Success = talent + luck
Great success = a little more talent + a lot of luck
-- Daniel Kahneman, Winner of ​Nobel Memorial Prize in Economic Sciences (2002)

If you’re so smart, why aren’t you rich? Turns out it’s just chance.
The most successful people are not the most talented, just the luckiest, a new computer model of wealth creation confirms. 

Granted 
A short story. Excerpt from my book Fredric.

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 2, to administer grants in science and engineering. At first, things moved along nicely, grants were submitted, handled in a timely manner, and decisions 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 full-time 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 humongous, 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 Director 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.”

Three.

Two.

One.

​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.

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.

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

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

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.

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

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.”

Three.

Two.

One.
​
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

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