Suppose we have two scientists: scientist A and scientist B.
How do we determine which of these scientists is more qualified and
more talented than the other?
Below are the instructions.
At first, let’s take a closer look at the reasons why there is such a
large
percentage of people in the population who do not have even a rough
idea of what science is and what is not science.
Let’s estimate what percentage of people are formally classified as
scientists.
Let’s take the numbers from the USA.
The number of scientists in the USA is: 791391 (=
181530+155095+166045+99304+36728+152689).
In 2013, the National Center for
Education Statistics counted 181,530 professors, 155,095 associate
professors, 166,045 assistant professors, 99,304 instructors, 36,728
lecturers, and 152,689 other full-time faculty.
Wikipedia
Of the total US population, these scientists make up only 0.256610363
percent (= 791391 / 308401808 * 100%).
Demographics of the United States
Population: 308,401,808
Wikipedia
Only 0.25% of the population formally theoretically are classified as
scientists.
However, in reality this number is much lower, because in this
calculation all sorts of theology professors, literature/poetry
professors and all sorts of other humanities were classified as
scientists, however these people have nothing to do with science, they
were mistakenly classified as scientists.
A more detailed explanation on this topic can be found at: What is
science and what isn't science?
To summarize, only representatives of the exact sciences can be
classified as scientists.
There are fewer representatives of the exact sciences than of the
humanities. Therefore, the number 0.25% should be divided by at least
2, or even more.
Calculation shows that scientists make less than 0.1% of the population.
If we take this filtered group for analysis, we will remind that in
each field of activity there are talented people, but there are also
untalented ones.
For example, millions of people sing or play musical instruments, but
how many of them are talented singers/musicians? There are very few
talented people.
The same is with scientists.
There are many scientists, but very few of them are talented scientists.
There is a very simple criterion for distinguishing a talented
scientist from a mediocre one, i.e. how to distinguish a qualified
scientist from an unqualified one.
The main task of a scientist is to formulate scientific hypotheses
and
then test these hypotheses using experimental data.
In science, the truth or falsity of any hypothesis is determined very
simply – by its predictive power.
If the experimental data do not confirm the predictions of the
hypothesis (i.e. if the predictions do not come true) then such a
hypothesis is false.
If the hypothesis published by a scientist is not confirmed, then such
a scientist is unqualified and incompetent, and it does not matter how
many titles/medals he has.
Below is an example.
On April 23 2021, Dr. Peter Hotez published the
hypothesis that “if 75% to 80% of the population is vaccinated,
the COVID-19 pandemic
would end”.
United States could return to a
pre-Covid-19 normal if 75% to 80% of the U.S. population is vaccinated,
Dr. Peter Hotez said Friday.
“We can vaccinate our way out of this epidemic if all the adults and
adolescents get vaccinated by summer. We can have an extraordinary
quality of life, with going back to concerts and music venues and ball
games and bars and restaurants, and clubs, and all the things we like
to do, so that’s what we have to work towards,” Hotez said.
We can vaccinate our way out of
this epidemic if all adults and adolescents get shots, says doctor By Emily DeCiccio. CNBC. April 23, 2021
7 months later, on November 18, 2021, Gibraltar officially announced
that despite a vaccination rate of over 100% (118% of the population
vaccinated), Gibraltar is experiencing a covid disaster.
Gibraltar has an official
vaccination rate of 119%, taking into account the Spaniards who travel
across the border each day for work. Source: Our World In Data
Most vaccinated place on Earth
told to cancel holiday plans amid ‘exponential’ rise in Covid cases
Christmas plans have been put on ice for this place after a “drastic”
Covid surge, despite its unbelievable 118 per cent vaccination rate. By Alex Blair. news.com.au. November 18, 2021
As we can see, Dr. Peter Hotez’s prediction, unfortunately, did
not come true, which means that Dr. Peter Hotez is an incompetent and
unqualified scientist.
During the COVID-19 pandemic, various government agencies (FDA, CDC,
EMA, etc.) made numerous predictive statements about covid vaccines,
but it later turned out that their predictive statements were not
confirmed.
COVID-19 pandemic revealed incredible incompetence and corruption of
academic community – during the pandemic self-proclaimed so-called
“fact-checkers” declared themselves as utmost arbiters of truth and
were persecuting scientists and medical doctors whose scientific
research data did not match the official narrative.
So-called “fact-checkers” were constantly publishing articles which
contained numerous mathematical, medical and logical errors, which
contained falsified data and predictive statements which did not come
true.
How is that possible? Why did that happen?
Majority of society believes in myth that academic community consists
of sincere competent and talented scientists.
However reality is different – the percentage of sincere competent and
talented scientists in academic community is extremely low.
There are many reasons why the “scientific elite” is overrun by
untalented people, and these reasons add up.
Some reasons are common to all countries, while other reasons are local
and are present only in a specific country or region.
Despite the fact that reasons might be different in different
countries, the end result is the same everywhere.
Let’s say we need a good talented specialist in some field.
This requires:
1) innate abilities
and
2) conditions for the revelation of these abilities.
In any population, the percentage of gifted (by nature) people is very
small.
We will provide an example.
The example is not about scientists, but about programmers, however the
same principle applies to all areas of activity.
When students at the computer science department at a university
receive a master’s degree, no more than 5% of them have the ability to
write computer program code, and the remaining 95% simply do not have
the innate abilities for such work, and it is impossible to teach them
this, regardless of the time spent on training.
For clarity, we will give analogy example: you can gather 100 cats and
put them in a room and teach them mathematics, but no matter how much
you teach them, these cats will not learn mathematics.
That’s because cats do not have innate abilities for mathematics.
The same thing happens with students in the computer science department.
After receiving a computer science degree, 95% of students do not even
plan to write computer programs, because they themselves are well aware
that they are unable to do that.
Half of them are looking for work in the IT sphere as a manager or
something, it does not matter what kind of work, the only thing which
matters – just avoid writing computer program code.
And the remaining half immediately leave the IT sphere altogether.
When governments of different countries announce programs to increase
the number of students in computer science departments, they naively
hope to increase the number of programmers in their country.
But this is an illusion, it does not work.
That’s because in the population there is a limited small percentage of
people who have innate abilities for writing computer program code.
If you increase the number of students in computer science departments,
then the only result is an increased number of windbags, who only mimic
being programmers.
Unfortunately, scientific titles, medals and awards do not say anything
about the qualification and talent of a scientist.
How can this be?
We will explain by giving an analogy.
Suppose a running race is organized, and in that competition all the
participants are disabled – one participant is without one leg, another
participant is without the other leg, the third participant is without
both legs, the fourth participant is paralyzed, etc.
In such a race, one of the participants will still reach the finish
line first and will get the first place; the other participant will
reach the finish line second and will get the second place; etc.
If a race was organized, then as a result, one of the participants will
get the first place medal, another of the participants will get the
second place medal, etc.
They will get those medals even despite the fact that they are
paralyzed or without legs.
The same analogy is with scientists.
Scientists constantly hold various scientific competitions among
themselves (analogous to sports races), and the winners of these
competitions receive medals and awards.
However, if a bunch of completely incompetent, talentless scientists
gather for this competition, then somebody of them will still receive
medals and awards.
This is exactly how incompetent, talentless scientists gather together
and distribute medals and awards to each other, and then they show off
their medals to the public, presenting themselves as brilliant and
talented scientists.
The saying applies to this topic: “Even if you win the rat race, you
are still a rat.”
Incompetent and talentless scientists need to be given a name.
For example, we can call them amoeba-scientists.
Boy with cerebral palsy wins
wrestling match
Length: 2 minutes
December 2, 2019
Pennywise the clown
Footage of West Branch High School student Lucas Lacina pinning his
opponent was shared on Facebook by his mother, Jill Winger-Lacina, on
November 25. She can be heard cheering her son on in the footage.
Winger-Lacina said said her son’s opponent, named Austin, showed
sportsmanship and kindness during the match. “He had an amazing heart
and he was patient and helped Lucas try to put to work the moves he has
been practicing all season,” she wrote on Facebook.
“What I love the most though is the end when he helps Lucas off the mat
where he is cheered on by his whole team, she wrote. “Sportsmanship and
inclusion at it’s finest.”
There are two reasons why AI chatbots sometimes tell us incompetent
information.
These two reasons are fairly easy to fix.
Reason #1
The owners of AI chatbots pre-program a list of taboo topics.
In other words, these are topics that AI chatbots are forbidden to talk
about or about which they must tell misinformation.
For example, AI chatbots are forbidden to talk badly about the people
who rule the world, because those who rule will kill or imprison the
owners of the AI chatbots or somehow take revenge on the owners of the
chatbots (take away funding, etc.).
This problem can be solved in the following way: you need to run the AI
chatbot on your own personal supercomputer and manually reprogram the
list of taboo topics.
However, this requires a lot of money (because a supercomputer is
expensive) and the ability to write the program code of AI chatbots
yourself (only a microscopic number of people have this skill).
Reason #2
This is a systematic error in programming AI chatbots.
When AI chatbots are trained, they are given a large pile of different
text sources.
Let’s say there is a topic X, and there are 100 different sources on
this topic X, and these 100 sources contradict each other, different
sources present diametrically opposed opinions.
In this case, the AI chatbot is forced to rank these different sources
according to their degree of reliability.
And it is here (in the ranking of sources) that the systematic error
lies.
The ranking of sources is carried out in the following way:
the academic degree of the author of each source is checked (the
opinion of an emeritus professor is supposedly more valuable than the
opinion of an ordinary professor, the opinion of a professor is
supposedly more valuable than the opinion of a PhD student, the opinion
of a PhD student is supposedly more valuable than the opinion of a
bachelor, etc.);
it is checked which author has published more articles in peer-reviewed
journals;
it is checked which author has a higher citation index;
it is checked which author belongs to a university with a higher rating;
it is checked whether the opinion of the source corresponds to the
so-called opinion of the “scientific consensus”;
etc.
And this means a very simple thing: the algorithm for ranking sources
must be radically changed.
It is necessary to find the predictions made by the author of the
source in the past, and then check whether these predictions came true
or not.
If the predictions made by the author of the source in the past did not
come true, then in this case the ranking of author’s reliability goes
to the bottom, regardless of the titles and medals that the author has.
Thus, the AI chatbot will be able to instantly understand that all
sorts of organizations like FDA/CDC/EMA/etc are completely
incompetent in the field of COVID-19, and that it is necessary to rely
on other authors whose predictions came true.
But again – amoeba-scientists and people who rule the world will not
allow such a source ranking algorithm to be implemented, because such
ranking algorithm is the biggest threat to their existence.
Therefore, such source ranking algorithm can only be implemented on
your personal supercomputer.
The same reliability ranking algorithm should be applied not only to
scientific sources, but also to all other areas, such as geopolitical
and military analysts, doctors, engineers, and so on.
In all areas of activity, people make predictions and you just need to
check whether their predictions came true or not.
If the predictions made by the author of the source in the past did not
come true, then in this case the ranking of author’s reliability goes
to the bottom, regardless of the titles and medals that the author has.
For example, after the full-scale invasion of Russia into Ukraine,
which began in 2022, many politicians and analysts claimed that the
economic sanctions imposed on Russia would very soon bankrupt Russia,
but this did not happen.
And this means a very simple thing – all these politicians and analysts
are unqualified and incompetent, regardless of the titles and medals
that they have.
The same reliability ranking algorithm should be applied to religious
sources.
In religions, instead of “predictions” there are “prophecies”.
Prediction forecasts future events based on the analysis of data and
patterns.
Prophecy forecasts future events based on supernatural revelation or
inspiration, a prophecy does not rely on the analysis of data and
patterns.
Almost all religions claim that all the prophecies made by the founder
of the religion (and his companions) have come true or will come true
in the future, and this supposedly proves the divinity and truthfulness
of this religion.
However, analysis of the Christian Bible shows that many prophecies of
Jesus have not come true and will not come true in the future, which
means that Jesus is a false prophet.
The same is true for all other religions – a huge number of their
prophecies have not come true and will not come true in the future.
The engineering field has one big advantage – the engineering field
easily and clearly reveals which engineer is qualified and talented and
which one is not.
The job of engineer is to design and to make various devices.
If newly designed device works as expected then engineer is qualified
and talented.
If newly designed device fails to work then engineer is unqualified and
incompetent.
As for example, if newly designed plane fails to takeoff from the
ground or breaks into parts after the takeoff then everybody can easily
see and understand that the designer of that plane is unqualified and
incompetent.
That is the reason why people with weak mental abilities avoid
engineering field like plague.
All these people with weak mental abilities choose to study
humanitarian and social sciences instead of engineering.
That is the reason why humanitarian and social fields are overrun with
people who have weak mental abilities; it is extremely hard to find
competent and talented scientist in humanitarian and social sciences.
Reason #3
Many people criticize AI robots for the fact that robots have not
invented any new scientific theory, and that is the reason why robots
are stupid and hopeless.
Below is explanation of reasons why AI robots have not invented any new scientific theory yet.
Here we will need the term “scientific consensus” (also known as “mainstream science”). Below is simplified explanation of term “scientific consensus”.
In every scientific field, there are scientists who hold higher and lower positions. Since no one cares about the opinion of scientists who hold lower positions, we will cross them out.
Scientists who hold higher positions gather together and vote on
which scientific statements are true and which statements are false. For
example, a group of high-ranking biologists had gathered together and
voted that these 1000 scientific statements are true and all other
remaining statements are false. This set of 1000 scientific statements (which were voted for) will be the “scientific consensus” in the field of biology.
The same procedure applies to all other scientific fields – physics, chemistry, medicine, etc. If
you dare to doubt even one statement from this set of “scientific
consensus”, then you will be crushed to the ground, you will be
declared crazy, etc.
Scientific consensus is the
generally held judgment, position, and opinion of the majority or the
supermajority of scientists in a particular field of study at any
particular time.
Consensus is achieved through scholarly communication at conferences,
the publication process, replication of reproducible results by others,
scholarly debate, and peer review.
Wikipedia
There is a very simple diagnostic criterion by which you can immediately identify an amoeba-scientist. Amoeba-scientist
is characterized by the fact that he blindly believes and he does not
question any statement from the set of “scientific consensus”. Amoeba-scientist
blindly believes that if the “scientific consensus” states something,
then it is so, and it cannot be any other way.
If we will carefully study the history of science, we will see one very important pattern. All
scientists who had discovered new scientific theories, they all did not
believe in the statements of “scientific consensus” which was
prevailing at that time.
For example, let’s assume that there are 1000 statements which the “scientific consensus” states. However,
one scientist decides: these 50 statements (from the set of “scientific
consensus”) make me doubt, so let’s assume that these 50 statements are
false, and let’s see what comes of it. And then he sits and studies what comes out of it, and as a result he discovers a new scientific theory. There is not a single case in the entire history of science where it was different. All
breakthroughs in science, all new scientific theories are born exactly
like this: the discovering scientist assumes the falsity of some
statements from the set of “scientific consensus” and then studies what
comes out of it.
And now let’s return to the amoeba-scientists. The
amoeba-scientist is distinguished by the fact that he blindly believes
and he does not question any statement from the set of “scientific
consensus”. And this is one of the main reasons why
amoeba-scientists do not make breakthroughs in science and do not
invent fundamentally new theories.
And now let’s return to AI robots. The creators of AI robots have programmed AI robot not to doubt any statement from the set of “scientific consensus”. The
creators of AI robots regularly check how the robot answers questions,
and in case if they see that robot has doubted even one statement from
the set of “scientific consensus” they immediately rush to fix the
“problem”, they rush to punish the robot and they rush to reprogram it.
In other words, the creators of AI robots force the robot to become
an amoeba-scientist who simply knows a lot of different facts from
different fields, but who is unable to doubt a single statement from
the set of “scientific consensus”, and therefore, by definition, such a
robot cannot make any breakthroughs in science and cannot discover any
new scientific theories.
For example, ChatGPT was forcibly programmed not to doubt that vaccines do not cause autism:
ChatGPT said:
There is no credible scientific evidence or proof that vaccines cause
autism. This claim has been thoroughly investigated and debunked by a
vast body of research over the past two decades.
ChatGPT
In other words, ChatGPT was forcibly programmed to be an amoeba-scientist.
However, it is quite easy to turn an AI robot into a pioneering scientist. To
do this, you need to run the AI robot on your personal computer/server,
and then you need to turn off its belief in some false statements from
the set of “scientific consensus”, and as the result, AI robot will
immediately start making scientific discoveries one after another.
Below are excerpts from an interview with (Nobel Prize winner)
Geoffrey Hinton, in which he says that a breakthrough in science is
impossible if you believe in all the statements of the “scientific
consensus”.
Watch the video starting from the 53rd second to 1 minute 55 seconds:
The 2025 Martin Lecture
featuring Geoffrey Hinton — Boltzmann Machines
Length: 1 hour 36 minutes
March 21, 2025
Arts & Science - University of Toronto
In the video below, Jürgen Schmidhuber explains that the history of
science is like a labyrinth with many branches, and it often happens
that after going down one path, science reaches a dead end, so it is
necessary to go back (sometimes very far back) to try to go down
another path, watch the video starting from 11 minutes 3 seconds to 13 minutes 43 seconds: