6.3 Beyond the data-driven world
Current AI research is centered around a set of computing technologies inspired by the ways people use their nervous system and sense organs to sense, learn, reason, and act. For example, deep learning is a form of machine learning based on layered representation of variables called neural networks, which are widely used in a variety of applications that rely on pattern recognition. Natural language processing (NLP) and knowledge representation and reasoning was used by IBM Watson to win the Jeopardy competition in 2011.94 Watson used a series of complex search algorithms and some heavy-duty processing firepower to determine an answer with the highest probability of being correct.95 (I have reservations about pursuing NLP. What needs to be done is developing a language that does not admit ambiguity because each of its messages can then be pinned to a specific context through a web of connections.) With present day NLP methods, when the context is narrow and unambiguous, it is powerful enough with potential for further growth in web searches, self-driving cars, health care diagnostics and targetted treatments, etc. AI and robotics applications already find a wide range of applications, such as in agriculture, food processing, fulfillment centers and factories that accentuate jobless economic growth. Once a new, context discriminating, man-machine lingua franca is created for universal use, AI will advance with exponential acceleration. Beyond this lies the vastly challenging task of discovering axiomatic systems that can encapsulate massive amounts of as yet unconnected data/observations. Here is a profound insight from Gregory Chaitin:
[A] scientific theory is a computer program that calculates the observations, and that the smaller the program is, the better the theory. If there is no theory, that is to say, no program substantially smaller than the data itself, considering them both to be finite binary strings, then the observations are algorithmically random, theory-less, unstructured, incomprehensible and irreducible.96
This, I believe, describes the heart of AI and sets a landmark goal for every researcher to pursue. From my perspective a successful AI-system should be able to build an axiomatic-system that looks at vast amounts of data, categorizes the data by doing a correlation analysis, creates a random set of samples and gleans common “features” among members of this random set, proposes a parsimonious set of axioms and rules of inference that would reproduce the observed features and predict new features as “theorems” where one is forever trying to see if x = y or not (x and y are two validly constructed statements or axioms in the axiomatic system). Parsimony is the key attribute by which an AI system must be measured for its excellence and effectiveness. While seeking parsimony one should always bear in mind the impossibility of proving the consistency and completeness of any axiomatic system to which Gödel’s theorems apply, the limitations of the computing powers of a Universal Turing Machine, and the limitations imposed by the postulates of quantum mechanics and that information is physical and hence governed by the laws of physics.
I see AI at a juncture where physics was in the early 1900s when quantum mechanics burst on the scene. It put physics on an entirely different conceptual footing. This is a time when AI researchers need to decide what aspects of AI they want to pursue. Long term survivability will be enhanced for those brilliant enough to pursue the said landmark goal. Above all, it will enable the integration of multiple technologies that we may use to disrupt the present constructively before we are disrupted.
Such a breakthrough in AI is expected when three technologies—synthetic biology, AI, and quantum computing—begin to synergistically integrate into creating new life forms through forced speciation that will likely lead to a superspecies, the humanoid. This will likely make the Homo sapiens extinct (all earlier species, e.g., H. habilis, H. erectus, and H. heidelbergensis as well as the Neanderthals (H. neanderthalensis), the early form of Homo sapiens called Cro-Magnon, and the enigmatic H. naledi in the Homo genus are now believed to be extinct97) or domesticated by them (as the wolves were domesticated into dogs). This will completely overturn all predictions about the rate and directions of AI advances that are currently favored.98 As I note in a forthcoming book chapter99
We envisage a world where genetic engineering, artificial intelligence (AI), and quantum computing (QC) will coalesce to bring about a forced speciation of the Homo sapiens. A forced speciation will drastically reduce the emergence time for a new species to a few years compared to Nature’s hundreds of millennia. In this chapter, we explain the basic concepts that would allow a forced speciation of the Homo sapiens to occur and its consequences on life on Earth thereafter. Accelerating speciation mediated by Homo sapiens via domestication, gene splicing, and gene drive mechanisms is now scientifically well understood. Synthetic biology can advance speciation far more rapidly using a combination of clustered regularly interspaced short palindromic repeats (CRISPR) technology, advanced computing technologies, and knowledge creation using AI. The day is perhaps not far off when Homo sapiens itself will initiate its own speciation once it advances synthetic biology to a level where it can safely modify the brain to temper emotion and enhance rational thinking as a means of competing against AI-embedded machines guided by quantum algorithms.
The repercussions of forced speciation will be enormous. The role of natural humans and humanity’s faith in spirituality if humanoids take charge will undergo a sea-change. Such a biological evolution of intelligent life, triggered by the Homo sapiens’ curiosity-driven quest to understand the Universe within a rational, axiomatized framework will force humanity to reassess the meaning of life, its place and significance in the Universe, and above all its ability to merely survive, much less survive with dignity.
[ 94 ] Best (2013).
[ 95 ] Lynley (2011).
[ 96 ] Chaitin (2003).
[ 97 ] Encyclopaedia Britannica (2019).
[ 98 ] See, e.g., Stone (2016).
[ 99 ] See, e.g., Bera (2019).