Am I a machine?

A question I’ve been obsessing over lately goes something like this: Is life a form of computation, and if so, is this thought somehow dangerous?

Of course, drawing an analogy between life and the most powerful and complex tool human beings have so far invented wouldn’t be a historical first. There was a time in which people compared living organisms to clocks, and then to steam engines, and then, perhaps surprisingly, to telephone networks.

Thoughtful critics, such as the roboticist Rodney Brooks think this computational metaphor has already exhausted its usefulness and that its overuse is proving to be detrimental to scientific understanding. In his view scientists and philosophers should jettison the attempt to see everything in terms of computation and information processing, which has blinded them from seeing other more productive metaphors that have been hidden by the glare of a galloping Moore’s Law. Perhaps today it’s just the computer’s turn and someday in the future we’ll have an even more advanced tool that will again seem the perfect model for the complexity of living beings. Or maybe not.

If instead we have indeed reached peak metaphor it will be because with computation we really have discovered a tool that doesn’t just resemble life in terms of features, but reveals something deep about the living world- because it allows us for the first time to understand life as it actually is. It would be as if we’ve managed to prove Giambattista Vico’s claim made right at the start of the scientific revolution “Verum et factum convertuntur” strangely right after all these years. Humanity can never know the natural world only what we ourselves have made. Maybe we will finally understand life and intelligence because we are now able to recreate a version of it in a different substrate (not to mention engineering it in the lab). We will know life because we are at last able to make it.

But let me start with the broader story…

Whether we’re blinded by the power of our latest uber-tool, or on the verge of a revolution in scientific understanding, however, might matter less than the unifying power of a universal metaphor. And I would argue that science needs just such a unifying metaphor. It needs one if it is to give us a vision of a rationally comprehensible world. It needs one for the purpose of education along with public understanding and engagement. A unifying metaphor is above all needed today as a ballast against over-specialization which traps the practitioners of the various branches of science (including the human sciences) in silos unable to communicate with one another and thereby formulate a reunified picture of a world that science itself has artificially divided up into fiefdoms as an essential first step towards understanding it. And of all the metaphors we have imagined, computation really does appear uniquely fruitful and revelatory and not just in biology but across multiple and radically different domains. A possible skeleton key for problems that have frustrated scientific understanding for decades.

One place where the computation/information analogy has grown over the past decades is in the area of fundamental physics, as an increasing number in the field have begun to borrow concepts from computer science in the hopes of bridging the gap between general relativity and quantum mechanics.

This informational turn in physics can perhaps be traced back to the Israeli physicist Jacob Bekenstein who way back in 1972 proposed what became known as the “Bekenstein bound”. An upper limit to entropy, to information, that can exist in a finite area of space. Pack information any tighter than 10⁶⁹ bits per square meter and that area will collapse to form a black hole. Physics, it seems, puts hard limits on our potential computational abilities (they’re a long way off), just as it places hard limits on our potential speed. What Bekenstein showed was that thinking of physics in terms of computation helped reveal something deeper not just about the physical world but about the nature of computation itself.

This recasting of physics in terms of computation, often called digital physics, really got a boost with an essay by the physicist John Wheeler in 1989 titled “It from Bit.” It’s probably safe to say that no one has ever really understood the enigmatic Wheeler who if one wasn’t aware that he was one of the true geniuses of 20th century physics might be confused for a mystic like Fritjof Capra, or heaven forbid, a woo-woo spouting conman- here’s looking at you- Deepak Chopra. The key idea of It from bit is captured in this quote from his aforementioned essay, a quote that also captures something of Wheeler’s koan-like style:

“It from bit symbolizes the idea that every item of the physical world has at bottom — at a very deep bottom, in most instances — an immaterial source and explanation; that what we call reality arises in the last analysis from the posing of yes-no questions and the registering of equipment-evoked responses; in short, that all things physical are information-theoretic in origin and this is a participatory universe.”

What distinguishes digital physics today from Wheeler’s late 20th century version is above all the fact that we live in a time when quantum computers have not only been given a solid theoretical basis, but have been practically demonstrated. A tool born from an almost offhand observation by the brilliant Richard Feynman who in the 1980s declared what should have been obvious. That: Nature isn’t classical . . . and if you want to make a simulation of Nature, you’d better make it quantum mechanical…”

What Feynman could not have guessed was that physics would make progress (to this point, at least) not from applying quantum computers to the problems of physics, so much as from applying the ideas of how quantum computation might work to the physical world itself. Leaps of imagination, such as seeing space itself as a kind of quantum error correcting code, reformulating space-time and entropy in terms of algorithmic complexity or compression, or explaining how, as with Schrodinger’s infamous cat, superpositions existing at the quantum level breakdown for macroscopic objects as a consequence of computational complexity. The Schrodinger equation unsolvable for large objects so long as P ≠ N.

There’s something new here, but perhaps also something very old, an echo of Empedocles vision of a world formed out of the eternal conflict between Love and Strife. If one could claim that for the ancient Greek philosopher life could only exist in the mid-point between total union and complete disunion, then we might assert that life can only exist in a universe with room for entropy to grow, neither curled up too small, or too dispersed for any structures to congregate- a causal diamond. In other words, life can only exist in a universe whose structure is computable.

Of course, one shouldn’t make the leap from claiming that everything is somehow explainable from the point of view of computation to making the claim that “a rock implements every finite state automaton” , which, as David Chalmers pointed out, is an observation verging on the vacuous. We are not, as some in Silicon Valley would have it, living in a simulation, so much as in a world that emerges from a deep and continual computation of itself. In this view one needs some idea of a layered structure to nature’s computations, whereby simple computations performed at a lower level open up the space for more complex computations on a higher stage.

Digital physics, however, is mainly theoretical at this point and not without its vocal critics. In the end it may prove just another cul de sac rather than a viable road to the unification of physics. Only time will tell, but what may bolster it could be the active development of quantum computers. Quantum theory and quantum technology, one can hope, might find themselves locked in an iterative process with each aiding the development of the other in the same way that the development of steam engines propelled the development of thermodynamics from which came yet more efficient steam engines in the 19th century. Above all, quantum computers may help physicists sort out the rival interpretations regarding what quantum mechanics actually means, where at the end of the day quantum mechanics will ultimately be understood as a theory of information. A point made brilliantly by the science writer Philip Ball in his book Beyond Weird.

There are thus good reasons for applying the computational metaphor to the realm of physics, but what about where it really matters for my opening question, that is when it comes to life. To begin, what is the point of connection between computation in merely physical systems and in those we deem alive and how do they differ?

By far the best answer to these questions that I’ve found was the case made by John E. Mayfield in his book The Engine of Complexity: Evolution as Computation.  Mayfield views both physics and biology in terms of the computation of functions. What distinguishes historical processes such as life, evolution, culture and technology from mere physical systems is their ability to pull specific structure from the much more general space of the physically possible. A common screwdriver, as an example, at the end of the day just a particular arrangement of atoms. It’s a configuration that while completely consistent with the laws of physics is also highly improbable, or, as the physicist Paul Davies put it in a more recent book on the same topic:

“The key distinction is that life brings about processes that are not only unlikely but impossible in any other way.”

Yet the existence of that same screwdriver is trivially understandable when explained with reference to agency. And this is just what Mayfield argues evolution and intelligence does. They create improbable yet possible structures in light of their own needs.

Fans of Richard Dawkins or students of scientific history might recognize an echo here of William Paley’s famous argument for design. Stumble upon a rock and its structure calls out for little explanation, a pocket- watch with its intricate internal parts, gives clear evidence of design. Paley was nothing like today’s creationists, he had both a deep appreciation for and understanding of biology, and was an amazing writer to boot. The problem is he was wrong. What Darwin showed was that a designer wasn’t required for even the most complex of structures, random mutation plus selection gave us what looks like miracles over sufficiently long periods of time.

Or at least evolution kind of does. The issue that most challenges the Theory of Natural Selection is the overwhelming size of the search space. When most random mutations are either useless or even detrimental, how does evolution find not only structure, but the right structure, and even highly complex structure at that? It’s hard to see how monkeys banging away on typewriters for the entire age of the universe get you the first pages of Hamlet let alone get you Shakespeare himself.

The key, again it seems, is to apply the metaphor of computation. Evolution is a kind of search over the space of possible structures, but these are structures of a peculiar sort. What makes these useful structures peculiar is that they’re often computationally compressible. The code to express them is much shorter than the expression itself. As Jordana Cepelewicz put it for a piece at Quanta:

“Take the well-worn analogy of a monkey pressing keys on a computer at random. The chances of it typing out the first 15,000 digits of pi are absurdly slim — and those chances decrease exponentially as the desired number of digits grows.

But if the monkey’s keystrokes are instead interpreted as randomly written computer programs for generating pi, the odds of success, or “algorithmic probability,” improve dramatically. A code for generating the first 15,000 digits of pi in the programming language C, for instance, can be as short as 133 characters.”

In somewhat of an analogy to what Turing’s “primitives” are for machine based computation, biological evolution requires only a limited number of actions to access a potentially infinite number of end states. Organisms need a way of exploring the search space, they need a way to record/remember/copy that information, along with the ability to compress this information so that it is easier to record, store and share. Ultimately, the same sort of computational complexity that stymes human built systems, may place limits on the types of solutions evolution itself is able to find.  

The reconceptualization of evolution as a sort of computational search over the space of easily compressible possible structures is a case excellently made by Andreas Wagner in his book Arrival of the Fittest. Interestingly, Wagner draws a connection between this view of evolution and Plato’s idea of the forms. The irony, as Wagner points out, being that it was Plato’s idea of ideal types applied to biology that may have delayed the discovery of evolution in the first place. It’s not “perfect” types that evolution is searching over, for perfection isn’t something that exists in an ever changing environment, but useful structures that are computationally discoverable. Molecular and morphological solutions to the problems encountered by an organism- analogs to codes for the first 15,000 digits of pi.

Does such evolutionary search always result in the same outcome? The late paleontologist and humanist, Stephen Jay Gould, would have said no. Replay the tape of evolution like what happens to George Bailey in “It’s a Wonderful Life” and you’d get radically different outcomes. Evolution is built upon historical contingency like Homer Simpson who wishes he didn’t kill that fish.

Yet our view of evolution has become more nuanced since Gould’s untimely death in 2002. In his book Improbable Destinies, Jonathan Losos lays out the current research on the issue. Natural Selection is much more a tinkerer than an engineer. It’s solutions, by necessity, clugee and a result of whatever is closest at hand. Evolution is constrained by the need for survival to move from next best solution to next best solution across the search space of adaptation like a cautious traveler walking from stone to stone across a stream.

Starting points are thus extremely important for what Natural Selection is able to discover over a finite amount of time. That said, and contra Gould, similar physics and environmental demands does often lead to morphological convergence- think bats and birds with flight. But even when species gravitate towards a particular phenotype there is often found a stunning amount of underlying genetic variety or indeterminacy. Such genetic variety within the same species can be levered into more than one solution to a challenging environment with one branch getting bigger and the other smaller in response to say predation. Sometimes evolution at the microscopic level can, by pure chance, skip over intermediate solutions entirely and land at something close to the optimal solution thanks to what in probabilistic terms should have been a fatal mutation.

The problem encountered here might be quite different from the one mentioned above, not finding the needle of useful structures in the infinite haystack of possible one, but how to avoid finding the same small sample of needles over and over again. In other words, if evolution really is so good at convergence, why does nature appear so abundant with variety?

Maybe evolution is just plain creative- a cabinet of freaks and wonders. So long as a mutation isn’t detrimental to reproduction, even if it provides no clear gain, Natural Selection is free to give it a whirl. Why do some salamanders have only four toes? Nobody knows.

This idea that evolution needn’t be merely utilitarian, but can be creative, is an argument made by the renowned mathematician Gregory Chaitin who sees deep connections between biology, computation, and the ultimately infinite space of mathematical possibility itself. Evolutionary creativity is also something that is found in the work of the ornithologist Richard O. Prum. His controversial The Evolution of Beauty arguing that we need to see the perception of beauty by animals in the service of reproductive or consumptive (as in bees and flowers) choice as something beyond a mere proxy measure for genetic fitness. Evolutionary creativity, propelled by sexual selection, can sometimes run in the opposite direction from fitness.

In Prum’s view evolution can be driven not by the search for fitness but by the way in which certain displays resonate with the perceivers doing the selection. In this way organisms become agents of their own evolution and the explored space of possible creatures and behaviors is vastly expanded from what would pertain were alignment to environmental conditions the sole engine of change.

If resonance with perceiving mates or pollinators acts to expand the search space of evolution in multicellular organisms with the capacity for complex perception- brains- then unicellular organisms do them one better through a kind of shared, parallel search over that space.

Whereas multicellular organisms can rely on sex as a way to expand the evolutionary search space, bacteria have no such luxury. What they have instead is an even more powerful form of search- horizontal gene transfer. Constantly swapping genes among themselves gives bacteria a sort of plug-n-play feature.

As Ed Yong in his I Contain Multitudes brilliantly lays out, in possession of nature’s longest lived and most extensive tool kit, bacteria are often used by multicellular organisms to do things such as process foods or signal mates, which means evolution doesn’t have to reinvent the wheel. These bacteria aren’t “stupid”. In their clonal form as bio-films, bacteria not only exhibit specialized/cooperative structures, they also communicate with one another via chemical and electrical signalling– a slime internet, so to speak.

It’s not just in this area of evolution as search that the application of the computational metaphor to biology proves fruitful. Biological viruses, which use the cellular machinery of their hosts to self-replicate bear a striking resemblance to computer viruses that do likewise in silicon. Plants too, have characteristics that resemble human  communications networks, as in forests whose trees communicate and coordinate responses to dangers and share resources using vast fungal webs.

In terms of agents with limited individual intelligence whose collective behavior can quite clearly be considered quite sophisticated, nothing trumps the eusocial insects, such as ants and termites. In such forms of swarm intelligence, computation is analogous to what we find in cellular automata where a complex task is tackled by breaking up a problem into numerous much smaller problems solved in parallel.

Dennis Bray in his superb book Wetware: A Computer in Every Living Cell has provided an extensive reading of biological function through the lens of computation/information processing. Bray meticulously details how enzyme regulation is akin to switches, their up and down regulation, like the on/off functions of a transistor, with chains of enzymes acting like electrical circuits. He describes how proteins form signal networks within an organism that allow cells to perform logical operations. Components linked not by wires, but molecular diffusion within and between cells. Structures of cells such as methyl groups allow cells to measure the concentration of attractants in the environment, in effect acting as counters- performing calculus.

Bray thinks that it is these protein networks rather than anything that goes on in the brain that are the true analog for computer programs such as artificial neural nets. Just as neural nets are trained, the connections between nodes in a network sculpted by inputs to derive the desired output, protein networks are shaped by the environment to produce a needed biological product.

The history of artificial neural nets, the foundation of today’s deep learning, is itself a fascinating study on the interitative relationship between computer science and our understanding of the brain. When Alan Turing first imagined his Universal Computer it was the human brain as then understood by the ascendent behavioral psychology that he used as its template.

It wasn’t until 1943 that the first computational model of a neuron was proposed by McCulloch and Pitts who would expand upon their work with a landmark paper in 1959 “What the frog’s eye tells the frog’s brain”. The title might sound like a snoozer, but it’s certainly worth a read as much for the philosophical leaps the authors make as for the science.

For what McCulloch and Pitts discovered in researching frog vision was that perception wasn’t passive but an active form of information processing pulling out distinct features from the environment such as “bug detectors”, what they, leaning on Immanuel Kant of all people, called a physiological synthetic a priori.

It was a year earlier in 1958 that Frank Rosenblatt superseded McCulloch and Pitts earlier and somewhat simplistic computational model of neurons with his perceptrons whose development would shape what was to be the rollercoaster like future of AI. Perceptrons were in essence single-layer artificial neural networks. What made them appear promising was the fact that they could “learn”. A single layer perceptron could, for example, be trained to identify simple shapes in a kind of analog for how the brain was discovered to be wired together by synaptic connections between neurons.

The early success of perceptrons, and it turned out the future history of AI, was driven into a ditch when in 1969 Marvin Minsky and Seymour Papert in their landmark book, Perceptrons, showed just how limited the potential of perceptions as a way of mimicking cognition actually were. Perceptrons struggled over all but the most simple of functions, and broke down when dealing with anything without really sharp boundaries such as AND/OR functions- XOR. In the 1970s AI researchers turned away from modeling the brain (connectionist) and towards the symbolic nature of thought (symbolist). The first AI Winter had begun.

The key to getting around these limitations proved to be using multiple layers of perceptrons, what we now call deep learning. Though we’ve known this since the 1980’s it took until now with our exponential improvement in computer hardware, and accumulation of massive data sets for the potential of perceptrons to come home.

Yet it isn’t clear that most of the problems with perceptrons identified by Minsky and Papert have truly been solved. Much of what deep learning does can still be characterized as “line fitting”. What’s clear is that, whatever deep learning is, it bears little resemblance to how the brains of animals actually work, which was well described by the authors at the end of their book.

“We think the difference in abilities comes from the fact that a brain is not a single, uniformly structured network. Instead, each brain contains hundreds of different types of machines, interconnected in specific ways which predestin that brain to become a large, diverse society of specialized agencies.” (273)

The mind isn’t a unified computer but an emergent property of a multitude of different computers, connected, yes, but also kept opaque from one another as a product of evolution and as the price paid for computational efficiency. It is because of this modular nature that minds remain invisible even to their possessor although these computational layers may be stacked with higher layers building off of end product of the lower, possibly like the layered, unfolding nature of the universe itself. The origin of the mysteriously hierarchical structure of nature and human knowledge?

If Minsky and Papert were right about the nature of mind, if in an updated version of their argument recently made by Kevin Kelly, or a similar one made AI researcher François Chollet that there are innumerable versions of intelligence many of which may be incapable of communicating with one another, then this has deep implications for the computational metaphor as applied to thought and life and sets limits on how far we will be able to go in engineering, modeling, or simulating life with machines following principles laid down by Turing.

The quest of Paul Davies for a unified theory of information to explain life might be doomed from the start. Nature would be shown to utilize a multitude of computational models fit for their purposes, and not just multiple models between different organisms, but multiple models within the same organism. In the struggle between Turing universality and nature’s specialized machines Feynman’s point that traditional computers just aren’t up to the task of simulating the quantum world may prove just as relevant for biology as it does for physics. To bridge the gap between our digital simulations and the complexity of the real we would need analog computers which more fully model what it is we seek to understand, though isn’t this what experiments are- a kind of custom built analog computer? No amount of data or processing speed will eliminate the need for physical experiments. Bray himself says as much:

“A single leaf of grass is immeasurably more complicated than Wolfram’s entire opus. Consider the tens of millions of cells it is built from. Every cell contains billions of protein molecules. Each protein molecule in turn is a highly complex three-dimensional array of tens of thousands of atoms. And that is not the end of the story. For the number, location, and particular chemical state of each protein molecule is sensitive to its environment and recent history. By contrast, an image on a computer screen is simply a two-dimensional array of pixels generated by an iterative algorithm. Even if you allow that pixels can show multiple colors and that underlying software can embody hidden layers of processing, it is still an empty display. How could it be otherwise? To achieve complete realism, a computer representation of a leaf would require nothing less than a life-size model with details down to the atomic level and with a fully functioning set of environmental influences.” (103)

What this diversity of computational models means is not that the Church-Turing Thesis is false (Turing machines would still be capable of duplicating any other model of computation), but that its extended version was likely false. The Extended Church-Turing Thesis claims that any possibly efficient computation can be efficiently performed by a Turing machine, yet our version of Turing machines might prove incapable of efficiently duplicating either the extended capabilities of quantum computers gained through leverage of the deep underlying structure of reality or the twisted structures utilized by biological computers “designed” by the multi-billion years force of evolutionary contingency. Yet even if the Extended Church-Turing Thesis turns out to be false, the lessons derived from reflection on idealized Turing machines and their real world derivatives will likely continue to be essential to understanding computation in both physics and biology.   

Even if science gives us an indication that we are nothing but computers all the way down, from our atoms to our cells, to our very emergence as sentient entities, it’s not at all clear what this actually means. To conclude that we are, at bottom, the product of a nested system of computation is to claim that we are machines. A very special type of machine, to be sure, but machines nonetheless.

The word machine itself conjures up all kinds of ideas very far from our notion of what it means to be alive. Machines are hard, deterministic, inflexible and precise, whereas life is notably soft, stochastic, flexible and analog. If living beings are machines they are also supremely intricate machines, they are, to borrow from an older analogy for life, like clocks. But maybe we’ve been thinking about clocks all wrong as well.

As mentioned, the idea that life is like an intricate machine and therefore is best understood by looking at the most intricate machines humans have made, namely clocks, has been with us since the very earliest days of the scientific revolution. Yet as the historian Jessica Riskin points out in her brilliant book The Restless Clock, since that day there has been a debate over what exactly was being captured in the analogy. As Riskin lays out, starting with Leibniz there was always a minority among the materialist arguing that life was clock like, that what it meant to be a clock was to be “restless”, stochastic and undetermined. In the view of this school, sophisticated machines such as clock or automatons gave us a window into what it meant to be alive, which, above all, meant to possess a sort of internally generated agency.

Life in an updated version of this view can be understood as computation, but it’s computation as performed by trillions upon trillions of interconnected, competing, cooperating, soft-machines- constructing the world through their senses and actions, each machine itself capable of some degree of freedom within an ever changing environment. A living world unlike a dead one is a world composed of such agents, and to the extent our machines have been made sophisticated enough to possess something like agency, perhaps we should consider them alive as well.

Barbara Ehenreich makes something like this argument in her caustic critique of American culture’s denial of death, Natural Causes:

“Maybe then, our animist ancestors were on to something that we have lost sight of in the last few hundred years of rigid monotheism, science, and Enlightenment. And that is the insight that the natural world is not dead, but swarming with activity, sometimes even agency and intentionality. Even the place where you might expect to find solidity, the very heart of matter- the interior of a proton or a neutron- turns out to be flickering with ghostly quantum fluxuations. I would not suggest that the universe is “alive”, since that might invite misleading biological analogies. But it is restless, quivering, and juddering from its vast patches to its tiniest crevices. “ (204)

If this is what it means to be a machine, then I am fine with being one. This does not, however, address the question of danger. For the temptation of such fully materialist accounts of living beings, especially humans, is that it provides a justification for treating individuals as nothing more than a collection of parts.

I think we can avoid this risk even while retaining the notion that what life consists of is deeply explained by reference to computation as long as we agree that it is only ethical when we treat a living being in light of the highest level at which it understands itself, computes itself. I may be a Matryoshka doll of molecular machines, but I understand myself as a father, son, brother, citizen, writer, and a human being. In other words we might all be properly called machines, but we are a very special kind of machine, one that should never be reduced to mere tools, living, breathing computers in possession of an emergent property that might once have been called a soul.

 

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Caves, Creationism and the Divine Wonder of Deep Time

The Mutiliation of Uranus by Saturn

Last week was my oldest daughter’s 5th birthday in my mind the next “big” birthday after the always special year one. I decided on a geology themed day one of whose components were her, me and my younger daughter who’s 3 taking a trip to a local limestone cave that holds walk through tours.

Given that we were visiting the cave on a weekday we had the privilege of getting a private tour: just me, the girls and our very friendly and helpful tour guide. I was really hoping the girls would get to see some bats, which I figured would be hibernating by now. Sadly, the bats were gone. In some places upwards of 90% of them had been killed by an epidemic with the understated name of “White Nose Syndrome” a biological catastrophe I hadn’t even been aware of and a crisis both for bats and farmers who depend on them for insect control and pollination.

For a local show-cave this was pretty amazing- lots of intricate stalactites and stalagmites, thin rock in the form of billowing ribbons a “living” growth moving so slowly it appears to be frozen in time. I found myself constantly wondering how long it took for these formations to take shape. “We tell people thousands of years” our guide told us. “We have no idea how old the earth is”. I thought to myself that I was almost certain that we had a pretty good idea of the age of the earth – 4.5 billion, but did not press- too interested in the caves features and exploring with the girls. I later realized I should have, it would have likely made our guide feel more at ease.

Towards the end of our tour we spotted a sea shell embedded in the low ceiling above us, and I picked up the girls one at a time so they could inspect it with their magnifying glass. I felt the kind of vertigo you feel when you come up against deep time. Here was the echo of a living thing from eons ago viewed by the living far far in its future.

Later I found myself thinking about our distance in time from the sea shell and the cave that surrounded it. How much time separated us and the shell? How old was the cave? How long had the things we had seen taken to form? Like any person who doesn’t know much about something does I went to our modern version of Delphi’s Oracle- Google.

When I Googled the very simple question: “how old are limestone caves?” a very curious thing happened. The very first link that popped up wasn’t Wikipedia or a geology site but The Institute for Creation Research.  That wasn’t the only link to creationist websites. Many, perhaps the majority, of articles written on the age of caves were by creationists, the ones that I read in seemingly scientific language, difficult for a non-scientist/non-geologist to parse. Creationists seem to be as interested in the age of caves as speleologist, and I couldn’t help but wonder, why?

Unless one goes looking or tries to remain conscious of it, there are very few places where human beings confront deep time- that is time far behind (or in front) of thousands of years by which we reckon human historical time.  The night sky is one of these places, though we have so turned the whole world into a sprawling Las Vegas that few of us can even see into the depths of night any more. Another place is natural history museums where prehistoric animals are preserved and put on display. Creationists have attempted to tackle the latter by designing their very own museums such as The Creation Museum replete with an alternative history of the universe where, among other things, dinosaurs once lived side-by-side with human beings like in The Flintstones.

Another place where a family such as my own might confront deep time is in canyons and caves. The Grand Canyon has a wonderful tour called The Trail of Time that gives some idea of the scale of geological time where tourists start at the top of the canyon in present time and move step by step and epoch by epoch to the point where the force of the Colorado River has revealed a surface 2 billion years old.

Caves are merely canyons under the ground and in both their structure and their slow growing features- stalactites and the like- give us a glimpse into the depths of geologic time. Creationists feel compelled to steel believers in a 5,000 year old earth against the kinds of doubts and questions that would be raised after a family walks through a cave. Hence all of the ink spilt arguing over how long it takes a stalagmite to grow five feet tall and look like a melting Santa Claus. What a shame.

It was no doubt the potential prickliness of his tourists that led our poor guide to present the age of the earth or the passage of time within the cave as open questions he could not address. After all, he didn’t know me from Adam and one slip of the word “million” from his mouth might have resulted in what should have been an exciting outing turning into a theological debate. As he said he was not, after all, a geologist and had merely found himself working in the cave after his father had passed away.

As regular readers of my posts well known, I am far from being an anti-religious person. Religion to me is one of the more wondrous inventions and discoveries we human beings have come up with, but religion, understood in this creationist sense seems to me a very real diminishment not merely of the human intellect but of the idea of the divine itself.

I do not mean to diminish the lives of people who believe in such pseudo-science. One of the most hardworking and courageous persons I can think of was a man blinded by a mine in Vietnam. Once we were discussing what he would most like to see were his sight restored and he said without hesitation “The Creation Museum!”. I think this man’s religious faith was a well spring for his motivation and courage, and this, I believe is what religions are for- to provide strength for us to deal with the trials and tribulations of human life. Yet, I cannot help but think that the effort to black- hole- like suck in and crush our ideas of creation so that it fits within the scope of our personal lives isn’t just an assault on scientific truth but a suffocation of our idea of the divine itself.

The Genesis story certainly offers believers and non-believers alike deep reflection on what it means to be a moral creature, but much of this opportunity for reflection is lost when the story is turned into a science text book. Not only that, both creation and creator become smaller. How limited is the God of creationists whose work they constrict from billions into mere thousand of years and whose overwhelming complexity and wonder they reduce to a mere 788,280 human words!  With bitter irony creationists diminish the depth of the work God has supposedly made so that man can exalt himself to the center of the universe and become the primary character of the story of creation. In trying to diminish the scale and depth of the universe in space and time they are committing the sin of Milton’s Satan- that is pride.

The more we learn of the universe the deeper it becomes. Perhaps the most amazing projects in NASA’s history were two very recent one- Kepler and Hubble. Their effects on our understanding of our place in the universe are far more profound than the moon landings or anything else the agency has done.

Hubble’s first Deep Field image was taken over ten consecutive days in December of 1995. What it discovered in the words of Lance Wallace over at The Atlantic:

What researchers found when they focused the Hubble over those 10 days on that tiny speck of darkness, Mather said, shook their worlds. When the images were compiled, they showed not just thousands of stars, but thousands of galaxies. If a tiny speck of darkness in the night sky held that many galaxies, stars and—as scientists were beginning to realize—associated planets … the number of galaxies, stars, and planets the universe contained had to be breathtakingly larger than they’d previously imagined.

The sheer increased scale of the universe has led scientist to believe that it is near impossible that we are “alone” in the cosmos. The Kepler Mission has filled in the details with recent studies suggesting that there may be billions of earth like planets in the universe.   If we combine these two discoveries with the understanding of planet hunter Dimitar Sasselov, who thinks that not only are we at the very beginning of the prime period for life in the universe because it has taken this long for stars to produce the heavy elements that are life’s prerequisites, but that we also have a very long time perhaps as much as 100 billion years for this golden age of life to play out, we get an idea of just how prolific creation is and will be beside which a God who creates only one living planet and one intelligent species seems tragically sterile.

To return underground, caves were our first cathedrals- witness Lascaux. It is even possible that our idea of the Underworld as the land of the dead grew out of the bronze age temple complex of Alepotrypa inspiring the Greek idea of Hades that served as the seed through which the similar ideas of Sheol held by the Jews and revved up by Christians to the pinnacle of horror shows with the idea of Hell.

I like to think that this early understanding of the Underworld as the land of the dead and the use of caves as temples reflects an intuitive understanding of deep time. Walking into a cave is indeed, in a sense, entering the realm of the dead because it is like walking into the earth’s past. What is seen there is the movement of time across vast scales. The shell my daughter’s peered at with their magnifying glass, for instance, was the exoskeleton of a creature that lived perhaps some 400 million years ago in the Silurian Period when what is now Pennsylvania was located at the equator and the limestone that was the product of decay the shells inhabitant’s relatives began to form.

Recognition of this deep time diminishes nothing of the human scale and spiritual meaning of this moment taken to stop and stare at something exquisite peeking at us from the ceiling- quite the opposite. Though I might be guilty of overwinding,  it was a second or two 400 million years or perhaps one might say 13 billion years in the making- who couldn’t help thanking God for that?

Saving Alexandria

One of the most dangerous speeches given by a public intellectual in recent memory was that of Richard Dawkins at the just held Atheist Rally in Washington DC.  Dawkins is a brilliant scientist, and a member of what the philosopher and fellow atheist Daniel Dennett has termed “the brights” a movement seeking to promote a naturalistic as opposed to supernatural view of the world. All this is for the good, and the brights were originally intended to be an inclusive movement that aimed to pull religious as well as non-religious people into a dialogue regarding some of the deeper questions of existence in so far as religious persons shared the same materialist assumptions and language as the secular and scientific mainstream of the movement.

This inclusiveness might have resulted in some very interesting public conversations, something that the neuroscientist David Eagleman has called possibilism– the space between what science definitively knows, and what religion and philosophy imagine. Instead, we have Dawkins’ speech in which he calls on atheist to challenge, “mock”, and “ridicule” the beliefs of religious people. Not only is this an invitation to incivility- where atheist are encouraged to intellectually mug religious persons who probably have not asked to engage in such conversations- it threatens to inflame the very anti-scientific tendencies of modern religion that Dawkins, rightly, opposes and detest.

To challenge religion where it has an immoral, intolerant, or dangerous effect on the larger political society is a duty of all citizens whatever their non-religious or religious persuasions.  Persons of a secular bent, among whom I include myself, need to constantly remind overly zealous religious people that theirs is not the only view and that the separation of church and state exists, not merely for their own, but for all of our protection.

Yet, the last thing science needs is to get into a fist-fight with sincerely religious people about subjects that have no effect whatsoever on the health of the public sphere. When the crowds roared in support of Dawkins’ call that they mock people who hold what he considers absurd beliefs such as that of Catholics regarding transubstantiation (an example he actually uses) one is left wondering whether the barbarians of the future might just as likely come from the secular rather than religious elements in society.

Continued in this vein, Dawkins would transform the otherwise laudable atheist movement into a lightening rod aimed right at the heart of science. No one should want a repeat of what Piss-Christ did to public funding for the arts.

Up until now, the ire of religion towards science has remained remarkably focused- evolution, reproductive technology, and, to a limited and much more dangerous extent- global warming- the last thing we need is for it to be turned on physics- cosmology, neurology or computer science.

Should the religious ever turn their attention to the singularity movement, which, after all, is a religion masking itself as science, they could stifle innovation and thus further exacerbate inequality. If the prophets of the singularity prove to be correct, they may find themselves in a state of war with traditional religion. A cynical minority of religious people may see the singularity as a major threat to their “business model”, but the majority may be reasonably inspired by their dispute with singularians over the necessarily spiritual question of what it means to be human, something the religious hold, with justification, to be their own turf.

Here, the religious may ironically actually hold the humanist higher ground. For it is difficult to see how the deep extension of the human lifespan and creation of artificial forms of intelligence promised by the singularity movement are humanistic ends given the divergence in mortality rates and educational levels between the developing and developed world. In other words, a humanist, as opposed to a trashumanist version of the future would aim at increasing the life expectancy of countries such as Chad, where a person is not expected to live past 50, rather than trying to extend ever outward the lifespan of the wealthy in the developed world. It would also be less inclined to race towards creating a new species of intelligent beings than towards making the most of the intelligent beings who are already here-us- through the old fashioned methods such as education- especially for girls.

In the not too far-off future, class and religious struggles might merge in dangerous and surprising ways, and the explosive growth of religion in the developing world might be mobilized in the name of traditional belief, and in the humanist cause of protecting the species.

Even should none of this dystopian scenario come to pass, religion is already full of anxiety in regards to science, and science imperialistic in its drive to submit every aspect of reality human and non-human alike to its “models” of reality. This anxiety and imperialism has been detrimental for religion and science both.

The confrontation between religion and science has resulted in religion becoming vulgar in the need to translate religious concepts into the “truths” of science- think the Shroud of Turin or the Creation Museum.

At the same time, science turns it sights not so much on undermining the religious world-view as the very nature of belief itself. It is equally vulgar for scientist to strap electrodes onto someone’s brain in the hopes of finding “the god spot” or some such nonsense- as if it means anything that religious belief is “proven” to be a part of neuro-anatomy- what else could it be?

We have known since the ancient Greeks that there are better ways to describe the natural world than religion. Religion isn’t, or shouldn’t be about that. It’s about the mystery of being, the search for meaning, on a human scale, a scale that science cannot provide, about good and evil.

Science may be extremely good at explaining a mental disease such as schizophrenia, and devising effective interventions. What it cannot do, what religion does so well, is to turn the devastating nature of such an illness into a sphere of meaning that can rescue purpose from the cold indifference of the universe. Without some variant of it we will freeze to death.