Chris Anderson: You were something of a mathematical phenom. You had already taught at Harvard and MIT at a young age. And then the NSA came calling. What was that about?
Chris Anderson: Bili ste svojevrsni matematički fenomen. Predavali ste na Harvardu i MIT-u već kao mladić. I onda vas je zvala NSA. Zbog čega?
Jim Simons: Well the NSA -- that's the National Security Agency -- they didn't exactly come calling. They had an operation at Princeton, where they hired mathematicians to attack secret codes and stuff like that. And I knew that existed. And they had a very good policy, because you could do half your time at your own mathematics, and at least half your time working on their stuff. And they paid a lot. So that was an irresistible pull. So, I went there.
Jim Simons: Pa, NSA -- to je Nacionalna sigurnosna agencija -- nisu me baš zvali. Imali su operaciju na Princetonu, gdje su zapošljavali matematičare da napadaju tajne kodove i takve stvari. I ja sam znao da to postoji. Oni su imali veoma dobru politiku, jer ste mogli pola svog vremena provoditi radeći svoju matematiku i barem pola radeći na njihovim zadacima. I puno su plaćali. Tako da je to bio neodoljiv poziv. Pa sam otišao tamo.
CA: You were a code-cracker.
CA: Bili ste razbijač kodova.
JS: I was.
JS: Tako je.
CA: Until you got fired.
CA: Dok vam nisu dali otkaz.
JS: Well, I did get fired. Yes.
JS: Pa, da, dobio sam otkaz. Da.
CA: How come?
CA: Kako to?
JS: Well, how come? I got fired because, well, the Vietnam War was on, and the boss of bosses in my organization was a big fan of the war and wrote a New York Times article, a magazine section cover story, about how we would win in Vietnam. And I didn't like that war, I thought it was stupid. And I wrote a letter to the Times, which they published, saying not everyone who works for Maxwell Taylor, if anyone remembers that name, agrees with his views. And I gave my own views ...
JS: Pa, kako to? Dobio sam otkaz, jer je bilo vrijeme Vijetnamskog rata i šef šefova u mojoj organizaciji je bio veliki obožavatelj rata i napisao je članak za New York Times, naslovnu priču za sekciju časopisa, o tome kako ćemo pobijediti u Vijetnamu. A meni se nije svidio taj rat, smatrao sam da je glup. I napisao sam pismo Timesu, koje su objavili, u kojem sam napisao da se, svi koji rade za Maxwella Taylora, ako se itko sjeća tog imena, ne slažu s njegovim pogledima. I dao sam svoje mišljenje ...
CA: Oh, OK. I can see that would --
CA: Oh, OK. To bi prouzročilo --
JS: ... which were different from General Taylor's. But in the end, nobody said anything. But then, I was 29 years old at this time, and some kid came around and said he was a stringer from Newsweek magazine and he wanted to interview me and ask what I was doing about my views. And I told him, "I'm doing mostly mathematics now, and when the war is over, then I'll do mostly their stuff." Then I did the only intelligent thing I'd done that day -- I told my local boss that I gave that interview. And he said, "What'd you say?" And I told him what I said. And then he said, "I've got to call Taylor." He called Taylor; that took 10 minutes. I was fired five minutes after that.
JS: ... koje je bilo drukčije od mišljenja generala Taylora. Ali na kraju, nitko nije ništa rekao. Ali tada, imao sam 29 godina, neki klinac je došao i rekao da je freelancer za časopis Newsweek i htio me intervjuirati i pitati što radim sa svojim mišljenjem. I reako sam mu: "Sada uglavnom radim matematiku, a kad rat završi, onda ću raditi većinom njihove stvari." Tada sam napravio jedinu inteligentnu stvar tog dana -- rekao sam svom lokalnom šefu da sam dao taj intervju. I on je pitao: " Što si rekao?" I rekao sam mu što sam rekao. I onda je on rekao: "Moram nazvati Taylora." Nazvao je Taylora; to je trajalo 10 minuta. Pet minuta nakon toga dobio sam otkaz.
CA: OK.
CA: OK.
JS: But it wasn't bad.
JS: Ali nije bilo loše.
CA: It wasn't bad, because you went on to Stony Brook and stepped up your mathematical career. You started working with this man here. Who is this?
CA: Nije bilo loše, jer ste otišli u Stony Brook i uznapredovali u svojoj matematičkoj karijeri. Počeli ste raditi s ovim čovjekom. Tko je to?
JS: Oh, [Shiing-Shen] Chern. Chern was one of the great mathematicians of the century. I had known him when I was a graduate student at Berkeley. And I had some ideas, and I brought them to him and he liked them. Together, we did this work which you can easily see up there. There it is.
JS: Oh, Shiing-Shen Chern. Chern je bio jedan od najboljih matematičara stoljeća. Upoznao sam ga dok sam bio student na Berkeleyju. Imao sam neke ideje, donio sam ih njemu i svidjele su mu se. Zajedno smo radili na ovome što lako možete vidjeti tamo gore. Eto ga.
CA: It led to you publishing a famous paper together. Can you explain at all what that work was?
CA: To je dovelo do vaše poznate objavljene studije s njim. Možete li imalo objasniti o čemu je bila?
JS: No.
JS: Ne.
(Laughter)
(Smijeh)
JS: I mean, I could explain it to somebody.
JS: Mislim, mogao bih ju objasniti nekome.
(Laughter)
(Smijeh)
CA: How about explaining this?
CA: A da objasnite ovo?
JS: But not many. Not many people.
JS: Ne mnogima. Ne mnogim ljudima.
CA: I think you told me it had something to do with spheres, so let's start here.
CA: Mislim da ste mi rekli da ima veze sa sferama pa hajdemo tu započeti.
JS: Well, it did, but I'll say about that work -- it did have something to do with that, but before we get to that -- that work was good mathematics. I was very happy with it; so was Chern. It even started a little sub-field that's now flourishing. But, more interestingly, it happened to apply to physics, something we knew nothing about -- at least I knew nothing about physics, and I don't think Chern knew a heck of a lot. And about 10 years after the paper came out, a guy named Ed Witten in Princeton started applying it to string theory and people in Russia started applying it to what's called "condensed matter." Today, those things in there called Chern-Simons invariants have spread through a lot of physics. And it was amazing. We didn't know any physics. It never occurred to me that it would be applied to physics. But that's the thing about mathematics -- you never know where it's going to go.
JS: Pa, ima, ali reći ću o tom radu -- imalo je neke veze s tim, ali prije nego što dođemo do toga -- taj rad je bila dobra matematika. Bio sam jako zadovoljan s tim; i Chern također. Time je čak započelo malo pod-područje koje sada cvijeta. Ali, još zanimljivije, ispalo je da je primijenjivo na fiziku, nešto o čemu nismo imali pojma -- barem ja nisam imao pojma o fizici, a mislim da ni Chern nije znao previše. I otprilike 10 godina nakon izlaska studije, čovjek zvan Ed Witten s Princetona je počeo primijenjivati to na teoriju struna i ljudi u Rusiji su je počeli primijenjivati na "kondenziranu tvar." Danas, te stvari nazvane Chern-Simons invarijante, su se proširile na mnoga područja fizike. I bilo je nevjerojatno. Nismo uopće poznavali fiziku. Nikad mi nije palo na pamet da će se primijeniti u fizici. Ali tako je s matematikom -- nikad ne znate kamo će otići.
CA: This is so incredible. So, we've been talking about how evolution shapes human minds that may or may not perceive the truth. Somehow, you come up with a mathematical theory, not knowing any physics, discover two decades later that it's being applied to profoundly describe the actual physical world. How can that happen?
CA: To je nevjerojatno. Pričali smo kako evolucija oblikuje ljudski um koji može ili ne može percipirati istinu. Vi ste nekako došli do matematičke teorije, bez da ste znali imalo fizike, otkrijete dva desetljeća poslije da se koristi u detaljnim opisima stvarnog fizičkog svijeta. Kako se to dogodi?
JS: God knows.
JS: Samo Bog zna.
(Laughter)
(Smijeh)
But there's a famous physicist named [Eugene] Wigner, and he wrote an essay on the unreasonable effectiveness of mathematics. Somehow, this mathematics, which is rooted in the real world in some sense -- we learn to count, measure, everyone would do that -- and then it flourishes on its own. But so often it comes back to save the day. General relativity is an example. [Hermann] Minkowski had this geometry, and Einstein realized, "Hey! It's the very thing in which I can cast general relativity." So, you never know. It is a mystery. It is a mystery.
Ali postoji poznati fizičar, Eugene Wigner, koji je napisao esej o nerazumljivoj učinkovitosti matematike. Ta matematika, koja je ukorijenjena u stvarnom svijetu na neki način -- učimo brojati, mjerimo, svi to rade -- onda sama procvijeta. Ali često se vraća da nas spasi. Opća relativnost je primjer. Hermann Minkowski je razvio tu geometriju i Einstein je shvatio "Hey! To je točno ono čime mogu prikazati opću relativnost." Dakle, nikad ne zante. To je misterij. Misterij.
CA: So, here's a mathematical piece of ingenuity. Tell us about this.
CA: Dakle, ovdje je matematički dio dosjetljivosti. Recite nam nešto o tome.
JS: Well, that's a ball -- it's a sphere, and it has a lattice around it -- you know, those squares. What I'm going to show here was originally observed by [Leonhard] Euler, the great mathematician, in the 1700s. And it gradually grew to be a very important field in mathematics: algebraic topology, geometry. That paper up there had its roots in this. So, here's this thing: it has eight vertices, 12 edges, six faces. And if you look at the difference -- vertices minus edges plus faces -- you get two. OK, well, two. That's a good number. Here's a different way of doing it -- these are triangles covering -- this has 12 vertices and 30 edges and 20 faces, 20 tiles. And vertices minus edges plus faces still equals two. And in fact, you could do this any which way -- cover this thing with all kinds of polygons and triangles and mix them up. And you take vertices minus edges plus faces -- you'll get two. Here's a different shape. This is a torus, or the surface of a doughnut: 16 vertices covered by these rectangles, 32 edges, 16 faces. Vertices minus edges comes out to be zero. It'll always come out to zero. Every time you cover a torus with squares or triangles or anything like that, you're going to get zero. So, this is called the Euler characteristic. And it's what's called a topological invariant. It's pretty amazing. No matter how you do it, you're always get the same answer. So that was the first sort of thrust, from the mid-1700s, into a subject which is now called algebraic topology.
JS: Pa, to je kugla -- sfera koja ima rešetku oko sebe -- znate, one kvadrate. Ono što ću ovdje pokazati je prvi uočio Leonhard Euler, veliki matematičar, 1700-ih. I to je postupno preraslo u važno polje matematike: algebarska topologija, geometrija. Ta studija tamo gore ima svoje korijene u ovome. Evo o čem se radi: ima osam točaka, 12 rubova, šest strana. I ako pogledate razliku -- točke minus rubovi plus strane -- dobijete dva. OK, dakle, dva. To je dobar broj. Evo drugog načina da to napravite -- ovo su trokuti -- 12 točaka i 30 rubova i 20 strana, 20 pločica. Točke minus rubovi plus strane je još uvijek dva. I zapravo biste ovo mogli napraviti na bilo koji način -- pokriti ovo sa svim vrstama poligona i trokuta i pomiješati ih. I onda točke minus rubovi plus strane -- i dobijete dva. Evo drugog oblika. Ovo je torus, ili površina krafne: 16 točaka pokrivenih ovim pravokutnicima, 32 ruba, 16 strana. Točke minus rubovi je nula. Uvijek će biti nula. Svaki put kad pokrijete torus kvadratima ili trokutima ili nečim sličnim, dobit ćete nulu. Ovo se zove Eulerova karakteristika. I to je ono što se zove topološka invarijanta. Doista je nevjerojatno. Kako god da napravite to, uvijek ćete dobiti isti odgovor. Dakle, to je bio prvi prodor, iz 1700-ih, u predmet koji se dans zove algebarska topologija.
CA: And your own work took an idea like this and moved it into higher-dimensional theory, higher-dimensional objects, and found new invariances?
CA: I vaš je rad preuzeo ovu ideju i promaknuo je u više-dimenzionalnu teoriju, više-dimenzionalne predmete, i našao nove invarijante?
JS: Yes. Well, there were already higher-dimensional invariants: Pontryagin classes -- actually, there were Chern classes. There were a bunch of these types of invariants. I was struggling to work on one of them and model it sort of combinatorially, instead of the way it was typically done, and that led to this work and we uncovered some new things. But if it wasn't for Mr. Euler -- who wrote almost 70 volumes of mathematics and had 13 children, who he apparently would dandle on his knee while he was writing -- if it wasn't for Mr. Euler, there wouldn't perhaps be these invariants.
JS: Da. Pa, već su postojale više-dimenzionalne invarijante: Pontryaginove klase -- zapravo, bile su Chernove klase. Postojala je hrpa tih vrsta invarijanti. Mučio sam se radeći na jednoj i modelirajući je kombinatorički, umjesto na način na koji se to tipično radi, i to je dovelo do ovog rada i otkrili smo nove stvari. Ali da nije bilo g.Eulera -- koji je napisao gotovo 70 djela o matematici i imao 13 djece, koje je navodno njihao na koljenima dok je pisao -- da nije bilo g.Eulera, vjerojatno ne bi bilo ovih invarijanti.
CA: OK, so that's at least given us a flavor of that amazing mind in there. Let's talk about Renaissance. Because you took that amazing mind and having been a code-cracker at the NSA, you started to become a code-cracker in the financial industry. I think you probably didn't buy efficient market theory. Somehow you found a way of creating astonishing returns over two decades. The way it's been explained to me, what's remarkable about what you did wasn't just the size of the returns, it's that you took them with surprisingly low volatility and risk, compared with other hedge funds. So how on earth did you do this, Jim?
CA: OK, to nam je dalo barem uvid u nevjerojatan um koji stoji iza toga. Pričajmo o Renaissance-i. Zato što ste promatrali taj nevjerojatan um i zato što ste bili razbijač kodova u NSA, počeli ste raditi kao razbijač kodova u financijskoj industriji. Vjerojatno niste vjerovali u učinkovitu tržišnu teoriju. Nekako ste našli način za kreaciju zadivljujućih prihoda u zadnjih 20 godina. Ovako su mi to objasnili, ono što je izvanredno u tome što ste napravili, nisu samo prihodi, nego kako ste ih stvorili sa začuđujuće niskim volatilitetom i rizikom, uspoređujući s drugim hedge fondovima. Kako ste, pobogu, to napravili, Jim?
JS: I did it by assembling a wonderful group of people. When I started doing trading, I had gotten a little tired of mathematics. I was in my late 30s, I had a little money. I started trading and it went very well. I made quite a lot of money with pure luck. I mean, I think it was pure luck. It certainly wasn't mathematical modeling. But in looking at the data, after a while I realized: it looks like there's some structure here. And I hired a few mathematicians, and we started making some models -- just the kind of thing we did back at IDA [Institute for Defense Analyses]. You design an algorithm, you test it out on a computer. Does it work? Doesn't it work? And so on.
JS: Napravio sam to skupivši divnu grupu ljudi. Kada sam se počeo baviti trgovinom, malo sam se umorio od matematike. Bio sam u kasnim 30-im, imao sam malo novca. Počeo sam trgovati i išlo je dobro. Zaradio sam dosta novca čistom srećom. Barem ja mislim da je bila čista sreća. Sigurno nije bilo matematičko modeliranje. Ali kada sam pogledao podatke, nakon nekog vremena sam shvatio: Izgleda kao da postoji neka struktura u tome. Zaposlio sam par matematičara i počeli smo izrađivati neke modele -- iste kakve smo radili na Institutu za obrambenu analizu. Dizajnirate algoritme, testirate ih na računalu. Funkcionira? Ne funkcionira? I tako dalje.
CA: Can we take a look at this? Because here's a typical graph of some commodity. I look at that, and I say, "That's just a random, up-and-down walk -- maybe a slight upward trend over that whole period of time." How on earth could you trade looking at that, and see something that wasn't just random?
CA: Možemo li pogledati ovo? Zato što je ovdje tipični graf neke robe. Gledam u to i kažem: "To je samo nasumičan hod gore-dolje -- možda mali trend povećanja tijekom cijelog vremena." Kako bi, pobogu, mogli trgovati gledajući u to i vidjeti nešto što nije samo nasumično?
JS: In the old days -- this is kind of a graph from the old days, commodities or currencies had a tendency to trend. Not necessarily the very light trend you see here, but trending in periods. And if you decided, OK, I'm going to predict today, by the average move in the past 20 days -- maybe that would be a good prediction, and I'd make some money. And in fact, years ago, such a system would work -- not beautifully, but it would work. You'd make money, you'd lose money, you'd make money. But this is a year's worth of days, and you'd make a little money during that period. It's a very vestigial system.
JS: U starim ddanima -- ovo je graf iz starih dana, roba ili valuta su imale tendenciju rasta. Ne nužno nizak rast kakav vidite ovdje, ali periodični rast. I ako ste odlučili, OK, predvidit ću danas, prema prosječnom pomicanju u posljednjih 20 godina -- možda bi to bilo dobro predviđanje i zaradio bih novac. I zapravo, prije mnogo godina, ovakav bi sustav funkcionirao -- ne lijepo, ali bi funkcionirao. Zaradili biste, izgubili novac, zaradili. Ali ovo je godišnja vrijednost i zaradili biste malo novca tijekom tog vremena. To je veoma neprofitabilan sustav.
CA: So you would test a bunch of lengths of trends in time and see whether, for example, a 10-day trend or a 15-day trend was predictive of what happened next.
CA: Dakle, vi biste testirali hrpu duljina porasta u vremenu i vidjeli predviđa li, na primjer, 10-dnevni porast ili 15-dnevni porast što će se sljedeće dogoditi.
JS: Sure, you would try all those things and see what worked best. Trend-following would have been great in the '60s, and it was sort of OK in the '70s. By the '80s, it wasn't.
JS: Naravno, probali biste sve to i vidjeli što najbolje funkcionira. Praćenje rasta bi bilo odlično 60-ih i bilo bi OK 70-ih. 80-ih više nije bilo.
CA: Because everyone could see that. So, how did you stay ahead of the pack?
CA: Zato što su svi mogli vidjeti to. Dakle, kako ste ostali ispred krda?
JS: We stayed ahead of the pack by finding other approaches -- shorter-term approaches to some extent. The real thing was to gather a tremendous amount of data -- and we had to get it by hand in the early days. We went down to the Federal Reserve and copied interest rate histories and stuff like that, because it didn't exist on computers. We got a lot of data. And very smart people -- that was the key. I didn't really know how to hire people to do fundamental trading. I had hired a few -- some made money, some didn't make money. I couldn't make a business out of that. But I did know how to hire scientists, because I have some taste in that department. So, that's what we did. And gradually these models got better and better, and better and better.
JS: Ostali smo ispred krda, jer smo pronašli druge pristupe -- kraće pristupe. Najvažnije je bilo prikupiti nevjerojatnu količinu podataka -- u početku smo to morali raditi ručno. Išli smo u Federalnu rezervu i kopirali povijesne trendove rasta i takve stvari, jer to nije postojalo na računalima. Dobili smo mnogo podataka. I mnogo pametnih ljudi -- to je bio ključ. Nisam znao kako da zaposlim ljude da se bave osnovnim trgovanjem. Zaposlio sam ih par -- neki su zarađivali, a neki ne. Nisam mogao stvoriti business iz toga. Ali znao sam kako zaposliti znanstvenike, jer sam se iskusio u tom području. Dakle, to smo i napravili. I postepeno su ti modeli postajali sve bolji i bolji, i bolji i bolji.
CA: You're credited with doing something remarkable at Renaissance, which is building this culture, this group of people, who weren't just hired guns who could be lured away by money. Their motivation was doing exciting mathematics and science.
CA: Zaslužni ste za nešto nevjerojatno što ste napravili u Renaissence-u, izgradili ste tu kulturu, tu grupu ljudi, koji nisu bili zamo zaposlene puške koje bi namamio novac. Njihova je motivacija bila uzbudljiva matematika i znanost.
JS: Well, I'd hoped that might be true. But some of it was money.
JS: Pa, nadao bih se da je to istina. Ali ipak je dio bio novac.
CA: They made a lot of money.
CA: Puno su zarađivali.
JS: I can't say that no one came because of the money. I think a lot of them came because of the money. But they also came because it would be fun.
JS: Ne mogu reći da nitko nije došao zbog novca. Mislim da ih je mnogo došlo zbog novca. Ali su također došli, jer bi bilo zabavno.
CA: What role did machine learning play in all this?
CA: Koju ulogu u svemu tome je imalo mašinsko učenje?
JS: In a certain sense, what we did was machine learning. You look at a lot of data, and you try to simulate different predictive schemes, until you get better and better at it. It doesn't necessarily feed back on itself the way we did things. But it worked.
JS: U određenom smislu, ono što smo mi radili je bilo mašinsko učenje. Pogledate puno podataka i pokušate simulirati različite predviđajuće sheme, dok ne postanete sve bolji i bolji u tome. Nije nužno dobiti povratnu informaciju na način na koji smo mi radili. Ali funkcioniralo je.
CA: So these different predictive schemes can be really quite wild and unexpected. I mean, you looked at everything, right? You looked at the weather, length of dresses, political opinion.
CA: Dakle, te različite predviđajuće sheme mogu biti prilično divlje i neočekivane. Mislim, vi ste sve gledali, je li tako? Gledali ste vrijeme, duljinu haljina, politička mišljenja.
JS: Yes, length of dresses we didn't try.
JS: Da, duljinu haljina nismo probali.
CA: What sort of things?
CA: Kakve stvari?
JS: Well, everything. Everything is grist for the mill -- except hem lengths. Weather, annual reports, quarterly reports, historic data itself, volumes, you name it. Whatever there is. We take in terabytes of data a day. And store it away and massage it and get it ready for analysis. You're looking for anomalies. You're looking for -- like you said, the efficient market hypothesis is not correct.
JS: Pa, sve. Sve je važno. Vrijeme, godišnja izvješća, kvartalna izvješća, povijesni podaci, obujmi, što god. Što god postoji. Uzimamo terabajte podataka svaki dan. I pohranjujemo ih te ih spremamo za analizu. Tražite anomalije. Tražite -- kako ste rekli, učinkovitu tržišnu hipotezu koja nije točna.
CA: But any one anomaly might be just a random thing. So, is the secret here to just look at multiple strange anomalies, and see when they align?
CA: Ali jedna anomalija može biti slučajna. Dakle, je li tajna ovdje samo gledati više čudnih anomalija i vidjeti zašto se redaju?
JS: Any one anomaly might be a random thing; however, if you have enough data you can tell that it's not. You can see an anomaly that's persistent for a sufficiently long time -- the probability of it being random is not high. But these things fade after a while; anomalies can get washed out. So you have to keep on top of the business.
JS: Svaka anomalija može biti slučajna; ipak, ako imate dovoljno podataka možete shvatiti da nije. Možete vidjeti anomaliju koja je dugotrajna -- vjerojatnost da je slučajna nije visoka. Ali te stvare izblijede nakon nekog vremena, anomalije se isperu. Zato morate biti na vrhu businessa.
CA: A lot of people look at the hedge fund industry now and are sort of ... shocked by it, by how much wealth is created there, and how much talent is going into it. Do you have any worries about that industry, and perhaps the financial industry in general? Kind of being on a runaway train that's -- I don't know -- helping increase inequality? How would you champion what's happening in the hedge fund industry?
CA: Mnogi ljudi gledaju hedge fond industriju i na neki način su šokirani njome, koliko se tamo bogatstva stvara i koliko talenta ide onamo. Imate li ikakve brige za tu industriju ili možda za financijsku industriju općenito? Nekako je na tračnici koja -- ne znam --- pomaže u povećanju nejednakosti? Kako biste opravdali sve što se događa u hedge fond industriji?
JS: I think in the last three or four years, hedge funds have not done especially well. We've done dandy, but the hedge fund industry as a whole has not done so wonderfully. The stock market has been on a roll, going up as everybody knows, and price-earnings ratios have grown. So an awful lot of the wealth that's been created in the last -- let's say, five or six years -- has not been created by hedge funds. People would ask me, "What's a hedge fund?" And I'd say, "One and 20." Which means -- now it's two and 20 -- it's two percent fixed fee and 20 percent of profits. Hedge funds are all different kinds of creatures.
JS: Mislim da u zadnje tri ili četiri godine hedge fondovi nisu posebno dobro prošli. Mi smo odlično poslovali, ali hedge fond industrija u cjelini, ne baš odlično. Dionice su divljale, povećavale se kao što svi znaju i omjer zarada je porastao. Dakle, ogromno bogatstvo koje je stvoreno u posljednjih -- ajmo reći, pet ili šest godina -- nisu stvorili hedge fondovi. Ljudi bi me pitali: "Što su hedge fondovi?" I ja bi reako :"Jedan i 20." Što znači -- danas je dva i 20 -- to je dva posto fiksnog uloga i 20 posto profita. Hedge fondovi su različite vrste bića.
CA: Rumor has it you charge slightly higher fees than that.
CA: Priča se da naplačujete nešto više od toga.
JS: We charged the highest fees in the world at one time. Five and 44, that's what we charge.
JS: U jednom trenutku smo naplačivali najviše u svijetu. Pet i 44, toliko mi naplačujemo.
CA: Five and 44. So five percent flat, 44 percent of upside. You still made your investors spectacular amounts of money.
CA: Pet i 44. Dakle, pet posto uloga, 44 posto više profita. I dalje ste svojim investitorima stvorili spektakularne zarade.
JS: We made good returns, yes. People got very mad: "How can you charge such high fees?" I said, "OK, you can withdraw." But "How can I get more?" was what people were --
JS: Imali smo dobre zarade, da. Ljudi su se jako naljutili: "Kako možete naplačivati toliko?" Ja sam rekao: "OK, možete se povući." Ali "Kako mogu dobiti više?", to je zanimalo ljude --
(Laughter)
(Smijeh)
But at a certain point, as I think I told you, we bought out all the investors because there's a capacity to the fund.
Ali u određenom trenutku, mislim da sam vam rekao to, kupili smo sve investitore, jer je postojao kapacitet fonda.
CA: But should we worry about the hedge fund industry attracting too much of the world's great mathematical and other talent to work on that, as opposed to the many other problems in the world?
CA: Ali trebamo li se brinuti da hedge fond industrija privlači previše svjetskih odličnih matematičara i drugih talenata da rade na tome, umjesto na mnogim drugim svjetskim problemima?
JS: Well, it's not just mathematical. We hire astronomers and physicists and things like that. I don't think we should worry about it too much. It's still a pretty small industry. And in fact, bringing science into the investing world has improved that world. It's reduced volatility. It's increased liquidity. Spreads are narrower because people are trading that kind of stuff. So I'm not too worried about Einstein going off and starting a hedge fund.
JS: Pa, nisu samo matematičari. Zapošljavamo astronome i fizičare i takve ljude. Mislim da ne bismo trebali previše brinuti o tome. To je još uvijek veoma mala industrija. I zapravo, dovođenje znanosti u svijet investicija je poboljšalo svijet. Smanjen volatilitet. Povećana likvidnost. Širenja su uža, jer ljudi trguju takvim stvarima. Tako da nisam zabrinut da će Einstein odustati od svog rada i osnovati hedge fond.
CA: You're at a phase in your life now where you're actually investing, though, at the other end of the supply chain -- you're actually boosting mathematics across America. This is your wife, Marilyn. You're working on philanthropic issues together. Tell me about that.
CA: Sada ste u fazi života kada zapravo investirate, iako s druge strane opskrbnog lanca -- vi zapravo potičete matematiku u cijeloj Americi. Ovo je vaša žena, Marilyn. Zajedno radite na filantropskim problemima. Recite mi nešto o tome.
JS: Well, Marilyn started -- there she is up there, my beautiful wife -- she started the foundation about 20 years ago. I think '94. I claim it was '93, she says it was '94, but it was one of those two years.
JS: Pa, Marilyn je osnovala -- eno je tamo gore, moja prekrasna supruga -- ona je osnovala zakladu prije otprilike 20 godina. Mislim '94. Ja tvrdim '93., ona kaže da je '94., ali bilo je jedne od te dvije godine.
(Laughter)
(Smijeh)
We started the foundation, just as a convenient way to give charity. She kept the books, and so on. We did not have a vision at that time, but gradually a vision emerged -- which was to focus on math and science, to focus on basic research. And that's what we've done. Six years ago or so, I left Renaissance and went to work at the foundation. So that's what we do.
Osnovali smo zakladu, samo kao prikladan način doniranja. Ona je bila knjigovođa, itd. U to vrijeme nismo imali viziju, ali postupno se ona pojavila -- a to je bilo fokusiranje na matematiku i znanost, na osnovna istraživanja. I to smo napravili. Prije šest godina ili tako nešto, napustio sam Reneissance i otišao raditi u zakladu. Dakle, to radimo.
CA: And so Math for America is basically investing in math teachers around the country, giving them some extra income, giving them support and coaching. And really trying to make that more effective and make that a calling to which teachers can aspire.
CA: Dakle, Math of America investira u profesore matematike iz cijele zemlje, daje im nešto više prihoda, daje im podršku i usavršavanja. I zapravo pokušava to učiniti učinkovitijim i učiniti to nečim čemu profesori teže.
JS: Yeah -- instead of beating up the bad teachers, which has created morale problems all through the educational community, in particular in math and science, we focus on celebrating the good ones and giving them status. Yeah, we give them extra money, 15,000 dollars a year. We have 800 math and science teachers in New York City in public schools today, as part of a core. There's a great morale among them. They're staying in the field. Next year, it'll be 1,000 and that'll be 10 percent of the math and science teachers in New York [City] public schools.
JS: Da -- umjesto da prebijamo loše profesore, što je proizvelo moralne probleme u cijeloj prosvjetnoj zajednici, posebice u matematici i znanostima, mi se fokusiramo na slavljenje dobrih i dajemo im status. Da, dajemo im dodatan novac, 15,000 dolara godišnje. Imamo 800 profesora matematike i znanosti u newyorškim javnim školama danas, kao dio jezgre. Velika je moralnost među njima. Ostaju u tom polju. Sljedeće godine, bit će ih 1,000 i to će biti 10 posto newyorških javnoškolskih profesora matematike i znanosti.
(Applause)
(Pljesak)
CA: Jim, here's another project that you've supported philanthropically: Research into origins of life, I guess. What are we looking at here? JS: Well, I'll save that for a second. And then I'll tell you what you're looking at. Origins of life is a fascinating question. How did we get here? Well, there are two questions: One is, what is the route from geology to biology -- how did we get here? And the other question is, what did we start with? What material, if any, did we have to work with on this route? Those are two very, very interesting questions. The first question is a tortuous path from geology up to RNA or something like that -- how did that all work? And the other, what do we have to work with? Well, more than we think. So what's pictured there is a star in formation. Now, every year in our Milky Way, which has 100 billion stars, about two new stars are created. Don't ask me how, but they're created. And it takes them about a million years to settle out. So, in steady state, there are about two million stars in formation at any time. That one is somewhere along this settling-down period. And there's all this crap sort of circling around it, dust and stuff. And it'll form probably a solar system, or whatever it forms. But here's the thing -- in this dust that surrounds a forming star have been found, now, significant organic molecules. Molecules not just like methane, but formaldehyde and cyanide -- things that are the building blocks -- the seeds, if you will -- of life. So, that may be typical. And it may be typical that planets around the universe start off with some of these basic building blocks. Now does that mean there's going to be life all around? Maybe. But it's a question of how tortuous this path is from those frail beginnings, those seeds, all the way to life. And most of those seeds will fall on fallow planets.
CA: Jim, evo još jednog projekta koji ste filantropski poduprli; Istraživanje postanka života, pretpostavljam. Što to ovdje gledamo? JS: Pa, pričekat ću sekundu. I onda ću vam reći što gledamo. Postanak života je fascinantno pitanje. Kako smo dospjeli ovdje? Pa, postoje dva pitanja: Jedno je, koji je put od geologije do biologije -- kako smo dospjeli ovdje? A drugo je pitanje, čime smo započeli? S kojim materijalom, ako s ikojim, smo morali raditi na ovom putu? To su dva veoma, veoma zanimljiva pitanja. Prvo je pitanje mučan put od geologije do RNA ili nešto tako -- kako je to sve funkcioniralo? A drugo, s čim moramo raditi? Pa, s više nego što mislimo. Dakle, ono što je na ovoj slici je zvijezda u nastanku. Svake godine u našoj Mliječnoj Stazi, koja ima 100 milijardi zvijezda, nastanu otprilike dvije nove zvijezde. Nemojte me pitati kako, ali nastanu. I treba im otprilike miljun godina da se raspadnu. Dakle, u stabilnom stanju ima oko dva miljuna zvijezda koje nastaju u bilo koje vrijeme. Ova je negdje u razdoblju stabiliziranja. I tu su sva ova sranja koja kruže oko nje, prašina i tome slično. I vjerojatno će formirati solarni sustav, ili što god već formira. Ali, evo u čemu je stvar -- u ovoj prašini koja okružuje zvijezde nađene su značajne organske molekule. Molekule, ne samo poput metana, nego poput formaldehida i cijanida -- stvari koje izgrađuju -- sjeme, može i tako -- života. To može biti tipično. I može biti tipično da planeti u svemiru nastaju od nekih osnovnih građevnih tvari poput tih. Znači li to da će život zauvijek postojati? Možda. Ali pitanje je koliko je mučan taj put od tih nepouzdanih početaka, tog sjemena, sve do života. I većina tog sjemena će pasti na neobrađene planete.
CA: So for you, personally, finding an answer to this question of where we came from, of how did this thing happen, that is something you would love to see.
CA: Dakle, za vas osobno, pronaći odgovor na pitanje odakle dolazimo, pitanje kako se ovo dogodilo, to je nešto što biste voljeli vidjeti.
JS: Would love to see. And like to know -- if that path is tortuous enough, and so improbable, that no matter what you start with, we could be a singularity. But on the other hand, given all this organic dust that's floating around, we could have lots of friends out there. It'd be great to know.
JS: Volio bih to vidjeti, I volio bih znati -- ako je taj put dovoljno težak i toliko nevjerojatan, da bez obzira s čim počnemo, mogli bismo biti jedinstveni. Ali s druge strane, kada vidimo svu tu organsku prašinu koja lebdi uokolo, mogli bismo imati puno prijatelja tamo. Bilo bi super znati.
CA: Jim, a couple of years ago, I got the chance to speak with Elon Musk, and I asked him the secret of his success, and he said taking physics seriously was it. Listening to you, what I hear you saying is taking math seriously, that has infused your whole life. It's made you an absolute fortune, and now it's allowing you to invest in the futures of thousands and thousands of kids across America and elsewhere. Could it be that science actually works? That math actually works?
CA: Jim, prije nekoliko godina sam imao priliku razgovarati s Elonom Muskom i pitao sam ga za tajnu njegova uspjeha, a on je odgovorio da je tajna njegova uspjeha što je ozbiljno shvaćao fiziku. Slušajući vas, ono što čujem da vi govorite je ozbiljno shvaćanje matematike, koja je obuzela vaš cijeli život. Obogatila vas je i sad vam dopušta da investirate u budućnost tisuće i tisuće djece u Americi i drugdje. Može li biti da znanost zapravo funkcionira? Da matematika zapravo funkcionira?
JS: Well, math certainly works. Math certainly works. But this has been fun. Working with Marilyn and giving it away has been very enjoyable.
JS: Pa, matematika zasigurno funkcionira. Matematika sigurno funkcionira. Ali ovo je bilo zabavno. Raditi s Marilyin i donirati je bilo veoma ugodno.
CA: I just find it -- it's an inspirational thought to me, that by taking knowledge seriously, so much more can come from it. So thank you for your amazing life, and for coming here to TED.
CA: Ja smatram -- za mene je to inspirativna misao, da kada znanje shvaćamo ozbiljno, toliko više može proizaći iz toga. Pa vam zahvaljujem na nevjerojatnom životu i na dolasku na TED.
Thank you.
Hvala Vam.
Jim Simons!
Jim Simons!
(Applause)
(Pljesak)