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.”