Peter Kuznick, co-author with Oliver Stone of Untold History of the United States, discusses Roosevelt's attitude towards Hitler and the Soviet Union.
Peter Kuznick Associate Professor Department of History
The author of Beyond the Laboratory: Scientists as Political Activists in 1930s America and coeditor of Rethinking Cold War Culture, Professor Kuznick is currently writing a book about scientists’ opposition to the Vietnam War. As director of American University’s award winning Nuclear Studies Institute, he takes students on an annual study abroad trip to Hiroshima and Nagasaki. He spearheaded the Committee for a National Discussion of Nuclear History and Current Policy in response to the Smithsonian’s Enola Gay exhibit and co-founded the Nuclear Education Project. He writes often and lectures frequently about nuclear issues in general and the atomic bombings in particular. He has recently completed a historically based Hollywood screenplay and teaches the path-breaking course Oliver Stone’s America. He regularly provides commentary to the media on a broad range of subject and was selected Organization of American Historians Distinguished Lecturer, 2004-2007.
Degrees: PhD, Rutgers University MA, Rutgers University BA, Rutgers University
William Oliver Stone (born September 15, 1946) is an American film director, screenwriter, and producer. Stone came into public attention in the mid 1980s and the early 1990s for writing and directing a series of films about the Vietnam War, in which he had participated as an infantry soldier. He won further attention—and controversy—with the films JFK, Natural Born Killers, and Nixon. Many of Stone's films focus on contemporary American political and cultural issues. Stone has received three Academy Awards for his work on the films Midnight Express, Platoon, and Born on the Fourth of July. Stone was also presented with the Extraordinary Contribution to Filmmaking Award at the 2007 Austin Film Festival. British newspaper The Guardian described Stone as "one of the few committed men of the left working in mainstream American cinema." Stone's films often combine different camera and film formats within a single scene (including VHS and 8 mm film) as evidenced in JFK, Natural Born Killers, and Nixon.
Č quanto affermato dal quotidiano israeliano Haaretz, nella sua edizione del 12 dicembre 2012 (haaretz.com) riferendosi alla situazione degli immigrati etiopi riconosciuti come ebrei da Tel Aviv, i cosiddetti “falascia”.
“Č difficile da credere ma in Israele nel 2012 le donne etiopi sono sottoposte con la forza al Depo-Provera, un’iniezione contraccettiva” si legge nell’articolo, che prosegue: “Questa iniezione non è un contraccettivo comunemente prescritto. (…) Di solito è riservato alle donne che soffrono di disabilità o che sono malate”.
Il giornale israeliano si rifà all’inchiesta pubblicata di recente dal programma “Vacuum documentary ” condotto da Gal Gabay, e trasmesso sulla Televisione Educativa israeliana, in cui si afferma che “il trattamento è imposto su un gran numero di immigrati etiopi”. Secondo il ricercatore Reuven Sava, che ha condotto l’indagine, più di quaranta donne sono state sottoposte, contro la loro volontà, all’iniezione di Depo-Provera, un medicinale molto forte che, come si può leggere tranquillamente su internet, viene usato per curare tumori, per la castrazione chimica e nelle terapie iniziali per cambiare sesso. Il Depo-Provera, che ha diverse controindicazioni, tra cui l’osteoporosi e a lungo andare la sterilità, ha una storia inquietante.
Secondo una relazione dell’organizzazione Sha L’Isha, l’iniezione contraccettiva è stata sperimentata, tra il 1967 e il 1978, nello Stato della Georgia, negli Stati Uniti, su più di 13mila donne povere, la metà delle quali erano nere. La maggior parte di loro, che non erano a conoscenza della sperimentazione, si sono ammalate. Molte altre sono morte. Medicinali contraccettivi come il Depo-Provera sono stati utilizzati spesso da Washington per ridurre il tasso di natalità dei poveri.
Nel 1960, gli Usa erano preoccupati per l’aumento della popolazione del Puerto Rico. Nel 1965 si è riscontrato che il 34% delle donne portoricane tra i 20 e il 49 anni erano state sterilizzate. Nel caso di Israele, si tratta di politiche “repressive” e “razziste” contro gli immigrati e contro gli “ebrei neri”. Lo si evince dalla storia: tra il 1980 e il 1990 migliaia di ebrei etiopi hanno trascorso mesi o anni nei campi di transito in Etiopia e in Sudan. Centinaia di loro sono morti perché “la più grande democrazia del Vicino Oriente” gli ha impedito di entrare nel Paese in quanto “non era il momento giusto”, non c’erano “le condizioni per integrarli” o meglio ancora “non erano sufficientemente ebrei”. Si è mai sentito parlare di ebrei neri?
Ancora oggi, gli immigrati falascià sono intrappolati nei campi di transito a causa della contorta burocrazia israeliana che cerca di “sfiancarli” prima di farli entrare nel Paese, sistemandoli in centri di integrazione, veri e propri ghetti, dove le donne ricevono le iniezioni di Depo-Provera e i bambini vengono mandati in “strutture di educazione speciali”. Secondo l’indagine condotta da “Vacuum documentary” e ripresa da Haaretz, il tutto avviene contro la loro volontà. Secondo l’American Jewish Joint Distribution, le dichiarazioni delle donne etiopi sono “sciocchezze”. Ma diverse organizzazioni umanitarie hanno lanciato l’allarme sul razzismo che negli ultimi tempi ha preso piede in Israele.
Ai primi di giugno il parlamento israeliano ha approvato una legge che prevede fino a tre anni di reclusione senza processo per gli immigrati illegali che saranno sorpresi in Israele. Si tratta perlopiù di africani, che fuggono dalla guerra (causata quasi sempre dall’Occidente) e che cercano rifugio al di là del Sinai. Si tratta di una legge che le associazioni per i diritti umani hanno già bollato come “contrarie” alla convenzione Onu sui rifugiati e a numerosi trattati internazionali che Tel Aviv, in quanto firmatario, è chiamata a rispettare. Ma la comunità internazionale non ha proferito parola in merito, nemmeno quando Tel Aviv ha espulso 1500 sud-sudanesi dal Paese trovando una becera giustificazione: “Gli immigrati minacciano l’identità ebraica” e “prendono il lavoro degli israeliani”. Parola del ministro degli Interni, Eli Yishai, secondo cui “bisogna mettere alle sbarre tutti gli immigrati clandestini” per “consentire ai cittadini israeliani nel sud di Tel Aviv e altrove di vivere in modo appropriato... in tranquillità e sicurezza”.
Quite easy, according to data scientist Nick Berry, founder of Data Genetics, a Seattle technology consultancy.
Berry analyzed passwords from previously released and exposed tables and security breaches, filtering the results to just those that were exactly four digits long [0-9]. There are 10,000 possible combinations that the digits 0-9 can be arranged into to form a four-digit code. Berry analyzed those to find which are the least and most predictable. He speculates that, if users select a four-digit password for an online account or other web site, it's not a stretch to use the same number for their four-digit bank PIN codes.
What he found, he says, was a "staggering lack of imagination" when it comes to selecting passwords. Nearly 11% of the 3.4 million four-digit passwords he analyzed were 1234. The second most popular PIN in is 1111 (6% of passwords), followed by 0000 (2%). (Last year SplashData compiled a list of the most common numerical and word-based passwords and found that "password" and "123456" topped the list.)
Berry says a whopping 26.83% of all passwords could be guessed by attempting just 20 combinations of four-digit numbers (see first table). "It's amazing how predictable people are," he says. We don't like hard-to-remember numbers and "no one thinks their wallet will get stolen," Berry says.
Days, Months, Years
Many of the commonly used passwords are, of course, dates: birthdays, anniversaries, year of birth, etc. Indeed, using a year, starting with 19__, helps people remember their code, but it also increases its predictability, Berry says. His analysis shows that every single 19__ combination be found in the top 20% of the dataset.
"People use years, date of birth — it's a monumentally stupid thing to do because, if you lose your wallet, your driver's license is in there. If someone finds it, they've got the date of birth on there. At least use a parent's date of birth [as a password]," says Berry. Somewhat intriguing was #22 on the most common password list: 2580. It seems random, but if you look at a telephone keypad (or ATM keypad), you'll see those numbers are straight down the middle — yet another sign that we're uncreative and lazy password makers.
The Least Predictable Password
The least-used PIN is 8068, Berry found, with just 25 occurrences in the 3.4 million set, which equates to 0.000744%. (See the second table for the least popular passwords.) Why this set of numbers? Berry guesses, "It's not a repeating pattern, it's not a birthday, it's not the year Columbus discovered America, it's not 1776." At a certain point, these numbers at the bottom of the list are all kind of "the lowest of the low, they're all noise," he says.
A few other interesting tidbits from Berry:
-The most popular PIN code (1234) is used more than the lowest 4,200 codes combined. - People have even less imagination in choosing five-digit passwords — 28% use 12345. - The fourth most popular seven-digit password is 8675309, inspired by the Tommy Tutone song. -People love using couplets for their PINs: 4545, 1313, etc. And for some reason, they don't like using pairs of numbers that have larger numerical gaps between them. Combinations like 45 and 67 occur much more frequently than 29 and 37. - The 17th most common 10-digit password is 3141592654 (for those of you who are not math nerds, those are the first digits of Pi).
Un studiu recent arata ca trimiterea unui mesaj de pe telefonul mobil în timpul traversarii unui drum este cea mai riscanta activitate, sansele fiind de patru ori mai mari ca oamenii sa ignore astfel traficul si luminile. De asemenea, din cauza sms-urilor traversarea unei intersectii aglomerate dureaza mai mult timp.
Un trecator din trei foloseste telefonul sau face ceva care îi distrage atentia atunci când travereseaza un drum.
La studiu au participat 1.000 de pietoni care au fost urmariti în mai multe intersectii aglomerate din Seattle la ore diferite. Cercetatorii au urmarit activitatile care distrag atentia cum ar fi vorbitul la telefon, trimiterea sms-urilor, ascultarea muzicii la casti si conversatiile cu alte persoane. Astfel, un pieton din 10 asculta muzica, 7% trimiteau mesaje si 6% vorbeau la telefon. Trimiterea sms-urilor este cea mai periculoasa activitate pentru ca trecatorii ignora luminile si înainte de a traversa nu se uita în ambele sensuri, putând aparea astfel accidente grave.
Conducatoarea studiului, Beth Ebel, a spus ca oamenii intra în transa atunci când folosec pe strada telefoanele mobile sau când asculta muzica la casti, aceasta stare fiind numita de psihologi "atentie divizata" sau "orbire". Mai multe studii au aratat ca accidentele în care sunt implicate persoanele care asculta muzica la casti pe strada s-au triplat.
Lavorerai con sudore, partorirai con dolore". Chissà se la maledizione biblica vedeva un collegamento tra le due cose. Che sia per l'aumento della popolazione, o per la crisi economico-finanziaria, la situazione del lavoro è tragica per tutto il pianeta.
In Italia, le notizie recenti riportano come la disoccupazione stia crescendo a livelli record, e che il 2013 sarà se possibile anche peggio. Dagli USA, arrivano notiziole di poco valore che dovrebbero essere confortanti, la disoccupazione in calo per miseri 146 mila posti di lavoro creati a novembre.
Se invece, come piace a noi, guardiamo i dati da una distanza un po' meno vicina che ieri mattina, ecco il drammatico grafico in apertura che si riferisce proprio agli States. Un crollo senza possibilità di scuse. Un Paese che, dal 95% di occupati del 1969, è passato a poco più del 58; dove un quarto della popolazione vive intorno alla soglia di povertà, dove chi lavora spesso deve fare tre lavori al giorno per arrivare a fine mese, dove i laureati lavorano gratis. La classe media è completamente distrutta.
Altrove non va meglio. In Spagna i senza lavoro sono 5 milioni, in Francia la disoccupazione è cresciuta al 10,3% nel terzo trimestre, in Grecia è al 26. Persino in Cina decine di migliaia di laureati e diplomati si affollano in file di ore per la speranza di un colloquio, mentre l'economia rallenta (guardate le foto, impressionanti).
La sensazione è che stia finendo il lavoro. Non ce n'è più per tutti, qualsiasi cosa si faccia, qualsiasi preparazione si abbia, qualsiasi capacità o conoscenza si sia in grado di offrire. E questa è probabilmente la prospettiva più agghiacciante nella fine del nostro sistema.
ZM Global Radio is a weekly radio show presented by various active coordinators of The Zeitgeist Movement in a rotational fashion. These broadcasts discuss the developments and aims of The Zeitgeist Movement.
About TZM:
The Zeitgeist Movement is a global sustainability activist group working to bring the world together for the common goal of species sustainability before it is too late. It is a social movement, not a political one, with over 1100 chapters across nearly all countries. Divisive notions such as nations, governments, races, political parties, religions, creeds or class are non-operational distinctions in the view of The Movement. Rather, we recognize the world as one system and the human species as a singular unit, sharing a common habitat. Our overarching intent could be summarized as “the application of the scientific method for social concern.”
Resembling little more than a credit card-sized scrap of exposed circuit board, the RPi is a fully programmable PC that runs a free, open-source Linux operating system, plugs into any TV, can power 3D graphics, connects to the Internet and, with a little ingenuity, be used to create your own personalized robot slave.
The computer's miniature frame is crowded with two USB ports, an SD card slot, an Ethernet connection and microchip in the middle -- all powered by a universal USB mobile charger.
Not only is it the world's smallest personal computer but, perhaps most importantly of all, at just $25 the RPi is also the world's cheapest.
Eben Upton, the UK-based University of Cambridge professor and inventor behind the wallet-friendly PC, says he set out to create a computer so affordable that every child in Britain could have one.
With its rough-around-the-edges aesthetic, however, he didn't expect it to catch on very fast and, in the early days of development, set a sales target of 10,000 units within his lifetime.
But when the RPi launched in February of this year, demand far outran supply, and all 10,000 sold out immediately -- crashing the distributing websites in the process.
It turned out there was a voracious appetite -- particularly among a growing class of DIY geeks -- for a cheap, easily-programmable, open-source piece of hardware that would allow them to let their imaginations run wild.
Now, far exceeding its inventor's original estimates, the RPi is set to sell as a million units within its first year of availability.
In a revealing interview with CNN, Upton tells all about why everyone wants a slice of Raspberry Pi.
CNN: What inspired you to invent the Raspberry Pi?
Eben Upton: A group of us here at the University of Cambridge were involved in trying to find 17, 18-year-olds to come and study computer science and what we found every year was a reduction in the numbers.
We went from 500 people in the 1990s applying for our 80 places, down to under 250 and, worse than that, the sorts of things those children knew how to do when they came in the door were much less impressive.
Really Raspberry Pi is an attempt to try and reboot some of that 1980s computer industry feel that had been responsible for giving us this stream of very talented students.
CNN: What in your view are the Pi's most distinguishing qualities?
EU: I think we really have to say the big, distinct feature about Raspberry Pi is the cost. This is a device that comes in two variants, one that cost $25 and one that costs $35.
These are designed to be the same price as a textbook; they're designed to be cheap enough that a child can buy on with their pocket money. They're designed to be cheap enough that you could equip a whole classroom for under a $1000. So, really the cheapness.
Almost everything you can do with a Raspberry Pi, you can do with a conventional PC, but you'd be doing it at 10 times the cost.
CNN: Did you have any idea what kind of response you'd get?
EU: Absolutely not, I think the response has been staggering. Even a year ago we were thinking of this shifting 10,000 units over a lifetime.
All we wanted was a few hundred more students -- or for the students we were getting to have had a little more experience when they came to the door.
CNN: What was your own reaction when you heard how many you'd sold on the first day?
EU: Terror, I guess. When you've scaled everything for a particular size and then you discover you have this enormous spike of demand, then you're always going to wonder if you can fill it. And there were big queues, there were back orders for months after that as we and our partners worked very hard to try and scale out production.
CNN: You set out to sell primarily to schools for use by children in class, who's it actually being bought by?
EU: Until September, it was being bought almost entirely by people like me -- technically literate adults who wanted to use it to do interesting projects. You know this is something you can plug your television into and play videos on; it's got stuff you can use to control a robot. For people like me this is gold dust.
From September onwards we've started to see a swing round towards what we were hoping for, which is educational engagement -- individual children buying them and schools buying classroom sets of them.
CNN: Any other surprise takers?
EU: We're also starting to see some industrial applications. We're seeing people who have been buying $300 industrial computers saying "hang on a second, why am I buying this special purpose computer when I can buy one of these. It does the same thing, it runs units. My software engineers can be very comfortable with it, why don't I just switch over to these?"
Another really interesting one that I should have anticipated was of course the developing world. These make very good entry-level productivity computers for the developing world, so we're starting to see an interest there as well.
CNN: How are you able to sell it so cheaply?
EU: One of things that allows us to hit our very low price point is that we have a very high level of integration -- there's just not that much stuff on the board. All of the main features are integrated onto the chip in the middle. It's our central processor and also our graphic processor that drives the display and does some of our peripheral functions, so that's the main chip.
CNN: Do you remember the first time you took it into a school and what kind of reaction you got?
EU: We were really surprised by the reaction we got. School kids today are used to their tablets and their mobile phones, so we thought we were going to have to put into a shiny box.
But one of the biggest reactions from the children was because they could actually see it and point to it and tell what the different bits do. Normally you don't get to see the green stuff and they really love that, there's been such a positive response.
It’s hard to believe you’d have an economy at all if you gave pink slips to more than half the labor force. But that—in slow motion—is what the industrial revolution did to the workforce of the early 19th century. Two hundred years ago, 70 percent of American workers lived on the farm. Today automation has eliminated all but 1 percent of their jobs, replacing them (and their work animals) with machines. But the displaced workers did not sit idle. Instead, automation created hundreds of millions of jobs in entirely new fields. Those who once farmed were now manning the legions of factories that churned out farm equipment, cars, and other industrial products. Since then, wave upon wave of new occupations have arrived—appliance repairman, offset printer, food chemist, photographer, web designer—each building on previous automation. Today, the vast majority of us are doing jobs that no farmer from the 1800s could have imagined.
It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation. Yes, dear reader, even you will have your job taken away by machines. In other words, robot replacement is just a matter of time. This upheaval is being led by a second wave of automation, one that is centered on artificial cognition, cheap sensors, machine learning, and distributed smarts. This deep automation will touch all jobs, from manual labor to knowledge work.
First, machines will consolidate their gains in already-automated industries. After robots finish replacing assembly line workers, they will replace the workers in warehouses. Speedy bots able to lift 150 pounds all day long will retrieve boxes, sort them, and load them onto trucks. Fruit and vegetable picking will continue to be robotized until no humans pick outside of specialty farms. Pharmacies will feature a single pill-dispensing robot in the back while the pharmacists focus on patient consulting. Next, the more dexterous chores of cleaning in offices and schools will be taken over by late-night robots, starting with easy-to-do floors and windows and eventually getting to toilets. The highway legs of long-haul trucking routes will be driven by robots embedded in truck cabs.
All the while, robots will continue their migration into white-collar work. We already have artificial intelligence in many of our machines; we just don’t call it that. Witness one piece of software by Narrative Science (profiled in issue 20.05) that can write newspaper stories about sports games directly from the games’ stats or generate a synopsis of a company’s stock performance each day from bits of text around the web. Any job dealing with reams of paperwork will be taken over by bots, including much of medicine. Even those areas of medicine not defined by paperwork, such as surgery, are becoming increasingly robotic. The rote tasks of any information-intensive job can be automated. It doesn’t matter if you are a doctor, lawyer, architect, reporter, or even programmer: The robot takeover will be epic.
And it has already begun.
Here’s why we’re at the inflection point: Machines are acquiring smarts.
We have preconceptions about how an intelligent robot should look and act, and these can blind us to what is already happening around us. To demand that artificial intelligence be humanlike is the same flawed logic as demanding that artificial flying be birdlike, with flapping wings. Robots will think different. To see how far artificial intelligence has penetrated our lives, we need to shed the idea that they will be humanlike.
Consider Baxter, a revolutionary new workbot from Rethink Robotics. Designed by Rodney Brooks, the former MIT professor who invented the best-selling Roomba vacuum cleaner and its descendants, Baxter is an early example of a new class of industrial robots created to work alongside humans. Baxter does not look impressive. It’s got big strong arms and a flatscreen display like many industrial bots. And Baxter’s hands perform repetitive manual tasks, just as factory robots do. But it’s different in three significant ways.
First, it can look around and indicate where it is looking by shifting the cartoon eyes on its head. It can perceive humans working near it and avoid injuring them. And workers can see whether it sees them. Previous industrial robots couldn’t do this, which means that working robots have to be physically segregated from humans. The typical factory robot is imprisoned within a chain-link fence or caged in a glass case. They are simply too dangerous to be around, because they are oblivious to others. This isolation prevents such robots from working in a small shop, where isolation is not practical. Optimally, workers should be able to get materials to and from the robot or to tweak its controls by hand throughout the workday; isolation makes that difficult. Baxter, however, is aware. Using force-feedback technology to feel if it is colliding with a person or another bot, it is courteous. You can plug it into a wall socket in your garage and easily work right next to it.
Second, anyone can train Baxter. It is not as fast, strong, or precise as other industrial robots, but it is smarter. To train the bot you simply grab its arms and guide them in the correct motions and sequence. It’s a kind of “watch me do this” routine. Baxter learns the procedure and then repeats it. Any worker is capable of this show-and-tell; you don’t even have to be literate. Previous workbots required highly educated engineers and crack programmers to write thousands of lines of code (and then debug them) in order to instruct the robot in the simplest change of task. The code has to be loaded in batch mode, i.e., in large, infrequent batches, because the robot cannot be reprogrammed while it is being used. Turns out the real cost of the typical industrial robot is not its hardware but its operation. Industrial robots cost $100,000-plus to purchase but can require four times that amount over a lifespan to program, train, and maintain. The costs pile up until the average lifetime bill for an industrial robot is half a million dollars or more.
The third difference, then, is that Baxter is cheap. Priced at $22,000, it’s in a different league compared with the $500,000 total bill of its predecessors. It is as if those established robots, with their batch-mode programming, are the mainframe computers of the robot world, and Baxter is the first PC robot. It is likely to be dismissed as a hobbyist toy, missing key features like sub-millimeter precision, and not serious enough. But as with the PC, and unlike the mainframe, the user can interact with it directly, immediately, without waiting for experts to mediate—and use it for nonserious, even frivolous things. It’s cheap enough that small-time manufacturers can afford one to package up their wares or custom paint their product or run their 3-D printing machine. Or you could staff up a factory that makes iPhones.
Baxter was invented in a century-old brick building near the Charles River in Boston. In 1895 the building was a manufacturing marvel in the very center of the new manufacturing world. It even generated its own electricity. For a hundred years the factories inside its walls changed the world around us. Now the capabilities of Baxter and the approaching cascade of superior robot workers spur Brooks to speculate on how these robots will shift manufacturing in a disruption greater than the last revolution. Looking out his office window at the former industrial neighborhood, he says, “Right now we think of manufacturing as happening in China. But as manufacturing costs sink because of robots, the costs of transportation become a far greater factor than the cost of production. Nearby will be cheap. So we’ll get this network of locally franchised factories, where most things will be made within 5 miles of where they are needed.”
That may be true of making stuff, but a lot of jobs left in the world for humans are service jobs. I ask Brooks to walk with me through a local McDonald’s and point out the jobs that his kind of robots can replace. He demurs and suggests it might be 30 years before robots will cook for us. “In a fast food place you’re not doing the same task very long. You’re always changing things on the fly, so you need special solutions. We are not trying to sell a specific solution. We are building a general-purpose machine that other workers can set up themselves and work alongside.” And once we can cowork with robots right next to us, it’s inevitable that our tasks will bleed together, and soon our old work will become theirs—and our new work will become something we can hardly imagine.
To understand how robot replacement will happen, it’s useful to break down our relationship with robots into four categories, as summed up in this chart:
The rows indicate whether robots will take over existing jobs or make new ones, and the columns indicate whether these jobs seem (at first) like jobs for humans or for machines.
Let’s begin with quadrant A: jobs humans can do but robots can do even better. Humans can weave cotton cloth with great effort, but automated looms make perfect cloth, by the mile, for a few cents. The only reason to buy handmade cloth today is because you want the imperfections humans introduce. We no longer value irregularities while traveling 70 miles per hour, though—so the fewer humans who touch our car as it is being made, the better.
And yet for more complicated chores, we still tend to believe computers and robots can’t be trusted. That’s why we’ve been slow to acknowledge how they’ve mastered some conceptual routines, in some cases even surpassing their mastery of physical routines. A computerized brain known as the autopilot can fly a 787 jet unaided, but irrationally we place human pilots in the cockpit to babysit the autopilot “just in case.” In the 1990s, computerized mortgage appraisals replaced human appraisers wholesale. Much tax preparation has gone to computers, as well as routine x-ray analysis and pretrial evidence-gathering—all once done by highly paid smart people. We’ve accepted utter reliability in robot manufacturing; soon we’ll accept it in robotic intelligence and service.
Next is quadrant B: jobs that humans can’t do but robots can. A trivial example: Humans have trouble making a single brass screw unassisted, but automation can produce a thousand exact ones per hour. Without automation, we could not make a single computer chip—a job that requires degrees of precision, control, and unwavering attention that our animal bodies don’t possess. Likewise no human, indeed no group of humans, no matter their education, can quickly search through all the web pages in the world to uncover the one page revealing the price of eggs in Katmandu yesterday. Every time you click on the search button you are employing a robot to do something we as a species are unable to do alone.
While the displacement of formerly human jobs gets all the headlines, the greatest benefits bestowed by robots and automation come from their occupation of jobs we are unable to do. We don’t have the attention span to inspect every square millimeter of every CAT scan looking for cancer cells. We don’t have the millisecond reflexes needed to inflate molten glass into the shape of a bottle. We don’t have an infallible memory to keep track of every pitch in Major League Baseball and calculate the probability of the next pitch in real time.
We aren’t giving “good jobs” to robots. Most of the time we are giving them jobs we could never do. Without them, these jobs would remain undone.
Now let’s consider quadrant C, the new jobs created by automation—including the jobs that we did not know we wanted done. This is the greatest genius of the robot takeover: With the assistance of robots and computerized intelligence, we already can do things we never imagined doing 150 years ago. We can remove a tumor in our gut through our navel, make a talking-picture video of our wedding, drive a cart on Mars, print a pattern on fabric that a friend mailed to us through the air. We are doing, and are sometimes paid for doing, a million new activities that would have dazzled and shocked the farmers of 1850. These new accomplishments are not merely chores that were difficult before. Rather they are dreams that are created chiefly by the capabilities of the machines that can do them. They are jobs the machines make up.
Before we invented automobiles, air-conditioning, flatscreen video displays, and animated cartoons, no one living in ancient Rome wished they could watch cartoons while riding to Athens in climate-controlled comfort. Two hundred years ago not a single citizen of Shanghai would have told you that they would buy a tiny slab that allowed them to talk to faraway friends before they would buy indoor plumbing. Crafty AIs embedded in first-person-shooter games have given millions of teenage boys the urge, the need, to become professional game designers—a dream that no boy in Victorian times ever had. In a very real way our inventions assign us our jobs. Each successful bit of automation generates new occupations—occupations we would not have fantasized about without the prompting of the automation.
To reiterate, the bulk of new tasks created by automation are tasks only other automation can handle. Now that we have search engines like Google, we set the servant upon a thousand new errands. Google, can you tell me where my phone is? Google, can you match the people suffering depression with the doctors selling pills? Google, can you predict when the next viral epidemic will erupt? Technology is indiscriminate this way, piling up possibilities and options for both humans and machines.
It is a safe bet that the highest-earning professions in the year 2050 will depend on automations and machines that have not been invented yet. That is, we can’t see these jobs from here, because we can’t yet see the machines and technologies that will make them possible. Robots create jobs that we did not even know we wanted done.
Finally, that leaves us with quadrant D, the jobs that only humans can do—at first. The one thing humans can do that robots can’t (at least for a long while) is to decide what it is that humans want to do. This is not a trivial trick; our desires are inspired by our previous inventions, making this a circular question.
When robots and automation do our most basic work, making it relatively easy for us to be fed, clothed, and sheltered, then we are free to ask, “What are humans for?” Industrialization did more than just extend the average human lifespan. It led a greater percentage of the population to decide that humans were meant to be ballerinas, full-time musicians, mathematicians, athletes, fashion designers, yoga masters, fan-fiction authors, and folks with one-of-a kind titles on their business cards. With the help of our machines, we could take up these roles; but of course, over time, the machines will do these as well. We’ll then be empowered to dream up yet more answers to the question “What should we do?” It will be many generations before a robot can answer that.
This postindustrial economy will keep expanding, even though most of the work is done by bots, because part of your task tomorrow will be to find, make, and complete new things to do, new things that will later become repetitive jobs for the robots. In the coming years robot-driven cars and trucks will become ubiquitous; this automation will spawn the new human occupation of trip optimizer, a person who tweaks the traffic system for optimal energy and time usage. Routine robo-surgery will necessitate the new skills of keeping machines sterile. When automatic self-tracking of all your activities becomes the normal thing to do, a new breed of professional analysts will arise to help you make sense of the data. And of course we will need a whole army of robot nannies, dedicated to keeping your personal bots up and running. Each of these new vocations will in turn be taken over by robots later.
The real revolution erupts when everyone has personal workbots, the descendants of Baxter, at their beck and call. Imagine you run a small organic farm. Your fleet of worker bots do all the weeding, pest control, and harvesting of produce, as directed by an overseer bot, embodied by a mesh of probes in the soil. One day your task might be to research which variety of heirloom tomato to plant; the next day it might be to update your custom labels. The bots perform everything else that can be measured.
Right now it seems unthinkable: We can’t imagine a bot that can assemble a stack of ingredients into a gift or manufacture spare parts for our lawn mower or fabricate materials for our new kitchen. We can’t imagine our nephews and nieces running a dozen workbots in their garage, churning out inverters for their friend’s electric-vehicle startup. We can’t imagine our children becoming appliance designers, making custom batches of liquid-nitrogen dessert machines to sell to the millionaires in China. But that’s what personal robot automation will enable.
Everyone will have access to a personal robot, but simply owning one will not guarantee success. Rather, success will go to those who innovate in the organization, optimization, and customization of the process of getting work done with bots and machines. Geographical clusters of production will matter, not for any differential in labor costs but because of the differential in human expertise. It’s human-robot symbiosis. Our human assignment will be to keep making jobs for robots—and that is a task that will never be finished. So we will always have at least that one “job.”
In the coming years our relationships with robots will become ever more complex. But already a recurring pattern is emerging. No matter what your current job or your salary, you will progress through these Seven Stages of Robot Replacement, again and again:
1. A robot/computer cannot possibly do the tasks I do.
[Later.]
2. OK, it can do a lot of them, but it can’t do everything I do.
[Later.]
3. OK, it can do everything I do, except it needs me when it breaks down, which is often.
[Later.]
4. OK, it operates flawlessly on routine stuff, but I need to train it for new tasks.
[Later.]
5. OK, it can have my old boring job, because it’s obvious that was not a job that humans were meant to do.
[Later.]
6. Wow, now that robots are doing my old job, my new job is much more fun and pays more!
[Later.]
7. I am so glad a robot/computer cannot possibly do what I do now.
This is not a race against the machines. If we race against them, we lose. This is a race with the machines. You’ll be paid in the future based on how well you work with robots. Ninety percent of your coworkers will be unseen machines. Most of what you do will not be possible without them. And there will be a blurry line between what you do and what they do. You might no longer think of it as a job, at least at first, because anything that seems like drudgery will be done by robots.
We need to let robots take over. They will do jobs we have been doing, and do them much better than we can. They will do jobs we can’t do at all. They will do jobs we never imagined even needed to be done. And they will help us discover new jobs for ourselves, new tasks that expand who we are. They will let us focus on becoming more human than we were.
Let the robots take the jobs, and let them help us dream up new work that matters.
THE END.
Source: wired.com - Author: Kevin Kelly (kk.org) is senior maverick of Wired and the author, most recently, of What Technology Wants.
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