Tom Slee on Evgeny Morozov’s “To Save Everything, Click Here”

Tom Slee, Whimsley, March 2013

Every­body loves Jane Jacobs.  I love Jane Jacobs and "Aus­trian" econ­o­mists with whom I dis­agree, like Alex Tabar­rok, love Jane Jacobs.Steven John­son says he loves Jane Jacobs in his recent book Future Per­fect, and so does Evgeny Moro­zov at the begin­ning of To Save Every­thing, Click Here – but Moro­zov is argu­ing against John­son. You prob­a­bly love Jane Jacobs.

So some­one has to be wrong. Much of this essay is an attempt to see why Moro­zov gets Jacobs right, while John­son and oth­ers are miss­ing some­thing important.

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From 2005 to 2007, Evgeny Moro­zov tells us, he thought that dig­i­tal tech­nol­ogy might be a way to rid the world of auto­cratic regimes. His dis­il­lu­sion­ment was chan­nelled into his influ­en­tial first book, The Net Delu­sion, a full-on attack on "the sheer cal­lous­ness and utopi­anism" of the "Inter­net Free­dom" project (p 354).

This time around, Morozov's tar­get is much broader, but still cen­tred in the world of dig­i­tal tech­nolo­gies, and par­tic­u­larly the Inter­net. He takes aim at the ide­olo­gies that have grown up around the Inter­net, and their many manifestations.

Chap­ter 7 is typ­i­cal of the book. Here is a col­lec­tion of peo­ple who record and track their every­day lives online, and then ana­lyze and quan­tify their exis­tence, from tooth­brush­ing to read­ing to fecal con­tents. These "data­sex­u­als" now have a social move­ment, of a sort, which they call the "Quan­ti­fied Self" move­ment. It would be easy to dis­miss the Quan­ti­fied Self­ers as harm­less eccentrics if they did not have a sig­nif­i­cant pres­ence among the opin­ion shapers and lead­ing lights of Sil­i­con Val­ley, and if the mind­set they embody was not clearly present, if in mod­er­ated form, in the wider dig­i­tal world, and if the assump­tions and goals were not ooz­ing out over the rest of us. From quan­ti­fy­ing one­self in a pri­vate con­text it is a short step to the pre­sen­ta­tion of self through these num­bers, and the use of them as a basis for opti­miza­tion and refine­ment. So Moro­zov cites Reid Hoff­man, founder of LinkedIn, who says that self track­ing is a way to "acknowl­edge that you have bugs, that there's new devel­op­ment to do on your­self" (237) so that we can algo­rith­mi­cally mea­sure, tweak, and refine our­selves and our self-presentation to the world.

From here it is just one more short step to the buy­ing and sell­ing of our per­sonal data: to insur­ers in return for lower pre­mi­ums, to adver­tis­ers in return for bet­ter deals. Our per­sonal data becomes a new "asset class" and exec­u­tives respond by "try­ing to shift the focus [of debate] from purely pri­vacy to what we call prop­erty rights" (235). New social pres­sures emerge as the dig­i­tiz­ers fol­low their path of bits, algo­rithms and mar­kets (career coun­sel­lors now rou­tinely rec­om­mend that build­ing a strong pres­ence on LinkedIn is a route to a bet­ter job), and we can replace debates about pri­vacy with reas­sur­ances about per­sonal choice. "Pri­vacy is mostly an illu­sion, but you'll have as much of it as you want to pay for" says Kevin Kelly (236). New com­pa­nies emerge to opti­mize our self-presentation on the web (reputation.com), new norms emerge as "If you're going out with some­one, and they don't have a Face­book pro­file, you should be sus­pi­cious" (Slate's Farhad Man­joo, quoted on p. 239). Why would you not share your real-time blood alco­hol lev­els with your employer if you don't have any­thing to hide? (240).

The impact of the dig­i­tal on our lives is such that, while the social con­se­quences of self-tracking seem immense, they are just one thread among many of the dig­i­tal rev­o­lu­tion. In sep­a­rate chap­ters, Moro­zov inves­ti­gates new devel­op­ments in polic­ing, arts and cul­ture, pol­i­tics, gov­ern­ment, social engi­neer­ing, civic life, health, the work­place, and the increas­ingly designed, archi­tected envi­ron­ments in which we live. There is no aspect of life that isn't ready to be tweaked, nudged, hacked and fil­tered into opti­mal performance.
How to respond to such a flood of changes? One is tempted to define one­self by an atti­tude to dig­i­tal tech­nolo­gies them­selves: to be unequiv­o­cally pro– or anti-technology. But to reject or to accept tech­nol­ogy whole­sale has no future: whole­sale rejec­tion entails rejec­tion, not just of inte­grated cir­cuits, but of the peo­ple con­nected by them: shap­ing the use of tech­nol­ogy lies not in the realm of indi­vid­ual choice, but of social choice. Whole­sale accep­tance seems fatal­is­tic – aban­don­ing the pos­si­bil­ity of hav­ing any say in the forces shap­ing the soci­eties in which we live.

Moro­zov under­takes two projects, one suc­cess­fully and one less so. The first is to pro­vide a frame­work in which to think about the new inven­tions that are being sold to us, and the pat­terns of thought behind them. Moro­zov iden­ti­fies a twin-tracked ide­ol­ogy behind the inven­tions and inven­tive­ness of the dig­i­tal world. One track is "Internet-centrism" – the prac­tice of "tak­ing a model of how the Inter­net works and apply­ing it to other endeav­ours". Writ­ers have imbued the Inter­net with "a way of work­ing"; it has a "grain" to which we must adapt; it has a cul­ture, a "way it is meant to be used", and it comes with a mythol­ogy in which iTunes and Wikipedia become mod­els to think about the future of pol­i­tics, and Zynga is a model for civic engage­ment (15). The sec­ond track is "solu­tion­ism": the recast­ing of social sit­u­a­tions as prob­lems with def­i­nite solu­tions; processes to be opti­mized (23).

Moro­zov does a fine job of artic­u­lat­ing Internet-centrism and solu­tion­ism as two facets of a sin­gle Sil­i­con Val­ley ide­ol­ogy, whose fol­low­ers include the Valley's soft­ware indus­try lead­ers, ven­ture cap­i­tal­ists, con­fer­ences and "thought lead­ers", as an evo­lu­tion of the "Cyber­selfish" ide­ol­ogy iden­ti­fied a decade ago by Paulina Bor­sook. The com­mon assump­tions, shared biases, and indi­vid­u­al­is­tic predil­ic­tions give a cohe­sive­ness and homo­gene­ity to the new ideas and inven­tions, actively con­struct­ing and shap­ing the dig­i­tal envi­ron­ment from which they claim to draw their inspi­ra­tion. The insis­tence on "dis­rupt­ing" our social and envi­ron­men­tal lives; the idea that the solu­tions inspired by and enabled by the Inter­net mark a clean break from his­tor­i­cal pat­terns, a never-before-seen oppor­tu­nity – these mean that the only lessons to learn from his­tory are those of pre­vi­ous tech­no­log­i­cal dis­rup­tions. The view of soci­ety as an institution-free net­work of autonomous indi­vid­u­als prac­tic­ing free exchange makes the social sci­ences, with the excep­tion of eco­nom­ics, irrel­e­vant. What's left is engi­neer­ing, neu­ro­science, an under­stand­ing of incen­tives (in the nar­rowly util­i­tar­ian sense): just right for those whose intel­lec­tual pre­dis­po­si­tions are to algo­rithms, design, and data struc­tures. Moro­zov argues that these ortho­dox­ies have had "a cor­ro­sive effect on pub­lic dis­course and on reform projects" (16) and it's dif­fi­cult to argue otherwise.

Morozov's approach to unpick­ing the hid­den assump­tions of solu­tion­ism, and the unpalat­able con­se­quences of its appli­ca­tion, is impres­sive but less suc­cess­ful. In order to avoid a blan­ket technopes­simism he makes two moves. The first is to adopt a broadly social con­struc­tion­ist approach to the world of dig­i­tal tech­nolo­gies. The Inter­net does not shape us, it is shaped by the soci­ety in which it is grow­ing. He is with Ray­mond Williams, against Mar­shall McLuhan. His stance here is blunt: he refuses to see "the Inter­net" as an agent of change, for good or bad. "The Inter­net" is not a cause; it does not explain things, it is the thing that needs to be explained. Chap­ter 2 is titled The Inter­net Tells Us Noth­ing (Because It Doesn't Actu­ally Exist).

The sec­ond, more sur­pris­ing move, is to adopt a cri­tique that was first described in a pejo­ra­tive sense by Albert Hirschmann. "In his influ­en­tial book The Rhetoric of Reac­tion, Hirschmann argued that all pro­gres­sive reforms usu­ally attract con­ser­v­a­tive crit­i­cisms that build on one of the fol­low­ing three themes: per­ver­sity (whereby the pro­posed inter­ven­tion only wors­ens the prob­lem at hand), futil­ity (whereby the inter­ven­tion yields no results what­so­ever), and jeop­ardy (whereby the inter­ven­tion threat­ens to under­mine some pre­vi­ous, hard-earned accom­plish­ment)" (6). Moro­zov does not see him­self as a con­ser­v­a­tive, but instead places him­self in the tra­di­tion of other thinkers who have stood against pro­grams of orga­nized effi­ciency; "Jane Jacobs attacks on the arro­gance of urban plan­ning, Michael Oakeshott's rebel­lion against ratio­nal­ists in all walks of human exis­tence, Hans Jonas's impa­tience with the cold com­fort of cyber­net­ics; and, more recently, James Scott's con­cern with how states have forced what he calls 'leg­i­bil­ity' on their sub­jects" (7). The list is an inter­est­ing one because, as I men­tioned at the begin­ning, it fea­tures the same cast of char­ac­ters that the solu­tion­ists — those whom Moro­zov opposes so implaca­bly — rou­tinely invoke as their own inspirations.

The Hirschmann frame­work pro­vides Moro­zov with a recipe for how to think about the many solu­tion­ist ini­tia­tives he tack­les, and many of the pas­sages in the book have a sim­i­lar struc­ture. Let's return to self-tracking for a moment. Morozov's first line of cri­tique is Hirschmann's "jeop­ardy": he invokes the 'tech­nos­truc­tural­ists' to ask not just what indi­vid­ual choices self-tracking offers, but to ask how it changes the envi­ron­ment we inhabit. A deci­sion not to share becomes a tacit acknowl­edge­ment that you have some­thing to hide. The dan­ger is that "if you are well and well-off, self-monitoring will only make things bet­ter for you. If you are none of these things, the per­sonal prospec­tus could make your life much more dif­fi­cult, with higher insur­ance pre­mi­ums, fewer dis­counts, and lim­ited employ­ment prospects" (240). It erodes pri­vacy, the abil­ity to make a clean start, and erodes risk-taking behav­iour given the con­se­quences of fail­ure. A sec­ond line of cri­tique is to ask what, as our quan­tifi­able aspects become the focus of atten­tion, is miss­ing in the quan­ti­fied por­trait that emerges: what intan­gi­ble aspects of our­selves become invis­i­ble. Do these num­bers, he asks, miss mean­ing? Where do ethics and aes­thet­ics go to in a world of num­bers? Moro­zov sur­veys the centuries-old debates over the virtues and per­ils of quan­tifi­ca­tion. Here the cri­tique stum­bles, as Moro­zov rolls out thinker after thinker in a parade of rea­sons to doubt the ben­e­fits of quan­tifi­ca­tion. From Niet­zsche to Nuss­baum, from nutri­tion­ism (the quan­tifi­ca­tion of food) to water-metering and the evo­lu­tion of clothes-washing norms, to the ben­e­fits of fric­tion and dis­so­nance in our every­day lives, there is no doubt he cov­ers an impres­sive amount of ground, but the argu­ment is scat­ter­shot; dis­joint. The end result is an eru­dite and widely-sourced list of the ways in which tech­nolo­gies may lead to bad out­comes – but it is still a list, and it lacks the force of a strong cen­tral the­sis behind it.

The other chap­ters fol­low a sim­i­lar pat­tern: the per­ver­sity, futil­ity, and jeop­ardy of solu­tion­ist agen­das show a breadth of inves­ti­ga­tion that should shame many of his more pop­ulist oppo­nents, and pro­vide valu­able con­texts in which to think about tech­no­log­i­cal pro­grammes. In par­tic­u­lar, his insis­tence on seek­ing out his­tor­i­cal prece­dents for today's argu­ments is a wel­come change from the lan­guage of "rup­ture" that many solu­tion­ists prefer.

If there is a uni­fied point of view behind the cri­tique, it can be traced back to the "anti-solutionists" with whom Moro­zov iden­ti­fies. Like Moro­zov and like Steven John­son, I'm a big admirer of Jane Jacobs's Death and Life of Great Amer­i­can Cities, and James Scott's See­ing Like a State: which makes me won­der how can they end up in such dif­fer­ent camps. The fault, you will not be sur­prised to hear, belongs with the solutionists.

One of the remark­able insights of com­puter sci­en­tists (and social sci­en­tists and nat­ural sci­en­tists in the com­puter age) is an under­stand­ing of how great com­plex­ity and diver­sity can be gen­er­ated by pop­u­la­tions of sim­ple agents fol­low­ing sim­ple rules. Just as schools of fish and flocks of star­lings cre­ate sweep­ing artis­tic dis­plays by pur­su­ing sim­ple indi­vid­ual rules, so the rich tapes­try of city life emerges from sim­ple every­day inter­ac­tions. The ideas of net­work the­o­rists lend them­selves to talk of self-organization, non-hierarchical struc­tures, and infor­ma­tional cas­cades. Com­puter sci­en­tists take ideas such as the "Game of Life", the stun­ning images of frac­tal shapes, and the rich behav­iour of net­works to illus­trate how com­plex­ity arises from sim­plic­ity. From spin-glasses in mag­nets to the sort­ing and emer­gence of pat­terns revealed by Schelling and his intel­lec­tual descen­dants, sim­ple "micro­mo­tives" give rise to sur­pris­ing and intri­cate pat­terns of "mac­robe­hav­iour". Such agent-based think­ing seems at first to mesh per­fectly with Jacobs's closely observed stud­ies of city life. She famously focused her pierc­ing, ana­lyt­i­cal eye on the details of every day life in large cities, and used her obser­va­tions to chal­lenge and then tri­umph over the grand visions and arro­gance of top-down city plan­ners. It's the bottom-up nature of her approach that inspires: the plan­ners are try­ing to impose pat­terns on pop­u­la­tions from above but they miss the rela­tion­ship between the large and the small. It is tempt­ing, then, to take the descrip­tions of Jacobs's cities and encode them in algo­rithms: agent-based sim­u­la­tions of the effects of block size on pedes­trian traf­fic pat­terns seem almost man­dated, so obvi­ous a next step do they seem from Jacobs's chap­ter on the topic.

Yet this step, I increas­ingly believe, is a mis­take. Solu­tion­ism is ulti­mately cen­tral plan­ning by another name. The arro­gance of the urban plan­ner reap­pears as the arro­gance of the agent-based mod­eller and the Inter­net entre­pre­neur: the plan is still mono­lithic, but now takes the shape of a net­work. As Steven John­son says, when his "peer pro­gres­sives" see a social prob­lem, they design a peer net­work to solve it. But what has hap­pened to the cit­i­zens in this net­work? They have been reduced to dumb fol­low­ers of sim­ple rules. The rich­ness and com­plex­ity – all the inter­est, in fact – lies in the struc­ture of the net­work. If the out­come isn't what you want, well tweak the incen­tives, adjust the topol­ogy of the net­work, pro­vide an addi­tional option for the nodes (sorry, peo­ple) to choose from. For all its talk of bottom-up, decen­tral­ized think­ing, the Internet-centric solu­tion­ists end up with an impov­er­ished per­spec­tive of indi­vid­ual behaviour.

Just because com­plex and rich behav­iours can arise from sim­ple rules doesn't mean that peo­ple are sim­ple beings. Any approach that applies both to mur­mu­ra­tions of star­lings or spin-glasses of mag­netic ions as well as to cities of humans is, almost by def­i­n­i­tion, miss­ing the dis­tinc­tive fea­tures of human soci­eties. Com­plex­ity can arise from sim­plic­ity at the small scale, but macro-level com­plex­ity also arises from micro-level com­plex­ity. The sub­tle and ill-understood nature of our own needs and inter­ac­tions will defeat the best efforts of solu­tion­ist plan­ning, just has it has defeated those of cen­tral plan­ning and of free markets.

In his final chap­ters, Moro­zov appeals to this par­tic­u­lar­ist view of the world, in which each node of a net­work is dif­fer­ent from oth­ers, and in which gen­eral solu­tions don't exist. To dis­card the impor­tance of the details of our daily inter­ac­tions, as the solu­tion­ists inevitably do, is to inevitably pro­voke unex­pected responses, unin­ten­tional side effects, and unan­tic­i­pated break­downs of the solu­tion­ist schemes. When Brian Chesky of AirBnB com­plains that there are 30,000 dif­fer­ent cities in which he wants to oper­ate, and that it's just not prac­ti­cal to nego­ti­ate with each one, he is not design­ing a bottom-up solu­tion, he is impos­ing a top-down net­work. He is demand­ing that cities become "leg­i­ble" in James Scott's ter­mi­nol­ogy, to his over­ar­ch­ing (and sim­plis­tic) algorithms.

To reach for an alter­na­tive vision, Moro­zov looks to artists who have engaged in "adver­sar­ial design" to illus­trate the impor­tance of acknowl­edg­ing micro-level com­plex­ity. But to look to the arti­fi­cial­ity of the arts is second-best here; there is enough vari­a­tion and rich­ness of detail in the nor­mal every­day world to illus­trate the impor­tance of vari­a­tion and local knowl­edge and unan­tic­i­pated interactions.

But despite these minor com­plaints, "Click Here" is an admirable and sig­nif­i­cant achieve­ment. It iden­ti­fies and makes a valu­able and intel­lec­tu­ally adven­tur­ous assault on what is becom­ing an increas­ingly obvi­ous prob­lem: the appro­pri­a­tion of demo­c­ra­tic and "bottom-up" visions by those who seek to impose their own top-down net­works on the rest of us, and who reduce us to sim­plis­tic nodes in the process. This is a valu­able book: now if only some­one could make a TED Talk of it.

I.B.M.’s Rometty on the Data Challenge to the Culture of Management

Steve Lohr, The New York Times, March 7, 2013

Five years ago, Samuel J. Palmisano, then chief executive of I.B.M., gave a speech at the Council on Foreign Relations on the big opportunity ahead for modern computing technology to help businesses, government agencies and other institutions operate more intelligently. His remarks captured an emerging trend that opened the way to a large, lucrative business for I.B.M.

On Thursday evening, his successor, Virginia M. Rometty, is scheduled to deliver a speech in the same setting, looking at how far technology has come and its implications for how business and government are managed.

In 2008, Mr. Palmisano spoke of the accelerating pace of computer processing, storage and software that exploited artificial intelligence. He pointed to the explosion in data from the Web, social networks and sensors. He mentioned I.B.M., if at all, only in passing.

But to the vision he sketched out Mr. Palmisano attached the term "Smarter Planet." It became the tagline for I.B.M.'s highly successful campaign to sell software and services — and sometimes hardware, too — to corporations and governments for an array of "smart" projects.

For Ms. Rometty, the script is similar in the sense that it is intended for an audience beyond the business community and refers to I.B.M. by name only once.

Still, the context is quite different. Mr. Palmisano gave his talk in the fall of 2008, when the financial crisis was sending the world's economies into a tailspin. His speech gained a wider audience only partly because of what he said about technology. He was the chief executive of a major global corporation declaring that it was a good time to invest for the long term rather than pull back in a defensive crouch.

Today the economy is growing, if slowly, and corporate America is thriving. Yet Ms. Rometty's speech — like her predecessor's — seeks to broaden the discussion of the opportunity and implications of technology trends. Hers, in some ways, is a more subtle message about the next step — the broad absorption of this advancing technology.

In an advance copy of the speech, Ms. Rometty points to three effects of the smart technology, which these days flies under the banner of Big Data. She calls them three lessons.

"Lesson 1: Decisions will be based not on 'gut instinct,' but on predictive analytics."

"Lesson 2: The social network is not just the new water cooler; it's the new production line."

"Lesson 3: Value will be created not for 'market segments' or demographics, but for individuals."

Each of the three themes or lessons is illustrated by examples: Memphis police using data analysis to curb crime; a Mexican cement maker tapping the crowdsourced intelligence of its workers in product development: and the Obama campaign's sifting of data to pinpoint get-out-the-vote efforts.

Ms. Rometty concludes by saying, "The challenge is not the technology. The challenge, as always, is culture — changing our entrenched ways of thinking, acting and organizing."

Her point echoes a message management experts have been making for some time. I shared a copy of the speech with Erik Brynjolfsson, director of the Center for Digital Business at MIT's Sloan School of Management.

In an e-mail, Mr. Brynjolfsson said, "I agree very much with the speech. The technology has been available for a few years now to create a management revolution based on big data, and now we're beginning to see more and more companies undertake the much harder job of reinventing their business processes and culture to take full advantage of those technologies."

Any speech by an executive, especially one that aspires to reach a wider audience, walks a line: How much is it fresh information and thoughtful insight versus a self-serving sales pitch?

Decide for yourself. Ms. Rometty's speech is scheduled to begin at 6 P.M. Eastern time on Thursday and to be webcast live on the Council on Foreign Relations site and on I.B.M.'s Smarter Planet blog.

By 7:30 P.M. or so, the text of Ms. Rometty's speech should also be on the I.B.M. blog.

The Google Glass feature no one is talking about

Mark Hurst, Creative Good, February 28, 2013

Google Glass might change your life, but not in the way you think. There's something else Google Glass makes possible that no one – no one – has talked about yet, and so today I'm writing this blog post to describe it.

To read the raving accounts of tech journalists who Google commissioned for demos, you'd think Glass was something between a jetpack and a magic wand: something so cool, so sleek, so irresistible that it must inevitably replace that fading, pitifully out-of-date device called the smartphone.

Sergey Brin himself said as much yesterday, observing that it is "emasculating" to use a smartphone, "rubbing this featureless piece of glass." His solution to that piece of glass, of course, is called Glass. And his solution to that emasculation is – well, as VentureBeat put it, "Sergey Brin calls smartphones 'emasculating' – but dorky Google Glass [is] A-OK."

Like every other shiny innovation these days, Google Glass will live or die solely on the experience it creates for people. The immediate, most visible problem in the Glass experience is how dorky the user looks while wearing it. No one wants to be the only person in the bar dressed like a cyborg from a 1992 virtual-reality movie. It's embarrassing. Early adopters will abandon Google Glass if they don't sense the social approval they seek while wearing it.

Google seems to have calculated this already and recently announced a partnership with Warby Parker, known for its designer glasses favored by the all-important younger demographic. (My own proposal, posted the day before, jokingly suggested that Google look into monocles.)

Except for the awkward physical design, the experience of using Google Glass has won high praise from reviewers. Seeing your bitstreams floating in the air in front of you, it would seem, is an ecstatic experience. Weather! Directions! Social network requests! Email overload! All floating in front of you, never out of your sight! For people who delight in a deluge of digital distractions, this is much more exciting than a smartphone, which forces you back to the boring offline world, every so often, when you put the phone away. Glass promises never to do that. In fact, in a feat of considerable chutzpah, Google is attempting to pitch Glass as an antidote to distraction, since users don't have to look down at a phone. Right, because now the distractions are all conveniently placed directly into your eyeball! (For a more accurate exploration of Glass-enabled distraction, see this darkly comic parody video. Even edgier is this parody – warning, some spicy language.)

As if all that wasn't enough, Google Glass comes with yet another, even more important feature: lifebits, the ability to record video of the people, places, and events around you, at all times. Veteran readers will remember that I predicted this six years ago in my book Bit Literacy. From Chapter 13:

The life bitstream will raise new and important issues. Should it be socially acceptable, for example, to record a private conversation with a friend? How will anyone be sure they're not being recorded, in public or private? … Corporations, police, even friends with 'life recorders' will capture the actions and utterances of everyone in sight, whether they like it or not.

Today, finally, that future has arrived: a major company offering the ability to record your life, store it, and share it – all with a simple voice command.

And this is where our story takes a turn, toward a ramification that dwarfs every other issue raised so far on Google Glass. Yes, the glasses look dorky – Google will fix that. And sure, Glass forces users to be permanently plugged-in to Google's digital world – that's hardly a concern for the company or, for that matter, most users out there. No. The real issue raised by Google Glass, which will either cause the project to fail or create certain outcomes you may not want (which I'll describe), has to do with the lifebits. Once again, it's an issue of experience.

The Google Glass feature that (almost) no one is talking about is the experience – not of the user, but of everyone other than the user. A tweet by David Yee introduces it well:

There is a kid wearing Google Glasses at this restaurant which, until just now, used to be my favorite spot.

The key experiential question of Google Glass isn't what it's like to wear them, it's what it's like to be around someone else who's wearing them. I'll give an easy example. Your one-on-one conversation with someone wearing Google Glass is likely to be annoying, because you'll suspect that you don't have their undivided attention. And you can't comfortably ask them to take the glasses off (especially when, inevitably, the device is integrated into prescription lenses). Finally – here's where the problems really start – you don't know if they're taking a video of you.

Now pretend you don't know a single person who wears Google Glass… and take a walk outside. Anywhere you go in public – any store, any sidewalk, any bus or subway – you're liable to be recorded: audio and video. Fifty people on the bus might be Glassless, but if a single person wearing Glass gets on, you – and all 49 other passengers – could be recorded. Not just for a temporary throwaway video buffer, like a security camera, but recorded, stored permanently, and shared to the world.

Now, I know the response: "I'm recorded by security cameras all day, it doesn't bother me, what's the difference?" Hear me out – I'm not done. What makes Glass so unique is that it's a Google project. And Google has the capacity to combine Glass with other technologies it owns.

First, take the video feeds from every Google Glass headset, worn by users worldwide. Regardless of whether video is only recorded temporarily, as in the first version of Glass, or always-on, as is certainly possible in future versions, the video all streams into Google's own cloud of servers. Now add in facial recognition and the identity database that Google is building within Google Plus (with an emphasis on people's accurate, real-world names): Google's servers can process video files, at their leisure, to attempt identification on every person appearing in every video. And if Google Plus doesn't sound like much, note that Mark Zuckerberg has already pledged that Facebook will develop apps for Glass.

Finally, consider the speech-to-text software that Google already employs, both in its servers and on the Glass devices themselves. Any audio in a video could, technically speaking, be converted to text, tagged to the individual who spoke it, and made fully searchable within Google's search index.

Now our stage is set: not for what will happen, necessarily, but what I just want to point out could technically happen, by combining tools already available within Google.

Let's return to the bus ride. It's not a stretch to imagine that you could immediately be identified by that Google Glass user who gets on the bus and turns the camera toward you. Anything you say within earshot could be recorded, associated with the text, and tagged to your online identity. And stored in Google's search index. Permanently.

I'm still not done.

The really interesting aspect is that all of the indexing, tagging, and storage could happen without the Google Glass user even requesting it. Any video taken by any Google Glass, anywhere, is likely to be stored on Google servers, where any post-processing (facial recognition, speech-to-text, etc.) could happen at the later request of Google, or any other corporate or governmental body, at any point in the future.
Remember when people were kind of creeped out by that car Google drove around to take pictures of your house? Most people got over it, because they got a nice StreetView feature in Google Maps as a result.
Google Glass is like one camera car for each of the thousands, possibly millions, of people who will wear the device – every single day, everywhere they go – on sidewalks, into restaurants, up elevators, around your office, into your home. From now on, starting today, anywhere you go within range of a Google Glass device, everything you do could be recorded and uploaded to Google's cloud, and stored there for the rest of your life. You won't know if you're being recorded or not; and even if you do, you'll have no way to stop it.

And that, my friends, is the experience that Google Glass creates. That is the experience we should be thinking about. The most important Google Glass experience is not the user experience – it's the experience of everyone else. The experience of being a citizen, in public, is about to change.
Just think: if a million Google Glasses go out into the world and start storing audio and video of the world around them, the scope of Google search suddenly gets much, much bigger, and that search index will include you. Let me paint a picture. Ten years from now, someone, some company, or some organization, takes an interest in you, wants to know if you've ever said anything they consider offensive, or threatening, or just includes a mention of a certain word or phrase they find interesting. A single search query within Google's cloud – whether initiated by a publicly available search, or a federal subpoena, or anything in between – will instantly bring up documentation of every word you've ever spoken within earshot of a Google Glass device.

This is the discussion we should have about Google Glass. The tech community, by all rights, should be leading this discussion. Yet most techies today are still chattering about whether they'll look cool wearing the device.

Oh, and as for that physical design problem. If Google Glass does well enough in its initial launch to survive to subsequent versions, forget Warby Parker. The next company Google will call is Bausch & Lomb. Why wear bulky glasses when the entire device fits into a contact lens? And that, of course, would be the ultimate expression of the Google Glass idea: a digital world that is even more difficult to turn off, once it's implanted directly into the user's body. At that point you'll not even know who might be recording you. There will be no opting out.

Smartphones May Enable Smart Cities

Bernie Meyerson, The Financial Times, March 4, 2013

Connected and predictive cities will transform city living, says IBM Fellow Bernie Meyerson

People who live in cities are starting to get a lot more information to help plan their daily lives. Smarter City systems are telling residents about traffic congestion, the time the next bus will arrive, the potential for flooding on a block-by-block basis, and a host of other useful updates.

The same wealth of data enables city service leaders to proactively send crews out to mitigate future damage, such as going out to replace water lines before small leaks become catastrophic sinkholes.

This is happening because cities are harnessing the streams of information from sensors to manage municipal systems better. Cities are complex systems of systems that have always generated vast amounts of information. Information flowing in from many small subsystems, such as local flood catch-basins, is integrated with other sources to provide guidance on the overall operation of a city's storm sewer system. This integrated data, from such systems of systems, is then given to city administrators in useful formats that can transform city living for the better.

The need for cities to maximise the efficient use of infrastructure and help residents be more productive without spending vast sums on new facilities is driving this process. The more efficient use of data and predictive analytics promises to help accomplish that goal.

In some cases, cities have moved well beyond just monitoring events in real time. They have begun to accurately predict future events likely to cause damage, and take action to avoid it. Predicting and altering the future may sound like science fiction, but it is here today and represents the future for urban management.
This feat begins with gathering as much data as possible on the issues that need to be addressed. Using the "internet of things", sensors embedded in a wide array of systems serving the public (traffic signals, buses, trains, parking spaces, water systems) report the status of the system they are monitoring via the Internet.
The number of devices such as these exceeded the number of people on the internet in 2008, and will reach 50bn in 2020, according to an article in the 8th November issue of the Journal of Sensor and Actuator Networks. Cities now gather the critical data required to build the models and predictive abilities to understand and alter the future for the better.

All these devices can constantly send information to central command posts where digital streams of data can be analysed and used to alert emergency responders or divert traffic in case of trouble.

Sensors that instantly detect a broken down subway car on a busy track can give repair crews valuable extra minutes of work time to correct matters. However, a true game-changer is the use of sensors to show that a bearing on a train car is running warmer than usual, allowing analytics software to alert maintenance workers and eliminate an impending problem long before a dangerous failure occurs.

IBM, has been using predictive analytics to help cities function better for almost a decade. For example, we have helped city police in Richmond, Virginia deploy their forces to predicted trouble spots and avert crimes. We have helped schools in Mobile, Alabama identify potential dropouts and target them for additional remediation.

In Rio de Janeiro, the city has built a sophisticated central command center in preparation for the World Cup in 2014, and Rio's mayor is counting on advanced analytics to monitor the deluge of data coming in from the region and spot problems long before any one individual could.

Rio currently employs sophisticated weather prediction systems capable of forecasting weather for areas as small as one square kilometer to alert city officials to the potential for heavy rainfall to cause highly localised mud slides in a specific neighborhood located on a steep mountain side. As the predictions are very precise, public safety officials can concentrate their safety teams at key locations, alerting residents to the potential danger and moving to evacuate them if deemed appropriate.

With super-cities of tens of millions of people now emerging, at a scale and complexity never before encountered, this ability to focus resources using deep understanding of the "big picture" becomes ever more critical. In 2010, for the first time in history, more people on the planet lived in cities than lived in rural areas. More importantly, in the next 20 years, some 2bn more people will move into cities, marking one of the most significant migrations in human history.

In some countries, India and China as examples, governments are building new cities to accommodate this transformation. However, on a worldwide basis, most migrants will move to existing cities where they may have personal relationships, prospects of a job, or some other incentive.

In those existing cities, the coming population growth will challenge even the most skilled city managers, and the legacy infrastructure of many such cities is not equipped with the ability to gather the data needed to guide city operations. Despite the seeming lack of data gathering infrastructure, another option exists given the shift from desktop computing to mobile devices and tablets.

Mobile technology has advanced dramatically in the past decade to the point where a lightweight smartphone may gather all manner of data, such as its location, velocity, local temperature and background noise levels. Furthermore, in the near future, additional capabilities are easily envisioned, where sensors could be embedded to detect both particulate and chemical pollution, local lighting conditions, vibration levels, and more.
This will enable a new era of participatory engagement by a city's population, where the very data required to optimise city operations is in part willingly provided by its citizens via smartphone apps.

It also raises complex societal issues that must be addressed as we face this new era, but similarly provides an unprecedented opportunity for the citizenry of a community to take an active role in the betterment of their community, not only in the reporting of critical environmental and transportation data, but in taking an active role in mitigating the very challenges their own data identifies.

It will be a cooperative and informed effort, with information technology enabling societal transformation, to address the growing challenges facing our cities in the coming decades.

Bernie Meyerson is an IBM fellow and vice president for innovation

The Promise and Peril of the 'Data-Driven Society'

Steve Lohr, The New York Times, February 25, 2013

A small group of academics, business executives and journalists gathered at the M.I.T. Media Lab last Thursday, and the purpose was to toss out ideas and discuss the concept of "Data-Driven Societies." A daunting topic, ambitious and vague at once, it seems.

Up to now, the focus on the power and implications of Big Data technology has been involved social media, business decision-making and online privacy. Those are big subjects in their own right. So it's not surprising that the notion of a data-driven society has not been much considered.

But someone who has was host of the meeting: Alex Pentland, a computational social scientist at the Media Lab. He put his intellectual stake in the ground last year in a presentation posted on Edge.org, "Reinventing Society in the Wake of Big Data."

Mr. Pentland's starting point is that the most important data that is becoming available on a vast new scale is information about people's behavior. For example, he cites location data from cellphones and evermore consumption data as people increasingly use credit cards for even the smallest purchases. He distinguishes this behavioral data from less-telling data — about people's beliefs like Facebook communications or Google searches.

The fine-grained behavioral data, according to Mr. Pentland, opens the way to changing how we think about society and how a society is governed. Adam Smith and Karl Marx, he explains, thought about markets and classes, respectively. "But those are aggregates," he said. "They're averages."

Yet now, Mr. Pentland says, it becomes possible to track social phenomena down to the individual level and the social and economic connections among individuals. The ability to monitor these "micro-patterns," Mr. Pentland said, means "we're entering a new era of social physics."

What might that mean in practice? Reed Hundt, the chairman of the Federal Communications Commission in the Clinton administration, observed at the meeting that Big Data played a major role in the last election — a reference to the Obama campaign's deft use of data analysis to identify potential Obama voters and encourage them to cast their ballots.

"You get elected with Big Data, but you govern without it," Mr. Hundt said. "How much sense does that make?"

Mr. Hundt, chief executive for the Coalition for Green Capital, a nonprofit organization, pointed to the waste in a range of government incentive and benefit programs, from tax credits for solar panels to Social Security, that results from the across-the-board approach — or policy by averages, as Mr. Pentland might put it.

Instead, a data-driven approach to solar-energy incentives would concentrate government incentives to where the payoff is greatest in terms of efficiently generating alternative energy — larger buildings with a lot of roof space instead of small houses, Mr. Hundt said. A by-the-data model for benefits programs, he added, would suggest means-testing Social Security payments as well as adjusting payments locally for differences in costs of living.

"So all men are created equal, but are subsidized individually," Mr. Hundt quipped.

Intriguing, and perhaps wise policy, but it would also seem to be a redefinition of fairness as it applies to broad benefit programs, like Social Security and Medicare, which typically make standard payments and avoid means testing.

What are the chances such a data-driven course would be politically acceptable? If the data points the way to greater efficiency, why not, Mr. Hundt replied. After all, he said, a major role of government is to transfer income to people who would benefit most — better data, closely analyzed, means government can perform that role more effectively, Mr. Hundt said.

An underlying assumption of tilting toward a data-driven society is that, as one participant put it, "information over time wins out." That is, data will change attitudes and policy, combating bias and causing policy-making to be more of a science. To data optimists, then, the endless political squabbling and stalemate in Washington points to all the room there is for improvement.

In a Big Data world, the data-mining for patterns and insights to guide policy will be done automatically — by software algorithms. Of course, algorithms are created by people and they contain inferences and assumptions coded in. Those coded-in values shape the output — computer-generated predictions, recommendations and simulations.

That raises question of the human design and control of the computerized helpers in policy-making, as in other realms of decision-making. "At some point, you're in the hands of the algorithm," observed John Henry Clippinger, chief executive of the Institute for Data Driven Design, a nonprofit research and educational organization. "You're whistling in the dark if you don't think that day is coming."

Kevin Kelly in Wired: Better Than Human

Kevin Kelly, Wired, December 24, 2012

Imagine that 7 out of 10 working Americans got fired tomorrow. What would they all do?

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.
Kevin Kelly (kk.org) is senior maverick of Wired and the author, most recently, of What Technology Wants.

Why The Simmering Social Revolution In The Workplace Will Boil Over In 2013

Brett Caine, Forbes, December 24

Brett Caine is senior vice president and general manager for the Citrix Online Services division.

So much has been said and written about being social – we tweet, we pin, we like. We have become a society that communicates and shares just about everything we do, with one notable exception – work. Work is the place where social firewalls go up when they really should come down. After all, our teams are about teamwork. Social is the perfect tool to get our teams to work more collaboratively.

And as it catches on, productivity is improving – people can work and play from anywhere and (finally) debunking the notion that workers need to be in an office to produce. The number of work-at-home employees is increasing dramatically and not just day-extenders. For the first time we are seeing companies implement work-at-home policies and practices that make it possible to work from home as a full member of the team. Everyone wants flexibility, more and more ask for it and the millennials will demand it. What does this changing workforce (and workplace) mean for leaders and managers in the workplace?

In full openness, I work for a company whose business, in part, is to help other businesses collaborate socially. We also use the tools ourselves. In embracing these trends, our company has delivered extremely high employee engagement scores over the past few years – increasing each year despite the challenges and complexities of an ever increasing distributed and global workforce.

With these trends as the backdrop, I predict that in 2013, we’ll start seeing some distinct changes in the workplace – changes that mark a leap forward in the “social” work revolution.

Here are five areas where the changes will take place – areas that all managers and employees need to be ready to address.

·       Email evolves into obscurity
Email is not the right tool for office communications. It’s been default as the best way to do it for too long. It’s broken and it (we) can’t keep up in a fast paced, continuous adaption of teams in the workforce.

Email was never intended to be used as a collaboration tool. It slows us down, kills productivity and leaves critical information in silos rather than being shared. This argument was made profoundly by a recent study, which found that the average worker spends nearly a third of his or her work week managing email and nearly 20 percent of his or her time looking for internal information and/or tracking down colleagues who can help with specific tasks. Leaders of major companies are shutting down internal email – simply to improve communication and get more work done.

In 2013, we will see a shift in how emails are used in the workplace. Increasingly, emails will evolve into notifications that will signal to readers that something is happening somewhere else – e.g., a brainstorming call at 2 p.m. The content and reference materials will be in the cloud and continuously updated, synchronized and easily available from anywhere.

·       Social collaboration becomes invisible
With more and more businesses turning to the cloud for effective collaboration solutions, the social enterprise isn’t the latest idea for companies. Microsoft, Salesforce and IBM proved that with major acquisitions of social networking companies for enterprises. When multiple, large industry players enter a space, it’s the beginning of a major trend and unlikely to fade away. In my view, the trend will become so common, social becomes the frame for collaboration in the future.

If you really want to social enable your company, figure out how to actually get to “work” with social collaboration instead of just talking about it. This will be the tipping point for users and companies to get the benefit in 2013 and beyond.

·       Needs for collaboration increase as we get better at preparing for unlikely events
Unfortunately, natural and man-made disasters are likely to increase in the years ahead. Companies therefore are updating their crisis management plans. As part of that preparation, it makes perfect sense for companies to invest in social collaboration tools that allow them to maintain business under any circumstances. Increasingly, companies and employee commutes and routines are getting disrupted by storms, transit strikes, outages and fires to name a few. In 2013, get ahead of this and invest in both tools and policies to enable work from anywhere business models. Certainly, social collaboration tools, remote access and support for employees regardless of location are smart steps to take to prepare your business to stay open even when the most unlikely events occur.

·       Your personal cloud will have a single access point
As businesses move to the cloud, this will also be the year where you will start seeing one point of access to your data regardless of where it’s stored. You will no longer have to login and check five, six or even seven different storage repositories for the information you need for work. One app will connect you with all your social channels – personal, professional, you name it. That ease-of-use is going to drive adoption in droves.

·       Casual Fridays will become Work-from-Home Fridays
The tablet generation has already redefined the workplace. This on-the-go generation is not defined by four walls and a desktop computer. They are mobile. Mobile is the new normal. It’s not the exception. We will see more small- and medium sized companies take this generation into consideration and consider virtual offices and workshifting to incentivize their employees.

With rising gas prices and the high costs of commercial real estate, it makes financial sense for SMBs in particular to consider alternatives to traditional corporate headquarters and hubs. Office-sharing in some regions has become quite popular, particularly among start-ups. In return, we will have a more dynamic workforce and work environment instead of the same old stagnant cube nation.

The future is indeed bright for going social in the workplace. It’s great to see work catch up to everyday life. It’s something we can all like.

What Turned Jaron Lanier Against the Web?

The digital pioneer and visionary behind virtual reality has turned against the very culture he helped create

Ron Rosenbaum, Smithsonian, January 2013 

I couldn’t help thinking of John Le Carré’s spy novels as I awaited my rendezvous with Jaron Lanier in a corner of the lobby of the stylish W Hotel just off Union Square in Manhattan. Le Carré’s espionage tales, such as The Spy Who Came In From the Cold, are haunted by the spectre of the mole, the defector, the double agent, who, from a position deep inside, turns against the ideology he once professed fealty to.

And so it is with Jaron Lanier and the ideology he helped create, Web 2.0 futurism, digital utopianism, which he now calls “digital Maoism,” indicting “internet intellectuals,” accusing giants like Facebook and Google of being “spy agencies.” Lanier was one of the creators of our current digital reality and now he wants to subvert the “hive mind,” as the web world’s been called, before it engulfs us all, destroys political discourse, economic stability, the dignity of personhood and leads to “social catastrophe.” Jaron Lanier is the spy who came in from the cold 2.0.

To understand what an important defector Lanier is, you have to know his dossier. As a pioneer and publicizer of virtual-reality technology (computer-simulated experiences) in the ’80s, he became a Silicon Valley digital-guru rock star, later renowned for his giant bushel-basket-size headful of dreadlocks and Falstaffian belly, his obsession with exotic Asian musical instruments, and even a big-label recording contract for his modernist classical music. (As he later told me, he once “opened for Dylan.” )

The colorful, prodigy-like persona of Jaron Lanier—he was in his early 20s when he helped make virtual reality a reality—was born among a small circle of first-generation Silicon Valley utopians and artificial-intelligence visionaries. Many of them gathered in, as Lanier recalls, “some run-down bungalows [I rented] by a stream in Palo Alto” in the mid-’80s, where, using capital he made from inventing the early video game hit Moondust, he’d started building virtual-reality machines. In his often provocative and astute dissenting book You Are Not a Gadget, he recalls one of the participants in those early mind-melds describing it as like being “in the most interesting room in the world.” Together, these digital futurists helped develop the intellectual concepts that would shape what is now known as Web 2.0—“information wants to be free,” “the wisdom of the crowd” and the like.

And then, shortly after the turn of the century, just when the rest of the world was turning on to Web 2.0, Lanier turned against it. With a broadside in Wired called “One-Half of a Manifesto,” he attacked the idea that “the wisdom of the crowd” would result in ever-upward enlightenment. It was just as likely, he argued, that the crowd would devolve into an online lynch mob.

Lanier became the fiercest and weightiest critic of the new digital world precisely because he came from the Inside. He was a heretic, an apostate rebelling against the ideology, the culture (and the cult) he helped found, and in effect, turning against himself.

***

And despite his apostasy, he’s still very much in the game. People want to hear his thoughts even when he’s castigating them. He’s still on the Davos to Dubai, SXSW to TED Talks conference circuit. Indeed, Lanier told me that after our rendezvous, he was off next to deliver the keynote address at the annual meeting of the Ford Foundation uptown in Manhattan. Following which he was flying to Vienna to address a convocation of museum curators, then, in an overnight turnaround, back to New York to participate in the unveiling of Microsoft’s first tablet device, the Surface.

Lanier freely admits the contradictions; he’s a kind of research scholar at Microsoft, he was on a first-name basis with “Sergey” and “Steve” (Brin, of Google, and Jobs, of Apple, respectively). But he uses his lecture circuit earnings to subsidize his obsession with those extremely arcane wind instruments. Following his Surface appearance he gave a concert downtown at a small venue in which he played some of them.

Lanier is still in the game in part because virtual reality has become, virtually, reality these days. “If you look out the window,” he says pointing to the traffic flowing around Union Square, “there’s no vehicle that wasn’t designed in a virtual-reality system first. And every vehicle of every kind built—plane, train—is first put in a virtual-reality machine and people experience driving it [as if it were real] first.”

I asked Lanier about his decision to rebel against his fellow Web 2.0 “intellectuals.”

“I think we changed the world,” he replies, “but this notion that we shouldn’t be self-critical and that we shouldn’t be hard on ourselves is irresponsible.”

For instance, he said, “I’d been an early advocate of making information free,” the mantra of the movement that said it was OK to steal, pirate and download the creative works of musicians, writers and other artists. It’s all just “information,” just 1’s and 0’s.

Indeed, one of the foundations of Lanier’s critique of digitized culture is the very way its digital transmission at some deep level betrays the essence of what it tries to transmit. Take music.

“MIDI,” Lanier wrote, of the digitizing program that chops up music into one-zero binaries for transmission, “was conceived from a keyboard player’s point of view...digital patterns that represented keyboard events like ‘key-down’ and ‘key-up.’ That meant it could not describe the curvy, transient expressions a singer or a saxophone note could produce. It could only describe the tile mosaic world of the keyboardist, not the watercolor world of the violin.”

Quite eloquent, an aspect of Lanier that sets him apart from the HAL-speak you often hear from Web 2.0 enthusiasts (HAL was the creepy humanoid voice of the talking computer in Stanley Kubrick’s prophetic 2001: A Space Odyssey). But the objection that caused Lanier’s turnaround was not so much to what happened to the music, but to its economic foundation.

I asked him if there was a single development that gave rise to his defection.

“I’d had a career as a professional musician and what I started to see is that once we made information free, it wasn’t that we consigned all the big stars to the bread lines.” (They still had mega-concert tour profits.)
“Instead, it was the middle-class people who were consigned to the bread lines. And that was a very large body of people. And all of a sudden there was this weekly ritual, sometimes even daily: ‘Oh, we need to organize a benefit because so and so who’d been a manager of this big studio that closed its doors has cancer and doesn’t have insurance. We need to raise money so he can have his operation.’

“And I realized this was a hopeless, stupid design of society and that it was our fault. It really hit on a personal level—this isn’t working. And I think you can draw an analogy to what happened with communism, where at some point you just have to say there’s too much wrong with these experiments.”

His explanation of the way Google translator works, for instance, is a graphic example of how a giant just takes (or “appropriates without compensation”) and monetizes the work of the crowd. “One of the magic services that’s available in our age is that you can upload a passage in English to your computer from Google and you get back the Spanish translation. And there’s two ways to think about that. The most common way is that there’s some magic artificial intelligence in the sky or in the cloud or something that knows how to translate, and what a wonderful thing that this is available for free.

“But there’s another way to look at it, which is the technically true way: You gather a ton of information from real live translators who have translated phrases, just an enormous body, and then when your example comes in, you search through that to find similar passages and you create a collage of previous translations.”

“So it’s a huge, brute-force operation?” “It’s huge but very much like Facebook, it’s selling people [their advertiser-targetable personal identities, buying habits, etc.] back to themselves. [With translation] you’re producing this result that looks magical but in the meantime, the original translators aren’t paid for their work—their work was just appropriated. So by taking value off the books, you’re actually shrinking the economy.”

The way superfast computing has led to the nanosecond hedge-fund-trading stock markets? The “Flash Crash,” the “London Whale” and even the Great Recession of 2008?

“Well, that’s what my new book’s about. It’s called The Fate of Power and the Future of Dignity, and it doesn’t focus as much on free music files as it does on the world of finance—but what it suggests is that a file-sharing service and a hedge fund are essentially the same things. In both cases, there’s this idea that whoever has the biggest computer can analyze everyone else to their advantage and concentrate wealth and power. [Meanwhile], it’s shrinking the overall economy. I think it’s the mistake of our age.”

The mistake of our age? That’s a bold statement (as someone put it in Pulp Fiction). “I think it’s the reason why the rise of networking has coincided with the loss of the middle class, instead of an expansion in general wealth, which is what should happen. But if you say we’re creating the information economy, except that we’re making information free, then what we’re saying is we’re destroying the economy.”

The connection Lanier makes between techno-utopianism, the rise of the machines and the Great Recession is an audacious one. Lanier is suggesting we are outsourcing ourselves into insignificant advertising-fodder. Nanobytes of Big Data that diminish our personhood, our dignity. He may be the first Silicon populist.
“To my mind an overleveraged unsecured mortgage is exactly the same thing as a pirated music file. It’s somebody’s value that’s been copied many times to give benefit to some distant party. In the case of the music files, it’s to the benefit of an advertising spy like Google [which monetizes your search history], and in the case of the mortgage, it’s to the benefit of a fund manager somewhere. But in both cases all the risk and the cost is radiated out toward ordinary people and the middle classes—and even worse, the overall economy has shrunk in order to make a few people more.”

Lanier has another problem with the techno-utopians, though. It’s not just that they’ve crashed the economy, but that they’ve made a joke out of spirituality by creating, and worshiping, “the Singularity”—the “Nerd Rapture,” as it’s been called. The belief that increasing computer speed and processing power will shortly result in machines acquiring “artificial intelligence,” consciousness, and that we will be able to upload digital versions of ourselves into the machines and achieve immortality. Some say as early as 2020, others as late as 2045. One of its chief proponents, Ray Kurzweil, was on NPR recently talking about his plans to begin resurrecting his now dead father digitally.

Some of Lanier’s former Web 2.0 colleagues—for whom he expresses affection, not without a bit of pity—take this prediction seriously. “The first people to really articulate it did so right about the late ’70s, early ’80s and I was very much in that conversation. I think it’s a way of interpreting technology in which people forgo taking responsibility,” he says. “‘Oh, it’s the computer did it not me.’ ‘There’s no more middle class? Oh, it’s not me. The computer did it.’

“I was talking last year to Vernor Vinge, who coined the term ‘singularity,’” Lanier recalls, “and he was saying, ‘There are people around who believe it’s already happened.’ And he goes, ‘Thank God, I’m not one of those people.’”

In other words, even to one of its creators, it’s still just a thought experiment—not a reality or even a virtual-reality hot ticket to immortality. It’s a surreality.

Lanier says he’ll regard it as faith-based, “Unless of course, everybody’s suddenly killed by machines run amok.”

“Skynet!” I exclaim, referring to the evil machines in the Terminator films.

At last we come to politics, where I believe Lanier has been most farsighted—and which may be the deep source of his turning into a digital Le Carré figure. As far back as the turn of the century, he singled out one standout aspect of the new web culture—the acceptance, the welcoming of anonymous commenters on websites—as a danger to political discourse and the polity itself. At the time, this objection seemed a bit extreme. But he saw anonymity as a poison seed. The way it didn’t hide, but, in fact, brandished the ugliness of human nature beneath the anonymous screen-name masks. An enabling and foreshadowing of mob rule, not a growth of democracy, but an accretion of tribalism.

It’s taken a while for this prophecy to come true, a while for this mode of communication to replace and degrade political conversation, to drive out any ambiguity. Or departure from the binary. But it slowly is turning us into a nation of hate-filled trolls.

Surprisingly, Lanier tells me it first came to him when he recognized his own inner troll—for instance, when he’d find himself shamefully taking pleasure when someone he knew got attacked online. “I definitely noticed it happening to me,” he recalled. “We’re not as different from one another as we’d like to imagine. So when we look at this pathetic guy in Texas who was just outed as ‘Violentacrez’...I don’t know if you followed it?”

“I did.” “Violentacrez” was the screen name of a notorious troll on the popular site Reddit. He was known for posting “images of scantily clad underage girls...[and] an unending fountain of racism, porn, gore” and more, according to the Gawker.com reporter who exposed his real name, shaming him and evoking consternation among some Reddit users who felt that this use of anonymity was inseparable from freedom of speech somehow.

“So it turns out Violentacrez is this guy with a disabled wife who’s middle-aged and he’s kind of a Walter Mitty—someone who wants to be significant, wants some bit of Nietzschean spark to his life.”

Only Lanier would attribute Nietzschean longings to Violentacrez. “And he’s not that different from any of us. The difference is that he’s scared and possibly hurt a lot of people.”

Well, that is a difference. And he couldn’t have done it without the anonymous screen name. Or he wouldn’t have.

And here’s where Lanier says something remarkable and ominous about the potential dangers of anonymity.
“This is the thing that continues to scare me. You see in history the capacity of people to congeal—like social lasers of cruelty. That capacity is constant.”

“Social lasers of cruelty?” I repeat.

“I just made that up,” Lanier says. “Where everybody coheres into this cruelty beam....Look what we’re setting up here in the world today. We have economic fear combined with everybody joined together on these instant twitchy social networks which are designed to create mass action. What does it sound like to you? It sounds to me like the prequel to potential social catastrophe. I’d rather take the risk of being wrong than not be talking about that.”

Here he sounds less like a Le Carré mole than the American intellectual pessimist who surfaced back in the ’30s and criticized the Communist Party he left behind: someone like Whittaker Chambers.

But something he mentioned next really astonished me: “I’m sensitive to it because it murdered most of my parents’ families in two different occasions and this idea that we’re getting unified by people in these digital networks—”

“Murdered most of my parents’ families.” You heard that right. Lanier’s mother survived an Austrian concentration camp but many of her family died during the war—and many of his father’s family were slaughtered in prewar Russian pogroms, which led the survivors to flee to the United States.

It explains, I think, why his father, a delightfully eccentric student of human nature, brought up his son in the New Mexico desert—far from civilization and its lynch mob potential. We read of online bullying leading to teen suicides in the United States and, in China, there are reports of well-organized online virtual lynch mobs forming...digital Maoism.

He gives me one detail about what happened to his father’s family in Russia. “One of [my father’s] aunts was unable to speak because she had survived the pogrom by remaining absolutely mute while her sister was killed by sword in front of her [while she hid] under a bed. She was never able to speak again.”
It’s a haunting image of speechlessness. A pogrom is carried out by a “crowd,” the true horrific embodiment of the purported “wisdom of the crowd.” You could say it made Lanier even more determined not to remain mute. To speak out against the digital barbarism he regrets he helped create.