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Ask A Genius (or Two): Conversation with Dr. Claus D. Volko and Rick Rosner on “The Nature of Intelligence” (Part Two)


Author(s): Scott Douglas Jacobsen

Publication (Outlet/Website): In-Sight: Independent Interview-Based Journal

Publication Date (yyyy/mm/dd): 2018/06/08


Rick Rosner and I conduct a conversational series entitled Ask A Genius on a variety of subjects through In-Sight Publishing on the personal and professional website for Rick. Rick exists on the World Genius Directory listing as the world’s second highest IQ at 192 based on several ultra-high IQ tests scores developed by independent psychometricians. Dipl.-Ing Dr. Claus D. Volko, B.Sc., earned a score at 172, on the Equally Normed Numerical Derivation Tests (ENNDT) by Marco Ripà and Gaetano Morelli. Both scores on a standard deviation of 15. A sigma of ~6.13 for Rick – a general intelligence rarity of 1 in 2,314,980,850 – and 4.80 for Claus – a general intelligence rarity of 1 in 1,258,887. Of course, if a higher general intelligence score, then the greater the variability in, and margin of error in, the general intelligence scores because of the greater rarity in the population. This amounts to a joint interview or conversation with Dr. Claus Volko, Rick Rosner, and myself on the “The Nature of Intelligence.”

Keywords: Carl Gustav Jung, Charles S. Cockell, Christopher Michael Langan, Dr. Claus Volko, Francis Galton, Geoffrey Hinton, intelligence, Jeff Hawkins, Marvin Minsky, nature, Oliver Selfridge, psychometricians, Ray Kurzweil, Rick Rosner, Seymour Papert, Stephen J. Gould.

Ask A Genius (or Two): Conversation with Dr. Claus D. Volko and Rick Rosner on “The Nature of Intelligence” (Part Two)[1],[2],[3],[4]

Scott Douglas Jacobsen: Claus, as computational intelligence research is a subdiscipline with computer science, the specialization in computational intelligence would, seems to me, imply the end goal of the robot butler example. An autonomous machine still with a utility defined by human needs and wants at any given moment.

I see this as the main point of contact: the notions in general culture and an end goal of the experts in computational intelligence. One question for you, Claus, out of “neural networks, machine learning, search algorithms, metaheuristics and evolutionary computation,” what one is the dominant methodology?

In the long-term, which one or set of them will likely provide the foundation for a fully autonomous machine? As a sub-question, why did you pick the latter two – metaheuristics and evolutionary computation – to focus research questions for yourself?

Also, does anyone within the field, or even outside who has valid thoughts about the field, disagree with the fundamental assumption about intelligent behavior arising from the basis of computation? It seems hard to disagree with the fundamental premise, but it seems wise to ask about it. Also, Claus, and sorry for more questions for yourself at the moment, your final statement struck me:

A computer is excellent at computing logical conclusions from given premises, but it lacks the ability to come up with new ideas of its own. It can only draw conclusions from data that is given to it.

Of course, it is debatable whether human beings are really different in this aspect. Perhaps it is also the norm for human beings to be only able to come up with new ideas by combining knowledge and experiences that have previously been acquired in a creative way.

Within computational intelligence research, if the assertion amounts to human beings as computational engines or information processors with the ability to create or generate premises, compute conclusions from the data, e.g., integrated sensory experience, connected with the premises, and act or behave in the world from those conclusions, then human beings would have one distinct trait from other computational intelligences – in some large set space of possible computational intelligences given current technology and methodologies, which would be the ability to “come up with new ideas.” Of course, you note this is in question, as well.

What may be the computational basis for the creation or generation of suited to circumstance new ideas? Or if, as some think, this generation of new ideas is something machines cannot do on their own, what would differentiate this trait of human computation from other computation known now? Rick, many of these questions apply to you too.

Dipl.-Ing Dr. Claus D. Volko, B.Sc.:The dominant methodology is definitely neural networks in combination with machine learning. As a matter of fact, neural networks is not a new concept. It has been around for decades. But the big problem connected with it was the inability of this method to classify data sets that were not linearly separable, as pointed out by Marvin Minsky, Oliver Selfridge and Seymour Papert (Minsky, M. L., & O. G. Selfridge, 1961, “Learning in Random Nets”, in C. Cherry (ed.), “Information Theory: Fourth Symposium (Royal Institution)”, London: Butterworth, pp. 335 – 347; also see “Unrecognizable Sets of Numbers” (with Seymour Papert), JACM 31, 2, April, 1966, pp. 281-286).

To my knowledge, it is mostly thanks to the achievements of a couple of researchers including Geoffrey Hinton that this problem was overcome. Hinton published a paper about the backpropagation algorithm already in 1986, but it took until about 2011 that the new technique of “deep learning” became well-established, resulting in great successes, with artificial intelligence becoming stronger and stronger ever since. Interesting enough, Hinton himself has recently turned to be skeptical of backpropagation since he believes that this is not the way the human brain really works (see also:

Even if it is right that the human brain works in a different way, I am convinced that the technology we have now would suffice to create fully autonomous machines, at least for serving certain defined purposes. However, when I have recently been at a demonstration of a language-processing robot here in Vienna, I was disappointed to see that the robot failed to recognize either of the words that had been spoken to it by the demonstrator. Still we should acclaim the progress artificial intelligence has made. Not only is Google Translate quite good already, there is also a website founded by German computer scientists called which is an even better translator of text documents, especially from German to English and from English to German. When I write my blog postings in German, I use this website to obtain an English version fast. The results need some post-processing, but far less than similar translation programs would have required only ten years ago.

The reason why I focused on metaheuristics and evolutionary computation during my days as a graduate student was mostly that I found these approaches to be fascinating, especially as I also have a background in biomedical sciences and a good understanding of Charles Darwin’s Theory of Evolution. Also, I am one of those people who are especially interested in algorithm design. I tend to believe that I have a special talent for that. For instance, I recently developed and implemented a complete mesh voxelizer from scratch, starting with the underlying algorithm. That is, a computer program that takes a description of a three-dimensional geometrical object (e.g. a cone, a sphere, or something even more complex) and converts it into a (possibly huge) set of identical blocks.

I am not aware that anybody working in the field of computational intelligence disagrees with “the fundamental assumption about intelligent behavior arising from the basis of computation”. If somebody disagrees with this fundamental assumption, then I guess he or she does not work in the field. Otherwise his/her behavior would be inconsistent.

Regarding your remark about human beings having “one distinct trait from other computational intelligences”, namely “the ability to come up with new ideas”, Ray Kurzweil wrote about this in his seminal book “The Singularity Is Near”, from 2005. He stated that human intelligence is particularly good at pattern recognition and that this is something machines are still weak at (although I must say that machines have dramatically improved on this in the past decade, just thinking of unsupervised learning and clustering). By contrast, according to Kurzweil machines are particularly good at storing huge amounts of data and retrieving this data within a very short time. That’s what he considers the strength of machine intelligence.

It is difficult to answer your question what is the computational basis for the creation of new ideas. I must say in this context that I am a big fan of the Swiss psychiatrist Carl Gustav Jung who invented the Jungian Function Theory which the Myers-Briggs Type Indicator and Socionics are based on – I consider him the greatest genius of all times (see also: Carl Gustav Jung defined eight psychological functions, one of them being introverted intuition. This function is defined as follows (from

“Introverted Intuition (Ni) deals with understanding how the world works through internal intuitive analysis. Ni relies on gut feelings and intuition about a situation to help them understand. Introverted Intuition does not look at what is seen. Introverted Intuition forms an internal map and framework of how things work. The map is slowly adapted and adjusted over time to allow the user to get a better sense of the ‘big picture of things’ and what steps to take to get the desired outcome. Introverted Intuition will take pieces of abstract information and make sense of it. It is not interested so much in concrete facts, as it is with the essence of ideas and theories, and how they all fit together. They are very good at recognizing patterns. […] Introverted Intuition asks questions like ‘what’s really going on here?’ or ‘where have I felt this way before?’ Introverted Intuition is one of the toughest functions to explain to someone else that doesn’t have it. Because of this, Ni has been labeled as ‘mystical’ and ‘psychic.’ And sure, it can appear that way to others, but it is more complex and involved than just ‘magically’ coming to conclusions.”

So, the human ability to come up with new ideas is related to what Carl Gustav Jung called “introverted intuition”. How this exactly works, science has not found an explanation for yet. We are still in the time of hypothesis generation regarding this aspect of the human psyche.

However, as already mentioned, machines do have the ability to discover non-obvious properties of given data, as is employed in the “clustering” method. For instance, if you feed a machine with data regarding name, eye color, size and weight, a machine might find out correlations between e.g. eye color and weight that would possible be non-obvious for a human being.

Rick Rosner: Claus comments that he has been skeptical of backpropagation because he does not consider this the way the human brain really works. Evolution is opportunistic. We can assume brains in general take advantage of anything that works.

That is easily made and energetically efficient. Evolution will follow easy, effective pathways, which may mean brains have more than one computational/information-processing strategy.

Because evolution not being a conscious force does not give a crap. Things that work tend to persist over time. There is discussion here about the strengths and weaknesses of machine intelligence.

I feel like that is somewhat entangled with information processing machines still being really primitive. That when they come into their own. They will have roughly the same abilities as the human brain.

It is that we are at such a beginning point. Being able to store data is barely machine intelligence. Comparing computer data storage to the brain is like comparing a pulley to an engine. I’ve talked with you (ed. Scott) about this a lot.

I was arguing with my buddy, Lance, last night about free will. I don’t see how free will can exist since thought has to be based on the information. I also don’t see why it is needed.

I prefer informed will: knowing why I am thinking everything I am thinking and without being subject to bias that I am not aware of. But when it comes down to it, I think machine thinking – not the thinking of machines now, but machines in the future or human-machine hybrids, or super powerful genetically tweaked humans in the future – will all be thinking based on the information.

I think Claus talks about it, as it is stated. Thought is a form of information processing. It is not this magical other thing. When you get powerful enough and flexible enough information processing, it is the equivalent of thought.

Free will is like a concept left over from a time before people thought in terms of information.

2. Jacobsen: Claus, in correspondence, you wisely wanted to redirect the conversation from artificial intelligence and computational intelligence into the more substantive unsolved problem of human intelligence in the context of a full framework for explanation.

Given the redirection from one sub-topic of artificial intelligence to another in human intelligence, to Claus and Rick, what defines human intelligence to you, e.g. parameters, limits, capabilities, measurements, observational markers, empirically verifiable general factors, and so on?

How does artificial intelligence differ from human intelligence? Can artificial intelligence replicate human intelligence in another substrate? If so, why does this seem possible in theory? If not, why does this not seem possible in theory?

Does intelligence amount to the currency of the universe? If so, how? If not, how not? How does human intelligence compare to other primate and mammalian intelligences? What appear to be the probabilities for extraterrestrial intelligences? How might human and other known intelligences shed light on the possible range and variety of extraterrestrial intelligences?

Volko: These are very interesting questions, thank you for asking them. First of all, I have recently watched a TED talk with Jeff Hawkins, a former IT entrepreneur who turned into an AI and brain researcher ( In my opinion, the definition of intelligence he provided in his talk is very reasonable. He stated that intelligence is all about making predictions. Indeed that is the case when solving IQ test tasks. You are presented with a list of numbers, for instance, and have to guess what numbers will follow if the principle the number pattern is based on is continuously applied. The same goes for tasks involving patterns, verbal analogies etc.

In fact there are many different definitions of intelligence, which is also why it is sometimes difficult if not even impossible to compare IQ scores obtained in two different tests. My late father, who had studied psychology at university (even though he did not complete the degree), used to prefer the definition that intelligence is the ability to get by novel situations not experienced before. Of course, this definition is compatible with Hawkins’ definition, since getting by novel situations requires to make predictions.

In his recent book “Life 3.0 – Being human in the age of Artificial Intelligence”, Max Tegmark, a professor of physics at the MIT, defines intelligence as the “ability to achieve complex goals”. He states that intelligence is multi-faceted and cannot be measured by a single IQ value, and also that while machines are superior to humans at particular types of intelligence such as arithmetics and a couple of strategy games (Chess, Go), there are various forms of human intelligence where machines have not reached a comparable level of performance yet, such as artistic intelligence, scientific intelligence, and social intelligence.

I personally prefer Hawkins’ definition of intelligence. In my opinion, many researchers and of course also laymen make the mistake to use the term intelligence for all sorts of abilities while in reality, intelligence is only a basic cognitive talent that may be required for accomplishing various sorts of intellectual tasks, but intelligence is not to be confused with these intellectual abilities themselves. Also, when Howard Gardner talks about multiple intelligences, I would say that much of what he calls types of intelligence is abilities which, of course, may be related to intelligence (the ability to make predictions), but general intelligence is only a basic requirement for developing these abilities, and the abilities themselves (such as social skills or musical talent) go way beyond intelligence as such.

For instance, as a child I was fond of computer games, and so it happened that I ended up trying to make computer games of my own. Computer games mainly consist of three components: graphics, music and code. I tried all the three things, but it turned out that I have only talent for code. Thus, I am able to create working computer programs, including games, but without assistance from other people, these games are destined to have rather weak graphics and music. I am intelligent, I usually score very high on IQ tests (as Rick can confirm, the two of us once took part in the beta-testing session of a novel, experimental numerical IQ test, and in this beta-testing session Rick obtained the second highest score of all 86 participants from the world, all having an IQ of 135 or higher according to traditional IQ tests, while I obtained the third highest score). Yet I lack talent at graphic design and music composition. Programming, however, comes natural to me. Probably that’s not only due to my level of intelligence but because I also have a special talent for algorithm design, which goes beyond what traditional IQ tests measure. After all, I also got to know some people scoring very high on traditional IQ tests who failed to solve basic programming exercises when they were required to do so in mandatory university courses for beginners.

So, there are some researchers who perceive intelligence as a set of general and several sets of special abilities (also called g and s, respectively), but I do not adhere to this notion. In my opinion, intelligence should be called cognitive talent and intelligence testing should be all about the basic ability to make predictions from given data. In this context, of course that is also what machine learning does, especially unsupervised learning and clustering. For this reason, it is definitely justified to call machine learning a form of (artificial) intelligence. When the computer makes predictions based on given sets of data, the computer in fact does behave in an intelligent manner. Being able to make intelligent predictions, on the other hand, does not imply being a life-form equipped with consciousness and self-awareness, as I have already stated.

I do not think intelligence can be called the currency of the universe. A currency is something that can be used to exchange goods. But intelligence cannot be used for that purpose. That said, I do think that animals are intelligent as well. I even think that animals are self-aware. I have a German Shepherd dog myself (hi, Archie!), and as my mother keeps saying, my dog seems to be able to understand everything that is going on around him and every word we are saying to him. Animals have something to them which machines such as computers do not yet have, even though machines are already able to make intelligent predictions. I am a strong advocate for animal rights, and I have even been pondering over bacterial rights recently, bacteria being a life-form themselves as well (Charles S. Cockell has published a few papers dealing with that matter, if you are interested, which can be freely downloaded from the Internet – I am corresponding with him these days as I am working on a related new scientific theory on my own, which is supposed to shed light on new ways of treating infectious diseases and cancer).

It is possible that there are also intelligent life-forms in outer space, but what makes me a bit skeptical about that is simply that we have not encountered any of them so far, at least not to my knowledge. However, even if we have not met extraterrestrial life-forms yet, that of course does not suffice to conclude with certainty that there are none. The universe is huge, so who knows what may be existing in a remote place where no man has ever gone before. I personally consider the SETI project a good thing, and I would also be ready to donate computational power to it if it was not the case that I am already donating my computational power to research projects in biomedical science (protein folding).

Rosner: This whole section is about machine intelligence versus human intelligence. I think the thing that differentiates them currently is that human intelligence; we perceive the world in great detail because our brains have 10^10neurons each with 10^3 dendrites.

So, in a lot of situations, the brain has reality constructing resources to spare. We do not notice the graininess of perception because our brains are big and powerful, though not infinitely big and powerful.

When you have so much perceptual and simulatory and, as Claus mentioned, predictive resources to throw at the world, you get good results without necessarily being conscious of mental strategies and algorithms.

You get a seamless feeling simulation of the world. I agree with Claus and the TED Talk guy, and Lisa Feldman Barrett who wrote How Emotions Are Made. She said the brain’s primary objective is to predict the world to allow you to most efficiently address the world.

Our brains answer the questions: what is going to happen next? What do I need to do with what is going to happen next? But given our brains are so powerful, we tend not to see the mechanics of thought in everyday life.

Say you are a thief and part of your caper is that you need to duplicate a key, if you are trying to duplicate a key, and if you only had tools that came out of Minecraft, for instance, they’d be blocky and clunky, and you would have to come up with a special strategy to duplicate the key.

In caper movies, you need to a wad of wax. The graininess of the wax, the scale of the particles in the wax, are smaller than the scale of the notches in the key. The graininess of that is not noticed.

You have material that you press the key into that has 10^10 atoms per millimetre. We do not notice the graininess. As machine intelligence becomes more powerful, we will less and less notice the graininess of the products of intelligence.

You can see that in video games. You started with one pixel with Pong. Then you went to these rough blocky things like the creatures in Dig Dug and Pac-Man. Now, we are deep into the or beyond the Uncanny Valley with most video games.

People look perfectly fleshy and have the right body dynamics. There is a lot of coding that has delivered that, but it is also in combination with raw computational power. 

3. Jacobsen: I paid attention to Hawkins for some time several years ago, almost a decade now. He talked about some models – some related to intelligence and others not, created by others and himself, as revolutionary at the time. It seems interesting to me, too.

Claus and Rick, you both perform exceptionally well on tests of general intelligence. The performance on the tests, on average, translate into general life performance or standard success metrics. If somebody performs well on an IQ test, they tend to succeed in school and life.

This seems truer than in the past with the Fourth Industrial Revolution and the knowledge economy: both ongoing. Each requires more education. Those who perform well on IQ tests tend to perform well in school, so better in the knowledge economy compared to others.

With the subject of human intelligence, I want to focus on the big pool of failed theories. What about the theories purported to explain human intelligence better than others but with failure in predictive validity?

Those theories with claims to validity, but do not predict success in different domains of human endeavour. In short, what theories claim to measure human intelligence while these lack the empirical evidence to support them? Claus, you touched on some. This may narrow the field of possibilities down a bit.

Also, if we can mathematicize the processes of the universe with descriptive laws, then we can mathematicize the processes of parts of the universe with descriptive laws. If the human brain and consciousness are part of the universe, then we can (in theory) mathematicize the brain and consciousness with descriptive laws.

This seems to lead to the main point about human intelligence within the bigger topic of the nature of intelligence: a set of descriptive laws for the processes of the human brain and consciousness, so human intelligence as well.

With such a set of descriptive laws, it would encapsulate human intelligence by implication. As we simulate the parts of the universe in digital computers, e.g. galactic mergers, rotation of planets around stars and satellites around planets, and so on, with the descriptive laws programmed into a digital computer, this may extend to human intelligence too.

Does this lead to an inevitable conclusion with human intelligence as replicable inside a substrate including digital computers with such a set of descriptive laws for human intelligence programmed as an algorithm into a digital computer?

Any speculations on the early form of this algorithm?

Volko: I am aware of some historical attempts at intelligence testing that have more or less failed. For instance, Francis Galton, the founder of the science of human genetics, invented some practical tasks such as guessing the weight of an item and believed that the majority of common people would fail these tasks. However, in reality the majority of the people he tested passed. So this test was not an adequate intelligence test assuming that the distribution of intelligence follows a Gaussian curve. I also know that in the middle of the 20th century, it sometimes happened that vocabulary tests were used as intelligence tests. In reality vocabulary tests give an advantage to people of a particular social class and lifestyle. I recall I once saw a test sheet from the 1950s and was unable to define some of the German words from this test (my native language is German) despite having a good general education. Some of this words were simply old-fashioned and not in use nowadays, and some, as said, referred to everyday items of people of a particular social class with a particular lifestyle which are more or less unknown to other people. I also recall that when I was learning English at high school, it was easy for me to memorize philosophical and scientific terms because I was interested in these things, while I had a hard time to memorize words that were about kitchen equipment, for instance. It is the same situation with these vocabulary tests – they are definitely not suitable for testing intelligence without bias.

I am also aware that many people have tried to “mathematicize” the universe and come up with their own “theories of everything”. Again, the problem with most of these theories is that they fail to come up with plausible explanations of the phenomenon of consciousness. Science in fact often assumes a “naturalist” worldview suggesting that everything that happens in the world can be explained by observable causes. I tend to believe that the focus on the physical world and the rejection of the possibility that something might exist out of the physical world, in a kind of immaterial world that cannot be observed with our five senses, is the reason why this approach to understanding the world will never lead to a complete explanation of everything. On the contrary, I do think that we need to speculate and enter the domain of metaphysics if we want to obtain a coherent theory of how the world might actually work. In this context, let me clearly state that I do not reject religion, I only reject dogmatism and the social mechanisms of enforcing a certain set of beliefs on other people and suppressing the non-believers. I myself am not religious, I have not even been brought up in a religious fashion, yet I do not consider myself an atheist but rather am of the opinion that there is something we cannot observe, something we probably cannot even measure indirectly (at least not without distortions and artifacts from other origins), and this could be called a “divine force” or God. I agree with atheists that it is silly to imagine God as an omnipotent old man with a long white beard, but I do believe in some sort of “divine force” that is stronger than anything else in the world, and that is why I consider myself a theist. The term “God” may be used as a metaphor for this “divine force”.

However, it might in fact be possible indeed to describe human intelligence by some set of laws, and by programming computers to obey these laws, computers might be equipped with the ability to come up with predictions just as human beings do. I actually believe that what we call human intelligence is a function of the brain, or perhaps of the central nervous system. While I am not sure whether consciousness is a product of the brain or whether a conscious “persona” or “psyche” exists in an immaterial world we cannot perceive with our sensory organs and is only, in some way, attached to a brain, I believe that the brain is the “computer” that enables us to make intelligent predictions. So what intelligence tests measure is a property of this “computer”.

At the moment I am spending some of my spare time reading about the “Cognitive-Theoretic Model of the Universe”, which is a “theory of everything” invented by the autodidact Christopher Langan. I have acquired only a basic understanding of this rather complex theory so far, but I am definitely able to say that it is an interesting read and I am particularly curious about learning how Langan explains phenomena such as consciousness which science fails to explain so far, and which science, as long as it limits itself to phenomena observable in the physical world, will probably never be able to fully explain.

Regarding the question what the algorithm employed by the human brain to make intelligent predictions might be, I would like to mention again that Geoffrey Hinton, the inventor of backpropagation, has recently stated that his own algorithm is definitely not the way the human brain works and that the artificial intelligence community should see to it that a replacement for it be found as soon as possible. To my mind, the only thing that can be definitely said about how human intelligence works is that the process of making predictions is basically a search algorithm in which syntactically possible, but contextually wrong solutions are excluded until only one solution remains, or until only a few solutions remain from which the brain chooses the one that appears to be the most reasonable one. Differences in human intelligence may be due to differences in the efficiency of the search algorithm employed by the proband. Efficiency is not only about raw speed. If you have the talent to come up with ways to exclude more possible solutions at the same time than other people, you will find the right solution sooner than another person with the same “raw processing speed” of the brain. Human intelligence definitely is not all about “raw speed”.

The more powerful computers become, the more possibilities, of course, we will have to simulate complex things such as human intelligence and possibly even living organisms. In the past year, I have read several papers and books about artificial life. This is a branch of science that is still in its infancy. While artificial intelligence has made tremendous progress since 2010, even though it will still need another revolution until we will have artificial general intelligence that matches or even surpasses human intelligence, not much progress has been made in the simulation of living organisms since the field of artificial life was coined by Christopher Langton (not the same person as Christopher Langan) 30 years ago. I have been even a bit surprised to see that the artificial life community nowadays mainly focuses on evolutionary algorithms, one of the things I learned about in my computer science studies, instead of trying to simulate living organisms. But a reason for this is certainly that it still requires an enormous amount of computational power even to simulate a few hundred nanoseconds of the folding of a protein. That is why existing artificial life systems are usually highly abstract and have little to do with actual living organisms. An exception to this rule might be the Open Worm project, which tries to simulate the nematode Caenorhabditis elegans in a computer and about which new publications appear on the Internet now and then.

As you wrote that people who score high on intelligence tests usually perform well at school: I can confirm this from my own experience. I was a very good student and even graduated from high school with a straight-A record. What I, however, would like to state in this context is that high intelligence does not seem to give you a benefit when studying things you are not really interested in. I recall I had a hard time memorizing things I was oblivious to, such as some areas of biology and geology. However, it seems to me that people who perform well on intelligence tests usually also have a rather wide range of interests. That is why they are able to acquire knowledge about many things without really having to study hard. And yet, scoring high on an intelligence test does not always imply that you will eventually become a polymath one day. There are many other factors that are relevant as well, such as your personality and the (social) environment in which you grow up.

Rosner: The field of intelligence testing and the related field of statistics have had pasts that are questionable, but they are even worse than that. A lot of the people associated with statistics and intelligence testing were racist or trying to reach racist or try to support racist conclusions.

Pearson, apparently, was racist. I do not know the whole history of this. If you want to read a history of this, though it is obsolete, then you can read Stephen J. Gould’s The Mismeasure of Man. That book is probably close to 40-years-old now.

There might be more recent books that talk about this better. Pearson is the guy who came up with the Pearson Coefficient, r, which is a huge part of statistics. Apparently, he was not a great guy.

I question the need for intelligence testing in a modern context. There are many measures of people. I can go along with IQ testing if you are using IQ testing for its original purpose – the purpose imagined by Binet when he came up with the idea, which is getting kids help in school, either because they are smarter than average or not as smart as average. Beyond that, when you start talking about national IQs and national average IQs, all that stuff is racist and doesn’t help anybody except racist assholes.

There is not much need for improvements in human intelligence testing. The rate at which technology is galloping along and the rate at which we will merge with information processing technology means we do not need anything as old school as everybody knowing their IQ to three purported digits.

Technology is making a lot of us stupider via social media and texting all the time. But in the aggregate and in the long run, technology is making us smarter. Native intelligence will be less and less of a factor.

What will be more and more of a factor will be how well we merge with the technologies and the technological social structures of the future, we are already seeing that. I call the 2016 election the first AI election. The American election was a complete mess because of all sorts of technology that we do not have a handle on yet. The social media manipulation of opinion. The angry electorate because of jobs lost in part due to automation.

America continues to be – and anywhere where Russia hd gotten its cybernetic and social media cyber paws – in semi-turmoil. England is a mess with Brexit. Russia has its paws over that too.

Russia tried to mess with France’s election. When Western nations lose power because we are governed by idiots and everyone is pissed at everybody else, Russia somehow gains power.

Appendix I: Footnotes

[1Dipl.-Ing. Dr. Claus D. Volko, B.Sc.: “I was born in 1983 in Vienna, Austria, Europe. My father wanted me to become a doctor while I was more interested in computers in my youth. After teaching myself to program when I was eight, I started editing an electronic magazine at age twelve and kept spending almost my entire sparetime on it – Hugi Magazine.

Upon graduation from high school, I studied medicine and computer science in parallel. In the end I became a software developer who occasionally participated in medical research projects as a leisure activity.

I am also the maintainer of the website 21st Century Headlines where I try to give interested readers an up-to-date overview of current trends in science and technology, especially biomedical sciences, computers and physics, and I recently founded the Web Portal on Computational Biology. I think there is no doubt I am a versatile mind and a true polymath.”

Rick G. Rosner: “According to semi-reputable sources, Rick Rosner has the world’s second-highest IQ. He earned 12 years of college credit in less than a year and graduated with the equivalent of 8 majors. He has received 8 Writer’s Guild Award and Emmy nominations, and was named 2013 North American Genius of the Year by The World Genius Registry.

He has written for Remote Control, Crank Yankers, The Man Show, The Emmy Awards, The Grammy Awards, and Jimmy Kimmel Live!. He has also worked as a stripper, a bouncer, a roller-skating waiter, and a nude model. In a TV commercial, Domino’s Pizza named him the World’s Smartest Man. He was also named Best Bouncer in the Denver Area by Westwood Magazine.

He spent the disco era as an undercover high school student. 25 years as a bar bouncer, American fake ID-catcher, 25+ years as a stripper, and nude art model, and nearly 30 years as a writer for more than 2,500 hours of network television.

He lost on Jeopardy!, sued Who Wants to Be a Millionaire over a bad question, and lost the lawsuit. He spent 35+ years on a modified version of Big Bang Theory. Now, he mostly sits around tweeting in a towel. He lives in Los Angeles, California with his wife and daughter.

You can send an email or a direct message via Twitter, or find him on LinkedIn, or see him on YouTube.”

[2] Individual Publication Date: June 8, 2018 at; Full Issue Publication Date: September 1, 2018 at


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