing the data structure of thought. The data structure of thought. That's a provocative idea.
Dobro, razgovarajmo sada o nečem drugom. Razgovarajmo o govoru. Ovo je govor. Govor je osnovni način komunikacije između svih nas. Ono što je zanimljivo kod govora jest da je govor jednodimenzionalan. Zašto je jednodimenzionalan? Jednodimenzionalan je zato što je zvuk. Također je jednodimenzionalan zato što su naša usta tako građena. Naša usta su građena tako da stvaraju jednodimenzionalni zvuk. Ali ako razmišljate o mozgu, misli koje imamo u našim glavama nisu jednodimenzionalne. Hoću reći, imamo te bogate, složene, višedimenzionalne ideje. Sad, meni se čini da jezik je zapravo izum mozga kojim pretvara te bogatu, višedimenzionalnu misao s jedne strane u govor s druge strane. Ono što je zanimljivo jest da mi u današnje vrijeme radimo mnogo posla u informacijama, a gotovo sve to se radi u domeni jezika. Uzmite naprimjer Google. Google iskopava bezbroj milijardi svih tih web stranica, koje su sve na engleskom, i kad želite koristiti Google, idete na Google pretraživanje, i tipkate na engleskom, i on sparuje engleski sa engleskim. Što ako bismo to mogli napraviti u FreeSpeechu? Imam sumnju da ako bismo to napravili, otkrili bismo da algoritmi kao pretraživanje, kao dohvaćanje, sve te stvari, su mnogo jednostavniji i također mnogo učinkovitiji, jer oni ne obrađuju podatkovnu strukturu govora. Umjesto toga obrađuju podatkovnu strukturu misli. Podatkovna struktura misli. To je provokativna ideja.
But let's look at this in a little more detail. So this is the FreeSpeech ecosystem. We have the Free Speech representation on one side, and we have the FreeSpeech Engine, which generates English. Now if you think about it, FreeSpeech, I told you, is completely language-independent. It doesn't have any specific information in it which is about English. So everything that this system knows about English is actually encoded into the engine. That's a pretty interesting concept in itself. You've encoded an entire human language into a software program. But if you look at what's inside the engine, it's actually not very complicated. It's not very complicated code. And what's more interesting is the fact that the vast majority of the code in that engine is not really English-specific. And that gives this interesting idea. It might be very easy for us to actually create these engines in many, many different languages, in Hindi, in French, in German, in Swahili. And that gives another interesting idea. For example, supposing I was a writer, say, for a newspaper or for a magazine. I could create content in one language, FreeSpeech, and the person who's consuming that content, the person who's reading that particular information could choose any engine, and they could read it in their own mother tongue, in their native language. I mean, this is an incredibly attractive idea, especially for India. We have so many different languages. There's a song about India, and there's a description of the country as, it says, (in Sanskrit). That means "ever-smiling speaker of beautiful languages."
Ali pogledajmo to malo detaljnije. Dakle, ovo je FreeSpeech ekosustav. Imamo Free Speech prikaz s jedne strane i imamo FreeSpeech motor, koji stvara engleski. Sada ako razmislite o tome, FreeSpeech, kao što sam vam rekao, je potpuno neovisan o jeziku. Nema nikakve specifične informacije u sebi koja je o engleskom jeziku. Sve što taj sustav zna o engleskom je kodirano u programu. To je vrlo zanimljiv koncept sam po sebi. Kodirali ste cijeli ljudski jezik u jedan program. Ali ako pogledate što je unutar programa, zapravo to nije tako složeno. To nije jako složen kod. I ono što je još zanimljivije je činjenica da velika većina koda u programu nije zapravo specifična za engleski. I iz toga proizlazi ova zanimljiva ideja. Moglo bi nam biti vrlo lako zapravo stvoriti takve programe u mnogo, mnogo različitih jezika, na hindskom, francuskom, njemačkom, swahiliju. A iz toga proizlazi još jedna zanimljiva ideja. Naprimjer, recimo da sam pisac, ne znam, za neke novine ili časopis. Mogao bih stvoriti sadržaj na jednom jeziku, FreeSpeechu, a osoba koja konzumira taj sadržaj, osoba koja čita te informacije mogla bi odabrati bilo koji program, i pročitati to na svojem materinjem jeziku, na svojem vlastitom jeziku. Hoću reći, to je vrlo privlačna ideja, posebno za Indiju. Imamo toliko mnogo različitih jezika. Ima jedna pjesma o Indiji, i postoji opis zemlje koji kaže, (na sanskrtskom) To znači „vječno nasmijan govornik prekrasnih jezika.“
Language is beautiful. I think it's the most beautiful of human creations. I think it's the loveliest thing that our brains have invented. It entertains, it educates, it enlightens, but what I like the most about language is that it empowers.
Jezik je prekrasan. Mislim da je to najljepša od svih ljudskih tvorevina. Mislim da je to najdivnija stvar koju su naši mozgovi izmislili. Zabavlja, obrazuje, prosvjetljuje, ali ono što mi se najviše sviđa kod jezika jest da osnažuje.
I want to leave you with this. This is a photograph of my collaborators, my earliest collaborators when I started working on language and autism and various other things. The girl's name is Pavna, and that's her mother, Kalpana. And Pavna's an entrepreneur, but her story is much more remarkable than mine, because Pavna is about 23. She has quadriplegic cerebral palsy, so ever since she was born, she could neither move nor talk. And everything that she's accomplished so far, finishing school, going to college, starting a company, collaborating with me to develop Avaz, all of these things she's done with nothing more than moving her eyes.
Htio bih završiti s ovim. Ovo je fotografija mojih suradnika, mojih najranijih suradnika kada sam počeo raditi na jeziku i autizmu i drugim stvarima. Ime djevojčice je Pavna, I to je njezina majka, Kalpana. I Pavna je poduzetnica, ali njezina priča je mnogo vrjednija pažnje no moja, jer Pavna ima oko 23 godine. Ima kvadriplegičnu cerebralnu paralizu, od vremena kad se rodila, nije se mogla micati ni govorit. I sve što je dosad postigla, završavanje škole, odlazak na fakultet, osnivanje tvrtke, suradnja sa mnom u razvijanju Avaza, sve te stvari je postigla s ništa više od micanja očiju.
Daniel Webster said this: He said, "If all of my possessions were taken from me with one exception, I would choose to keep the power of communication, for with it, I would regain all the rest." And that's why, of all of these incredible applications of FreeSpeech, the one that's closest to my heart still remains the ability for this to empower children with disabilities to be able to communicate, the power of communication, to get back all the rest.
Daniel Webster je rekao ovo: Rekao je, „Kad bi mi sve što imam bilo oduzeto s jednom iznimkom, odabrao bih zadržati moć komunikacije, jer bi s njom ponovno stekao sve ostalo.“ I zbog toga, od svih ovih nevjerojatnih primjena FreeSpeecha, ona koja mi je najbliža srcu još uvijek ostaje njegova mogućnost da omogući djeci s poteškoćama sposobnost komunikacije, snagu komunikacije, kako bi dobila nazad sve ostalo.
Thank you. (Applause) Thank you. (Applause) Thank you. Thank you. Thank you. (Applause) Thank you. Thank you. Thank you. (Applause)
Hvala. (Pljesak) Hvala. (Pljesak) Hvala. Hvala. Hvala. (Pljesak) Hvala. Hvala. Hvala. (Pljesak)