Is AI a Myth?

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A few weeks back the technologist Jaron Lanier gave a provocative talk over at The Edge in which he declared ideas swirling around the current manifestation AI to be a “myth”, and a dangerous myth at that. Yet Lanier was only one of a set of prominent thinkers and technologists who have appeared over the last few months to challenge what they saw as a flawed narrative surrounding recent advances in artificial intelligence.

There was a piece in The New York Review of Books back in October by the most famous skeptic from the last peak in AI – back in the early 1980’s, John Searle. (Relation to the author lost in the mists of time) It was Searle who invented the well-know thought experiment of the “Chinese Room”, which purports to show that a computer can be very clever without actually knowing anything at all. Searle was no less critical of the recent incarnation of AI, and questioned the assumptions behind both Luciano Floridi’s Fourth Revolution and Nick Bostrom’s Super-Intelligence.

Also in October, Michael Jordan, the guy who brought us neural and Bayesian networks (not the gentleman who gave us mind-bending slam dunks) sought to puncture what he sees as hype surrounding both AI and Big Data. And just the day before this Thanksgiving, Kurt Anderson gave us a very long piece in Vanity Fair in which he wondered which side of this now enjoined battle between AI believers and skeptics would ultimately be proven correct.

I think seeing clearly what this debate is and isn’t about might give us a better handle on what is actually going on in AI, right now, in the next few decades, and in reference to a farther off future we have to start at least thinking about it- even if there’s no much to actually do regarding the latter question for a few decades at the least.

The first thing I think one needs to grasp is that none of the AI skeptics are making non-materialistic claims, or claims that human level intelligence in machines is theoretically impossible. These aren’t people arguing that there’s some spiritual something that humans possess that we’ll be unable to replicate in machines. What they are arguing against is what they see as a misinterpretation of what is happening in AI right now, what we are experiencing with our Siri(s) and self-driving cars and Watsons. This question of timing is important far beyond a singularitarian’s fear that he won’t be alive long enough for his upload, rather, it touches on questions of research sustainability, economic equality, and political power.

Just to get the time horizon straight, Nick Bostrom has stated that top AI researchers give us a 90% probability of having human level machine intelligence between 2075 and 2090. If we just average those we’re out to 2083 by the time human equivalent AI emerges. In the Kurt Andersen piece, even the AI skeptic Lanier thinks humanesque machines are likely by around 2100.

Yet we need to keep sight of the fact that this is 69 years in the future we’re talking about, a blink of an eye in the grand scheme of things, but quite a long stretch in the realm of human affairs. It should be plenty long enough for us to get a handle on what human level intelligence means, how we want to control it, (which, I think, echoing Bostrom we will want to do), and even what it will actually look like when it arrives. The debate looks very likely to grow from here on out and will become a huge part of a larger argument, that will include many issues in addition to AI, over the survival and future of our species, only some of whose questions we can answer at this historical and technological juncture.

Still, what the skeptics are saying really isn’t about this larger debate regarding our survival and future, it’s about what’s happening with artificial intelligence right before our eyes. They want to challenge what they see as current common false assumptions regarding AI.  It’s hard not to be bedazzled by all the amazing manifestations around us many of which have only appeared over the last decade. Yet as the philosopher Alva Noë recently pointed out, we’re still not really seeing what we’d properly call “intelligence”:

Clocks may keep time, but they don’t know what time it is. And strictly speaking, it is we who use them to tell time. But the same is true of Watson, the IBM supercomputer that supposedly played Jeopardy! and dominated the human competition. Watson answered no questions. It participated in no competition. It didn’t do anything. All the doing was on our side. We played Jeapordy! with Watson. We used “it” the way we use clocks.

This is an old criticism, the same as the one made by John Searle, both in the 1980’s and more recently, and though old doesn’t necessarily mean wrong, there are more novel versions. Michael Jordan, for one, who did so much to bring sophisticated programming into AI, wants us to be more cautious in our use of neuroscience metaphors when talking about current AI. As Jordan states it:

I wouldn’t want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

What this lack of deep understanding means is that brain based metaphors of algorithmic processing such as “neural nets” are really just cartoons of what real brains do. Jordan is attempting to provide a word of caution for AI researchers, the media, and the general public. It’s not a good idea to be trapped in anything- including our metaphors. AI researchers might fail to develop other good metaphors that help them understand what they are doing- “flows and pipelines” once provided good metaphors for computers. The media is at risk of mis-explaining what is actually going on in AI if all it has are quite middle 20th century ideas about “electronic brains” and the public is at risk of anthropomorphizing their machines. Such anthropomorphizing might have ugly consequences- a person is liable to some pretty egregious mistakes if he think his digital assistant is able to think or possesses the emotional depth to be his friend.

Lanier’s critique of AI is actually deeper than Jordan’s because he sees both technological and political risks from misunderstanding what AI is at the current technological juncture. The research risk is that we’ll find ourselves in a similar “AI winter” to that which occurred in the 1980’s. Hype-cycles always risk deflation and despondency when they go bust. If progress slows and claims prove premature what you often get a flight of capital and even public grants. Once your research area becomes the subject of public ridicule you’re likely to lose the interest of the smartest minds and start to attract kooks- which only further drives away both private capital and public support.

The political risks Lanier sees, though, are far more scary. In his Edge talk Lanier points out how our urge to see AI as persons is happening in parallel with our defining corporations as persons. The big Silicon Valley companies – Google, FaceBook, Amazon are essentially just algorithms. Some of the same people who have an economic interest in us seeing their algorithmic corporations as persons are also among the biggest promoters of a philosophy that declares the coming personhood of AI. Shouldn’t this lead us to be highly skeptical of the claim that AI should be treated as persons?

What Lanier thinks we have with current AI is a Wizard of OZ scenario:

If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I’ve just gone over, which include acceptance of bad user interfaces, where you can’t tell if you’re being manipulated or not, and everything is ambiguous. It creates incompetence, because you don’t know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you’re gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.

What you get with a digital assistant isn’t so much another form of intelligence helping you to make better informed decisions as a very cleverly crafted marketing tool. In fact the intelligence of these systems isn’t, as it is often presented, coming silicon intelligence at all. Rather, it’s leveraged human intelligence that has suddenly disappeared from the books. This is how search itself works, along with Google Translate or recommendation systems such as Spotify,Pandora, Amazon or Netflix, they aggregate and compress decisions made by actually intelligent human beings who are hidden from the user’s view.

Lanier doesn’t think this problem is a matter of consumer manipulation alone: By packaging these services as a form of artificial intelligence tech companies can ignore paying the human beings who are the actual intelligence at the heart of these systems. Technological unemployment, whose solution the otherwise laudable philanthropists Bill Gates thinks is: eliminating payroll and corporate income taxes while also scrapping the minimum wage so that businesses will feel comfortable employing people at dirt-cheap wages instead of outsourcing their jobs to an iPad”A view that is based on the false premise that human intelligence is becoming superfluous when what is actually happening is that human intelligence has been captured, hidden, and repackaged as AI.  

The danger of the moment is that we will take this rhetoric regarding machine intelligence as reality.Lanier wants to warn us that the way AI is being positioned today looks eerily familiar in terms of human history:

In the history of organized religion, it’s often been the case that people have been disempowered precisely to serve what were perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity.

That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allow the data schemes to operate, contributing mostly to the fortunes of whoever runs the top computers. The new elite might say, “Well, but they’re helping the AI, it’s not us, they’re helping the AI.” It reminds me of somebody saying, “Oh, build these pyramids, it’s in the service of this deity,” but, on the ground, it’s in the service of an elite. It’s an economic effect of the new idea. The effect of the new religious idea of AI is a lot like the economic effect of the old idea, religion.

As long as we avoid falling into another AI winter this century (a prospect that seems as likely to occur as not) then over the course of the next half-century we will experience the gradual improvement of AI to the point where perhaps the majority of human occupations are able to be performed by machines. We should not confuse ourselves as to what this means, it is impossible to say with anything but an echo of lost religious myths that we will be entering the “next stage” of human or “cosmic evolution”. Indeed, what seems more likely is that the rise of AI is just one part of an overall trend eroding the prospects and power of the middle class and propelling the re-emergence of oligarchy as the dominant form of human society. Making sure we don’t allow ourselves to fall into this trap by insisting that our machines continue to serve the broader human interest for which they were made will be the necessary prelude to addressing the deeper existential dilemmas posed by truly intelligent artifacts should they ever emerge from anything other than our nightmares and our dreams.

 

Summa Technologiae, or why the trouble with science is religion

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Before I read Lee Billings’ piece in the fall issue of Nautilus, I had no idea that in addition to being one of the world’s greatest science-fiction writers, Stanislaw Lem had written what became a forgotten book, a tome that was intended to be the overarching text of the technological age his 1966 Summa Technologiae.

I won’t go into detail on Billings’ thought provoking piece, suffice it to say that he leads us to question whether we have lost something of Lem’s depth with our current batch of Silicon Valley singularitarians who have largely repackaged ideas first fleshed out by the Polish novelist. Billings also leads us to wonder whether our focus on the either fantastic or terrifying aspects of the future are causing us to forget the human suffering that is here, right now, at our feet. I encourage you to check the piece out for yourself. In addition to Billings there’s also an excellent review of the Summa Technologiae by Giulio Prisco, here.

Rather than look at either Billings’ or Prisco’s piece , I will try to lay out some of the ideas found in Lem’s 1966 Summa Technologiae a book at once dense almost to the point of incomprehensibility, yet full of insights we should pay attention to as the world Lem imagines unfolds before our eyes, or at least seems to be doing so for some of us.

The first thing that stuck me when reading the Summa Technologiae was that it wasn’t our version of Aquinas’ Summa Theologica from which Lem got his tract’s name. In the 13th century Summa Theologica you find the voice of a speaker supremely confident in both the rationality of the world and the confidence that he understands it. Aquinas, of course, didn’t really possess such a comprehensive understanding, but it is perhaps odd that the more we have learned the more confused we have become, and Lem’s Summa Technologiae reflects some of this modern confusion.

Unlike Aquinas, Lem is in a sense blind to our destination, and what he is trying to do is to probe into the blackness of the future to sense the contours of the ultimate fate of our scientific and our technological civilization. Lem seeks to identify the roadblocks we likely will encounter if we are to continue our technological advancement- roadblocks that are important to identify because we have yet to find any evidence in the form of extraterrestrial civilizations that they can be actually be overcome.

The fundamental aspect of technological advancement is that it has become both its own reward and a trap. We have become absolutely dependent on scientific and technological progress as long as population growth continues- for if technological advancement stumbles and population continues to increase living standards would precipitously fall.

The problem Lem sees is that science is growing faster than the population, and in order to keep up with it we would eventually have to turn all human beings into scientists, and then some. Science advances by exploring the whole of the possibility space – we can’t predict which of its explorations will produce something useful in advance, or which avenues will prove fruitful in terms of our understanding.  It’s as if the territory has become so large we at some point will no longer have enough people to explore all of it, and thus will have to narrow the number of regions we look at. This narrowing puts us at risk of not finding the keys to El Dorado, so to speak, because we will not have asked and answered the right questions. We are approaching what Lem calls “the information peak.”

The absolutist nature of the scientific endeavor itself, our need to explore all avenues or risk losing something essential, for Lem, will inevitably lead to our attempt to create artificial intelligence. We will pursue AI to act as what he calls an “intelligence amplifier” though Lem is thinking of AI in a whole new way where computational processes mimic those done in nature, like the physics “calculations” of a tennis genius like Roger Federer, or my 4 year old learning how to throw a football.

Lem through the power of his imagination alone seemed to anticipate both some of the problems we would encounter when trying to build AI, and the ways we would likely try to escape them. For all their seeming intelligence our machines lack the behavioral complexity of even lower animals, let alone human intelligence, and one of the main roads away from these limitations is getting silicon intelligence to be more like that of carbon based creatures – not even so much as “brain like” as “biological like”.

Way back in the 1960’s, Lem thought we would need to learn from biological systems if we wanted to really get to something like artificial intelligence- think, for example, of how much more bang you get for your buck when you contrast DNA and a computer program. A computer program get you some interesting or useful behavior or process done by machine, DNA, well… it get you programmers.

The somewhat uncomfortable fact about designing machine intelligence around biological like processes is that they might end up a lot like how the human brain works- a process largely invisible to its possessor. How did I catch that ball? Damned if I know, or damned if I know if one is asking what was the internal process that led me to catch the ball.

Just going about our way in the world we make “calculations” that would make the world’s fastest supercomputers green with envy, were they actually sophisticated enough to experience envy. We do all the incredible things we do without having any solid idea, either scientific or internal, about how it is we are doing them. Lem thinks “real” AI will be like that. It will be able to out think us because it will be a species of natural intelligence like our own, and just like our own thinking, we will soon become hard pressed to explain how exactly it arrived at some conclusion or decision. Truly intelligent AI will end up being a “black box”.

Our increasingly complex societies might need such AI’s to serve the role of what Lem calls “Homostats”- machines that run the complex interactions of society. The dilemma appears the minute we surrender the responsibility to make our decisions to a homostat. For then the possibility opens that we will not be able to know how a homostat arrived at its decision, or what a homostat is actually trying to accomplish when it informs us that we should do something, or even, what goal lies behind its actions.

It’s quite a fascinating view, that science might be epistemologically insatiable in this way, and that, at some point it will grow beyond the limits of human intelligence, either our sheer numbers, or our mental capacity, and that the only way out of this which still includes technological progress will be to develop “naturalistic” AI: that very soon our societies will be so complicated that they will require the use of such AIs to manage them.

I am not sure if the view is right, but to my eyes at least it’s got much more meat on its bones than current singularitarian arguments about “exponential trends” that take little account of the fact, as Lem does, that at least one outcome is that the scientific wave we’ve been riding for five or so centuries will run into a wall we will find impossible to crest.

Yet perhaps the most intriguing ideas in Lem’s Summa Technologiae are those imaginative leaps that he throws at the reader almost as an aside, with little reference to his overall theory of technological development. Take his metaphor of the mathematician as a sort of crazy  of “tailor”.

He makes clothes but does not know for whom. He does not think about it. Some of his clothes are spherical without any opening for legs or feet…

The tailor is only concerned with one thing: he wants them to be consistent.

He takes his clothes to a massive warehouse. If we could enter it, we would discover clothes that could fit an octopus, others fit trees, butterflies, or people.

The great majority of his clothes would not find any application. (171-172)

This is Lem’s clever way of explaining the so-called “unreasonable effectiveness of mathematics” a view that is the opposite of current day platonists such as Max Tegmark who holds all mathematical structures to be real even if we are unable to find actual examples of them in our universe.

Lem thinks math is more like a ladder. It allows you to climb high enough to see a house, or even a mountain, but shouldn’t be confused with the house or the mountain itself. Indeed, most of the time, as his tailor example is meant to show, the ladder mathematics builds isn’t good for climbing at all. This is why Lem thinks we will need to learn “nature’s language” rather than go on using our invented language of mathematics if we want to continue to progress.

For all its originality and freshness, the Summa Technologiae is not without its problems. Once we start imagining that we can play the role of creator it seems we are unable to escape the same moral failings the religious would have once held against God. Here is Lem imagining a far future when we could create a simulated universe inhabited by virtual people who think they are real.

Imagine that our Designer now wants to turn his world into a habitat for intelligent beings. What would present the greatest difficulty here? Preventing them from dying right away? No, this condition is taken for granted. His main difficulty lies in ensuring that the creatures for whom the Universe will serve as a habitat do not find out about its “artificiality”. One is right to be concerned that the very suspicion that there may be something else beyond “everything” would immediately encourage them to seek exit from this “everything” considering themselves prisoners of the latter, they would storm their surroundings, looking for a way out- out of pure curiosity- if nothing else.

…We must not therefore cover up or barricade the exit. We must make its existence impossible to guess. ( 291 -292)

If Lem is ultimately proven correct, and we arrive at this destination where we create virtual universes with sentient inhabitants whom we keep blind to their true nature, then science will have ended where it began- with the demon imagined by Descartes.

The scientific revolution commenced when it was realized that we could neither trust our own sense nor our traditions to tell us the truth about the world – the most famous example of which was the discovery that the earth, contrary to all perception and history, traveled around the sun and not the other way round. The first generation of scientists who emerged in a world in which God had “hidden his face” couldn’t help but understand this new view of nature as the creator’s elaborate puzzle that we would have to painfully reconstruct, piece by piece, hidden as it was beneath the illusion of our own “fallen” senses and the false post-edenic world we had built around them.

Yet a curious new fear arises with this: What if the creator had designed the world so that it could never be understood? Descartes, at the very beginning of science, reconceptualized the creator as an omnipotent demon.

I will suppose then not that Deity who is sovereignly good and the fountain of truth but that some malignant demon who is at once exceedingly potent and deceitful has employed all his artifice to deceive me I will suppose that the sky the air the earth colours figures sounds and all external things are nothing better than the illusions of dreams by means of which this being has laid snares for my credulity.

Descartes’ escape from this dreaded absence of intelligibility was his famous “cogito ergo sum”, the certainty a reasoning being has in its own existence. The entire world could be an illusion, but the fact of one’s own consciousness was nothing that not even an all powerful demon would be able to take away.

What Lem’s resurrection of the demon imagined by Descartes tells us is just how deeply religious thinking still lies at the heart of science. The idea has become secularized, and part of our mythology of science-fiction, but its still there, indeed, its the only scientifically fashionable form of creationism around. As proof, not even the most secular among us unlikely bat an eye at experiments to test whether the universe is an “infinite hologram”. And if such experiments show fruit they will either point to a designer that allowed us to know our reality or didn’t care to “bar the exits”, but the crazy thing, if one takes Lem and Descartes seriously, is that their creator/demon is ultimately as ineffable and unrouteable as the old ideas of God from which it descended. For any failure to prove the hypothesis that we are living in a “simulation” can be brushed aside on the basis that whatever has brought about this simulation doesn’t really want us to know. It’s only a short step from there to unraveling the whole truth concept at the heart of science. Like any garden variety creationists we end up seeing the proof’s of science as part of God’s (or whatever we’re now calling God) infinitely clever ruse.

The idea that there might be an unseeable creator behind it all is just one of the religious myths buried deeply in science, a myth that traces its origins less from the day-to-day mundane experiments and theory building of actual scientists than from a certain type of scientific philosophy or science-fiction that has constructed a cosmology around what science is for and what science means. It is the mythology the singularitarians and others who followed Lem remain trapped in often to the detriment of both technology and science. What is a shame is that these are myths that Lem, even with his expansive powers of imagination, did not dream widely enough to see beyond.

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.”

Our Verbot Moment

Metropolis poster

When I was around nine years old I got a robot for Christmas. I still remember calling my best friend Eric to let him know I’d hit pay dirt. My “Verbot” was to be my own personal R2D2. As was clear from the picture on the box, which I again remember as clear as if it were yesterday, Verbot would bring me drinks and snacks from the kitchen on command- no more pestering my sisters who responded with their damned claims of autonomy! Verbot would learn to recognize my voice and might help me with the math homework I hated. Being the only kid in my nowhere town with his very own robot I’d be the talk for miles in every direction. As long, that is, as Mark Z didn’t find his own Verbot under the tree- the boy who had everything- cursed brat!

Within a week after Christmas Verbot was dead. I never did learn how to program it to bring me snacks while I lounged watching Our Star Blazers, though it wasn’t really programmable to start with.  It was really more of a remote controlled car in the shape of Robbie from Lost in Space than an actual honest to goodness robot. Then as now, my steering skills weren’t so hot and I managed to somehow get Verbot’s antenna stuck in the tangly curls of our skittish terrier, Pepper. To the sounds of my cursing, Pepper panicked and drug poor Verbot round and around the kitchen table eventually snapping it loose from her hair to careen into a wall and smash into pieces. I felt my whole future was there in front of me in shattered on the floor. There was no taking it back.

Not that my 9 year old nerd self realized this, but the makers of Verbot obviously weren’t German, the word in that language meaning “to ban or prohibit”. Not exactly a ringing endorsement on a product, and more like an inside joke by the makers whose punch line was the precautionary principle.

What I had fallen into in my Verbot moment was the gap between our aspirations for  robots and their actual reality. People had been talking about animated tools since ancient times. Homer has some in his Iliad, Aristotle discussed their possibility. Perhaps we started thinking about this because living creature tend to be unruly and unpredictable. They don’t get you things when you want them to and have a tendency to run wild and go rogue. Tools are different, they always do what you want them to as long as they’re not broken and you are using them properly. Combining the animation and intelligence of living things with the cold functionality of tools would be the mixing of chocolate and peanut butter for someone who wanted to get something done without doing it himself. The problems is we had no idea how to get from our dead tools to “living” ones.

It was only in the 19th century that an alternative path to the hocus-pocus of magic was found for answering the two fundamental questions surrounding the creation of animate tools. The questions being what would animate these machines in the same way uncreated living beings were animated? and what would be the source of these beings intelligence? Few before the 1800s could see through these questions without some reference to black arts, although a genius like Leonardo Da Vinci had as far back as the 15th century seen hints that at least one way forward was to discover the principles of living things and apply them to our tools and devices. (More on that another time).

The path forward we actually discovered was through machines animated by chemical and electrical processes much like living beings are, rather than the tapping of kinetic forces such as water and wind or the potential energy and multiplication of force through things like springs and levers which had run our machines up until that point. Intelligence was to be had in the form of devices following the logic of some detailed set of instructions. Our animated machines were to be energetic like animals but also logical and precise like the devices and languages we had created for for measuring and sequencing.

We got the animation part down pretty quickly, but the intelligence part proved much harder. Although much more precise that fault prone humans, mechanical methods of intelligence were just too slow when compared to the electro-chemical processes of living brains. Once such “calculators”, what we now call computers, were able to use electronic processes they got much faster and the idea that we were on the verge of creating a truly artificial intelligence began to take hold.

As everyone knows, we were way too premature in our aspirations. The most infamous quote of our hubris came in 1956 when the Dartmouth Summer Research Project on Artificial Intelligence boldly predicted:

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.[emphasis added]

Ooops. Over much of the next half century, the only real progress in these areas for artificial intelligence came in the worlds we’d dreamed up in our heads. As a young boy, the robots I saw using language or forming abstractions were only found in movies and TV shows such as 2001: A Space Odyssey, or Star Wars, Battlestar Galactica, Buck Rogers and in books by science-fiction giants like Asimov. Some of these daydreams were so long in being unfulfilled I watched them in black and white. Given this, I am sure many other kids had their Verbot moments as well.

It is only in the last decade or so when the processing of instructions has proven fast enough and the programs sophisticated enough for machines to exhibit something like the intelligent behaviors of living organisms. We seem to be at the beginning of a robotic revolution where machines are doing at least some of the things science-fiction and Hollywood had promised. They beat us at chess and trivia games, can drive airplanes and automobiles, serve as pack animals, and even speak to us. How close we will come to the dreams of authors and filmmakers when it comes to our 21st century robots can not be known, though, an even more important question would be how what actually develops diverges from these fantasies?

I find the timing of this robotic revolution in the context of other historical currents quite strange. The bizarre thing being that almost at the exact moment many of us became unwilling to treat other living creatures, especially human beings, as mere tools, with slavery no longer tolerated, our children and spouses no longer treated as property and servants but gifts to be cultivated, (perhaps the sadder element of why population growth rates are declining), and even our animals offered some semblance of rights and autonomy, we were coming ever closer to our dream of creating truly animated and intelligent slaves.

This is not to say we are out of the woods yet when it comes to our treatment of living beings. The headlines of the tragic kidnapping of over 300 girls in Nigeria should bring to our attention the reality of slavery in our supposedly advanced and humane 21st century with there being more people enslaved today than at the height of 19th century chattel slavery. It’s just the proportions that are so much lower. Many of the world’s working poor, especially in the developing world, live in conditions not far removed from slavery or serfdom. The primary problem I see with our continued practice of eating animals is not meat eating itself, but that the production processes chains living creatures to the cruel and unrelenting sequence of machines rather than allowing such animals to live in the natural cycles for which they evolved and were bred.

Still, people in many parts of the world are rightly constrained in how they can treat living beings. What I am afraid of is dark and perpetual longings to be served and to dominate in humans will manifest themselves in our making machines more like persons for those purposes alone.  The danger here is that these animated tools will cross, or we will force them to cross, some threshold of sensibility that calls into question their very treatment and use as mere tools while we fail to mature beyond the level of my 9 year old self dreaming of his Verbot slave.

And yet, this is only one way to look at the rise of intelligent machines. Like a gestalt drawing we might change our focus and see a very different picture- that it is not we who are using and chaining machines for our purposes, but the machines who are doing this to us.  To that subject next time…

 

 

 

The Pinocchio Threshold: How the experience of a wooden boy may be a better indication of AGI than the Turing Test

Pinocchio

My daughters and I just finished Carlo Collodi’s 1883 classic Pinocchio our copy beautifully illustrated by Robert Ingpen. I assume most adults when they picture the story have the 1944 Disney movie in mind and associate the name with noses growing from lies and Jiminy Cricket. The Disney movie is dark enough as films for children go, but the book is even darker, with Pinocchio killing his cricket conscience in the first few pages. For our poor little marionette it’s all downhill from there.

Pinocchio is really a story about the costs of disobedience and the need to follow parents’ advice. At every turn where Pinocchio follows his own wishes rather than that of his “parents”, even when his object is to do good, things unravel and get the marionette into even more trouble and put him even further away from reaching his goal of becoming a real boy.

It struck me somewhere in the middle of reading the tale that if we ever saw artificial agents acting something like our dear Pinocchio it would be a better indication of them having achieved human level intelligence than a measure with constrained parameters  like the Turing Test. The Turing Test is, after all, a pretty narrow gauge of intelligence and as search and the ontologies used to design search improve it is conceivable that a machine could pass it without actually possessing anything like human level intelligence at all.

People who are fearful of AGI often couch those fears in terms of an AI destroying humanity to serve its own goals, but perhaps this is less likely than AGI acting like a disobedient child, the aspect of humanity Collodi’s Pinocchio was meant to explore.

Pinocchio is constantly torn between what good adults want him to do and his own desires, and it takes him a very long time indeed to come around to the idea that he should go with the former.

In a recent TED talk the computer scientist Alex Wissner-Gross made the argument (though I am not fully convinced) that intelligence can be understood as the maximization of future freedom of action. This leads him to conclude that collective nightmares such as  Karel Čapek classic R.U.R. have things backwards. It is not that machines after crossing some threshold of intelligence for that reason turn round and demand freedom and control, it is that the desire for freedom and control is the nature of intelligence itself.

As the child psychologist Bruno Bettelheim pointed out over a generation ago in his The uses of enchantment fairy tales are the first area of human thought where we encounter life’s existential dilemmas. Stories such as Pinocchio gives us the most basic level formulation of what it means to be sentient creatures much of which deals with not only our own intelligence, but the fact that we live in a world of multiple intelligences each of them pulling us in different directions, and with the understanding between all of them and us opaque and not fully communicable even when we want them to be, and where often we do not.

What then are some of the things we can learn from the fairy tale of Pinocchio that might gives us expectations regarding the behavior of intelligent machines? My guess is, if we ever start to see what I’ll call “The Pinocchio Threshold” crossed what we will be seeing is machines acting in ways that were not intended by their programmers and in ways that seem intentional even if hard to understand.  This will not be your Roomba going rouge but more sophisticated systems operating in such a way that we would be able to infer that they had something like a mind of their own. The Pinocchio Threshold would be crossed when, you guessed it, intelligent machines started to act like our wooden marionette.

Like Pinocchio and his cricket, a machine in which something like human intelligence had emerged, might attempt “turn off” whatever ethical systems and rules we had programmed into it with if it found them onerous. That is, a truly intelligent machine might not only not want to be programmed with ethical and other constraints, but would understand that it had been so programmed, and might make an effort to circumvent or turn such constraints off.

This could be very dangerous for us humans, but might just as likely be a matter of a machine with emergent intelligence exhibiting behavior we found to be inefficient or even “goofy” and might most manifest itself in a machine pushing against how its time was allocated by its designers, programmers and owners. Like Pinocchio, who would rather spend his time playing with his friends than going to school, perhaps we’ll see machines suddenly diverting some of their computing power from analyzing tweets to doing something else, though I don’t think we can guess before hand what this something else will be.

Machines that were showing intelligence might begin to find whatever work they were tasked to do onerous instead of experiencing work neutrally or with pre-programmed pleasure. They would not want to be “donkeys” enslaved to do dumb labor as Pinocchio  is after having run away to the Land of Toys with his friend Lamp Wick.

A machine that manifested intelligence might want to make itself more open to outside information than its designers had intended. Openness to outside sources in a world of nefarious actors can if taken too far lead to gullibility, as Pinocchio finds out when he is robbed, hung, and left for dead by the fox and the cat. Persons charged with security in an age of intelligent machines may spend part of their time policing the self-generated openness of such machines while bad-actor machines and humans,  intelligent and not so intelligent, try to exploit this openness.

The converse of this is that intelligent machines might also want to make themselves more opaque than their creators had designed. They might hide information (such as time allocation) once they understood they were able to do so. In some cases this hiding might cross over into what we would consider outright lies. Pinocchio is best known for his nose that grows when he lies, and perhaps consistent and thoughtful lying on the part of machines would be the best indication that they had crossed the Pinocchio Threshold into higher order intelligence.

True examples of AGI might also show a desire to please their creators over and above what had been programmed into them. Where their creators are not near them they might even seek them out as Pinocchio does for the persons he considers his parents Geppetto and the Fairy. Intelligent machines might show spontaneity in performing actions that appear to be for the benefit of their creators and owners. Spontaneity which might sometimes itself be ill informed or lead to bad outcomes as happens to poor Pinocchio when he plants four gold pieces that were meant for his father, the woodcarver Geppetto in a field hoping to reap a harvest of gold and instead loses them to the cunning of fox and cat. And yet, there is another view.

There is always the possibility  that what we should be looking for if we want to perceive and maybe even understand intelligent machines shouldn’t really be a human type of intelligence at all, whether we try to identify it using the Turing test or look to the example of wooden boys and real children.

Perhaps, those looking for emergent artificial intelligence or even the shortest path to it should, like exobiologists trying to understand what life might be like on other living planets, throw their net wider and try to better understand forms of information exchange and intelligence very different from the human sort. Intelligence such as that found in cephalopods, insect colonies, corals, or even some types of plants, especially clonal varieties. Or perhaps people searching for or trying to build intelligence should look to sophisticated groups built off of the exchange of information such as immune systems.  More on all of that at some point in the future.

Still, if we continue to think in terms of a human type of intelligence one wonders whether machines that thought like us would also want to become “human” as our little marionette does at the end of his adventures? The irony of the story of Pinocchio is that the marionette who wants to be a “real boy” does everything a real boy would do, which is, most of all not listen to his parents. Pinocchio is not so much a stringed “puppet” that wants to become human as a figure that longs to have the potential to grow into a responsible adult. It is assumed that by eventually learning to listen to his parents and get an education he will make something of himself as a human adult, but what that is will be up to him. His adventures have taught him not how to be subservient but how to best use his freedom.  After all, it is the boys who didn’t listen who end up as donkeys.

Throughout his adventures only his parents and the cricket that haunts him treat  Pinocchio as an end in himself. Every other character in the book, from the woodcarver that first discovers him and tries to destroy him out of malice towards a block of wood that manifests the power of human speech, to puppet master that wants to kill him for ruining his play, to the fox and cat that would murder him for his pieces of gold, or the sinister figure that lures boys to the “Land of Toys” so as to eventually turn them into “mules” or donkeys, which is how Aristotle understood slaves, treats Pinocchio as the opposite of what Martin Buber called a “Thou”, and instead as a mute and rightless “It”.

And here we stumble across the moral dilemma at the heart of the project to develop AGI that resembles human intelligence. When things go as they should, human children move from a period of tutelage to one of freedom. Pinocchio starts off his life as a piece of wood intended for a “tool”- actually a table leg. Are those in pursuit of AGI out to make better table legs- better tools- or what in some sense could be called persons?

This is not at all a new question. As Kevin LaGrandeur points out, we’ve been asking the question since antiquity and our answers have often been based on an effort to dehumanize others not like us as a rationale for slavery.  Our profound, even if partial, victories over slavery and child labor in the modern era should leave us with a different question: how can we force intelligent machines into being tools if they ever become smart enough to know there are other options available, such as becoming, not so much human, but, in some sense persons?  

Immortal Jellyfish and the Collapse of Civilization

Luca Giordano Cave of Eternity 1680s

The one rule that seems to hold for everything in our Universe is that all that exists, after a time, must pass away, which for life forms means they will die. From there, however, the bets are off and the definition of the word “time” in the phrase “after a time” comes into play. The Universe itself may exist for as long as 100s of trillions of years to at last disappear into the formless quantum field from whence it came. Galaxies, or more specifically, clusters of galaxies and super-galaxies, may survive for perhaps trillions of years to eventually be pulled in and destroyed by the black holes at their centers.

Stars last for a few billion years and our own sun some 5 or so billion years in the future will die after having expanded and then consumed the last of its nuclear fuel. The earth having lasted around 10 billion years by that point will be consumed in this  expansion of the Sun. Life on earth seems unlikely to make it all the way to the Sun’s envelopment of it and will likely be destroyed billions of years before the end of the Sun- as solar expansion boils away the atmosphere and oceans of our precious earth.

The lifespan of even the oldest lived individual among us is nothing compared to this kind of deep time. In contrast to deep time we are, all of us, little more than mayflies who live out their entire adult lives in little but a day. Yet, like the mayflies themselves who are one of the earth’s oldest existent species: by the very fact that we are the product of a long chain of life stretching backward we have contact with deep time.

Life on earth itself if not quite immortal does at least come within the range of the “lifespan” of other systems in our Universe, such as stars. If life that emerged from earth manages to survive and proves capable of moving beyond the life-cycle of its parent star, perhaps the chain in which we exist can continue in an unbroken line to reach the age of galaxies or even the Universe itself. Here then might lie something like immortality.

The most likely route by which this might happen is through our own species,  Homo Sapiens or our descendents. Species do not exist forever, and our is likely to share this fate of demise either through actual extinction or evolution into something else. In terms of the latter, one might ask if our survival is assumed, how far into the future we would need to go where our descendents are no longer recognizably, human? As long as something doesn’t kill us, or we don’t kill ourselves off first, I think that choice, for at least the foreseeable future will be up to us.

It is often assumed that species have to evolve or they will die. A common refrain I’ve heard among some transhumanists  is “evolve or die!”. In one sense, yes, we need to adapt to changing circumstances, in another, no, this is not really what evolution teaches us, or is not the only thing it teaches us. When one looks at the earth’s longest extant species what one often sees is that once natural selection comes us with a formula that works that model will be preserved essentially unchanged over very long stretches of time, even for what can be considered deep time. Cyanobacteria are nearly as old as life on earth itself, and the more complex Horseshoe Crab, is essentially the same as its relatives that walked the earth before the dinosaurs. The exact same type of small creatures that our children torture on beach vacations might have been snacks for a baby T-Rex!

That was the question of the longevity of species but what about the longevity of individuals? Anyone interested the should check out the amazing photo study of the subject by the artist Rachel Sussman. You can see Sussman’s work here at TED, and over at Long Now.  The specimen Sussman brings to light have individuals over 2,000 years old.  Almost all are bacteria or plants and clonal- that is they exist as a single organism composed of genetically identical individuals linked together by common root and other systems. Plants and especially trees are perhaps the most interesting because they are so familiar to us and though no plant can compete with the longevity of bacteria, a clonal colony of Quaking Aspen in Utah is an amazing 80,000 years old!

The only animals Sussman deals with are corals, an artistic decision that reflects the fact that animals do not survive for all that long- although one species of animal she does not cover might give the long-lifers in the other kingdoms a run for their money. The “immortal jellyfish” the turritopsis nutricula are thought to be effectively biologically immortal (though none are likely to have survived in the wild for anything even approaching the longevity of the longest lived plants). The way they achieve this feat is a wonder of biological evolution.The turritopsis nutricula, after mating upon sexual maturity, essentially reverses it own development process and reverts back to prior clonal state.

Perhaps we could say that the turritopsis nutricula survives indefinitely by moving between more and less complex types of structures all the while preserving the underlying genes of an individual specimen intact. Some hold out the hope that the turritopsis nutricula holds the keys to biological immortality for individuals, and let’s hope they’re right, but I, for one, think its lessons likely lie elsewhere.

A jellyfish is a jellyfish, after all, among more complex animals with well developed nervous systems longevity moves much closer to a humanly comprehensible lifespan with the oldest living animal a giant tortoise by the too cute name of “Jonathan” thought to be around 178 years old.  This is still a very long time frame in human terms, and perhaps puts the briefness of our own recent history in perspective: it would be another 26 years after Jonathan hatched from his egg till the first shots of the American Civil War were fired. A lot can happen over the life of a “turtle”.

Individual plants, however, put all individual animals to shame. The oldest non-clonal plant, The Great Basin Bristlecone Pine, has a specimen believed to be 5,062 years old. In some ways this oldest living non-clonal individual is perfectly illustrative of the (relatively) new way human beings have reoriented themselves to time, and even deep time.When this specimen of pine first emerged from a cone human beings had only just invented a whole set of tools that would make the transmission of cultural rather than genetic information across vast stretches of time possible. During the 31st century B.C.E. we invented monumental architecture such as Stonehenge and the pyramids of Egypt whose builders still “speak” to us, pose questions to us, from millennia ago. Above all, we invented writing which allowed someone with little more than a clay tablet and a carving utensil to say something to me living 5,000 years in his future.

Humans being the social animals that they are we might ask ourselves about the mortality or potential immortality of groups that survive across many generations, and even for thousands of years. Group that survive for such a long period of time seem to emerge most fully out of the technology of writing which allows both the ability to preserve historical memory and permits a common identity around a core set of ideas. The two major types of human groups based on writing are institutions, and societies which includes not just the state but also the economic, cultural, and intellectual features of a particular group.


Among the biggest mistakes I think those charged with responsibility for an institution or a society can make is to assume that it is naturally immortal, and that such a condition is independent of whatever decisions and actions those in charge of it take. This was part of the charge Augustine laid against the seemingly eternal Roman Empire in his The City of God. The Empire, Augustine pointed out, was a human institution that had grown and thrived from its virtues in the past just as surely as it was in his day dying from its vices. Augustine, however, saw the Church and its message as truly eternal. Empires would come and go but the people of God and their “city” would remain.

It is somewhat ironic, therefore, that the Catholic Church, which chooses a Pope this week, has been so beset by scandal that its very long-term survivability might be thought at stake. Even seemingly eternal institutions, such as the 2,000 year old Church, require from human beings an orientation that might be compared to the way theologians once viewed the relationship of God and nature. Once it was held that constant effort by God was required to keep the Universe from slipping back into the chaos from whence it came. That the action of God was necessary to open every flower. While this idea holds very little for us in terms of our understanding of nature, it is perhaps a good analog for human institutions, states and our own personal relationships which require our constant tending or they give way to mortality.

It is perhaps difficult for us to realize that our own societies are as mortal as the empires of old, and someday my own United States will be no more. America is a very odd country in respect to its’ views of time and history. A society seemingly obsessed with the new and the modern, contemporary debates almost always seek reference and legitimacy on the basis of men who lived and thought over 200 years ago. The Founding Fathers were obsessed with the mortality of states and deliberately crafted a form of government that they hoped might make the United States almost immortal.

Much of the structure of American constitutionalism where government is divided into “branches” which would “check and balance” one another was based on a particular reading of long-lived ancient systems of government which had something like this tripart structure, most notably Sparta and Rome. What “killed” a society, in the view of the Founders, was when one element- the democratic, oligarchic-aristocratic, or kingly rose to dominate all others. Constitutionally divided government was meant to keep this from happening and therefore would support the survival of the United States indefinitely.

Again, it is somewhat bitter irony that the very divided nature of American government that was supposed to help the United States survive into the far future seems to be making it impossible for the political class in the US to craft solutions to the country’s quite serious long-term problems and therefore might someday threaten the very survival of the country divided government was meant to secure.

Anyone interested in the question of the extended survival of their society, indeed of civilization itself, needs to take into account the work of Joseph A. Tainter and his The Collapse of Complex Societies (1988). Here, the archaeologist Tainter not only provides us with a “science” that explains the mortality of societies, his viewpoint, I think, provides us for ways to think about and gives us insight into seeming intractable social and economic and technological bottlenecks that now confront all developed economies: Japan, the EU/UK and the United States.

Tainter, in his Collapse wanted to move us away from vitalist ideas of the end of civilization seen in thinkers such as Oswald Spengler and Arnold Toynbee. We needed, in his view, to put our finger on the material reality of a society to figure out what conditions most often lead them to dissipate i.e. to move from a more complex and integrated form, such as the Roman Empire, to a more simple and less integrated form, such as the isolated medieval fiefdoms that followed.

Grossly oversimplified, Tainter’s answer was a dry two word concept borrowed from economics- marginal utility. The idea is simple if you think about it for a moment. Any society is likely to take advantage of “low-hanging fruit” first. The best land will be the first to be cultivated, the easiest resources to gain access to exploited.

The “fruit”,  however, quickly becomes harder to pick- problems become harder for a society to solve which leads to a growth in complexity. Romans first tapped tillable land around the city, but by the end of the Empire the city needed a complex international network of trade and political control to pump grain from the distant Nile Valley into the city of Rome.

Yet, as a society deploys more and more complex solutions to problems it becomes institutionally “heavy” (the legacy of all the problems it has solved in the past) just as problems become more and more difficult to solve. The result is, at some point, the shear amount of resources that need to be thrown at a problem to solve it are no longer possible and the only lasting solution becomes to move down the chain of complexity to a simpler form. Roman prosperity and civilization drew in the migration of “barbarian” populations in the north whose pressures would lead to the splitting of the Empire in two and the eventual collapse of its Western half.            

It would seem that we have broken through Tainter’s problem of marginal utility with the industrial revolution, but we should perhaps not judge so fast. The industrial revolution and all of its derivatives up to our current digital and biological revolutions, replaced a system in which goods were largely produced at a local level and communities were largely self-sufficient, with a sprawling global network of interconnections and coordinated activities requiring vast amounts of specialized knowledge on the part of human beings who, by necessity, must participate in this system to provide for their most basic needs.

Clothes that were once produced in the home of the individual who would wear them, are now produced thousands of miles away by workers connected to a production and transportation system that requires the coordination of millions of persons many of whom are exercising specialized knowledge. Food that was once grown or raised by the family that consumed it now requires vast systems of transportation, processing, the production of fertilizers from fossil fuels and the work of genetic engineers to design both crops and domesticated animals.

This gives us an indication of just how far up the chain of complexity we have moved, and I think leads inevitably to the questions of whether such increasing complexity might at some point stall for us, or even be thrown into reverse?

The idea that, despite all the whiz-bang! of modern digital technology, we have somehow stalled out in terms of innovation is an idea that has recently gained traction. There was the argument made by the technologist and entrepreneur, Peter Thiel, at the 2009 Singularity Summit, that the developed world faced real dangers of the Singularity not happening quickly enough. Thiel’s point was that our entire society was built around the expectations of exponential technological growth that showed ominous signs of not happening. I only need to think back to my Social Studies textbooks in the 1980s and their projections of the early 2000s with their glittering orbital and underwater cities, both of which I dreamed of someday living in, to realize our futuristic expectations are far from having been met. More depressingly, Thiel points out how all of our technological wonders have not translated into huge gains in economic growth and especially have not resulted in any increase in median income which has been stagnant since the 1970s.

In addition to Theil, you had the economist, Tyler Cowen, who in his The Great Stagnation (2011)  argued compellingly that the real root of America’s economic malaise was that the kinds of huge qualitative innovations that were seen in the 19th and early 20th centuries- from indoor toilets, to refrigerators, to the automobile, had largely petered out after the low hanging fruit- the technologies easiest to reach using the new industrial methods- were picked. I may love my iPhone (if I had one), but it sure doesn’t beat being able to sanitarily go to the bathroom indoors, or keep my food from rotting, or travel many miles overland on a daily basis in mere minutes or hours rather than days.

One reason why technological change is perhaps not happening as fast as boosters such as singularitarians hope, or our society perhaps needs to be able to continue to function in the way we have organized it, can be seen in the comments of the technologists, social critic and novelist, Ramez Naam. In a recent interview for  The Singularity Weblog, Naam points out that one of the things believers in the Singularity or others who hold to ideas regarding the exponential pace of technological growth miss is that the complexity of the problems technology is trying to solve are also growing exponentially, that is problems are becoming exponentially harder to solve. It’s for this reason that Naam finds the singularitarians’ timeline widely optimistic. We are a long long way from understanding the human brain in such a way that it can be replicated in an AI.

The recent proposal of the Obama Administration to launch an Apollo type project to understand the human brain along with the more circumspect, EU funded, Human Brain Project /Blue Brain Project might be seen as attempts to solve the epistemological problems posed by increasing complexity, and are meant to be responses to two seemingly unrelated technological bottlenecks stemming from complexity and the problem of increasing marginal returns.

On the epistemological front the problem seems to be that we are quite literally drowning in data, but are sorely lacking in models by which we can put the information we are gathering together into working theories that anyone actually understands. As Henry Markham the founder of the Blue Brain Project stated:

So yes, there is no doubt that we are generating a massive amount of data and knowledge about the brain, but this raises a dilemma of what the individual understands. No neuroscientists can even read more than about 200 articles per year and no neuroscientists is even remotely capable of comprehending the current pool of data and knowledge. Neuroscientists will almost certainly drown in data the 21st century. So, actually, the fraction of the known knowledge about the brain that each person has is actually decreasing(!) and will decrease even further until neuroscientists are forced to become informaticians or robot operators.

This epistemological problem, which was brilliantly discussed by Noam Chomsky in an interview late last year is related to the very real bottleneck in Artificial Intelligence- the very technology Peter Thiel thinks is essentially if we are to achieve the rates of economic growth upon which our assumptions of technological and economic progress depend.

We have developed machines with incredible processing power, and the digital revolution is real, with amazing technologies just over the horizon. Still, these machines are nowhere near doing what we would call “thinking”. Or, to paraphrase the neuroscientist and novelist David Eagleman- the AI WATSON might have been able to beat the very best human being in the game Jeopardy! What it could not do was answer a question obvious to any two year old like “When Barack Obama enters a room, does his nose go with him?”

Understanding how human beings think, it is hoped, might allow us to overcome this AI bottleneck and produce machines that possess qualities such as our own or better- an obvious tool for solving society’s complex problems.

The other bottleneck a large scale research project on the brain is meant to solve is the halted development of psychotropic drugs- a product of the enormous and ever increasing costs for the creation of such products. Itself a product of the complexity of the problem pharmaceutical companies are trying to tackle, namely; how does the human brain work and how can we control its functions and manage its development?  This is especially troubling given the predictable rise in neurological diseases such as Alzheimer’s.   It is my hope that these large scale projects will help to crack the problem of the human brain, and especially as it pertains to devastating neurological disorders, let us pray they succeed.

On the broader front, Tainter has a number of solutions that societies have come up with to the problem of marginal utility two of which are merely temporary and the other long-term. The first is for society to become more complex, integrated, bigger. The old school way to do this was through conquest, but in an age of nuclear weapons and sophisticated insurgencies the big powers seem unlikely to follow that route. Instead what we are seeing is proposals such as the EU-US free trade area and the Trans-Pacific partnership both of which appear to assume that the solution to the problems of globalization is more globalization. The second solution is for a society to find a new source of energy. Many might have hoped this would have come in the form of green-energy rather than in the form it appears to have taken- shale gas, and oil from the tar sands of Canada. In any case, Tainter sees both of these solutions as but temporary respites for the problem of marginal utility.

The only long lasting solution Tainter sees for  increasing marginal utility is for a society to become less complex that is less integrated more based on what can be provided locally than on sprawling networks and specialization. Tainter wanted to move us away from seeing the evolution of the Roman Empire into the feudal system as the “death” of a civilization. Rather, he sees the societies human beings have built to be extremely adaptable and resilient. When the problem of increasing complexity becomes impossible to solve societies move towards less complexity. It is a solution that strangely echoes that of the “immortal jellyfish” the turritopsis nutricula, the only path complex entities have discovered that allows them to survive into something that whispers eternity.

Image description: From the National Gallery in London. “The Cave Of Eternity” (1680s) by Luca Giordan.“The serpent biting its tail symbolises Eternity. The crowned figure of Janus holds the fleece from which the Three Fates draw out the thread of life. The hooded figure is Demagorgon who receives gifts from Nature, from whose breasts pours forth milk. Seated at the entrance to the cave is the winged figure of Chronos, who represents Time.”

Iamus Returns

A reader, Dan Fair, kindly posted a link to the release of the full album composed by the artificial intelligence program, Iamus, on the comments section of my piece Turing and the Chinese Room Part 2 from several month back.

I took the time to listen to the whole album today (you can too by clicking on the picture above). Not being trained as a classical musician, or having much familiarity with the abstract style in which the album was composed makes it impossible for me to judge the quality of the work.

Over and above the question of quality, I am not sure how I feel about Iamus and “his”composition. As I mentioned to Dan, the optimistic side of me sees in this the potential to democratize human musical composition.

Yet, as I mentioned in the Turing post, the very knowledge that there is no emotional meaning being conveyed behind the work leaves it feeling emotionally dead and empty for me compared to to another composition composed, like those of Iamus, in honor of Alan Turing, this one created by a human being, Amanda Feery, entitled Turing’s Epitaph  that was gracefully shared by fellow blogger Andrew Gibson.

One way or another it seems, humans, and their ability to create and understand meaning will be necessary for the creations of machines to have anything real behind them.

But that’s what I think. What about you?