hazard Posted November 17, 2015 Share Posted November 17, 2015 Can computers replace scientists? - Slate Delovi ovog članka su klasičan senzacionalizam. Given data about several different biological functions within the cell, the computer did something mind-blowing. “We found this really beautiful, elegant equation that described how the cell worked, and that tended to hold true over all of our new experiments,” Schmidt says. There was only one problem: The humans had no idea why the equation worked, or what underlying scientific principle it suggested. It was, Schmidt says, as if they’d consulted an oracle. This should terrify scientists. If robots can now outsmart us, what’s left for people to do? More importantly, if we’re entering an age in which machines will be the primary discoverers of new scientific wisdom, what will that mean for human knowledge? We may be able to make use of the laws and equations uncovered by computers, but it’s quite possible that some of it will be too complex for even the smartest humans to understand. A onda objasnjenje (koje mi je bilo ocigledno cim sam procitao prvu recenicu o tome sta taj program radi): At the moment, Schmidt and Lipson’s machine is still very much subservient to humans—it depends on people to feed it data, and to direct it toward novel research problems. Eureqa is quite simple in design. After it’s fed data about a particular process (the swinging of a pendulum, the dynamics of a cell), the computer generates a huge field of potential equations. These initial equations are random, and the vast majority of them will not apply. But a few of these random equations will show some agreement with the physical world. “We take the ones that are slightly better than the others, and we randomly recombine them to get new equations—and then we repeat the process over and over again, billions and billions and billions of times, until we’ve exhausted the space of short, simple equations,” Schmidt says. In the end, this Darwinian process tends to come up with equations that describe “invariant relationships”—that is, equations that apply across all the data. Such invariant relationships are often associated with fundamental laws of nature: the conservation of energy, Newton’s laws of motion, the mass-energy equivalence. OK, znaci kompjuter uzme masu podataka, i onda nasumično generiše jednačine koje bi te podatke mogle da povezuju, i onda isproba svaku dok ne nađe pravu. Super mega-giga fancy curve-fitting algorithm. A šta ako su podaci suviše suženi pa uradi overfit za jedan opseg (tj. ne nađe generalni zakon nego ,,lokalnu" jednačinu gde je možda jedan od koeficijenata za generalni zakon nula)? To se zove ,,computers outsmarting scientists"? Dalje But there are two problems for human scientists expecting to find long-term employment as meaning-finders. First, Lipson is already working on ways for the computer to explain itself—that is, to describe, in terms that humans might understand, what its equations mean. For the pendulum, we might explain to the computer that we understand a certain quantity as representing energy. Then the software will have to explain its new finding using only the concepts that we’ve taught it. “It’s a little bit like if a child asks you, ‘What’s an airplane?’ and you say, ‘Well, it has wings like a bird, and it has an engine, like a car.’ ” At some point, though, the computer might discover laws that are impossible for us to understand. “It would be like trying to explain Shakespeare to a dog,” Lipson says. M'ajde? Napravio si super curve-fitter, i sa'će odma' AI koji tumači jednačine? Klasično prodavanje magle. Strogatz believes our window of insight is closing—that “we’re reaching our limitations.” In several fields, we seem to be approaching the limits of our intellectual abilities. “People talk about hundreds of billions of things in economics, in the brain, in genes,” Strogatz says. “Once you start talking about that kind of number, lots of interesting interactions occur, and that’s where the scientific frontier problems are—and we’re just not very good at thinking about those kinds of numbers.” And computers are. Gluposti. Kompjuteri ne misle o brojevima, kompjuteri ne misle uopšte. Kompjuteri računaju - sa vrlo konkretnim brojevima, bez ikakve suštinske moći da se bave apstraktnim, generalizovanim brojevima. Da, naravno, kompjuteri mnogo bolje računaju i rade matematiku sa brojevima sa 6 i 9 nula nego što to čine ljudi. To rade zato što su takve računice mehaničke operacije koje se mogu raditi bez mozga i naravno da to brže radi mašina nego čovek. Kompjuter ne ume da reši jednačinu sa jednom nepoznatom x + 2 = 5 na način koji to radi čovek - analitički. Naravno kompjuter ume da nađe vrlo dobru aproksimaciju za jednačinu na n-ti stepen koristeći numeričke metode (koje su izumeli ljudi, ali koje kompjuter daleko brže izvršava) i to jednačine gde je analitičko rešenje nemoguće (i gde je neki matematičar analitiči dokazao da je tako), i može na isti način da reši x + 2 = 5, ali je u tom slučaju daleko manje efikasan nego čovek. Neither Strogatz nor Lipson have a date in mind for when humans will lose their mastery over science. Even as the robots get smarter and smarter, there will still be many human traits that science will depend on. For instance, taste—the ability to choose interesting, creative areas of science to investigate. But make no mistake: Our time is limited. “As thinking machines, they have a lot of advantages over us—this is obvious,” Strogatz says. “We’re not going to be the best players in town. I do think we’ll be put out of business. This is really going to happen.” Ovo je već rejkurcvajlovsko pričanje u prazno. OMG the robotz can fit an equation to big data! oh noes we're d00med!!! Quote Link to comment
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