Why the Castles of Silicon Valley are Built out of Sand

Ambrogio_Lorenzetti Temperance with an hour glass Allegory of Good Government

If you get just old enough, one of the lessons living through history throws you is that dreams take a long time to die. Depending on how you date it, communism took anywhere from 74 to 143 years to pass into the dustbin of history, though some might say it is still kicking. The Ptolemaic model of the universe lasted from 100 AD into the 1600’s. Perhaps even more dreams than not simply refuse to die, they hang on like ghost, or ghouls, zombies or vampires, or whatever freakish version of the undead suits your fancy. Naming them would take up more room than I can post, and would no doubt start one too many arguments, all of our lists being different. Here, I just want to make an argument for the inclusion of one dream on our list of zombies knowing full well the dream I’ll declare dead will have its defenders.

The fact of the matter is, I am not even sure what to call the dream I’ll be talking about. Perhaps, digitopia is best. It was the dream that emerged sometime in the 1980’s and went mainstream in the heady 1990’s that this new thing we were creating called the “Internet” and the economic model it permitted was bound to lead to a better world of more sharing, more openness, more equity, if we just let its logic play itself out over a long enough period of time. Almost all the big-wigs in Silicon Valley, the Larry Pages and Mark Zuckerbergs, and Jeff Bezos(s), and Peter Diamandis(s) still believe this dream, and walk around like 21st century versions of Mary Magdalene claiming they can still see what more skeptical souls believe has passed.

By far, the best Doubting Thomas of digitopia we have out there is Jaron Lanier. In part his power in declaring the dream dead comes from the fact that he was there when the dream was born and was once a true believer. Like Kevin Bacon in Hollywood, take any intellectual heavy hitter of digital culture, say Marvin Minsky, and you’ll find Lanier having some connection. Lanier is no Luddite, so when he says there is something wrong with how we have deployed the technology he in part helped develop, it’s right and good to take the man seriously.

The argument Lanier makes in his most recent book Who Owns the Future? against the economic model we have built around digital technology in a nutshell is this: what we have created is a machine that destroys middle class jobs and concentrates information, wealth and power. Say what? Hasn’t the Internet and mobile technology democratized knowledge? Don’t average people have more power than ever before? The answer to both questions is no and the reason why is that the Internet has been swallowed by its own logic of “sharing”.

We need to remember that the Internet really got ramped up when it started to be used by scientists to exchange information between each other. It was built on the idea of openness and transparency not to mention a set of shared values. When the Internet leapt out into public consciousness no one had any idea of how to turn this sharing capacity and transparency into the basis for an economy. It took the aftermath of dot com bubble and bust for companies to come up with a model of how to monetize the Internet, and almost all of the major tech companies that dominate the Internet, at least in America- and there are only a handful- Google, FaceBook and Amazon, now follow some variant of this model.

The model is to aggregate all the sharing that the Internet seems to naturally produce and offer it, along with other “compliments” for “free” in exchange for one thing: the ability to monitor, measure and manipulate through advertising whoever uses their services. Like silicon itself, it is a model that is ultimately built out of sand.

When you use a free service like Instagram there are three ways its ultimately paid for. The first we all know about, the “data trail” we leave when using the site is sold to third party advertisers, which generates income for the parent company, in this case FaceBook. The second and third ways the service is paid for I’ll get to in a moment, but the first way itself opens up all sorts of observations and questions that need to be answered.

We had thought the information (and ownership) landscape of the Internet was going to be “flat”. Instead, its proven to be extremely “spiky”. What we forgot in thinking it would turn out flat was that someone would have to gather and make useful the mountains of data we were about to create. The big Internet and Telecom companies are these aggregators who are able to make this data actionable by being in possession of the most powerful computers on the planet that allow them to not only route and store, but mine for value in this data. Lanier has a great name for the biggest of these companies- he calls them Siren Servers.

One might think whatever particular Siren Servers are at the head of the pack is a matter of which is the most innovative. Not really. Rather, the largest Siren Servers have become so rich they simply swallow any innovative company that comes along. FaceBook gobbled up Instagram because it offered a novel and increasingly popular way to share photos.

The second way a free service like Instagram is paid for, and this is one of the primary concerns of Lanier in his book, is that it essentially cannibalizes to the point of destruction the industry that used to provide the service, which in the “old economy” meant it also supported lots of middle class jobs.

Lanier states the problem bluntly:

 Here’s a current example of the challenge we face. At the height of its power, the photography company Kodak employed more than 140,000 people and was worth $28 billion. They even invented the first digital camera. But today Kodak is bankrupt, and the new face of digital photography is Instagram. When Instagram was sold to FaceBook for a billion dollars in 2012, it employed only thirteen people.  (p.2)

Calling Thomas Piketty….

As Bill Davidow argued recently in The Atlantic the size of this virtual economy where people share and get free stuff in exchange for their private data is now so big that it is giving us a distorted picture of GDP. We can no longer be sure how fast our economy is growing. He writes:

 There are no accurate numbers for the aggregate value of those services but a proxy for them would be the money advertisers spend to invade our privacy and capture our attention. Sales of digital ads are projected to be $114 billion in 2014,about twice what Americans spend on pets.

The forecasted GDP growth in 2014 is 2.8 percent and the annual historical growth rate of middle quintile incomes has averaged around 0.4 percent for the past 40 years. So if the government counted our virtual salaries based on the sale of our privacy and attention, it would have a big effect on the numbers.

Fans of Joseph Schumpeter might see all this churn as as capitalism’s natural creative destruction, and be unfazed by the government’s inability to measure this “off the books” economy because what the government cannot see it cannot tax.

The problem is, unlike other times in our history, technological change doesn’t seem to be creating many new middle class jobs as fast as it destroys old ones. Lanier was particularly sensitive to this development because he always had his feet in two worlds- the world of digital technology and the world of music. Not the Katy Perry world of superstar music, but the kinds of people who made a living selling local albums, playing small gigs, and even more importantly, providing the services that made this mid-level musical world possible. Lanier had seen how the digital technology he loved and helped create had essentially destroyed the middle class world of musicians he also loved and had grown up in. His message for us all was that the Siren Servers are coming for you.

The continued advance of Moore’s Law, which, according to Charlie Stross, will play out for at least another decade or so, means not so much that we’ll achieve AGI, but that machines are just smart enough to automate some of the functions we had previously thought only human beings were capable of doing. I’ll give an example of my own. For decades now the GED test, which people pursue to obtain a high school equivalency diploma, has had an essay section. Thousands of people were necessary to score these essays by hand, the majority of whom were likely paid to do so. With the new, computerized GED test this essay scoring has now been completely automated, human readers made superfluous.

This brings me to the third way this new digital capabilities are paid for. They cannibalize work human beings have already done to profit a company who presents and sells their services as a form of artificial intelligence. As Lanier writes of Google Translate:

It’s magic that you can upload a phrase in Spanish into the cloud services of a company like Google or Microsoft, and a workable, if imperfect, translation to English is returned. It’s as if there’s a polyglot artificial intelligence residing up there in that great cloud of server farms.

But that is not how cloud services work. Instead, a multitude of examples of translations made by real human translators are gathered over the Internet. These are correlated with the example you send for translation. It will almost always turn out that multiple previous translations by real human translators had to contend with similar passages, so a collage of those previous translations will yield a usable result.

A giant act of statistics is made virtually free because of Moore’s Law, but at core the act of translation is based on real work of people.

Alas, the human translators are anonymous and off the books. (19-20)

The question all of us should be asking ourselves is not “could a machine be me?” with all of our complexity and skills, but “could a machine do my job?” the answer to which, in 9 cases out of 10, is almost certainly- “yes!”

Okay, so that’s the problem, what is Lanier’s solution? His solution is not that we pull a Ned Ludd and break the machines or even try to slow down Moore’s Law. Instead, what he wants us to do is to start treating our personal data like property. If someone wants to know my buying habits they have to pay a fee to me the owner of this information. If some company uses my behavior to refine their algorithm I need to be paid for this service, even if I was unaware I had helped in such a way. Lastly, anything I create and put on the Internet is my property. People are free to use it as they chose, but they need to pay me for it. In Lanier’s vision each of us would be the recipients of a constant stream of micropayments from Siren Servers who are using our data and our creations.

Such a model is very interesting to me, especially in light of other fights over data ownership, namely the rights of indigenous people against bio-piracy, something I was turned on to by Paolo Bacigalupi’s bio-punk novel The Windup Girl, and what promises to be an increasing fight between pharmaceutical/biotech firms and individuals over the use of what is becoming mountains of genetic data. Nevertheless, I have my doubts as to Lanier’s alternative system and will lay them out in what follows.

For one, such a system seems likely to exacerbate rather than relieve the problem of rising inequality. Assuming most of the data people will receive micropayments for will be banal and commercial in nature, people who are already big spenders are likely to get a much larger cut of the micropayments pie. If I could afford such things it’s no doubt worth a lot for some extra piece of information to tip the scales between me buying a Lexus or a Beemer, not so much if it’s a question of TIDE vs Whisk.

This issue would be solved if Lanier had adopted the model of a shared public pool of funds where micropayments would go rather than routing them to the actual individual involved, but he couldn’t do this out of commitment to the idea that personal data is a form of property. Don’t let his dreadlocks fool you, Lanier is at bottom a conservative thinker. Such a fee might balance out the glaring problem that Siren Servers effectively pay zero taxes

But by far the biggest hole in Lanier’s micropayment system is that it ignores the international dimension of the Internet. Silicon Valley companies may be barreling down on their model, as can be seen in Amazon’s recent foray into the smartphone market, which attempts to route everything through itself, but the model has crashed globally. Three events signal the crash, Google was essentially booted out of China, the Snowden revelations threw a pale of suspicion over the model in an already privacy sensitive Europe, and the EU itself handed the model a major loss with the “right to be forgotten” case in Spain.

Lanier’s system, which accepts mass surveillance as a fact, probably wouldn’t fly in a privacy conscious Europe, and how in the world would we force Chinese and other digital pirates to provide payments of any scale? And China and other authoritarian countries have their own plans for their Siren Servers, namely, their use as tools of the state.

The fact of the matter is their is probably no truly global solution to continued automation and algorithmization, or to mass surveillance. Yet, the much feared “splinter-net”, the shattering of the global Internet, may be better for freedom than many believe. This is because the Internet, and the Siren Servers that run it, once freed from its spectral existence in the global ether, becomes the responsibility of real territorially bound people to govern. Each country will ultimately have to decide for itself both how the Internet is governed and define its response to the coming wave of automation. There’s bound to be diversity because countries are diverse, some might even leap over Lanier’s conservativism and invent radically new, and more equitable ways of running an economy, an outcome many of the original digitopians who set this train a rollin might actually be proud of.

 

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Jumping Off The Technological Hype-Cycle and the AI Coup

Robotic Railroad 1950s

What we know is that the very biggest tech companies have been pouring money into artificial intelligence in the last year. Back in January Google bought the UK artificial intelligence firm Deep Mind for 400 million dollars. Only a month earlier, Google had bought the innovative robotics firm Boston Dynamics. FaceBook is in the game as well having also in December 2013 created a massive lab devoted to artificial intelligence. And this new obsession with AI isn’t only something latte pumped-up Americans are into. The Chinese internet giant Baidu, with its own AI lab, recently snagged the artificial intelligence researcher Andrew Ng whose work for Google included the breakthrough of creating a program that could teach itself to recognize pictures of cats on the Internet, and the word “breakthrough” is not intended to be the punch line of a joke.

Obviously these firms see something that make these big bets and the competition for talent seem worthwhile with the most obvious thing they see being advances in an approach to AI known as Deep Learning, which moves programming away from a logical set of instructions and towards the kind bulking and pruning found in biological forms of intelligence. Will these investments prove worth it? We should know in just a few years, yet we simply don’t right now.

No matter how it turns out we need to beware of becoming caught in the technological hype-cycle. A tech investor, or tech company for that matter, needs to be able to ride the hype-cycle like a surfer rides a wave- when it goes up, she goes up, and when it comes down she comes down, with the key being to position oneself in just the right place, neither too far ahead or too far behind. The rest of us, however, and especially those charged with explaining science and technology to the general public, namely, science journalists, have a very different job- to parse the rhetoric and figure out what is really going on.

A good example of what science journalism should look like is a recent conversation over at Bloggingheads between Freddie deBoer and Alexis Madrigal. As Madrigal points out we need to be cognizant of what the recent spate of AI wonders we’ve seen actually are. Take the much over-hyped Google self-driving car. It seems much less impressive to know the areas where these cars are functional are only those areas that have been mapped before hand in painstaking detail. The car guides itself not through “reality” but a virtual world whose parameters can be upset by something out of order that the car is then pre-programmed to respond to in a limited set of ways. The car thus only functions in the context of a mindbogglingly precise map of the area in which it is driving. As if you were unable to make your way through a room unless you knew exactly where every piece of furniture was located. In other words Google’s self-driving car is undriveable in almost all situations that could be handled by a sixteen year old who just learned how to drive. “Intelligence” in a self-driving car is a question of gathering massive amounts of data up front. Indeed, the latest iteration of the Google self-driving car is more like tiny trolley car where information is the “track” than an automobile driven by a human being and able to go anywhere, without the need of any foreknowledge of the terrain, so long, that is, as there is a road to drive upon.

As Madrigal and deBoer also point out in another example, the excellent service of Google Translate isn’t really using machine intelligence to decode language at all. It’s merely aggregating the efforts of thousands of human translators to arrive at approximate results. Again, there is no real intelligence here, just an efficient way to sort through an incredibly huge amount of data.

Yet, what if this tactic of approaching intelligence by “throwing more data at it”  ultimately proves a dead end? There may come a point where such a strategy shows increasingly limited returns. The fact of the matter is that we know of only one fully sentient creature- ourselves- and the more data strategy is nothing like how our own brains work. If we really want to achieve machine intelligence, and it’s an open question whether this is a worthwhile goal, then we should be exploring at least some alternative paths to that end such as those long espoused by Douglas Hofstadter the author of the amazing Godel, Escher, Bach,  and The Mind’s I among others.

Predictions about the future of capacities of artificially intelligent agents are all predicated on the continued exponential rise in computer processing power. Yet, these predictions are based on what are some less than solid assumptions with the first being that we are nowhere near hard limits to the continuation of Moore’s Law. What this assumption ignores is increased rumblings that Moore’s Law might be in hospice and destined for the morgue.

But even if no such hard limits are encountered in terms of Moore’s Law, we still have the unproven assumption that greater processing power almost all by itself leads to intelligence, or even is guaranteed to bring incredible changes to society at large. The problem here is that sheer processing power doesn’t tell you all that much. Processing power hasn’t brought us machines that are intelligent so much as machines that are fast, nor are the increases in processing power themselves all that relevant to what the majority of us can actually do.  As we are often reminded all of us carry in our pockets or have sitting on our desktops computational capacity that exceeds all of NASA in the 1960’s, yet clearly this doesn’t mean that any of us are by this power capable of sending men to the moon.

AI may be in a technological hype-cycle, again we won’t really know for a few years, but the dangers of any hype-cycle for an immature technology is that it gets crushed as the wave comes down. In a hype-cycle initial progress in some field is followed by a private sector investment surge and then a transformation of the grant writing and academic publication landscape as universities and researchers desperate for dwindling research funding try to match their research focus to match a new and sexy field. Eventually progress comes to the attention of the general press and gives rise to fawning books and maybe even a dystopian Hollywood movie or two. Once the public is on to it, the game is almost up, for research runs into headwinds and progress fails to meet the expectations of a now profit-fix addicted market and funders. In the crash many worthwhile research projects end up in the dustbin and funding flows to the new sexy idea.

AI itself went through a similar hype-cycle in the 1980’s, back when Hofstander was writing his Godel, Escher, Bach  but we have had a spate of more recent candidates. Remember in the 1990’s when seemingly every disease and every human behavior was being linked to a specific gene promising targeted therapies? Well, as almost always, we found out that reality is more complicated than the current fashion. The danger here was that such a premature evaluation of our knowledge led to all kinds of crazy fantasies and nightmares. The fantasy that we could tailor design human beings through selecting specific genes led to what amount to some pretty egregious practices, namely, the sale of selection services for unborn children based on spurious science- a sophisticated form of quackery. It also led to childlike nightmares, such as found in the movie Gattaca or Francis Fukuyama’s Our Posthuman Future where we were frightened with the prospect of a dystopian future where human beings were to be designed like products, a nightmare that was supposed to be just over the horizon.  

We now have the field of epigenetics to show us what we should have known- that both genes and environment count and we have to therefore address both, and that the world is too complex for us to ever assume complete sovereignty over it. In many ways it is the complexity of nature itself that is our salvation protecting us from both our fantasies and our fears.

Some other examples? How about MOOCS which we supposed to be as revolutionary as the invention of universal education or the university? Being involved in distance education for non-university attending adults I had always known that the most successful model for online learning was where it was “blended” some face-to-face, some online. That “soft” study skills were as important to student success as academic ability. The MOOC model largely avoided these hard won truths of the field of Adult Basic Education and appears to be starting to stumble, with one of its biggest players UDACITY loosing its foothold.  Andrew Ng, the AI researcher scooped up by Baidu I mentioned earlier being just one of a number of high level MOOC refugees having helped found Coursera.

The so-called Internet of Things is probably another example of getting caught on the hype-cycle. The IoT is this idea that people are going to be clamoring to connect all of their things: their homes, refrigerators, cars, and even their own bodies to the Internet in order to be able to constantly monitor those things. The holes in all this are that not only are we already drowning in a deluge of data, or that it’s pretty easy to see how the automation of consumption is only of benefit to those providing the service if we’re either buying more stuff or the automators are capturing a “finder’s fee”, it’s above all, that anything connected to the Internet is by that fact hackable and who in the world wants their homes or their very bodies hacked? This isn’t a paranoid fantasy of the future, as a recent skeptical piece on the IoT in The Economist pointed out:

Last year, for instance, the United States Fair Trade Commission filed a complaint against TrendNet, a Californian marketer of home-security cameras that can be controlled over the internet, for failing to implement reasonable security measures. The company pitched its product under the trade-name “SecureView”, with the promise of helping to protect owners’ property from crime. Yet, hackers had no difficulty breaching TrendNet’s security, bypassing the login credentials of some 700 private users registered on the company’s website, and accessing their live video feeds. Some of the compromised feeds found their way onto the internet, displaying private areas of users’ homes and allowing unauthorised surveillance of infants sleeping, children playing, and adults going about their personal lives. That the exposure increased the chances of the victims being the targets of thieves, stalkers or paedophiles only fuelled public outrage.

Personalized medicine might be considered a cousin of the IoT, and while it makes perfect sense to me for persons with certain medical conditions or even just interest in their own health to monitor themselves or be monitored and connected to health care professionals, such systems will most likely be closed networks to avoid the risk of some maleficent nerd turning off your pacemaker.

Still, personalized medicine itself, might be yet another example of the magnetic power of hype. It is one thing to tailor a patient’s treatment based on how others with similar genomic profiles reacted to some pharmaceutical and the like. What would be most dangerous in terms of health care costs both to individuals and society would be something like the “personalized” care for persons with chronic illnesses profiled in the New York Times this April, where, for instance, the:

… captive audience of Type 1 diabetics has spawned lines of high-priced gadgets and disposable accouterments, borrowing business models from technology companies like Apple: Each pump and monitor requires the separate purchase of an array of items that are often brand and model specific.

A steady stream of new models and updates often offer dubious improvement: colored pumps; talking, bilingual meters; sensors reporting minute-by-minute sugar readouts. Ms. Hayley’s new pump will cost $7,350 (she will pay $2,500 under the terms of her insurance). But she will also need to pay her part for supplies, including $100 monitor probes that must be replaced every week, disposable tubing that she must change every three days and 10 or so test strips every day.

The technological hype-cycle gets its rhetoric from the one technological transformation that actually deserves the characterization of a revolution. I am talking, of course, about the industrial revolution which certainly transformed human life almost beyond recognition from what came before. Every new technology seemingly ends up making its claim to be “revolutionary” as in absolutely transformative. Just in my lifetime we have had the IT , or digital revolution, the Genomics Revolution, the Mobile Revolution, The Big Data Revolution to name only a few.  Yet, the fact of the matter is not merely have no single one of these revolutions proven as transformative as the industrial revolution, arguably, all of them combined haven’t matched the industrial revolution either.

This is the far too often misunderstood thesis of economists like Robert Gordon. Gordon’s argument, at least as far as I understand it, is not that current technological advancements aren’t a big deal, just that the sheer qualitative gains seen in the industrial revolution are incredibly difficult to sustain let alone surpass.

The enormity of the change from a world where it takes, as it took Magellan propelled by the winds, years, rather than days to circle the globe is hard to get our heads around, the gap between using a horse and using a car for daily travel incredible. The average lifespan since the 1800’s has doubled. One in five of the children born once died in childhood. There were no effective anesthetics before 1846.  Millions would die from an outbreak of the flu or other infectious disease. Hunger and famine were common human experiences however developed one’s society was up until the 20th century, and indoor toilets were not common until then either. Vaccinations did not emerge until the late 19th century.

Bill Gates has characterized views such as those of Gordon as “stupid”. Yet, he himself is a Gordonite as evidenced by this quote:

But asked whether giving the planet an internet connection is more important than finding a vaccination for malaria, the co-founder of Microsoft and world’s second-richest man does not hide his irritation: “As a priority? It’s a joke.”

Then, slipping back into the sarcasm that often breaks through when he is at his most engaged, he adds: “Take this malaria vaccine, [this] weird thing that I’m thinking of. Hmm, which is more important, connectivity or malaria vaccine? If you think connectivity is the key thing, that’s great. I don’t.”

And this is all really what I think Gordon is saying, that the “revolutions” of the past 50 years pale in comparison to the effects on human living of the period between 1850 and 1950 and this is the case even if we accept the fact that the pace of technological change is accelerating. It is as if we are running faster and faster at the same time the hill in front of us gets steeper and steeper so that truly qualitative change of the human condition has become more difficult even as our technological capabilities have vastly increased.

For almost two decades we’ve thought that the combined effects of three technologies in particular- robotics, genetics, and nanotech were destined to bring qualitative change on the order of the industrial revolution. It’s been fourteen years since Bill Joy warned us that these technologies threatened us with a future without human beings in it, but it’s hard to see how even a positive manifestation of the transformations he predicted have come true. This is not to say that they will never bring such a scale of change, only that they haven’t yet, and fourteen years isn’t nothing after all.

So now, after that long and winding road, back to AI. Erik Brynjolfsson, Andrew McAfee and Jeff Cummings the authors of the most popular recent book on the advances in artificial intelligence over the past decade, The Second Machine Age take aim directly at the technological pessimism of Gordon and others. They are firm believers in the AI revolution and its potential. For them innovation in the 21st century is no longer about brand new breakthrough ideas but, borrowing from biology, the recombination of ideas that already exist. In their view, we are being “held back by our inability to process all the new ideas fast enough” and therefore one of the things we need are even bigger computers to test out new ideas and combinations of ideas. (82)

But there are other conclusions one might draw from the metaphor of innovation as “recombination”.  For one, recombination can be downright harmful for organisms that are actually working. Perhaps you do indeed get growth from Schumpeter’s endless cycle of creation and destruction, but if all you’ve gotten as a consequence are minor efficiencies at the margins at the price of massive dislocations for those in industries deemed antiquated, not to mention society as a whole, then it’s hard to see the game being worth the candle.

We’ve seen this pattern in financial services, in music, and journalism, and it is now desired in education and healthcare. Here innovation is used not so much to make our lives appreciably better as to upend traditional stakeholders in an industry so that those with the biggest computer, what Jaron Lanier calls “Sirene Servers” can swoop in and take control. A new elite stealing an old elite’s thunder wouldn’t matter all that much to the rest of us peasants were it not for the fact that this new elite’s system of production has little room for us as workers and producers, but only as consumers of its goods. It is this desire to destabilize, reorder and control the institutions of pre-digital capitalism and to squeeze expensive human beings from the process of production that is the real source of the push for intelligent machines, the real force behind the current “AI revolution”, but given its nature, we’d do better to call it a coup.

Correction:

The phrase above: “The MOOC model largely avoided these hard won truths of the field of Adult Basic Education and appears to be starting to stumble, with one of its biggest players UDACITY loosing its foothold.”

Originally read: “The MOOC model largely avoided these hard won truths of the field of Adult Basic Education and appears to be starting to stumble, with one of its biggest players UDACITY  closing up shop, as a result.”