The tool for doing this is a structured language that comes with a set of symbols, and a grammar for creating valid symbol patterns. This notion is encapsulated in Shannon’s information theory.^{51} The notion of computation and computability is captured in the mathematically defined Universal Turing Machine,^{52} and the notion of axiomatic systems is formalized in Gödel’s theorem.^{53} Creation of knowledge from data about which we know nothing begins with the assumption, and therefore the search for an axiomatic system, that there is at least one nugget of knowledge to be found. The way to acquire it or at the least the way humans go about acquiring it is the way we find the prime factors of composite numbers, a systematic process of guessing. It begins with an intuitive guess (which itself indicates the current biased state of the brain), we call a conjecture (another way of saying that I am curious to find out if my guess is right).

The common man’s intuition or common sense usually turns out to be mundane and its accuracy lamentably dubious. It is a very low-level knowledge acquisition capability shared by almost all *Homo sapiens*. Only a miniscule minority of the human population ever gets an opportunity to be formally trained, and an even smaller number who get the privilege of being mentored and becoming mentors of such people. It is this minority of the minority that advances human knowledge by making it more and more compact or abstract. Gauss, Fourier, Gödel, Turing, Galileo, Newton, Maxwell, Clausius, Einstein, Heisenberg, Schrödinger, Shannon, *etc.* were among them. Such geniuses compact and abstract knowledge to such a level that a machine fed with it can answer questions by interpolation, extrapolation, and derivation using mathematics.

Our discussion about knowledge, intelligence, and perception must necessarily be restricted to the axiomatic systems the brain-mind system can handle. An axiomatic system must be consistent both in the axioms and the theorems derivable from it. It does not allow an iota of inconsistency. As far as we can tell, the Universe is an axiomatic system; its axioms are inviolable laws of Nature. A desire to understand Nature is an attempt to minimise an information gap – the information we try to prise from Nature and Nature’s ability to withhold by making us run around in circles.

Physics gives rise to observer-participancy; observer-participancy gives rise to information; and information gives rise to physics.

^{54}– John Wheeler

What we call the laws of physics is the information we extract from Nature, compact and package within a falsifiable conjecture which we have so far failed to refute. Because Nature plays hide-and-seek, we are forced to conjecture (or hypothesize) and endlessly refine, discard, and amend our conjectures by strenuously trying to refute them.^{55}

Since all axiomatic systems can be recast as an arithmetic system (that is why AI systems run on computers), axiomatic mathematics provably tells us that in an inconsistent axiomatic (or belief) system, you can prove or disprove anything in your irrational way, thus rendering the system useless.^{56} It is also known that barring very trivial systems, it is not possible to prove the consistency of an axiomatic system. Hence, using rational arguments to seek unanimity in answers requires caution. Scientists make conjectures, and a conjecture is scientific only if there is potential scope of finding an error.^{57} The basic premise is that we can learn from our mistakes. “Though [a mistake] stresses our fallibility it does not resign itself to scepticism, for it also stresses the fact that knowledge can grow, and that science can progress–just because we can learn from our mistakes.”^{58} The process is criticism controlled. If you cannot bear the stress of being refuted, you cannot be a scientist; you can be a dogmatist. Religion insidiously teaches dogmatism. Hark Richard Dawkins, “not only is science corrosive to religion; religion is corrosive to science. [Religion] teaches people to be satisfied with trivial, supernatural non-explanations and blinds them to the wonderful real explanations that we have within our grasp. It teaches them to accept authority, revelation and faith instead of always insisting on evidence.”^{59} Therefore, religion cannot be axiomatized.

Our sole responsibility is to produce something smarter than we are; any problems beyond that are not ours to solve … [T]here are no hard problems, only problems that are hard to a certain level of intelligence. Move the smallest bit upwards [in level of intelligence], and some problems will suddenly move from “impossible” to “obvious.” Move a substantial degree upwards, and all of them will become obvious.

^{60}– Eliezer S. Yudnowsky, Staring into the Singularity, 1996

AI machines are on their way to make some hard problems solvable. In 1936, Alan Turing proved that mathematics, once formalized, is completely mechanizable and thus created computer science.^{61} Now it appears that human intelligence and emotion are mechanizable too. Perhaps not surprising since Max Tegmark notes: “Our reality isn’t just described by mathematics – it is mathematics … Not just aspects of it, but all of it, including you.” Thus, “our external physical reality is a mathematical structure”^{62}. To this we add another powerful observation, from Karl Popper, whose influence on modern scientists far exceeds that of any other philosopher: “We are products of nature, but nature has made us together with our power of altering the world, of foreseeing and of planning for the future, and of making far-reaching decisions for which we are morally responsible. Yet, responsibility, decisions, enter the world of nature only with us”^{63}

AI mimics thinking with unprecedented memorization and computation; it treats thought as a mathematical process. The human species conjured AI into existence, just as it did the laws of Nature in its mind. Chance favors the prepared mind is the mantra every millennial must believe in, not as a matter of faith but as a law of Nature. It is an analogous version of the law of phase transition in graph theory. Continuous random acquisition of knowledge favors those with perseverance.

## 5 AI-charged society

Socio-economic dynamics is ruled by perceptions derived from limited facts, their assumed relevance, reliability, and accuracy in a certain context, held beliefs (axioms), man-made rules of socio-economic governance, ingrained personal biases in interpreting, interpolating, and extrapolating information, *etc*. Perceptions are refined iteratively (as in a closed loop feedback system). The results that emerge from this dynamics include socio-economic structures based on division of labor (including the creation and support of the fine arts, STEM related activities, order, disorder, stability, chaos, phase transitions, *etc.*), and ways of attaching certain values (say, monetary, fame, place in society, *etc.*) to products, processes, and services that fleetingly manifest. Of such a dynamical system, we ask what products, processes, and services it can produce at any given place and time. Our ability to model the system mathematically will generally decide our ability to predict the system’s behavior; anything less would be hand-waving.

### 5.1 Structure determines function

As in biology, structure determines function given a context. What we can conjecture are the possible ways society might restructure and within those structures how the millennials may be forced to function. Despite the fragility of human knowledge, the survival and development of the millennials will depend on their ability to create, communicate, conserve, archive and transmit knowledge seamlessly to succeeding generations. AI-enabled machines come embedded with these abilities. Further, they are unburdened of religion and the fear of divine retribution. Humanoids, genetically coded to be oblivious of pain, and devoid of emotions, would be similarly blessed. AI presents both a cultural and a technical shift as have other inflexion points in past stages of human advancement, *e.g.,* the introduction of the printing press, the railways, the telephone, the ocean liner, *etc.*

During the industrial stage, most people reached their peak capacity to educate and skill themselves in activities (including earning a living) that required mechanizable “intelligent” rote education. That AI machines, in principle, can far surpass humans in such activities had become evident when Alan Turing showed how arithmetical calculations can be mechanized^{64} and mathematicians showed that any axiomatic system can be arithmetized^{65}. This meant that any form of rational knowledge could be axiomatized and rote education embedded in machines. While creating new knowledge would still require human creativity, once that knowledge had matured and was formalized into an axiomatic system, it would be mechanizable and expandable. It would then be a matter of time that humans would increasingly face competition from machines and eventually be overwhelmed by them. Kurzweil has predicted^{66} that this would happen by 2029. Recent advances in AI indicate it is very likely to be so. Further, advances in deep learning by machines indicate that through self-learning they can become highly creative and creators of original technology (the patent system will go for a toss) and scientific discoveries without human intervention may well become the norm. When that happens, who will decide the destiny of mankind and which religion will rescue man from machine bondage?

^{[ 51 ]} Shannon (1948)

^{[ 52 ]} Turing (1936).

^{[ 53 ]} Gödel, K. (1931).

^{[ 54 ]} Wheeler (1989).

^{[ 55 ]} Popper (1963).

^{[ 56 ]} See, e.g., Gödel, K. (1931); Raatikainen (2018).

^{[ 57 ]} Popper (1963).

^{[ 58 ]} Popper (1963).

^{[ 59 ]} Dawkins (2002).

^{[ 60 ]} As quoted in Kurzweil (2005), Chapter 2.

^{[ 61 ]} Turing (1936).

^{[ 62 ]} Tegmark (2014).

^{[ 63 ]} Popper (1994), p. 59.

^{[ 64 ]} Turing (1936).

^{[ 65 ]} Gödel (1931).

^{[ 66 ]} Fox News (20170316).