The practice of evolutionary algorithms involves a mundane yet inescapable phase, namely, finding parameters that work well. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? All these nagging questions need good answers if one is to embrace success. Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters. We aver that this renders the life of the practitioner that much easier, and cap off our study with an advisory digest for the weary.
Wanna learn more? The full paper is here.
Excerpt from my book Machine Nature
“He wanted to dream a man: he wanted to dream him with minute integrity and insert him into reality.” This was the goal of the silent man who came from the South, in Jorge Luis Borges’s short story, “The Circular Ruins.” From Pygmalion, Frankenstein, and the Golem to Star Trek’s Lieutenant Commander Data, the dream of administering the breath of life has fascinated humankind since antiquity.
We’ve seen how human-made systems can be made to evolve, to learn, to adapt, and to develop, as well as to exhibit a host of other characteristics that are usually not associated with machines, but rather with living beings. Can our creations one day take a life of their own? This question moved from the realm of science fiction to that of science with the advent of the field known as artificial life. The term was coined by Christopher G. Langton, organizer of the first artificial life conference, which took place in Los Alamos in 1987.
“Artificial Life,” wrote Langton (in the proceedings of the second conference), “is a field of study devoted to understanding life by attempting to abstract the fundamental dynamical principles underlying biological phenomena, and recreating these dynamics in other physical media — such as computers — making them accessible to new kinds of experimental manipulation and testing.” While biological research is essentially analytic, trying to break down complex phenomena into their basic components, artificial life is synthetic, attempting to construct phenomena from their elemental units, as such adding powerful new tools to the scientific toolkit. This is, however, only part of the field’s mission. As put forward by Langton “In addition to providing new ways to study the biological phenomena associated with life here on Earth, life-as-we-know-it, Artificial Life allows us to extend our studies to the larger domain of the ‘bio-logic’ of possible life, life-as-it-could-be, whatever it might be made of and wherever it might be found in the universe.”
Before talking about artificial life, shouldn’t we try to define what life is? Well … No. I’ll steer clear of this issue since it is in fact quite a controversy in science; as things stand today, there is no agreed-upon scientific definition of Life. For now, we’ll just have to accept its being one of those “you-know-it-when-you-see-it” qualities: Your dog is obviously alive, while your washing machine is obviously not. The question is, then, can we create something that is “obviously alive”?
This question seems reasonably clear, or is it now? You’d think that it’s the “life” part of “artificial life” that eludes our definition. Well, there’s a further subtlety: What exactly does the “artificial” part mean? If you look up “artificial” in the dictionary (Merriam-Webster online), you’ll find a number of definitions. So let’s see which one sits well with “artificial life.” Artificial might mean “lacking in natural or spontaneous quality < an artificial smile > < an artificial excitement > .” This can’t be it. An extraterrestrial might be unnatural and unspontaneous, and yet obviously alive; artificial life cannot be about life that lacks in natural or spontaneous quality. What about “imitation, sham < artificial flavor >”? This is no good: By definition our putative “artificially alive” creature is going to be an imitation in some sense; the point is, in what sense, and how good an imitation (“That’s not a real dog? I never would’ve guessed in a million years!”). Saying that artificial life is synonymous with imitation life doesn’t get us very far. We’re obviously trying to imitate life, in the proverbial “imitation is the sincerest flattery” sense.
I’m not merely engaging here in armchair philosophy, but rather trying to arrive at the essence of what “artificial life” means. What seems to be missing in the two definitions of the previous paragraph is the creation aspect. So let’s try what is actually the first definition appearing under “artificial”: “man-made < an artificial limb > < artificial diamonds > .” Ah! now we’re cooking. This seems to be the right one. It accords perfectly with the definition given by Langton in the proceedings of the first artificial-life conference: “The study of man-made systems that exhibit behaviors characteristic of natural living systems.”
Artificial life is thus life created by humans rather than by Nature. Simple. Well ... I hate to be so fussy, but “human-made” can mean at least three different things. One way to create life is through the union of a male, known as “daddy, ” and a female, known as “mommy,” thus giving life to a male or a female known as “baby.” You might be frowning now, thinking to yourself that it’s rather silly of me to even mention this since this is quite obviously not artificial life. This is the natural way of creating life, whatever that may mean. But then again, what about artificial insemination? This involves the introduction of semen into the uterus or oviduct by other than natural means, yet no one would claim that this produces artificial babies. Nonetheless, there is a definite intervention by humans, thus rendering this process somewhat less than 100 percent natural.
We often invoke the term “human-made” when speaking of objects such as cars. This image of artificial life might involve some kind of production line, where heads, arms, feet, and torsos are assembled into complete beings, after which the proverbial switch is pulled, thus breathing life into them. (The assembly line need by no means produce but humanoid life; it could in fact produce a range of beings, from artificial bacteria to artificial whales.) This is the most common image where fiction is concerned (Victor Frankenstein creating a humanoid monster, for one).
There is yet a third way by which life may be created by humans: through the process of evolution, and most likely open-ended at that (as we discussed in Chapter 12). This raises an interesting question: While we may sow the seeds of life, setting off such an open-ended process, whatever emerges — numerous generations later — might be far removed from our original design; just how “human-made,” then, is this form of life?
My intention in the somewhat philosophical discussion above has been to show you just how intricate this seemingly simple term — artificial life — really is. The concept of “artificial” is quite elusive where life is concerned, and even if we agree on emphasizing the “creation” aspect, there are a number of fundamentally different modes of creation.
Artificial life might in fact be an oxymoron. After all, how can life be artificial? If something is truly alive — assuming we can somehow agree on this fact — then what’s artificial about it? Even if we take what could arguably be considered the most artificial route of creation, that of the assembly line, once we’re done, the creature is no longer artificially alive; it’s alive — period. This takes us right back to Langton’s definition of artificial life, life-as-it-could-be, “whatever it might be made of and wherever it might be found in the universe.” Whether flesh-and-blood, man-woman-made, or nuts-and-bolts, factory-made, life is life. Perhaps rather than speak of artificial life, which is somewhat problematic, we should talk about life created by humans. In fact, even “created” might be too strong a word (think of the evolutionary scenario for one). Let’s settle for human-induced life. This emphasizes the relevant difference between Nature and humans, namely, the manner by which life arrives on the scene; the end result though is — in both cases — bona fide life.
Life may be many things: perhaps “a tale told by an idiot, full of sound and fury, signifying nothing” (Shakespeare), or maybe “colour and warmth and light, and a striving evermore for these” (Julian Grenfell), or indeed “a glorious cycle of song, a medley of extemporanea” (Dorothy Parker). At the heart of artificial life research lies the belief that whatever life is, it is not about carbon; life is not about the medium but about the mediated. It is a process that we do not yet understand in full, but which we may nonetheless be able to create, or perhaps we should say re-create: After all, Nature has beaten us to it.
Let’s get down to earth now and consider some of the issues involved in inducing life. As I’ve discussed above there are at least three ways of going about this. We might imagine some far-future extension of current medical practice (such as artificial insemination) that will result in a new form of life. Since this involves many technical biological and medical details, I think I’ll leave it at that for the present discussion.
The second way to induce life is to produce a full-blown living being. As I briefly mentioned in Chapter 11, there is much ongoing research on mimicking Nature’s gadgets, building such devices as eyes, ears, and hearts. While many of these are intended to serve as prostheses for humans, some are also used in robots. Perhaps at some point in the future we’ll be in possession of enough parts to construct an entire being. This might in fact come sooner rather than later: While speaking of “inducing life” usually tends to evoke in us images of humanoid life, let’s not be Homo sapien chauvinists. As I’ve mentioned time and again, constructing the equivalent of even a single-celled organism would be a huge achievement (not to mention a beetle or a fly), and this might come about sooner than we expect. (John Wyndham’s short story Female of the Species provides an amusingly gruesome vision of this production-line scenario. When visited by two inspectors of the Society for the Suppression of the Maltreatment of Animals, Doctor Dixon — the Frankenstein-like protagonist — explains: “The crux of this is that I have not, as you are suspecting, either grafted, or readjusted, nor in any way distorted living forms. I have built them.”)
And then there’s the third way of inducing life, by creating the necessary conditions for open-ended evolution to take place. In Chapter 1 we noted that evolution rests on four principles:
Tierra inventor Thomas Ray wrote, “I would consider a system to be living if it is self-replicating, and capable of open-ended evolution.” (The Tierran world was in fact set up to discover not how self-replication arrives on the scene, but what happens after it does, namely, how does a diverse ecosystem come to evolve.)
The study of self-replicating structures in human-made (or human-induced) systems began in the late 1940s, when John von Neumann — one of the twentieth century’s most eminent mathematicians and physicists — posed the question of whether a machine can self-replicate (produce copies of itself). He wrote that, “living organisms are very complicated aggregations of elementary parts, and by any reasonable theory of probability or thermodynamics highly improbable. That they should occur in the world at all is a miracle of the first magnitude; the only thing which removes, or mitigates, this miracle is that they reproduce themselves. Therefore, if by any peculiar accident there should ever be one of them, from there on the rules of probability do not apply, and there will be many of them, at least if the milieu is reasonable.”
Von Neumann was not interested in building an actual machine, but rather in studying the theoretical feasibility of self-replication from a mathematical point of view. He succeeded in proving (mathematically) that machines can self-replicate, laying down along the way a number of fundamental principles involved in this process. During the decade following his work (in the 1950s), when the basic genetic mechanisms had begun to unfold, it turned out that Nature had “adopted” von Neumann’s principles. (It is quite fascinating to see how his results predated the actual biological findings.)
The study of self-replication has been taking place now for more than half a century. Much of this work is in fact quite separate from artificial life and is motivated by the desire to understand the fundamental principles involved in this process. This research might better our understanding of self-replication in Nature, as well as find many technological applications. There is much talk today of nanotechnology, where self-replication is of vital import. You’d like to be able to build one tiny machine, which would than sally forth and multiply. For example, you’d inject a small nanomachine into your body to fight off some mean virus, and this nanomachine would be able to self-replicate, thereby increasing the size of your internal army. One of my favorite application examples is the self-replicating lunar factory, which is not drawn from some science-fiction novel but was actually proposed by NASA researchers in 1980. Imagine planting a “seed” factory on the moon that would then self-replicate to populate a large surface, using local lunar material. This multitude of factories could manufacture necessary products for lunar settlers or for shipping back to Earth. And all you have to do is plant the first one.
On our way to inducing life, self-replication is of crucial import. We know a bit more about this issue today than we did 50 years ago, though there is still no lack of unanswered questions, which is music to researchers’ ears. The next item on our life-inducing agenda is trying to come up with an open-ended context for our self-replicating critters (just as Ray set out to do with his Tierran world); this issue has both genotypic and phenotypic aspects (Chapter 12).
The phenotypic aspect of open-endedness concerns the environment. The grand challenges posed by an open-ended environment vis-à-vis its inhabitants are to be able to move around and to sense the surroundings to a degree sufficient to achieve the necessary maintenance of life and reproduction. What seems to us to be really difficult — as in playing chess — may in fact be quite easy once this essence of being and reacting is available. Remember, elephants don’t play chess (Chapter 11).
Chess is in fact quite an instructive example. It has been one of the holy grails in the field of artificial intelligence since the 1950s. In those early days a number of researchers had managed to come up with programs that were able to play a decent game, winning against average human players (though they were easily beaten by chess experts). The ruling opinion at the time was that very soon there would be a chess machine able to beat any human player. The problem, though, turned out to be harder than believed, demonstrating what is known as the fallacy of the first step: It’s easier to go from ignorance to mediocrity than it is to go from mediocrity to excellence (think of the difference between playing tennis and playing tennis well). Mediocre chess-playing computers were available as far back as the 1960s, though only very recently has a computer (IBM’s Deep Blue) been able to beat a world champion (Garry Kasparov).
It took 40 years to come up with a good chess-playing computer, and frankly — chess is easy! I’m not saying it doesn’t require a form of genius to excel at the game, nor am I belittling the arduous task faced by the designers of a chess-playing machine; I’m referring to the facileness of the chess environment; it is yet another illustrative example of non-open-endedness. Chess is defined by a very small number of well-known, fixed rules, and there’s really no dynamically changing environment to speak of (in this sense it is similar to our basketball environment of Chapter 12).
How do we increase the open-endedness of the environment? Thomas Ray took what is perhaps the first shot at providing an answer to this question with his simulated Tierra world. Another possibility is to subject our critter to the most complex environment known to date: ours. This is the route taken by adaptive-robotics researchers. In Chapter 4 we saw how real robots are subjected to a real-world environment (as opposed to simulated robots in a simulated computer environment); for this reason the approach is also known as situated or embodied robotics.
An argument that is often raised against embodied robotics is that it is too costly and too slow. You’d be much better off running the evolutionary process in a simulated environment within the computer, plucking out but the end result — the best simulated robot that has evolved — and implementing it in the real world. The problem is that often this does not work: When you go from the simulated to the real, the robot no longer functions properly. Our environment is full of many hidden complexities that often escape our notice, rendering it very hard to implement them in a computer; it’s just plain easier to use the real world.
Nature’s open-endedness manifests itself not only at the phenotypic level but also at the genotypic level; it can tinker with the genome so as to produce entirely novel designs, which give rise to new phenotypes better able to rough it. This point has also been receiving increased attention of late: How can we set up an evolutionary scenario in which fundamental genomic changes can occur? For example, in Chapter 4 we evolved only the behavior of the robots, their small, neural-network brains. Their body, on the other hand, did not change at all, which is nothing like natural evolution. Nature possesses the ability to bring about not only behavioral changes but also morphological modifications in her creatures. A number of researchers have recently begun looking into the possibility of doing this for robots as well, evolving both behavior and morphology. While quite rudimentary at the moment, this is yet another step toward increased open-endedness.
Next to the natural world a new universe has sprung up in the past few years, which is both complex and open-ended: the Internet. It is evolving at a breathtaking speed, already exhibiting enough complexity to merit the attention of scigineers. This is not surprising. The Internet’s evolution is mediated by self-proclaimed intelligent beings known as Homo sapiens. This process is more akin to Lamarckian evolution, where a beneficial survival trick can be immediately incorporated within the evolving population.
Will we someday see the rise of network life? Even as you read these lines, there are thousands and thousands of small programs — known as agents — roaming the network, seeking to find information that will appease their human masters. Currently they are quite limited, lacking in both intelligence and autonomy. Little by little, though, they might develop into more autonomous critters. This might come about by employing some of the techniques we discussed in this book, giving rise to what I call Egents, for Evolving Agents, and double AAgents, for Adaptive Agents. These agents will be denizens of the network universe, whereas we will not; it is they who will be in their element. We may have built the house, but we are not the ones living in it. I just hope those double AAgents will work for you, their master, and not for some unknown party behind the cyber curtain.
The borders between the living and the nonliving, between the Nature-made and the human-made appear to be constantly blurring.
As in dreams.
The silent man who came from the South eventually succeeded in dreaming a man and inserting him into reality. And what became of the dreamer? “With relief, with humiliation, with terror, he understood that he too was a mere appearance, dreamt by another.”
Excerpt from my book Machine Nature
Science and engineering have traditionally proceeded along separate tracks. The scientist is a detective who’s up against the mysteries of Nature: He analyzes natural processes, wishing to explain their workings, ultimately seeking to predict their future behavior. Scientists ask questions such as: What goes on inside the Sun? And how long will it keep on burning? How does the weather system work? And how can we predict whether it will rain tomorrow or not? What are the fundamental physical laws that underpin the workings of the known universe?
The engineer, on the other hand, is a builder: Faced with social and economic needs, she tries to create useful artifacts. Engineers ask questions such as: How can we build a car with a cruising speed of 150 kilometers per hour, a fuel consumption of 20 kilometers per liter, and a price tag of no more than $8000? How do we design a computer chip that is twice as fast as the fastest extant chip? How can we build an autonomous lawn mower? “To put it briefly,” wrote Lewis Wolpert in The Unnatural Nature of Science, “science produces ideas whereas technology results in the production of usable objects.” And if I may add my own little epigram, science is about making sense whereas engineering is about making cents ...
In a chapter entitled “Technology is not Science,” Wolpert discussed the differences between the two, noting that technology is very much older than science, and that science did almost nothing to aid technology until the nineteenth century. “Technology may well have used a series of ad hoc hypotheses and conjectures, but these were entirely directed to practical ends and not to understanding,” he wrote. Humans have been able to construct artifacts — such as tools and arms — and improve their existence via agriculture and animal domestication thousands of years before the arrival of modern science (in the sixteenth and seventeenth centuries). Though engineers have only recently begun to put science to use, scientists had always relied on the existing technology. To quote Wolpert: “Science by contrast has always been heavily dependent on the available technology, both for ideas and for apparatus. Technology has had a profound influence on science, whereas the converse has seldom been the case until quite recently.”
The emergence of technology long before science is not at all surprising. “The goals of the ordinary person in those times,” wrote Wolpert, “were practical ends such as sowing and hunting, and that practical orientation does not serve pure knowledge. Our brains have been selected to help us survive in a complex environment; the generation of scientific ideas plays no role in this process.” Thomas S. Kuhn considered science and technology in one of the most influential works in the philosophy of science, The Structure of Scientific Revolutions, writing: “Just how special that community must be if science is to survive and grow may be indicated by the very tenuousness of humanity’s hold on the scientific enterprise. Every civilization of which we have records has possessed a technology, an art, a religion, a political system, laws, and so on. In many cases those facets of civilization have been as developed as our own. But only the civilizations that descend from Hellenic Greece have possessed more than the most rudimentary science. The bulk of scientific knowledge is a product of Europe in the last four centuries. No other place and time has supported the very special communities from which scientific productivity comes.” So perhaps we should count ourselves lucky to have science at all!
During the twentieth century the use of scientific knowledge in advancing the state of the art of our technology has picked up quite dramatically. Today all but the simplest artifacts rest on strong scientific foundations, everything from computer chips to automobile tires, through T-shirts, sugarless bubble gum, and space shuttles.
Science and engineering go hand in hand nowadays, both drinking from and helping to fill the other’s fountain. We’ve seen how engineers not only apply our current scientific understanding of Nature in order to build better artifacts, but are indeed coming full circle, trying to make these objects more Naturelike. Biology serves as a source of inspiration, with processes such as evolution, learning, and ontogeny implemented in artificial media. Nature can even be directly co-opted for engineering purposes, as with the use of DNA molecules to solve problems in computing.
The betrothal of science and engineering, and the ensuing period of blissful courtship, have finally led, in my opinion, to marriage. I believe that the recent years have seen the rise of a new kind of professional (and profession): the scigineer, a combination of both scientist and engineer, holding a test tube in one hand and a proverbial slide rule in the other.
What is a scigineer? Let me go about explaining this by way of example. In Chapter 2, we saw how computer programs in the form of trees can be evolved, noting that evolution tends to produce “spaghetti” programs: huge trees with lots of weird branches and offshoots. If the program works to your satisfaction, you can of course simply go ahead and use it; if you want to understand what makes it tick, though, then you’re in a position that’s rather like that of a biologist trying to decode our own program (the human genome). We even noted that when you delve into these evolved programs, you frequently find loads of “junk”: computer code that is of no use at all, a situation which is similar to Nature. Our genomes also contain junk code: unused portions of our DNA program.
The scigineer has two hats — that of a scientist and that of an engineer — which she constantly alternates. First, she puts on the engineer’s hat, picks up her slide rule, and sets the stage, say, for the evolution of computer programs; then, she puts on the scientist’s hat and the white coat, setting out to analyze the creatures (programs) that have emerged in her artificial universe.
The robots of Chapter 4 also constitute a case in point. They are an artifact created by the scigineer, who subjects them to an environment in which they evolve and learn. We saw how they can come to avoid obstacles, but exactly how do they accomplish this? Though we’re talking about an artifact — an object created by humans — it has evolved into something that we do not fully comprehend. Even though as stage designers we seem to have a privileged position, the actors have taken their own routes so as to better themselves. The scigineer must now take out his scientific toolbox in order to analyze this little robotic creature, just as a scientist analyzes a cockroach. Though such a current-day robot is still a far cry from a cockroach, it’s already complex enough to require the donning of a white coat.
Let me give you another well-known example, that of the Tierra world. Tierra is a virtual universe — embedded within a computer — that was set up in an attempt to explore the idea of open-ended evolution. It comprises computer programs that can evolve; unlike those of Chapter 2, however, where an explicit goal (and hence fitness criterion) is imposed by the user (for example, compute taxes), the Tierran creatures receive no such guidance. Rather, they compete for the natural resources of their computerized environment: time and space. You may remember from Chapter 6 that a standard computer consists of two major elements, the processor — that actually runs the program and the memory — the storehouse that acts as a repository for programs. These two components represent Tierra’s natural resources, and — just as in Nature — they are limited: The processor can only run one program at a given moment, and the memory can contain no more than a certain number of programs. This gives rise to a fierce battle for survival, the Tierran creatures having to vie for the processor’s precious time and for a place in the jungle known as memory. Failure means death: A program that is unsuccessful in procuring these resources disappears from the evolutionary stage.
Tierra was invented not by a computing scientist but by an ecologist, Thomas Ray, who had worked for years in the Costa Rican rain forest before turning from natural evolution to digital evolution. Ray inoculated his Tierran world with a single organism — a self-replicating program called the “ancestor,” which was able to co-opt the processor to produce copies of itself elsewhere in memory. This organism, a program written by Ray himself, was the only engineered (human-made) creature in Tierra. The replication process is not perfect: Errors, or mutations, may occur, thus driving the evolutionary process. Ray then set his system loose and witnessed the emergence of an ecosystem-in-a-bottle, right there inside his computer, including organisms of various sizes, and such beasties as parasites and hyperparasites. Ray wrote that “much of the evolution in the system consists of the creatures discovering ways to exploit one another. The creatures invent their own fitness functions through adaptation to their biotic environment.”
Large programs such as the ancestor have several instructions that form part of their “body”; these program instructions are used to copy the organism from one memory location to another, thus effecting replication. The evolved parasites are small creatures (programs) that use the replication instructions of such larger organisms to self-replicate. In this manner they proliferate rapidly in the memory jungle without the need for the excess replication code. As in Nature, the evolved ecology exhibits a delicate balance: If all large creatures were to disappear, then the parasites would die, having no replication code to appropriate. Tierra had even managed to outdo its creator, who wrote: “Comparison to the creatures that have evolved shows that the one I designed is not a particularly clever one.”
Ray first engineered this world, which he then proceeded to analyze as a scientist: “Trained biologists will tend to view synthetic life in the same terms that they have come to know organic life. Having been trained as an ecologist and evolutionist, I have seen in my synthetic communities, many of the ecological and evolutionary properties that are well known from natural communities.” (If you’re interested in learning how the humble Tierran beginnings ultimately lead to the rise of the “TechnoCore” artificial intelligences [AIs], I recommend The Rise of Endymion — the final volume of Dan Simmons’s Hyperion tetralogy.)
In April 1998, while leafing through the weekly issue of Science, I was surprised to find two out-of-the-ordinary articles. Science, one of the top two scientific journals (the other being Nature), publishes almost exclusively hard-core scientific papers in physics, chemistry, biology, and the like. If your paper is good enough to grace Science’s pages, then it’s probably about the natural world — the object of scientific study. Yet in browsing this particular issue, I suddenly came across a couple of articles that dealt with an artificial world, created entirely by humans: the World Wide Web. One article looked into the efficiency of search tools, while the other studied patterns of behavior as information foragers move from one hyperlinked document to the next. It’s almost as if we were talking about a tropical jungle.
This is a cogent example of scigineering that is totally unrelated to biology or biological inspiration. Here is a universe created entirely by humans, which has become so complex — much more so than a car or an elevator — and so interesting in and of itself, that it merits the attention of scientists — and the consecration of Science. We’ve engineered the World Wide Web, and then we turn to study this brave new world. The era of scigineering is upon us.
The rival journal, Nature, waited until August 1999 to finally “give in.” In an article entitled “Genome Complexity, Robustness and Genetic Interactions in Digital Organisms,” Richard Lenski, Charles Ofria, Travis Collier, and Christoph Adami explored the effects of genetic mutations in both simple and complex digital organisms, which inhabited the artificial, Tierra-like world called “Avida.” Commenting on their work, Inman Harvey from the Centre for the Study of Evolution at the University of Sussex cautioned that “considerable debate can be expected before a consensus is reached on just what is necessary for results from a synthesized world to be seen as relevant to the natural world.” The scigineer might study her world and glean much about it, but she must be cautious in applying her conclusions to the world at large.
The scigineer has one up on the scientist in that he can render his world easier to analyze, whereas a scientist must make do with what Nature affords him. Evolutionists would love to have the entire Tree of Life at their disposal, including all the lost species, yet this is but wishful thinking; geological reality, alas, is harsher on them, revealing but bits and pieces of the whole story. With artificial worlds, though, wishes are granted: You can easily save the entire evolutionary history of your artificial creatures to later analyze it at your leisure.
Remember from the previous chapter how the protagonist of Permutation City describes the result of the Autoverse experiment — the result of billions of years of evolution in this artificial world? He says: “All demonstrably [my emphasis] descended from a single organism which lived three billion years ago ...” In this artificial planet, one can demonstrate that all the organisms have descended from a single origin since the entire evolutionary trace is available. The scigineer might not possess perfect knowledge of his engineered world, but he at least has the power — unlike scientists — to render his analysis job easier.
In reminiscing about his illustrious career, Isaac Newton remarked: “I do not know what I may appear to the world; but to myself I seem to have been only like a boy playing on the seashore, and diverting myself in now and then finding a smoother pebble or a prettier shell than ordinary, whilst the great ocean of truth lay all undiscovered before me.”
We’re no longer content to walk the shores of Nature’s oceans of truth, finding whatever pebbles may have been laid for us. We’re now creating new oceans, and with them we beget new shores to walk.
Excerpt from my book Machine Nature
Tyger! Tyger! burning bright
In the forests of the night,
What immortal hand or eye
Could frame thy fearful symmetry?
Who indeed framed the tyger? Two hundred years after William Blake wrote the beautiful opening stanza of The Tyger, we have a better idea of how tigers come about: through the process of evolution. There is neither a Master Plan, nor a "hand of god," nor any ultimate goal; the driving force is the short-term objective of survival, with the process consisting of the slow accumulation over millenia of numerous small — yet profitable — variations. In the forests of evolution burn many a creature, with nature's immortal evolutionary hand slowly framing the fearful symmetry of the tiger.
Natural evolution is an open-ended process, and is thus distinguished from artificial evolution which is guided, admitting a "hand of god": the (human) user who defines the problem to be solved. When we apply evolution with a well-defined goal in mind — such as designing a bridge, constructing a robotic brain, or developing a computer program — what we are doing is akin to animal husbandry. Farmers have been using the power of evolution for hundreds of years, in effect doing evolutionary computation on domestic animals. In order to "design," say, a faster horse they mate swift stallions with speedy mares, seeking to see even faster offspring emerge from this coupling. This is quite similar to the use of evolutionary techniques discussed in this book: the farmer defines the fitness criterion (say, speed) and performs the selection process by hand (by choosing the fastest individuals in the equine population); he then lets nature work out the genetic details involved in the coupling act. It's rather interesting to note that farmers and breeders had started using this method long before either evolution or genetics came under the scrutiny of science.
Farmers can start out with slow horses and evolve fast ones — but can they evolve tigers? Engineers can start out with bad bridges and evolve good ones — but can they evolve a town like Cambridge? Robotics researchers can evolve robots that manage to amble decently — but can they evolve a robotic housemaid. Programmers can evolve computer programs that solve various well-defined problems — but can they evolve truly intelligent software? Natural evolution has done it all: complex organisms, sophisticated structures, intelligent beings; and it did so by being open-minded, ready to accommodate any improvement that came along.
The Merriam-Webster online dictionary (www.m-w.com) defines "open-ended" as something that is "not rigorously fixed: as a : adaptable to the developing needs of a situation b: permitting or designed to permit spontaneous and unguided responses." Open-endedness is thus the flip side of guidedness, and it is a crucial aspect of natural evolution. Since nature has no specific goal in mind she can easily change course so as to face the winds of change, and in so doing she explores numerous designs out of what is essentially an infinitude of possibilities. Man, on the other hand, even when using evolutionary techniques does have an ultimate goal in mind — be it a retractable bridge or a program that computes taxes.
When we apply evolutionary techniques the ingredients are all there: a (possibly huge) population of individuals, survival of the fittest, and the equivalent of genetic operators. Yet the hand of god is ever-present in the background: at every step of the way an individual's fate is decided in accordance with its ability to perform in the arena set up by the puppet master; and the master wants his puppets to do some very specific tricks. This places a fundamental a priori limit on what evolution can achieve: if we set about to find fast horses, then we might succeed in doing so — but we'll not suddenly see the emergence of tigers.
Nature's open-endedness runs deeper, though, than the mere absence of a goal and a god — of a teleology and a master. It has to do with her ability not only to play the game but indeed to change the rules altogether. Let me drive this point home by way of a sportive example. The game of basketball is played on a court 90 feet long by 50 feet wide between two opposing teams of five players, who score by tossing an inflated ball through a raised goal. The rules are well known and rigid, with changes being rare, minor (for example, adding the three-point shot), and human-mediated (say, the NBA committee). This scenario is analogous to that of guided evolution: a human designer sets the stage (or in this case court) that gives rise to a (fiercely competitive) evolutionary process, from which but one kind of creature may emerge: basketball players. The process is not open-ended since there is a precisely defined goal (scoring more points), with virtually immutable rules. Though superb basketball players can (and do) evolve, this arena does not give rise to first-rate opera singers.
Playing nature's "basketball" game is quite different. For one thing, there is no clear objective; at best, one can speak of a very basic goal, that of coming out of the match alive. What's more — and this is where open-endedness comes into play — nature keeps changing the rules of the game, both in time and in space. Being seven foot tall might be good at a certain place and time, whereas elsewhere or else-when it might be downright deleterious. And sometimes the rules are such that having a superb tenor voice is a match winner. Nature's game of basketball is more of a meta-game, where you want to score more points — but have to figure out how points are scored.
In Chapter 1 we discussed an important distinction in nature, that between genotype and phenotype. An organism's genotype is its genetic constitution, the DNA chain that contains the instructions necessary for the making of the individual. The phenotype is the mature organism that emerges through execution of the instructions written in the genotype. It is the phenotype that engages in the battle for survival, whereas it is the genotype — safely cached in each cell of the organism — that accrues the evolutionary benefits.
Setting a specific goal — as with artificial, guided evolution — means that there is a highly restricted environment; the basketball-player phenotype faces an environment in which it is demanded to perform a very specific task: playing basketball. Natural environments are not only much more complex but also highly dynamic — the phenotypes must face ever-changing circumstances.
Nature's open-endedness manifests itself not only at the phenotypic level but also at the genotypic level: not only can the rules of the playground change, but so too can the rules for making players. The genome of a red ant is quite different from that of an orangutan (though as both are branches of the Tree of Life, they also bear many similarities). As we've seen, artificial-evolution scenarios to date are limited, being goal-oriented, with but very little maneuverability in changing the genetic makeup. A bridge genome will always produce a bridge — perhaps a superb one at that — but never a skyscraper. Nature, though, can tinker with the genome, thus changing the underlying construction plan, so as to produce entirely different beings, including skyscrapers (giraffes) and towns (ant colonies). This is a crucial aspect of her open-endedness.
We've seen how artificial evolution is used to design complex objects, which stretch — or overstretch — our classical engineering techniques. The results are often quite impressive and at times those who use them are even reputed to cry out: "Wow, I'd have never come up with such a solution." But this is still at the level of evolving super bridges or superb basketball players; moreover, it might even be limited at that since an entirely novel bridge design or a new form of basketball player might necessitate genomic tinkering that is beyond the system's reach. Can something truly astounding — something entirely new — emerge out of an artificially set stage? In my mind this is one of our grandest challenges, and it may still be many years in the coming. I like to think of this defy as that of building a system that Knocks Your Socks OFF. Following the time-honored tradition in computing science of coining acronyms, this might be dubbed the KYS OFF challenge — which leads me to wonder whether such a system would kiss us off ...
As our artifacts become more and more complex, so does their design become more arduous. One way out is to employ the powerful process of open-ended evolution. But wait a minute — by definition, that would mean ... removing the designer from the equation! Then who controls the design process — who's the boss? It seems that you can't have your cake and eat it too — something has to give. With guided evolution the guide — or designer — maintains a great deal of control over his system, and though he'll often be overwhelmed by the results obtained, his socks will remain firmly in place. Open-ended evolution might indeed knock your socks off, but at the price of giving up some of that precious control that we've grown used to.
Strangely enough then, it is less design, meaning more open-endedness, that increases our design power. Uhm ... did I just say less design? Actually, you have to set up the stage so as to be more open-ended, that is, you have to design the system to exhibit ... less design! That's the essence of the KYS OFF challenge, which only nature has met so far — but then again, she's been at it for the past threenand a half billion years. (While I've been concentrating my discussion of the open-ended versus the guided on evolution this is by no means the only process of interest. Learning, for one, augments a system's open-endedness.)
Open-ended goes hand in hand with less control — though with the potential of more spectacular results. Parents usually want their children to grow up to be independent and able to think for themselves. But in many ways child-raising is open-ended — with no guarantees: What if the child decides to be a rock star? (Result: horrified parents.) Or a doctor? (Result: delighted parents.) In Chapter 4 we discussed the application of biological processes, such as evolution and learning, in the field of adaptive robotics. We saw that one of the central goals is that of attaining more autonomous robots; I doubt, however, that we're ready to see them declare autonomy ...
With an open-ended process not only do you not control the precise shape that the final outcome will take, you're not even sure what this outcome will be. When we look at nature's magnificent products with awe and with envy, we should always keep in mind the billions of years that their production necessitated. If you set off such a process and then patiently wait for a couple of billion years, you might find — a posteriori — lots of wonderful devices, such as eyes, toes, flowers, brains, and wings. You might be quite happy with this plethora of gadgets that will bring you fame and fortune. But this process is open-ended, which means you don't know in advance what the final products will be. In fact, it might not even get off the ground: it took nature almost three billion years before things really started to pick up and the Tree of Life began to grow. What if it never gets off the ground? Or if you simply get tired of waiting?
The possibility of creating an artificial scenario in which open-ended evolution takes place is at the heart of Greg Egan's excellent science fiction novel Permutation City. Explaining to the researcher her mission, the protagonist says: "I want you to construct a seed for a biosphere ... I want you to design a pre-biotic environment — a planetary surface, if you'd like to think of it that way — and one simple organism which you believe would be capable, in time, of evolving into a multitude of species and filling all the potential ecological niches.'' Having succeeded in creating such a biospheric seed, evolution is then set lose in this artificial universe known as the Autoverse, to work its magic over the eons: "We've given the Autoverse a lot of resources; seven thousand years, for most of us, has been about three billion for Planet Lambert.'' And the outcome? "There are six hundred and ninety million species currently living on Planet Lambert. All obeying the laws of the Autoverse. All demonstrably descended from a single organism which lived three billion years ago — and whose characteristics I expect you know by heart. Do you honestly believe that anyone could have designed all that? " The answer is no; be it in an artificial or a natural world, open-ended evolution will knock your socks off ...
If we give up our control, can't things get out of hand, leading to a system run amok? This is a tough question, which needs to be addressed on a case-per-case basis. We should come up with fail-safes (à la Asimov's three laws of robotics). We might well wish to place checks and bounds (for example, limit the robots' autonomy). We'd like to maintain the possibility of pulling the plug if things get downright ugly. But this issue is by no means an open-and-shut case: how does one juggle between control and autonomy — between guided and open-ended? This issue will probably gain more prominence as our technology advances, enabling us to build systems that are somewhat less controlled — and less controllable.
Which brings me back to William Blake and the closing lines of "The Tyger,'' where the "Could'' of the first stanza has been conspicuously replaced by "Dare":
Tyger! Tyger! burning bright
In the forests of the night,
What immortal hand or eye
Dare frame thy fearful symmetry?
Dare we frame a tyger?
by Moshe Sipper
We can plainly see why nature is prodigal in variety, though niggard in innovation.
This beautiful statement was written by Charles Darwin in Origin of Species. As a corollary, I might add that, given nature’s prodigal resources, she needn’t be too smart, and — to paraphrase a famed Darwinist — can be seen to trudge along quite blindly.
Yet, in the field of Evolutionary Computation we practice the opposite of this Darwinian tenet, demonstrating at every conference, journal, and whatnot how cleverly prodigal we are in innovation (be it theoretical, algorithmic, or applicative), what an inventive evolutionary system we have designed, using — more often than not — quite niggardly means.
Might this be not the practice of evolutionary computation, but something else? A thing that tastes like evolution, feels like it, maybe even has that familiar smell of evolution — but isn’t?
In light of the argument pleaded before us, perchance the fruitful endeavor deserves a new name, similar yet distinct? Should the jury vote yea, I propose Volutionary Computation, deriving from “volution” (“a rolling or revolving motion”). After all, the metaphorical ball rolls in the search space, and if the system has been set up smartly — it shall end up being on a roll.
(Moreover, volutionary rolls off the tongue, now, doesn’t it?)
Copyright © 2016 by Moshe Sipper
Adapted from Hamlet's soliloquy by Moshe Sipper
To run, or not to run — that is the question:
Whether 'tis nobler in the mind to suffer
The boils and carbuncles of outrageous shoes
Or to take arms against a sea of shoe manufacturers
And by opposing run barefoot. To lie down, to sleep —
No more — and by a sleep to say we end
The heart’s race, and the thousand unnatural shocks
That running flesh is heir to. 'Tis a consummation
Devoutly to be wished. To lie down, to sleep —
To sleep — perchance to scream ENOUGH: ay, there's the rub,
For in that sleep of dearth what screams may come
When we have shuffled off this morbid soil,
Must give us pause. There's the respect
That makes calamity of so long distances.
For who would bear the whips and scorns of much time per furlong,
Th' oppressor's rhythm, the proud man's sweat
The pangs of despised joggers, the loo's delay,
The insole of office, and the spurns
That patient merit of th' unworthy takes,
When he himself might his quietus make
With a bare foot? Who would nip guards wear,
To grunt and sweat under a weary coach,
But that the dread of something after finish line,
The undiscovered country, from whose bourn
No runner returns, puzzles the will,
And makes us rather swear those spills we have
Than jog to others that we know not of?
Thus conscience does make cowards of us all,
And thus the native hue of new year’s resolution
Is sicklied o'er with the pale cast of thought,
And enterprise of great niche and showmen
With this regard their steps turn awry
And lose the name of action. — Soft you now,
The fair Marathon! — Lymph, in thy orisons
Be all my wins remembered.
by Moshe Sipper
Once upon a time there was a school for young dogs of fine breeding. The venerable establishment was quite selective, and accepted only male dogs whose noble line could be traced back at least five generations.
One day a cat walked into the school, to the astonishment of all the dogged students and doggy teachers. Nor was it a fine, well-bred cat. Oh no. This particular cat was quite scruffy, of an undefined greyish color, with not a single noble bone in its entire scrawny body, and — to make matters far worse — the cat was … female!
The feline newcomer ambled straight into the headmaster’s office, as if she owned the entire school along with the surrounding grounds, and demanded to be admitted. The highborn headmaster, along with his entire staff, were flabbergasted, for the cat was able to cite rather precisely all the pertinent anti-discrimination laws.
The kitty was on the verge of being admitted as a student, which would have caused a ruckus to end all ruckuses. However, the headmaster’s mother happened by and without further ado chased the impudent cat away, in an impressive display of feminine doghood.
Moral: Mom can solve everything. Especially if she’s a bitch.
Copyright © 2016 by Moshe Sipper
by Moshe Sipper
In a realm of science and merit, a professor once received tenure. Whereupon she began to think — an action, I might add, which had garnered her the prized academic station.
“Do I really need all that I possess?” she said out loud, for why should she not speak her mind. After all, she had tenure! “No,” she immediately replied respectfully. “My ears are of no use any more, for I no longer need to listen to colleagues or students.” And so she gave her ears forthwith to a corn.
“My legs? I need not leave my office ever again.” And so she broke a leg — and then the other. “Eyes? Hah, I’ve seen it all. Out with them. Nose? This place stinks anyway. Mouth? Hmm … I still need to give lectures, but I’ll have my grad students deliver those.”
Piece by piece her needs and body were reduced to the bare essentials, until she was left solely with a single digit of her left hand. For one day, there would land on her doorstep an invitation to travel to Sweden to collect a Nobel Prize. And then, reasoned the esteemed scholar, she would need that middle finger.
Why the hell should she bother herself to travel all the way to Sweden? She had tenure, for fuck’s sake
Copyright © 2016 by Moshe Sipper
by Moshe Sipper
In a faraway kingdom, known as Boozdom, ruled benevolently King Beer and Queen Wine. Their son, Prince Scotch, was beloved by all, and the fine citizens of the land were always merry and slightly wobbly.
One day an evil pirate named Sober landed on the shores of this joyous realm. And he would have brought the downfall of Boozdom, were it not for love.
For Sober fell in love with Princess Bloody Mary, and their wedding was the wooziest the kingdom had ever known.
And everybody lived tipsily ever after.
Copyright © 2016 by Moshe Sipper
by Moshe Sipper
Accept: We’re a shitty journal and we’ll accept anything. Even your pathetic rubbish.
Accept with Minor Changes: The referees haven’t read your piece of crap. All they ask is that you add every single one of their own papers to your bibliography section.
Accept with Major Changes: Your paper is worthless. However, if you change the Introduction, Previous Work, Methodologies, Setup, Results, and Conclusion sections, we might change our mind. (Or not.)
Revise and Resubmit: We still won’t accept your insignificant drivel but it will help our submission statistics when the publisher moves to shut us down.
Reject: Really?? You need that one explained?
Copyright © 2016 by Moshe Sipper
A short story by Moshe Sipper
The last man on earth sat alone in a room. There was a knock on the door.
It was the last woman. “Forgot your keys again?” asked the last man calmly. He was quite used to his wife’s key-loss-phy by now — and he loved her for it.
“Sorry dear,” spoke the last woman softly as she entered the house.
“How are the kids doing?” asked the last man somewhat anxiously. “Are they okay?”
“I’d say they’re taking this pretty well,” chuckled the last woman. “Can’t you hear them playing outside?”
“Now that you mention it ...”
The last boy and the last girl were undoubtedly having lots of fun, playing as they were outside on the front lawn. The last man could hear cries of joy and barks of pleasure. These latter came from Sparky — the last dog.
“It’s almost time,” said the last man. “Shouldn’t we call them in?”
“Give me another minute, love,” replied the last woman distractedly. “I still haven’t found where Mr. Perkins is hiding.” Mr. Perkins was the last cat.
“Have you looked under our bed?” asked the last man. “You know how Mr. Perkins loves to hide there.” After a moment he heard the last woman’s shout from upstairs: “You were right dear, I found him.”
Then the last man called his kids inside the house.
And all of them — the last man, the last woman, the last boy, the last girl, the last dog, the last cat, and even a couple of last mice — got into the transporter and left.
Eventually, everybody came back. Even the mice.
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
The thing I hated most was when people told me I wasn’t to blame, because, after all, I was only seven years old when the 'unpleasantness' began.
(This story appeared in the journal Nature, 6 December 2012)
Excerpt from my book Machine Nature
An eagle flaps its wings; a Boeing 747 doesn’t. A dolphin wiggles its body and jiggles its fins — a submarine just has a motor in the back. A dog walks on legs; a Mercedes-Benz rolls on wheels. A rose runs on water and light; a flashlight runs on batteries. A tiger develops in a womb from a single cell to a magnificent multicellular beast — a toy tiger is constructed full blown in a factory. A piano player goes through years of intensive training, learning to hone her talent; a piano learns nothing. Homo sapiens have evolved by means of natural selection; watches are designed by watchmakers.
Engineers and Nature have usually taken distinct routes in their creation of complex objects, differing both in the final artifacts produced as well as in the design process itself. And the recent movement that seeks inspiration in Nature has come up not only with novel objects but also with entirely new ways of designing objects. Thus, current-day robots may possess legs, fins, or wings; electronic circuits may develop in a manner akin to that of multicellular living beings; watches can heal themselves; computers can learn to play a mean game of backgammon; and bridges can be evolved.
Having visited several lands in the Terra Nova of computing, and having acquired along the way many new colorful approaches, we shall now use these colors in the remainder of the book to paint the big picture. In this chapter I’d like to take a closer look at the main differences between human’s work and that of Nature, specifically focusing on how these relate to our current engineering efforts. When does it pay to be biological, and when is it better to use the traditional, by-ole-logic way? As a concrete example I’ll consider two different kinds of flying machines: birds and airplanes. When engineers set about to design an airplane, they proceed in what is known as a top-down approach: They start with the general issues and questions (the top) and go all the way down to the nitty-gritty. At the top there is the decision — usually made by senior management — to build a new airplane. Next comes the requirements analysis phase, which basically answers the question: What should this new machine be able to do? It might be required, among others, to carry up to 600 passengers, to take off and land on short runways, and to handle severe weather. Having defined the problem, it is time for the engineers to enter in force, their job being to find a solution; now that we know what we want, it’s time to see how we go about building it.
The design process continues in a top-down fashion, breaking the big problem into smaller and smaller subproblems; one doesn’t jump immediately to the nuts-and-bolts level. This breaking-down process might be done by identifying key parts — such as the cockpit, the fuselage, the engines, and the wings — and assigning their design to different teams (which obviously must cooperate among themselves. After all, there is but a single final object being built: the airplane). Each such key design problem is further divided into smaller subproblems; the wings team, for example, will be considering flaps, spoilers, ailerons, and other such beasties. The design process is by no means simply a forward march; often one must go back to the drawing board since the part in question doesn’t function as it should. This back-and-forth process ends up with a design specification — a complete plan of the airplane (such a complex object might require years of design work). Now it’s time to fabricate the machine, a task which in itself may be quite elaborate for such an artifact. It might, in fact, require a separate design process since in all likelihood new fabrication techniques for the new airplane will have to be developed.
Engineering designers thus start out with a clear top-level goal in mind, then work their way downward toward the most minute details, ultimately coming up with a comprehensive solution. Nature works quite differently. For one thing, Nature has no explicit, a priori goal; Nature does not embark upon a lengthy R & D project whose final objective is the construction of a bird. Nature employs evolution, and evolution is shortsighted: The only goal, the only thing that matters, is immediate survival. Nature, if any designer at all, is a blind one at that. The ability to fly emerges over eons since it confers some advantage to the animals that possess it. Thus, when speaking of evolution’s goal, one can at best describe it as an implicit, short-term one: survival. (In The Blind Watchmaker Richard Dawkins proposed a way by which wings might have evolved. His scenario starts out with wingless animals that leap between tree boughs. Small flaps of skin that help extend the jump or break the fall — by acting as an airfoil — will bestow an immediate survival benefit upon their owner. Little by little, over the course of many generations, the accumulation of small, ever-better modifications to these flaps might end up in full-fledged wings.)
Evolution is further distinguished from engineering in that it is a bottom-up process: Its “products” emerge from the myriad of interactions that take place in the biosphere. There is no top-down process that starts out with a major, far-sighted goal that is then broken down successively into smaller and smaller subgoals, until they become doable. There are just numerous interactions, both among organisms, as well as between them and the elements, out of which emerge all the wonderful devices we see around us (and in us), such as wings, eyes, feet, nervous systems, and rock stars.
Nature’s open-ended, short-sighted, bottom-up style as opposed to engineering’s guided, far-sighted, top-down approach is the crux of the difference between the two. It entails several other distinctions between the engineering enterprise and Nature’s workings.
Engineers usually seek not only to create a widget that works, such as an airplane or a coffee machine, but indeed one that works well; often they evoke terms such as “efficient” and “optimal” to describe their desired product. Nature, on the other hand, cares nothing for these qualities; designs need neither be the best, nor the fastest, nor the most efficient; rather, Nature’s after “just-do-the-trick” solutions, namely, ones that can survive. If an organism has even the slightest advantage over its confreres, then that’s all it takes — it’ll be the winner in the survival race and its genes will pass on to the next generation.
“But how then,” you might be asking yourself, “has Nature come up with all those marvelous designs we see out there — such things as seeing gadgets, delicate manipulators, and thinking machines, which are still way beyond our current engineering capabilities?” First off, let’s not forget that Nature has had a bit of a head start — 3.5 billion years to be precise. This figure should not be brushed aside lightly: It is a huge amount of time, practically impossible for us to grasp. As noted by Charles Darwin in the Origin of Species: “The mind cannot possibly grasp the full meaning of the term of a hundred million years; it cannot add up and perceive the full effects of many slight variations, accumulated during an almost infinite number of generations.” Our inability to grasp such a vast period of time is not so surprising if you think about the environment in which our minds have evolved to function. During most of our evolutionary history, there was no survival value in being able to comprehend the expanse of a million years (nor, for that matter, of a millionth of a second). It is only very recently (no more than a few thousand years) that we have begun dealing with such huge numbers, our minds coming to appreciate time out of mind. While for engineers time is of the essence, for Nature the essence is time.
In coming up with her flying machine, Nature thus spent a little more than the few years engineers spend in designing a Boeing 747. The chirping critters we see today outside the window are superb beasts, yet their beginnings — the ancestral forms that flew the Earth millions and millions of years ago — were probably much less impressive. It’s hard to match our current engineering achievements with those of Nature, but then again, it might also be somewhat unfair. We should probably compare our current-day devices not with modern flora and fauna, but rather with Nature’s first attempts, those that had been in existence so many millions of years ago (and which are now — for the most part — extinct).
Nature not only takes her time but also makes use of a huge amount of resources. Charles Darwin remarked that the evolutionary process goes on “for millions on millions of years; and during each year on millions of individuals of many kinds ...” While an engineer usually tries to cut costs wherever possible, Nature is lavishly wasteful. She works by trial and error, indeed lots of trials and lots of errors. Charles Darwin quoted Milne Edwards as quipping that “nature is prodigal in variety, but niggard in innovation.” There are many more extinct species than surviving ones, or, as Richard Dawkins said: “however many ways there may be of being alive, it is certain that there are vastly more ways of being dead …”
Evolution is basically a forward process: Any new entity must be immediately functional, or else it dies out. As we’ve seen above, engineers can (and often do) go back to the drawing board in order to fix a flawed design. Nature, on the other hand, cannot move backward; there is no drawing board to go back to, no possibility of deciding, “Well, this new wing design isn’t so good, so let’s go back to the old one and try to improve it in another way.” In Nature, no good means no life (as in dead).
Another difference between engineered devices and natural ones has to do with “leftovers.” In human-made systems essentially every single part is accounted for and serves some purpose; if not, then it is removed without further ado. Nature, on the other hand, tends to accumulate junk, her motto being: “If it’s not harmful then it’s none of my business.” Why waste effort on removing innocuous parts? Modern creatures thus carry vestiges of past epochs that might have served some purpose at one time, but which are totally useless today (our tail bones, for example).
Let’s take stock of what we’ve gleaned so far about the biological versus the by-ole-logic. When engineers design a product, they have a clear goal in mind; they proceed in a top-down manner, seeking to create an artifact that is — as much as possible — the best solution to the problem at hand. Nature, on the other hand, has but a single, short-term goal in mind, survival; she relies on the process of evolution to “design” her products, slowly proceeding in a bottom-up manner, sparing no expense and taking no heed of her extravagant wastefulness. With respect to expenditure one might say that engineers are like Ebenezer Scrooge whereas Nature is like Santa Claus. In a nutshell, Nature designs by evolution while engineers design, well … by design.
Nature has come up not only with ingenious solutions to specific problems — for example, structural designs such as eyes or wings — but indeed has found (and founded) entirely new processes to aid in the emergence of complex organisms. Two of the most important ones are ontogeny (the development of a multicellular organism from a single mother cell) and learning.
Engineers and computing scientists have been turning of late more and more toward Nature, wishing to learn from her ways and means. In building novel artifacts they seek inspiration in a wide range of phenomena, from general processes such as evolution, ontogeny, and learning to more specific natural inventions, such as immune systems, eyes, and ears.
Why are we so enthralled by the biological? After all, the by-ole-logic way is methodical and precise while the biological is so much “mushier.” Think of (or in my case imagine) that sleek, black Porsche 911, comfortably reposing in your garage — a triumph of modern engineering. Since every step of its design and construction involved traditional engineering techniques, we know exactly what it is capable of, and of what it is incapable: how fast it can go, its fuel efficiency, its ability to withstand shocks, its maneuverability along curves, its braking distance, and so on. Contrast this with Nature’s creations, where we are often at loss to answer such questions as: Does it work; if so, why? If not, why not? Does it work well? Does it work well all the time? How far can we push the system? What are its limits? We know how to answer such questions when it comes to a Porsche, whereas a dung beetle presents us with a far more difficult case.
You could argue that a dung beetle is a problem for biologists, whereas we’re interested in a “hard” engineering problem, building Porsches. The problem is that once we move from the by-ole-logic to the biological, using techniques such as those described in this book, we find ourselves on murkier grounds. Consider the robots discussed in Chapter 4, whose brains consist of artificial neural networks that emerge by means of evolution. We find ourselves faced with an engineered machine — the robot — for which we are very hard put to answer all those questions of the previous paragraph (we’ll elaborate on this issue when we talk about scigineering).
It might seem that I come to bury the biological, not to praise it: Why use those mushy, biologically inspired techniques to build Porsches when we have such good, well-known classical methodologies? Well, despite appearances to the contrary, most of our engineering achievements to date are quite simple, at least in comparison to Nature’s. A Porsche is less complicated by far than a dung beetle; in fact, I’d probably be risking very little in claiming that a Porsche is simpler than any one cell of your body! Our engineering techniques have worked wonders in erecting modern civilization, but our appetites keep growing; technology feeds upon itself by creating new niches that bring about new needs and desires for more technology.
The more elaborate our artifacts become, the more difficult it is to find solutions by using only traditional computing and engineering techniques. That’s when we supplement the by-ole-logic with the biological. Notice my use of the term supplement: We’re not rushing to chuck the ole techniques; rather, we want to eat the cake and have it too, combining the by-ole-logic and the biological. There’s no point in being a traditionalist or a Young Turk just for its own sake; the goal is to build better artifacts, whatever the means.
And just what good is the biological to engineers? We’ve been answering this question throughout most of this book; let’s try to summarize some of the benefits we’ve encountered. As I’ve just remarked, technology keeps getting more and more complex, which means that our traditional methodologies run up against a wall much sooner than before; more and more often they are overstretched to their limit — and then some. That’s when we start considering the biological, which often permits us to make do with but a partial design — to be completed through evolution, learning, and other biologically inspired techniques. (Incidentally, even automobile companies have recently started employing techniques such as evolutionary computation and artificial neural networks to design certain parts of their cars.)
When the by-ole-logic is stretched to the limit, it’s worth trying the biological, though one must remember that it is not a panacea. I hope I’ve managed to convey the intricacy of applying these techniques in the preceding chapters. It’s not easy to get a good bridge to evolve or to have a robot learn to walk.
Another salient difference between Nature’s devices and those of human has to do with their robustness. This term means different things in different domains, but it basically boils down to the ability to cope with a wide range of circumstances. Place a cockroach in virtually any imaginable terrain, and it’ll have no problem in walking the Earth; a robot, on the other hand, has a much harder time breaking new ground. (As we saw in Chapter 4, the robotic soccer teams played much better at their home institutes than at the match site, having grown accustomed to the home terrain.) You can suffer a severe blow and still keep on ticking; the same cannot be said of your Porsche. Plants have an uncanny ability to grow toward the light, wherever it may be. A computer recognition system has a much harder time than a human in identifying a previously bearded man who suddenly shows up clean-shaven. From bacteria to brains, there are endless examples of just how robust natural creatures are, a quality that we’d like to instill in our artifacts.
Nature places its creatures in a continual lifetime struggle for survival. Moreover, every living creature today comes from a long line of distinguished ancestors that had one thing in common: They were survivors (at least long enough to engender a dynasty). Small wonder they’ve evolved to be so versatile. After all, robustness is decidedly a boon to survival.
To emphasize just what it means to pass through the evolutionary sieve, let me recount a short tale. The 11 o’clock news announces the founding of a new airline company whose rates are three times cheaper than the cheapest of airlines. How do they manage? Simple: no humans! At Robo Airways every job — onboard personnel, reservation clerks, ground crews — is handled by computers and robots. Would you fly the robotic skies? I’d bet the company would go bankrupt very quickly for one major reason: No one would want to fly without a human pilot aboard. Why is that? After all, any modern-day aircraft has an automatic, onboard pilot that performs much of the drudgery of piloting, and you don’t have to stretch your imagination too far to envisage a fully automated flight system. What’s so special about a human pilot? Well, it’s not so much the piloting abilities as the pilot’s humanness. Obviously, there is a psychological angle that comes into play; a human pilot being much more similar to us than a machine. Let’s dig a little deeper, though.
According to robotics researcher Rodney A. Brooks, an examination of the evolution of life on Earth reveals that most of the time was spent developing basic intelligence. He wrote that: “This suggests that problem solving behavior, language, expert knowledge and application, and reason, are all rather simple once the essence of being and reacting are available. That essence is the ability to move around in a dynamic environment, sensing the surroundings to a degree sufficient to achieve the necessary maintenance of life and reproduction. This part of intelligence is where evolution has concentrated its time — it is much harder.” Playing chess, reading newspapers, and piloting airplanes are very recent skills that piggyback on our versatile brains, which have evolved over millions and millions of years. The title of Brooks’s paper — “Elephants Don’t Play Chess” — nicely captures this idea: While not able to play chess, elephants are nonetheless robust and intelligent, and able to survive and reproduce in a complex, dynamic environment.
When Nature comes up with a new product line, it is immediately subjected to the most grueling series of tests ever invented: evolution. That’s why we can trust the human pilot much more than we can the automatic one: Piloting skills are but a mere add-on to a powerful system whose design has been millions of years in the making. Or, consider another example: Any human can tell the difference between a baby and a doll, our visual system having evolved to be able to keenly distinguish our kin. Yet with Dean, the housemaid robot of Chapter 4, this is far from obvious. How can we be sure it won’t confuse one with the other (with the consequences being anything from comic to disastrous)?
The biological approach to engineering is a powerful sword to be wielded when the old tools fail, or when they yield unsatisfactory solutions. Applying processes such as evolution and learning does have its price, though, since we’ve seen how lavish the biological tends to be. We do have, however, the benefit of very fast artifacts, such as computers; thus, the biological, when applied to engineering, need not necessarily take millions of years (as with natural evolution) or years (as with human learning). Moreover, the biological approach has the potential of yielding more robust solutions, ones that do not fold with the slightest breeze. And let’s not forget that another possible biological approach to engineering is to seek inspiration not in Nature’s grand processes but rather mimic some of her solutions, examples of which are artificial retinas and artificial cochleae.
As I’ve remarked above we need not replace the by-ole-logic with the biological but rather combine the two, thus enjoying the best of all possible worlds. And when opting for the biological, we don’t necessarily have to remain 100 percent faithful to Nature; we can even at times take a bio-illogic path. Let me give just one example, that of Darwinian versus Lamarckian evolution.
The Chevalier de Lamarck was an eighteenth-century intellectual who argued in favor of evolution many years before Darwin. In this he was right. What he got wrong was the mechanism, now known as Lamarckism, or Lamarckian evolution, which is based on two principles: the principle of use and disuse and the inheritance of acquired characteristics. The first principle asserts that those parts of an organism’s body that are used grow larger, and those that are not used tend to wither away. The second principle states that such acquired characteristics are then inherited by future generations. Thus, a bodybuilder bequeaths his developed muscular physique to his children. Or, consider the following story about giraffes: The early ones had rather short necks and so they strained desperately to reach high leaves on trees. These mighty efforts resulted in longer neck muscles and bones, which they passed on to their offspring; each generation of giraffes thus stretched its neck a bit, a head start which it passed on to its offspring.
Lamarckian evolution seems reasonable. In fact, it seems rather enticing: Wouldn’t it be great to have — from day one — all those acquired characteristics of your ancestors? Alas, that’s not how things work, and so the Darwinian theory of evolution has supplanted the Lamarckian theory. The giraffe does not directly pass its long neck — acquired during its lifetime — to its offspring. Darwinism is more roundabout: Some giraffes are genetically predisposed to develop into mature animals with long necks. These will then have an advantage (however slight) over others since they will be able to reach higher leaves. Thus, they will stand a better chance of surviving and leaving offspring, which will in turn inherit the genetic predisposition (which might then be further enhanced through favorable mutations).
While the biological theory of evolution has shifted from Lamarckism to Darwinism, this does not preclude the use of Lamarckian evolution in artificial settings. It can greatly accelerate evolution since a good acquired trait can be immediately incorporated into the genome. There is still a debate as to the use and usefulness of artificial Lamarckian evolution, though my intention here has simply been to show that we need not remain 100% faithful to Nature.
The biological blazes new trails that lead to fascinating lands. But the lesson to take home is that whether by-ole-logic, bio-logic, or bio-illogic, what matters is the end result: By hook or by crook, just get it to work.
A short story by Moshe Sipper
Please excuse my handwriting — it’s been quite a while since I actually used pen and paper to write anything more than my signature, but that’s the only writing medium I found here.
I have to admit the view outside the window is breathtaking, even with the full knowledge of its mortal danger. Perhaps this is because the ticking countdown on the small digital display next to me foretells my demise in fifty-seven minutes, whether I remain indoors or venture outdoors. Maybe someday someone will come and find my note. If so, let me tell you about myself — and about Diana McFarlan.
As for me, well, what can I say? It’s a fairly dull story. I had a normal childhood, from which I emerged at the age of eighteen knowing full well what my future would look like: I’d go to college, get an undergraduate degree in computer science, followed by a Ph.D., and end up a professor, happily ensconced in some lovely green campus. Along the way I’d also meet a sweetheart whom I’d marry, and life would be blissfully calm and happy.
Which is exactly what happened. I am indeed a computer science professor, I married a wonderful girl, and we have three beautiful children.
The only hitch in my planned life was a fiery redhead named Diana McFarlan. We met in grad school — that was before I met my wife — and our affair was … explosive. Never in my life until then had I met anyone so full of life, so passionate, so loving. And with a mind as sharp as a steel trap to boot. While I was working on my one thesis, she was blasting her way toward two Ph.D.s: one in electrical engineering, the other in physics.
Our affair was as short as it was volatile, and it quickly turned sour. Diana was just … too much for me. I guess a mind like hers has its downsides as well. She lived in constant fear I was cheating on her, and insisted I tell her where I was every minute of every day. She threw tantrums the likes of which I hope I never see again (and as I’ve now only thirty-one minutes to live, I guess this particular wish shall be granted).
Diana also had this weird sense of humor. Once, after making love, she’d gone out of the bedroom and I heard the click of the door being locked. I jumped out of bed anxiously and ran to try the doorknob, only to receive a very unpleasant electrical shock.
Diana found this hilarious.
I knew I had to end our relationship before I got hurt, both emotionally and physically. Though I tried to do this in the gentlest way possible, Diana took it very hard. I still remember her shouts in the restaurant (naively, I assumed a nice, quiet dinner would promote a friendly breakup). Indeed, I think every person in that place probably remembers those shrieks: “You’re dumping me? You’re dumping me??” Then she took hold of the wine bottle — not her glass, mind you — and spilled its entire contents on me, after which she stomped out of the restaurant. That was the last I saw of Diana McFarlan.
Until this morning, when, twenty years later, I ran into her on campus. Despite a few additional wrinkles she was still very beautiful. She looked calmer than I’d remembered her, more at peace with herself, and I readily accepted her invitation to chat about old times in the cafeteria.
A move I now deeply regret as the countdown continues.
It was too late for the breakfast crowd and too early for the lunch crowd, so we found ourselves the only patrons, seated in a secluded spot off to one corner, almost as if we were lovers again. She sat quietly, staring at me intently — too intently — as I told her about my career and my family. She in turn told me that she’d gone into business after obtaining her degrees and had amassed quite a fortune. Enough to retire and pursue her own pet project.
“Which is?” I asked.
She cast a conspiratorial look around us and said quietly, “I’ve invented a teleportation device.”
I was sure she was joking but her stern look suggested otherwise. “It uses some interesting quantum effects,” she added mildly, pulling out of her purse a small device that looked nothing so much as a run-of-the-mill smartphone.
Then she smiled that smile of hers that caused my heart to melt all those years ago — and still had a profound effect. “All you have to do is set the coordinates to any place you want to go and presto, you’re there.”
“This isn’t a joke, is it?” I finally managed to mumble.
Wordlessly, she placed the device in my hand — and that was the last I saw of her. There was a momentary sensation of blackness, and then I found myself in this cabin in the wilderness.
My oxygen is about to run out. Whoever finds this, please show it to my family.
Jane, Danny, Scott, Shelly: I love you all so very much. I’d give anything in the world — in two worlds — not to be the first (or is it second?) man on Mars.
Addendum: I don’t really have to complete this account, now that I’m back home, but I felt a strange desire to do so. There’s not much more to recount, anyway.
When the countdown reached zero I felt again that fleeting darkness and found myself back in the cafeteria staring at a cackling Diana.
Did I mention she had a weird sense of humor?
Copyright © 2013 by Moshe Sipper
A short story by Moshe Sipper
Janet Cohen could tell the outcome as soon as she saw Dr. Barnaby Finch walk out the door. His ashen face spoke louder than words.
“They rejected it, right?” she asked tremulously.
“I’m so very sorry, Janet,” replied Dr. Finch in a broken voice. “I … It’s … If only —”
Janet interjected softly, trying to put on a brave face. “Barnaby, it’s not your fault. We knew it was risky.” But then her composure dissipated like grains of sand in the wind and the tears started rolling out in a trickle that soon became a torrent.
Dr. Finch wanted more than anything to hug her tightly and murmur reassuring words in her ear. But he was Janet’s thesis adviser and they were standing out in the corridor, just outside the Ph.D. committee’s door, with all those stuffed shirts soon to pour out.
So all he did was motion his lachrymose student gently to follow him, as he led her back to the lab, which they were relieved to find empty.
Empty of humans, that is. Being a primatology lab there were, of course, several primates in attendance. As they entered, Dr. Finch felt something nagging at the back of his mind, until it finally hit him after a moment: silence. The lab, usually full of ruckus made by the chimps, orangutans, and all the other merrily caged animals, was eerily silent, as if they sensed the sadness of the moment.
Dr. Finch placed his hand on Janet’s shoulder. “They … They said the thesis was interesting but … somewhat … unsubstantiated.”
A tiny smile appeared on Janet’s pretty face. “Unsubstantiated? I bet Professor Higgledy-Smythes said it was crap.”
Dr. Finch smiled too. “Well, being English, the term he used was actually ‘bollocks’.”
At that they both burst into much-needed laughter, which also ended the silence in the lab as the apes joined in with shrieks of their own. Only Mr. Nuttles, the chimp who was Janet’s favorite, quietly eyed the whole scene. Standing beside his cage Janet now felt much better.
The smile on Dr. Finch’s face faded. “As we expected their main claim was that your analysis of the dating data was incorrect.”
“Pompous idiots,” said Janet in a fiery tone. “The dating is perfect. And I’m right. I know it in my mind and in my heart: I am right!” She pointed to the epitomic cartoon hanging on the lab wall showing the evolution of humans: the ancestral, hunched-down ape to the left, followed by Homo habilis, Homo erectus, and finally Homo sapiens to the right.
Janet banged loudly on Dr. Finch’s desk and repeated her thesis mantra: “Right to left”.
Dr. Finch said nothing, being, ipso facto, intimately familiar with Janet’s thesis. She had come to him four years earlier after reading a paper on some recent findings by Kenyan primatologists suggesting that humans had come upon the evolutionary scene before apes. The findings were pooh-poohed by the establishment as nothing more than bad science. But Janet was sufficiently intrigued to dig deeper. Dr. Finch recalled now how he’d warned her against picking such a thorny, contentious, perhaps even inflammatory topic for her thesis. But Janet was, self-admittedly, “pigheaded to a fault”, and she’d hammered Dr. Finch until he’d relented, agreeing to be her adviser on this risky venture.
She was eager, tireless, and highly motivated. Within a month she’d flown off to Kenya, to meet the authors of the controversial paper in question. Thence, she’d remained on the African continent for most of the past four years, only making short hops back to the university, to discuss her findings with Dr. Finch. She’d been to Ethiopia, Nigeria, Congo, Gabon, Angola, and Namibia, the latter trip almost proving fatal as she’d been captured by a gang of outlaws. She’d only managed to negotiate her release when it had turned out the head of the gang was an enthusiastic primatologist. Indeed, he’d led her to some interesting fossils, which had later proved pivotal to her thesis.
“Right to left,” repeated Janet, referring to her crowning conclusion: the evolution of man actually occurred in the opposite direction: from Homo sapiens to ape.
“So what now?” sighed Dr. Finch, as he grabbed a chair and dropped onto it dejectedly.
“Don’t worry about it, Barnaby,” said Janet optimistically. She’d always had a knack for bouncing back quickly from even the direst setback. “I’ve got some great offers in Africa. And you know how I’ve come to love that place.”
Dr. Finch smiled sadly, realizing he’d miss her terribly. Even though she’d spent little time in the lab her spirit seemed to dominate the place. And whenever she was back the apes would lavish so much attention on her it was almost eerie.
“Janet…” he started softly.
“It’s OK, Barnaby, really,” said the young woman.
“No, this has to be said,” Dr. Finch declared firmly. “You bewitched me into letting you run wild with your thesis, and I for one believe your data is one hundred percent valid. But I’m still your adviser and as such I should have steered you into other directions.” He paused for a moment and rubbed his chin. “Directions that would have ended in a Ph.D.”
“Bollocks,” said Janet, and again the two burst into laughter. “Seriously,” she continued, “I don’t give a damn about the degree any more. Frankly, I don’t need it, either.”
“That’s my girl,” came a gruff voice from the cage next to Janet.
After a moment of stunned silence the young woman finally managed to murmur, “Mr … Nuttles?”
“Actually, it’s Doctor Nuttles,” said the chimp, grinning widely. “I rather dazzled my Ph.D. committee.”
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
Twenty-one minutes and thirty-two seconds.
Professor Artie Mensch viewed the remaining time shown in large, bold numerals on the iWall and then he turned to his young colleague. “You know, this whole system is only ten years old.”
“Is it now?” said Dr. Tommy Bing. “Well, ten years ago I was still an undergraduate.”
“Ah, youth,” sighed Artie smilingly.
“We’ll know soon enough whether we’ve nailed that grant,” said Tommy apprehensively. This was his third year as an untenured Assistant Professor and he’d yet to secure a grant. This year he’d teamed up with Artie, who as a tenured Full Professor was able to view things with far more serenity.
Eighteen minutes and seventeen seconds.
“So why did they change the system?” asked Tommy after a moment. He knew the answer, of course, but Artie loved to explain things, and Tommy felt that hearing him was better than anxiously counting the seconds.
“Well,” began Artie cheerily, “the National Science Foundation was founded in 1950, just a few years after World War II, to administer grants in science and engineering. At first things moved along nicely, grants were submitted, handled in a timely manner, and replies were then issued. But, as the years went by, the system got bogged down. The number of grant applications grew by leaps and bounds, and so, accordingly, the NSF too had to grow. Obviously, the budget for grants increased — though far less than we scientists would wish for.”
Artie chuckled as Tommy nodded. He knew all this but hearing his older colleague’s voice helped pass the time.
Thirteen minutes and forty-one seconds.
Artie went on. “But, more ominously, so did the administrative budget grow. The NSF needed more and more staff to handle the explosive number of grants submitted. I think at its peak there were about four thousand fulltime employees at the NSF. They actually had plans for constructing yet another giant building — the third — to house them all!”
Tommy was silently watching the iWall.
Nine minutes and eighteen seconds.
“And that’s just the money spent by the NSF itself to administer the grants,” said Artie forcefully. “Compound that with the work done by the reviewers. I mean, these were professors and researchers who had a full schedule as it was, and yet were still asked to take the time to review proposals. Billions were lost because of wasted reviewers’ time alone.”
“But the worst part,” added Artie emphatically, “as far as I’m concerned, was not the obscene amount of money spent — wasted! — no, the worst part was how people felt.”
At that Tommy perked up. There was nothing new about the history lesson so far, but the part about people’s feelings was unknown to him.
“You see, Tommy, scientists who submitted grants — and I can attest to this personally, mind you — often felt that the results were totally unfair. Sometimes it was obvious the reviewer hadn’t really taken the time to read through the proposal. Even worse, often a reviewer would write a scathing review just because he hated the author or perhaps because he simply had little respect for the author’s domain.”
“Humans.” Artie sighed and raised his hands in a what-can-you-do gesture. “Power corrupts and all that. You know, I once submitted a grant with Chuck Adams over at Geology about using cloud computing to study the effects of global warming in the southern hemisphere. You know what this one reviewer wrote?”
Tommy shook his head.
Five minutes and forty-one seconds.
“He wrote — and I remember it verbatim to this day: `The authors are advised to remove their heads from the cloud they inhabit and descend back to earth’. Can you believe that?”
Tommy smiled, actually forgetting for a moment the seconds ticking away.
Artie laughed boisterously and waved his hand. “Water under the bridge. Anyway, the whole thing had become a humungous, unfair, and hideous mess. And we’re supposed to be scientists — I mean, we’re the smart guys, right?”
“Yup,” said Tommy, echoing Artie’s words. “We’re the smart guys.”
“Then,” said Artie, “along came Dr. Sangria. As soon as he was appointed head of the NSF he set up a team to examine the unfortunate situation and come up with solutions. Of course, being a committee and all, they failed miserably.”
“Of course,” repeated Tommy, only half listening by now.
“Luckily, Dr. Sangria was saved by his son. Well, lucky for all of us, I guess.”
“His son?” asked Tommy, his interest once again piqued.
Two minutes and twenty-two seconds.
“Yeah, not many people know this part — everybody thinks Dr. Sangria came up with the idea on his own. But I met him a few years ago and he told me the whole story. Seems he and his boy were at a baseball game, which was going badly for their team, when the boy blurted out, ‘they might as well save money on an umpire and use a coin toss instead’. And that remark changed the course of science. Well, at least that of science funding.”
The countdown disappeared and was replaced by an announcement:
The National Science Foundation wishes to thank all the dedicated researchers who submitted grant applications.
The lottery has now finished.
To learn whether you have won a grant please click here.
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
She was just too beautiful — and he, too shy. Would that he could work up the courage to ask her out. But, alas, affairs of the heart are never easy. Not by far.
For hours and hours every day he would gaze upon her, observe her, study her. Every minute detail of her fine-boned visage he would scrutinize with unending yearning. How he craved to caress that magnificent shock of raven hair. How he longed to touch — ever so gently — that noble neck.
In his dreams they were so happy. She, his loving, adoring wife, and he, a rhapsody of passion. Together, they would walk through fields of poppies and tall grass, the wind breezing softly, parting the flowery bed just for them — a miracle worthy of Moses. But not one of parting, no, it was a miracle of uniting. In his dreams everything was so perfect. Until the cruel claws of dawn would tear him away.
Bleary-eyed, before washing or shaving, he would go directly to the telescope, imbibing the first glimpse of the day, like a weary desert traveler who stumbles onto an oasis. Quaffing the magnified image of the woman of his dreams, now that reality had so brutally asserted its dominion.
His friend Marco would often jeer at him. “Leo, my good friend,” Marco would say, “she’s just a woman. Contrary to what you may have heard” — here Marco would chuckle lightly — “they’re quite human. Come now, my good man, you march up to her house right this instant and woo her!”
Countless a time they had had this conversation. But it would never advance the state of the (non-existent) affair. Marco was so confident, so worldly, so adored and adoring. Sure, for Marco it was all too easy — just a game.
But Marco refused to give up. He would nag, tease, persuade, cajole, snap, plead — all to no avail. Once Marco had even threatened to smash the telescope! But seeing the look of horror that had spread upon his friend’s face, Marco’s apology had followed with breathtaking swiftness.
The days would fly, riding the waves of time with frightening rapidity. But still he would not, could not, did not.
Until Marco had had enough of it all — and had dragged the damsel in question to his friend’s house one bright spring morning. And when the latter had been revived with the aid of a stiff drink, introductions had finally been made.
It had taken a while therefrom, but love had eventually sprung, and a wedding finally took place. After much dancing, laughter, and wine, Marco — slightly inebriated — sidled up to his friend and asked, “What will you do now with the ol’ telescope, Leo?”
“Hmm ... Maybe point it upwards? I’ve always fancied studying the stars,” Galileo answered dreamingly.
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
Dr. Marvel Sky was the greatest computer scientist of his day. He had but a slight problem with his career in that his unique greatness was uniquely recognized by himself alone. And so, at the age of forty-five, he was still but a lowly assistant professor.
And untenured to boot. Indeed, he’d just exited what had proved to be his penultimate tenure hearing, and the words of Professor Jed Newman, Head of the Tenure Committee, still rang loudly in his head. “Dr. Sky,” that arrogant jerk had said, “this will be the fifth and final extension of your contract as an untenured assistant professor. Should you fail within the next year to produce sufficient proof of your worthiness as a leading researcher in Artificial Intelligence, I’m afraid we shall be unable to grant you tenure and promotion to the rank of associate professor.”
In other words, one year and you’re out, thought Sky gloomily. Then his face cheered up as he recalled his latest brilliant idea, the last in a series of exceptional ideas hitherto unrecognized by his peers. Worthiness, he thought brazenly. I’ll show you worthiness.
Back at his office he continued relentlessly to work at programming his wondrous AI agent. The idea had come to him when he’d chanced upon a cartoon depicting a bald, bespectacled, white-coated, epitomic scientist with a paper in his hand, facing a path flanked by fellow scientists holding swords, axes, clubs, and other implements of war, with the end of the road marked by a large sign: “Paper Accepted”. The caption read: “Most scientists regarded the new streamlined peer-review process as `quite an improvement’.”
That’s when it had hit him: While in many ways the scientific endeavor had advanced since his graduate days, the one thing that had remained constant — indeed hadn’t changed much since the advent of post-war academia — was the peer-review process. And poor Sky had had much experience with said process, most of it rather negative: Lengthy, error-prone, subjective, sometimes tainted by ugly politics, with the end result often serving to display the referee’s sheer stupidity rather than the author’s — namely Sky’s — well … worthiness.
But that will change, Sky had thought in a flash of inspiration. For a moment he’d actually felt the heat of the proverbial light bulb above his head. Wasting not a second, Sky had sat down to program the referee to end all referees — at least of the human ilk. He ate little, slept even less, and was only given to preposterous interruptions such as tenure hearings.
Nine months following that fatal meeting AutoRef was ready. Now he had to deploy it — and quickly, before the year was out. Sky then thought of Jake Cart, a friend from his undergraduate days. Well, “friend” would be stretching it a bit, perhaps “acquaintance who didn’t flinch when he saw him” would better define their relationship. Anyhow, Cart was now a respected member of the scientific community, and — more to the point — he was on the Editorial Board of none other than NatSci.
Surprisingly, Cart did not flinch when Sky contacted him. Even more surprisingly, NatSci agreed to “test-drive” AutoRef! And so, one month prior to his final, life-or-death tenure hearing, the vaunted journal was offering submitting authors the option of a speedy review should they choose to forgo the human review process. Speedy indeed: Sky’s AI agent would take all of approximately forty nanoseconds to reach a decision.
I’m almost there, thought Sky in delight. Now I just have to write up the paper describing my wonderful design and that bastard Newman will not only have to grant me tenure, he’ll have to promote me to Full Professor! A smile began spreading over Sky’s face as he thought, No, that’s not it. Newman will have to beg me to stay, as I’ll have offers pouring in from all the top places. His smile broadened. And, boy, beg he will.
While Sky liked doing research well enough, he hated writing up the subsequent requisite papers. But this time his voice carried strong and confident as he dictated the paper to his iDic. In less than a week the research was written up, packaged, sealed, and ready to be shipped out to a top journal.
As he voiced that final command — “Submit” — causing the paper to be cyberspatially whisked forthwith to NatSci, an image of Newman with egg covering his entire face provoked a burst of laughter in Sky that actually brought tears to his eyes. Needless to say he had selected the AutoRef option. And so, even before the laughter had subsided and his tears had dried out, the journal’s reply was floating in beautiful, holographic glory before his eyes:
“Dear Dr. Sky,
We are grateful for considering NatSci as a venue for publishing your paper titled “Autonomous Refereeing by a Semi-Cognizant Agent”. At this time we would like to inform you that the review process has finished. Regrettably, we will be unable to publish your paper. A detailed account follows below —”
Sky’s angry shout was heard far and wide. Indeed, in years to come phrases such as “a year after the Big Shout” were fairly common around campus.
But by then the shout’s owner no longer cared, for he’d left academia to find utter bliss occupying the position of Chief Editorial Cognizer for the SciNat group. While some might consider this a fortuitous though somewhat orthogonal move, the truth was Marvel Sky simply delighted in seeing a computer rejecting his former colleagues.
He found it far better than egg over their faces.
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
“Seventy-nine?!” cried Linda Ryan, Head of the Planetary Survey. “You're sitting here with a straight face, telling me the Takeinians have seventy-nine different sexes?”
“Yes,” replied Mark Yang tersely and undauntedly. Head of the Contact Team, he was always terse and undaunted. But damn competent, thought Ryan. She knew he had much to do with the success of their mission. They'd already got to a point where they could carry on halting conversations with the Takeinians, which was remarkable considering they’d only arrived to this new world eight months ago.
“Don't tell me you need a member of each and every one of those seventy-nine sexes to reproduce?” mused Ryan.
“No,” replied Yang. “Apparently, it takes between three to seven different-sex members to produce offspring.”
“The number of combinations of possible sexual matings is huge,” mumbled Ryan.
“Well, there seem to be some non-viable configurations, but basically you're right. There are billions of possible mating combinations,” Yang said evenly.
Nature sure had a field day on this planet, thought Ryan. The Takeinians were actually quite friendly once you got to know them, yet Ryan was still befuddled to learn of the existence of so many sexes. When they had first got here, she had been sure Takeinians came in but one flavor.
“How can they tell the difference?” asked Ryan.
“Their equivalent of an olfactory sense is much more developed than ours. Apparently, they have no problem telling one gender from another.”
We can hardly handle two, thought Ryan, and these guys ... get to not understand seventy-eight other sexes ... “Don’t they ever get tired of this big mess?” she asked Yang pointedly.
“Actually, they do,” replied the young scientist. A gleam of amusement appeared in his eyes, a rare occurrence indeed. “But they’ve developed a way to counteract the anxiety of sexual multiplicity.” He paused for a moment and stared at Ryan.
“For relaxation they entertain relationships with members of the same sex.”
“You mean —” started Ryan.
“Yes,” interjected Yang. “Homosexuality is their idea of uncomplicated bliss. And it comes in seventy-nine different flavors.”
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
As Stewart was about to descend the final rung he heard a shout on the helmet radio:
Which engendered the unfortunate consequence that the first words uttered by a human being on Mars were:
“Who the hell was that?”
Years of planning. Months of travel. This was not how things were supposed to unfold, thought Commander William Stewart as the realization sank in: he was standing on the planet surface. On Mars. A small step, et cetera, he brooded. Damn.
Stewart was about to unearth (unmars?) his would-be comedian crew member, who would sorely regret the day he was born, when he noticed in the distance a rapidly approaching cloud of red dust.
Totally defenseless, there was nothing at all Stewart could do — so he just stood there and waited. A few tense minutes later the dust cloud settled neatly a few feet before him. Quickly it cleared — to reveal a sleek, black Corvette, inside of which were seated two men.
Men, that is, if you ignored their greenish complexion.
The Martians stepped out of the car. “Hi William,” said the driver, his voice somehow transmitted over the radio link. He was grinning broadly, like a Cheshire cat.
A green Cheshire cat.
Commander Stewart was as solid as they came. It was the first — and last — time in his entire adult life he literally fell flat on his ass. All he could finally mumble, after several minutes, was, “A black Corvette ... A black Corvette ...”
“See, he doesn’t like it,” said the second Martian, the one who had been riding shotgun. “I told you we should’ve replicated a Porsche 911, but would you listen? Nooooooooo.”
Communication from his shipmates in the Marslander had in the meantime confirmed Stewart’s sanity — there really were two bona fide Martians about. He pulled himself together.
“Where have you been all this time? Why have we never discovered you? We scanned every damn —”
“My, my,” interrupted the green driver, “aren’t we inquisitive. All in good time.”
“But first thing’s first,” said the second Martian, as the first one looked eagerly on.
“Do you happen to have a set of four original Corvette hubcaps? Preferably chrome-plated!”
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
The head of the Special Presidential Task Force was not pleased. Not pleased at all. “Let’s recap again what we know,” said Aaron Sloan in a voice revealing his state of near-exhaustion, the result of three intensive and frustrating months. “John, why don’t you begin?”
John McFinley, Head of the Physics Group, cleared his throat. “Well,” he proceeded at his habitual slow pace, “we’ve established beyond any doubt that the objects are not of natural origin. They are artifacts, probably made by intelligent beings.”
“How many of them have fallen so far upon the earth’s surface?” inquired Sloan.
“We only have estimates, calculated on the basis of observed average number-of-objects per one square kilometer,” replied a petite woman in the back — Shirley Newman, the geophysicist. “Our current estimate runs at just a little over twelve billion. Of course, being smaller than a raindrop, the margins of error are quite large.”
“Okay, let’s stop fiddling around with the small potatoes.” Sloan raised his voice: “What the hell are they?”
This time, no one rushed to speak. “Rachel?” Sloan finally turned to Rachel Stein, Head of Exobiology.
“We’re pretty certain that each teardrop” — that was how the objects had come to be called — “contains a form of encoded message. The problem is, at the moment we have only vague ideas as to the code involved. It may actually be some form of nanoetching, which is quite beyond our current technology. And that’s just the material, substrate question. Then there’s the whole issue of deciphering the language — and the message itself. Or rather messages. That’s one thing we’re pretty sure of: each teardrop contains an entirely different configuration, which means it’s probably a different message. Of course, there’s also the possibility that some kind of error-correcting code is used, with random combinations.”
Sloan felt one of those nasty headaches coming on. “Enough for today,” he said. “Just ... Oh, hell, just get on with your work. I’ve got a president to assuage.”
“Hold all my calls,” Sloan told his secretary as soon as everyone had left his office. He then dimmed the lights and set his tablet to play one of his favorite albums, Best of The Police.
Walked out this morning, don’t believe what I saw, sang Sting, as Sloan stretched out on the sofa and closed his eyes.
A hundred billion bottles washed up on the shore. This was one of his favorite songs.
Seems I’m not alone in being alone.
Already, Sloan felt more relaxed. Hell, let the president wait.
A hundred billion castaways looking for a home.
Copyright © 2012 by Moshe Sipper
A short story by Moshe Sipper
It was a tranquil Sunday afternoon, a rarity I savored greatly but which annoyed Olmes to the utmost. It did not last long, though. The calm was suddenly interrupted when Olmes sprang like an arrow and proclaimed:
“The King’s emissary shall present himself at our abode within the moment.”
“I suppose some amazing explanation of this deductive feat of yours is forthcoming?” I said, trying hard to stifle a yawn.
“Elementary, my dear Atson,” said he, “I simply recognized the gentleman’s odor.”
And at that there was a knock on the door. The King’s emissary, of course. Olmes’s olfactory capacities were unarguably much keener than my own.
“Welcome, welcome to our humble abode.” Olmes made no effort to hide his delight at what he no doubt considered a fortuitous breach of our pleasant idleness. The nondescript yet tastefully attired man entered our domicile, while Olmes reclined in his favorite armchair, and lit up his cherished pipe.
“You seem a tad nervous,” remarked Olmes as the man settled into the proffered chair, his hands visibly shaking. “Atson, I do believe the gentleman is in need of a glass of sherry.”
The potable seemed to soothe the emissary. “This is an exceptionally fine drink,” he said, after having quaffed several sips. “Thank you, Doctor Atson.”
“Royal Sherry,” said the good doctor with a modicum of pride. “Only the best.”
“Now,” said Olmes in that special trust-instilling voice of his. “Let me see if I can apply some simple logic to deduct the cause of this royal visit.” He paused for a moment. “The King’s daughter, Princess Ann, has gone missing.”
The man was agape. “Mr. Olmes, you are nothing shy of a genius! How on earth did you arrive so quickly at the correct conclusion?”
“Well,” relaxed Olmes as he assumed his famed explanatory posture, “the princess is obviously a virgin.”
“Most obviously.” The man seemed somewhat indignant, which did not perturb Olmes in the least.
“She is of the right age, is she not?” Olmes continued his chain of reasoning.
“Yesterday was her eighteenth birthday, Mr. Olmes. That is when she disappeared.” The awe emanating from the man’s eyes was unmistakable.
“I know exactly what happened,” proclaimed Olmes in a voice evidencing his flair for the dramatics, with his eager, stooping face shining in delight. The emissary leaned in closer.
“She was kidnapped by two dragons!” Olmes declared triumphantly.
“No!” said the man. “How can you be so sure?”
“Elementary,” said Olmes, as he rekindled his pipe with a puff of fire. “You see, it was Atson and I who flew into Princess Ann’s chambers late last night and spirited the lovely damsel away.”
“You must understand,” Olmes added in earnest, “we were running out of Royal Sherry.”
Copyright © 2012 by Moshe Sipper
by Moshe Sipper
We were watching TV,
My friend Billy and I,
When Mommy came in
With a hot apple pie.
She said, “Boys have a piece
Of this hot apple pie,
I’ve made it just now,
Go ahead, don’t be shy.”
I ate one big piece,
And Billy had two,
My mommy makes pies
Like no one we knew!
Then Mommy said, “Boys,
Why sit in the dark?
It’s so sunny outside,
Go play in the park.”
So I looked at Billy
And he looked at me,
We knew that the park
Was where he would be.
But Mommy would not
Let us stay in the house,
So we had to go out
And play cat and mouse.
We walked very slowly,
A little afraid,
We remembered what happened
The last time we played.
But there in the park
We met Danny and Jimmy,
And Bobby and Chad
And Alan and Timmy.
So all of us laughed,
We had a good time,
And I got to show
How well I could climb.
Then Billy just stopped
Like he swallowed a cat,
And we all saw him then,
The brat with the bat.
His real name is Robert
And he’s only ten,
But don’t call him that,
Or you won’t walk again.
The brat was alone
With his only real friend,
That hard wooden bat,
He’d use it to send,
Danny and Jimmy
And Bobby and Chad,
And Billy and me,
Running like mad.
So the brat came to us,
It was too late to run,
He said, “I’ll fuck you all,
This is gonna be fun!”
And he laughed like one
Of the bad guys you see,
Just before shooting
All those men on TV.
Then the brat raised his bat
And broke Danny’s arm,
He cried, “Fuck, this is great!
Like rats on a farm.”
He hit Jimmy and Bobby,
Kicked Alan and Chad,
And Timmy and Billy
Were bleeding real bad.
I tried to help Billy,
But the brat got to me,
He used his old bat
And busted my knee.
And as we were lying
Out there in the park,
Bleeding and broken,
Alone in the dark,
We heard this man shouting,
Gosh, was he fat,
That man was the dad
Of the brat with the bat.
“Come here good-for-nothing,
Slime piece of shit,
Come, you son of a bitch,
Copyright © 2013 by Moshe Sipper