Freaks of Science

What do scientists believe in?

“When you’re one step ahead of the crowd you’re a genius. When you’re two steps ahead, you’re a crackpot.”

— Shlomo Riskin

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Science is called science, because it is supposed to follow “the scientific method”:

The same process under the same circumstances brings about the same result.

It’s easy to admit that in reality, there is no such thing as exactly the same circumstances, so the condition can hardly ever, if ever, be met.

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When it comes to judging the work of scientists, there are four levels of complexity and reliability. Well, there used to be three before I added the fourth in 1998.

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First: The first scientists worked on a trial-and-error basis, but that didn’t necessarily prevent success. In fact, the vast majority of inventions have been the results of experimenting or accidental findings. A Greek slave, for instance, built the prototype of a steam engine in ancient Rome. A lot later, Newton’s apple was an ad-hoc event that gave birth to the concept of gravity. While gravity in Physics can be calculated well enough for practical purposes, nobody knows what gravity is even today.

So, the most ancient criterion of “scientific” proof was that a method worked. However, being functional never proved that the method was based on truth. Even bloodletting worked in medieval “Medicine,” but not because the theory behind it was true. What turned out was that every profession, especially the “academic” ones, needs some explanation that is so mind-boggling that nobody dares to challenge it. After all, something has to explain why people need “experts.” The wise doctors of old blandly assumed that they were creating openings for the “bad fluids” to leave the body, while the procedure merely activated the body’s reserves, which often resulted in the patient’s recovery. Strangely, nobody dared to ask how the “bad fluids” knew that it was time to go and why the “good” ones decided to stay. The “profession” held its privileged “knowledge” inviolable and “unqualified” laymen were not allowed to question the “experts.” (Some things never change, eh?) The alchemist followed the same method, trying to turn waste into gold. Most, if not all, marketed products follow the same principle of usability even today.

Scientific research is also limited by the available tools and instruments, which resembles the joke in which a drunk is searching for his lost keys:

A drunk is crawling around in circles in the middle of the night on all fours under a streetlight. A helpful policeman asks him,

“What are you doing here, Sir?”

“Looking for my keys.”

Okay, the policeman also goes down on all fours, joining the search. After like half an hour, he asks the drunk,

“Man, is this where you lost your keys?”

“No.”

“Then why are you looking for them here?”

“Because there is light here.”

The procedure is more common with “experts” than a commoner would like to believe. “Scientists” these days cannot afford to buy their own research facilities, so they are not even given the option to search anywhere else but in the limited domain permitted by their employers and they are denied to find anything that their employer dislikes (“researchers” usually sign a waiver that their findings and their inventions belong to their employers).

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Second: Following the trial-and-error method, the next step up to a more demanding requirement of science is that the theory gained from a method that works has explanatory power. In the case of bloodletting, the method worked even after the theory was refuted. A theory, employed under the same circumstances, is nothing else but a mundane analogy (it’s not that shameful, because at least 99.9% of human decisions are based on analogies, anyway).

Another joke can easily illuminate the ridiculousness of the assumption that a theory can reliably explain anything:

A biologist is studying the African flea.

He carefully checks that the flea has all its six legs, places it on the table and says,

“Jump!”

The flea jumps.

He tears out a couple of legs, puts it down again and says,

“Jump!”

The flea jumps.

He removes another two legs and tells the flea to jump. The flea jumps.

He removes the remaining two legs and says,

“Jump!”

The flea doesn’t budge.

He raises his voice,

“Jump!!”

The flea stays put.

He screams at the top of his voice,

“Juuump!!!”

The flea doesn’t jump.

The scientists is satisfied and writes down his conclusion:

“The African flea, after all its legs are removed, loses its hearing.”

Again, mixing up causes or replacing effect with cause is extremely common in “science,” especially in Medicine.

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Third: The highest step on the ladder is represented by the requirement that a theory can explain, but it can also predict outcomes. The joke about the African flea sufficiently demonstrates the fallacy in the fancy condition, but sometimes prediction can actually work, as in the following joke:

A man is sitting on the branch of a tree and cutting the branch off. A passer-bye pauses next to him and warns him,

“If you keep doing that, you will fall!”

The man ignores the warning and once the branch is cut, he falls off.

He wastes no time, but runs after the passer-bye, because he wants to meet someone who can see the future.

All in all, much of what people believe is science, is only smoke and mirrors.

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Fourth: In 1998, I must have been one of the first, if not the first linguist, who introduced a new criterion: computability. I also correctly predicted that a simulation of the world was going to be constructed in the near future, using live data. I also declared that human programming would never solve the problem; the process must be left to the computer, including segmentation, prioritization, and compartmentalization according to problem-solving hubs in an open system that dynamically changes its multi-dimensional structure and internal data paths according to its needs towards solving specific problems. The programmers would have no idea what the machine is doing, and will have to wait until the self-improving algorithm evolves to the point that it can explain past events and predict the future, and develop its modules for solving specific problems, including its own system maintenance both in the simulation and for real.

My colleagues were looking at me like I was a nutcase, but that is exactly what the technocrats are using these days: a live global simulation. And it works.