I'd like to tell you about two games of chess. The first happened in 1997, in which Garry Kasparov, a human, lost to Deep Blue, a machine. To many, this was the dawn of a new era, one where man would be dominated by machine. But here we are, 20 years on, and the greatest change in how we relate to computers is the iPad, not HAL.
Želim vam pričati o dvije partije šaha. Prva se dogodila 1997., u kojoj je Garry Kasparov, čovjek, izgubio od Deep Bluea, stroja. Za mnoge, ovo je bilo svitanje nove ere, one u kojoj će stroj dominirati nad čovjekom. No, evo nas 20 godina poslije i najveća promjena u našem pogledu na računala je iPad, ne HAL.
The second game was a freestyle chess tournament in 2005, in which man and machine could enter together as partners, rather than adversaries, if they so chose. At first, the results were predictable. Even a supercomputer was beaten by a grandmaster with a relatively weak laptop. The surprise came at the end. Who won? Not a grandmaster with a supercomputer, but actually two American amateurs using three relatively weak laptops. Their ability to coach and manipulate their computers to deeply explore specific positions effectively counteracted the superior chess knowledge of the grandmasters and the superior computational power of other adversaries. This is an astonishing result: average men, average machines beating the best man, the best machine. And anyways, isn't it supposed to be man versus machine? Instead, it's about cooperation, and the right type of cooperation.
Druga partija je bilo natjecanje u šahu slobodnog stila 2005., u kojoj su se računalo i čovjek mogli prijaviti zajedno kao partneri, a ne protivnici, ako bi tako odabrali. Na startu, rezultati su bili predvidljivi. Čak je i velemajstor s relativno slabim laptopom pobijedio super računalo. Iznenađenje je stiglo na kraju. Tko je pobijedio? Nije velemajstor sa super računalom, već dva američka amatera koristeći tri relativno slaba laptopa. Njihova sposobnost da manipuliraju svojim računalima tako da dublje istraže određene pozicije efektivno je kontrirala superiornom znanju šaha velemajstora i superiornu moć računanja ostalih protivnika. To je zapanjujuć rezultat: prosječni ljudi, prosječni strojevi, pobjeđuju najbolje ljude, najbolje strojeve. I uostalom, ne bi li trebalo biti čovjek protiv stroja? Umjesto toga, radi se o suradnji, i to pravoj vrsti suradnje.
We've been paying a lot of attention to Marvin Minsky's vision for artificial intelligence over the last 50 years. It's a sexy vision, for sure. Many have embraced it. It's become the dominant school of thought in computer science. But as we enter the era of big data, of network systems, of open platforms, and embedded technology, I'd like to suggest it's time to reevaluate an alternative vision that was actually developed around the same time. I'm talking about J.C.R. Licklider's human-computer symbiosis, perhaps better termed "intelligence augmentation," I.A.
Pridavali smo dosta pažnje viziji Marvina Minskya o umjetnoj inteligenciji tijekom zadnjih 50 godina. Ta vizija je seksi i mnogi su je prihvatili. Postala je dominantna misao u računalnim znanostima. No, kako ulazimo u doba velikih podataka, mrežnih sustava, otvorenih platformi i ugrađene tehnologije, volio bih predložiti da je vrijeme za revaluaciju alternativnih vizija koje su razvijene u isto doba. Govorim o simbiozi računala i čovjeka J. C. R. Licklidera, možda bolje nazvanom "proširenjem inteligencije".
Licklider was a computer science titan who had a profound effect on the development of technology and the Internet. His vision was to enable man and machine to cooperate in making decisions, controlling complex situations without the inflexible dependence on predetermined programs. Note that word "cooperate." Licklider encourages us not to take a toaster and make it Data from "Star Trek," but to take a human and make her more capable. Humans are so amazing -- how we think, our non-linear approaches, our creativity, iterative hypotheses, all very difficult if possible at all for computers to do. Licklider intuitively realized this, contemplating humans setting the goals, formulating the hypotheses, determining the criteria, and performing the evaluation. Of course, in other ways, humans are so limited. We're terrible at scale, computation and volume. We require high-end talent management to keep the rock band together and playing. Licklider foresaw computers doing all the routinizable work that was required to prepare the way for insights and decision making.
Licklider je bio titan računalnih znanosti i imao je znatan utjecaj na razvoj tehnologije i interneta. Njegova vizija je bila omogućiti suradnju čovjeka i stroja u donošenju odluka, kontroli kompleksnih situacija bez fleksibilne ovisnosti o predprogramiranim programima Primijetite riječ "suradnja". Licklider nas ohrabruje da ne uzimamo toster i napravimo Datu iz "Star Treka", već da uzmemo čovjeka i napravimo ga sposobnijim. Ljudi su čudesni -- kako mislimo, naši nelinearni pristupi, naša kreativnost, stalne hipoteze, vrlo teške ako uopće moguće za obradu računalom. Licklider to intuitivno shvaća promatrajući ljude kako postavljaju ciljeve, postavljaju hipoteze, određuju kriterije i obavljaju procjene. Za neke stvari ljudi su vrlo ograničeni. Užasni smo u mjerenju, računanju i veličini. Trebamo vrhunsko upravljanje talentima kako bi rock bend opstao i nastavio svirati. Licklider je predvidio da će računala obavljati sav rutinski posao potreban za pripremu dolaska do uvida i donošenje odluka.
Silently, without much fanfare, this approach has been compiling victories beyond chess. Protein folding, a topic that shares the incredible expansiveness of chess — there are more ways of folding a protein than there are atoms in the universe. This is a world-changing problem with huge implications for our ability to understand and treat disease. And for this task, supercomputer field brute force simply isn't enough. Foldit, a game created by computer scientists, illustrates the value of the approach. Non-technical, non-biologist amateurs play a video game in which they visually rearrange the structure of the protein, allowing the computer to manage the atomic forces and interactions and identify structural issues. This approach beat supercomputers 50 percent of the time and tied 30 percent of the time. Foldit recently made a notable and major scientific discovery by deciphering the structure of the Mason-Pfizer monkey virus. A protease that had eluded determination for over 10 years was solved was by three players in a matter of days, perhaps the first major scientific advance to come from playing a video game.
Tiho, bez mnogo galame, ovaj pristup je sakupljao pobjede i dalje od šaha. Savijanje proteina, tema koja dijeli nevjerojatnu širinu šaha -- postoji više načina za savijanje proteina nego atoma u svemiru. Ovo je svjetski problem s velikim značajem za našu sposobnost liječenja bolesti. Za ovaj zadatak, sirova snaga super računala jednostavno nije dovoljna. Foldit, igra koju su razvili računalni znanstvenici, prikazuje vrijednost pristupa. Amateri koji nisu tehničari niti biolozi igraju igru u kojoj vizualno preslaguju strukturu proteina, dopuštajući računalu da upravlja snagama atoma, interakcijama i da prepoznaje probleme u strukturi. Ovaj pristup pobjeđuje super računalo u 50% slučajeva i igra nerješeno s njim u 30%. Foldit je nedavno napravio značajno i veliko znanstveno otkriće dešifrirajući strukturu Mason-Pfizer majmunskog virusa. Proteaze koje su izmicale otkriću 10 godina riješila su tri igrača u nekoliko dana, možda prvo veliko znanstveno otkriće koje dolazi iz igranja video igara.
Last year, on the site of the Twin Towers, the 9/11 memorial opened. It displays the names of the thousands of victims using a beautiful concept called "meaningful adjacency." It places the names next to each other based on their relationships to one another: friends, families, coworkers. When you put it all together, it's quite a computational challenge: 3,500 victims, 1,800 adjacency requests, the importance of the overall physical specifications and the final aesthetics. When first reported by the media, full credit for such a feat was given to an algorithm from the New York City design firm Local Projects. The truth is a bit more nuanced. While an algorithm was used to develop the underlying framework, humans used that framework to design the final result. So in this case, a computer had evaluated millions of possible layouts, managed a complex relational system, and kept track of a very large set of measurements and variables, allowing the humans to focus on design and compositional choices. So the more you look around you, the more you see Licklider's vision everywhere. Whether it's augmented reality in your iPhone or GPS in your car, human-computer symbiosis is making us more capable.
Prošle godine, na mjestu srušenih blizanaca, otvorio se spomenik za 9/11. Prikazuje imena tisuća žrtava koristeći predivan koncept zvan "značajno susjedstvo." Postavlja imena jedno pored drugoga na temelju njihovih međusobnih veza: prijatelja, obitelji, suradnika. Kada spojite sve zajedno, popriličan je računalni izazov: 3500 žrtava, 1800 zahtjeva za susjedstvo, važnost sveukupnih fizičkih odredbi i završne estetike. Kada su mediji prvi puta javili to, cijela zasluga je dana algoritmu iz New Yorške dizajnerske tvrtke Local Projects. Istina je ponešto drugačija. Dok je korišten algoritam za razvoj osnovnog okvira, ljudi su koristili taj okvir za dizajn završnog rezultata. Dakle u ovom slučaju, računalo je procijenilo milijune mogućih rasporeda, upravljalo kompleksnim sustavom odnosa, i pratilo vrlo velik set mjera i varijabli, dopuštajući ljudima da se usmjere na dizajn i kompozicijske izbore. Što više gledate, sve više vidite Lickliderovu viziju. Bilo da je proširena stvarnost u vašem iPhoneu ili GPS-u u autu, simbioza čovjeka i računala nas čini sposobnijima.
So if you want to improve human-computer symbiosis, what can you do? You can start by designing the human into the process. Instead of thinking about what a computer will do to solve the problem, design the solution around what the human will do as well. When you do this, you'll quickly realize that you spent all of your time on the interface between man and machine, specifically on designing away the friction in the interaction. In fact, this friction is more important than the power of the man or the power of the machine in determining overall capability. That's why two amateurs with a few laptops handily beat a supercomputer and a grandmaster. What Kasparov calls process is a byproduct of friction. The better the process, the less the friction. And minimizing friction turns out to be the decisive variable.
Dakle, ako želite poboljšati tu simbiozu, što možete napraviti? Možete početi stavljanjem čovjeka u proces. Umjesto da razmišljate što računalo može napraviti da riješi problem, stvorite rješenje oko onoga što će i čovjek napraviti. Kada ovo napravite, brzo ćete shvatiti da ste potrošili svo vrijeme na sučelju između čovjeka i stroja, posebno na uklanjanju trvenja u interakciji. Ustvari, to trvenje je važnije nego snaga čovjeka ili stroja za sveukupnu sposobnost. To je razlog zašto dva amatera s laptopima mogu pobijediti super računalo i velemajstora. Ono što Kasparov zove procesom, je nusprodukt trvenja. Što je bolji proces, manje je trvenja. Smanjenje trvenja je, čini se, odlučujuća varijabla.
Or take another example: big data. Every interaction we have in the world is recorded by an ever growing array of sensors: your phone, your credit card, your computer. The result is big data, and it actually presents us with an opportunity to more deeply understand the human condition. The major emphasis of most approaches to big data focus on, "How do I store this data? How do I search this data? How do I process this data?" These are necessary but insufficient questions. The imperative is not to figure out how to compute, but what to compute. How do you impose human intuition on data at this scale?
Ili uzmite drugi primjer: veliki podaci. Svaka interakcija koju imamo u svijetu je snimljena vječno rastućim brojem senzora: vaš mobitel, vaša kreditna kartica, vaše računalo. Rezultat su veliki podaci, i daju nam priliku da dublje razumijemo ljudsko stanje. Najveći naglasak na većini ovih pristupa je fokus na "Kako spremim ove podatke? Kako ih pretražujem? Kako ih obrađujem?" Ovo su važna ali nedovoljna pitanja. Imperativ nije na shvaćanju kako računati, nego što računati. Kako umetnuti ljudsku intuiciju u tolikim podacima?
Again, we start by designing the human into the process. When PayPal was first starting as a business, their biggest challenge was not, "How do I send money back and forth online?" It was, "How do I do that without being defrauded by organized crime?" Why so challenging? Because while computers can learn to detect and identify fraud based on patterns, they can't learn to do that based on patterns they've never seen before, and organized crime has a lot in common with this audience: brilliant people, relentlessly resourceful, entrepreneurial spirit — (Laughter) — and one huge and important difference: purpose. And so while computers alone can catch all but the cleverest fraudsters, catching the cleverest is the difference between success and failure.
Ponovo, počinjemo stavljanjem čovjeka u proces. Kad je PayPal počinjao svoj posao, njihov najveći izazov nije bio "Kako šaljem novac tamo - amo preko mreže?" Bio je "Kako da to napravim bez da me prevari organizirani kriminal?" Zašto je toliki izazov? Jer, dok računala mogu naučiti prepoznati prevaru na temelju uzoraka, ne mogu to napraviti na temelju uzoraka koje nisu nikada vidjeli, a organizirani kriminal ima mnogo toga zajedničkog s ovom publikom: briljantni ljudi, neumoro snalažljivi, poduzetnički duh -- (Smijeh) -- i jedna ključna razlika: svrha. I dok računala sama mogu uhvatiti sve osim najpametnijih prevaranata, hvatanje najpametnijih je razlika između uspjeha i neuspjeha.
There's a whole class of problems like this, ones with adaptive adversaries. They rarely if ever present with a repeatable pattern that's discernable to computers. Instead, there's some inherent component of innovation or disruption, and increasingly these problems are buried in big data.
Postoji mnogo ovakvih problema, onih s prilagodljivim protivnicima. Rijetko, ako ikad ponavljaju uzorak primjetan računalima. Umjesto, postoji nasljedna sastavnica inovacije ili remećenja, i taj rastući broj problema je zakopan u velikim podacima.
For example, terrorism. Terrorists are always adapting in minor and major ways to new circumstances, and despite what you might see on TV, these adaptations, and the detection of them, are fundamentally human. Computers don't detect novel patterns and new behaviors, but humans do. Humans, using technology, testing hypotheses, searching for insight by asking machines to do things for them. Osama bin Laden was not caught by artificial intelligence. He was caught by dedicated, resourceful, brilliant people in partnerships with various technologies.
Na primjer, terorizam. Teroristi se uvijek prilagode na bolje ili lošije načine novim okolnostima i unatoč viđenom na TV-u, ove prilagodbe, i njihovo primjećivanje, su temeljno ljudske. Računala ne raspoznaju nove uzorke ili ponašanja, ali ljudi da. Ljudi, koristeći tehnologiju, testirajući hipoteze, tražeći uvide traženjem strojeva da urade nešto za njih. Osamu bin Ladena nije uhvatila umjetna inteligencija. Uhvatili su ga predani, snalažljivi, genijalni ljudi u partnerstvu s raznim tehnologijama.
As appealing as it might sound, you cannot algorithmically data mine your way to the answer. There is no "Find Terrorist" button, and the more data we integrate from a vast variety of sources across a wide variety of data formats from very disparate systems, the less effective data mining can be. Instead, people will have to look at data and search for insight, and as Licklider foresaw long ago, the key to great results here is the right type of cooperation, and as Kasparov realized, that means minimizing friction at the interface.
Koliko god zvučalo primamljivo, ne možete algoritamski iskopati svoj put do odgovora. Ne postoji gumb "nađi terorista", a što više podataka integriramo iz iznimnog broja izvora preko širokog spektra formata podataka iz različitih sustava, to kopanje može biti manje učinkovito. Umjesto toga, ljudi moraju gledati podatke i tražiti uvide, i kako je Licklider davno predvidio, ključ sjajnih rezultata je prava vrsta suradnje, i kako je Kasparov shvatio, to znači smanjivati trvenje na sučelju.
Now this approach makes possible things like combing through all available data from very different sources, identifying key relationships and putting them in one place, something that's been nearly impossible to do before. To some, this has terrifying privacy and civil liberties implications. To others it foretells of an era of greater privacy and civil liberties protections, but privacy and civil liberties are of fundamental importance. That must be acknowledged, and they can't be swept aside, even with the best of intents.
Ovaj pristup omogućava stvari poput pročešljavanja svih mogućih podataka iz drugačijih izvora, prepoznati ključne veze i stavljati ih na jedno mjesto, nešto što je ranije bilo nemoguće izvesti. Za neke, ovo ima užasan utjecaj na privatnost i građanska prava. Drugima, predskazuje doba veće privatnosti i zaštite građanskih prava, no privatnost i građanska prava su od ključne važnosti. To mora biti obznanjeno i ne smiju biti stavljeni sa strane, čak ni iz najboljih namjera.
So let's explore, through a couple of examples, the impact that technologies built to drive human-computer symbiosis have had in recent time.
Dakle, istražujmo kroz nekoliko primjera, utjecaj koji su tehnologije, napravljene da služe simbiozi računala i čovjeka, imale u zadnje vrijeme.
In October, 2007, U.S. and coalition forces raided an al Qaeda safe house in the city of Sinjar on the Syrian border of Iraq. They found a treasure trove of documents: 700 biographical sketches of foreign fighters. These foreign fighters had left their families in the Gulf, the Levant and North Africa to join al Qaeda in Iraq. These records were human resource forms. The foreign fighters filled them out as they joined the organization. It turns out that al Qaeda, too, is not without its bureaucracy. (Laughter) They answered questions like, "Who recruited you?" "What's your hometown?" "What occupation do you seek?"
U listopadu, 2007., SAD i koalicijske snage su pretresli sigurnu kuću Al Qaede u gradu Sinjaru na sirijskoj granici s Irakom. Našli su bogatstvo dokumenata: 700 biografskih skica stranih boraca. Ti strani borci su ostavili svoje obitelji u Zaljevu, Levantu i sjevernoj Africi da bi se pridružili Al Qaedi u Iraku. Ovi zapisi su formulari kadrovske. Strani borci su ih ispunjavali kako su se pridruživali organizaciji. Čini se kako ni Al Qaeda nije bez birokracije. (Smijeh) Odgovarali su na pitanja poput: "Tko te unovačio? Gdje ti je rodni grad? Koju poziciju tražiš?"
In that last question, a surprising insight was revealed. The vast majority of foreign fighters were seeking to become suicide bombers for martyrdom -- hugely important, since between 2003 and 2007, Iraq had 1,382 suicide bombings, a major source of instability. Analyzing this data was hard. The originals were sheets of paper in Arabic that had to be scanned and translated. The friction in the process did not allow for meaningful results in an operational time frame using humans, PDFs and tenacity alone. The researchers had to lever up their human minds with technology to dive deeper, to explore non-obvious hypotheses, and in fact, insights emerged. Twenty percent of the foreign fighters were from Libya, 50 percent of those from a single town in Libya, hugely important since prior statistics put that figure at three percent. It also helped to hone in on a figure of rising importance in al Qaeda, Abu Yahya al-Libi, a senior cleric in the Libyan Islamic fighting group. In March of 2007, he gave a speech, after which there was a surge in participation amongst Libyan foreign fighters.
U tom zadnjem pitanju otkriven je iznenađujuć uvid. Velika većina stranih boraca je htjela biti bombaš samoubojica zbog mučeništva -- vrlo važno, između 2003. i 2007., u Iraku se dogodilo 1.382 samoubilačkih bombaških napada, velik izvor nestabilnosti. Analiza podataka je bila teška. Originali su bili na arapskom i morali su biti skenirani i prevedeni. Trvenje u procesu nije dozvoljavalo važne rezultate u operativnom vremenu samo korištenjem ljudi, PDF-ova i ustrajnošću. Istraživači su morali poduprijeti svoje umove tehnologijom kako bi zaronili dublje, istražili ne očite hipoteze i, ustvari, dobili su rezultate. 20% stranih boraca je bilo iz Libije, 50% njih je iz istog grada u Libiji, vrlo važno s obzirom da prijašnja statistika taj broj određuje na 3%. Također je pomoglo u približavanju osobi rastuće važnosti u Al Qaedi, Abu Yahya al-Libiju, starijem kleriku u libijskoj islamskoj borbenoj grupi. U ožujku 2007., održao je govor nakon kojeg se dogodio snažan rast prijava među libijskim borcima.
Perhaps most clever of all, though, and least obvious, by flipping the data on its head, the researchers were able to deeply explore the coordination networks in Syria that were ultimately responsible for receiving and transporting the foreign fighters to the border. These were networks of mercenaries, not ideologues, who were in the coordination business for profit. For example, they charged Saudi foreign fighters substantially more than Libyans, money that would have otherwise gone to al Qaeda. Perhaps the adversary would disrupt their own network if they knew they cheating would-be jihadists.
Možda najpametnije od svega, iako najmanje očito, okretanjem podataka naopako, istraživači su mogli dublje istražiti koordinacijske mreže u Siriji koje su bile odgovorne za prihvat i transport stranih boraca na granicu. To su bile mreže plaćenika, ne ideologa, koji su u koordinacijskom poslu bili zbog profita. Na primjer, naplaćivali su saudijskim borcima, značajno više nego libijskim, novac koji bi inače išao Al Qaedi. Možda bi protivnici prekinuli vlastitu mrežu da su znali da varaju buduće džihadiste.
In January, 2010, a devastating 7.0 earthquake struck Haiti, third deadliest earthquake of all time, left one million people, 10 percent of the population, homeless. One seemingly small aspect of the overall relief effort became increasingly important as the delivery of food and water started rolling. January and February are the dry months in Haiti, yet many of the camps had developed standing water. The only institution with detailed knowledge of Haiti's floodplains had been leveled in the earthquake, leadership inside. So the question is, which camps are at risk, how many people are in these camps, what's the timeline for flooding, and given very limited resources and infrastructure, how do we prioritize the relocation? The data was incredibly disparate. The U.S. Army had detailed knowledge for only a small section of the country. There was data online from a 2006 environmental risk conference, other geospatial data, none of it integrated. The human goal here was to identify camps for relocation based on priority need. The computer had to integrate a vast amount of geospacial information, social media data and relief organization information to answer this question. By implementing a superior process, what was otherwise a task for 40 people over three months became a simple job for three people in 40 hours,
U siječnju 2010., razorni potres od 7.0 po Richteru je pogodio Haiti, treći najsmrtonosniji potres ikad je ostavio milijun ljudi, 10% stanovništva, bez krova nad glavom. Jedan mali aspekt cjelokupnog pokušaja olakšanja je postajao sve važniji kako su hrana i voda počeli stizati. Siječanj i veljača su suhi mjeseci na Haitiju, no mnogi kampovi su bili poplavljeni. Jedina institucija s detaljnim znanjem o poplavnim područjima Haitija je sravljena sa zemljom u potresu zajedno s vodstvom. Pitanje je koji su kampovi rizični, koliko ljudi ima u tim kampovima, koji je raspored plavljenja i uz vrlo ograničene resurse i infrastrukturu, kako prioritizirati premještaj? Podaci su bili vrlo različiti. Američka vojska je imala detaljno znanje za samo mali dio države. Postoje podaci na mreži s konferencije o okolišnom riziku iz 2006., drugi geospacijalni podaci, ništa nije integrirano. Ljudski cilj je bio prepoznati kampove za relokaciju na temelju prioritetnih potreba. Računalo je trebalo integrirati iznimnu količinu geospacijalnih informacija, podataka s društvenih mreža i podataka organizacije za humanitarnu pomoć kako bi odgovorilo na ovo pitanje. Primjenjujući superiorni proces, zadatak za 40 ljudi kroz 3 mjeseca je postao jednostavan posao za troje ljudi u 40 sati,
all victories for human-computer symbiosis.
sve pobjede za simbiozu čovjeka i računala.
We're more than 50 years into Licklider's vision for the future, and the data suggests that we should be quite excited about tackling this century's hardest problems, man and machine in cooperation together. Thank you. (Applause) (Applause)
Više smo od 50 godina u Lickliderovoj viziji budućnosti, i podaci govore da bismo trebali biti poprilično uzbuđeni oko savladavanja najtežih problema ovog stoljeća, čovjek i stroj u zajedničkoj suradnji. Hvala vam. (Pljesak)