The world had not anticipated, much less prepared itself for such rapid convergence of multiple and powerful technologies—AI, robotics, quantum computing, biotechnology, cloud computing, 3D precision manufacturing, etc. Their convergence is rapidly approaching a tipping point at which significant technological changes are likely to occur suddenly and without adequate warning. Already, Google, Toyota, Facebook, Microsoft and some others with deep pockets have collectively invested billions of dollars into AI, robotics and quantum computing research because they see it as the next frontier for profits. Significant breakthroughs in AI will have a profound effect on society and social structure. The minimal upgradable science, technology, engineering and mathematical (STEM) skills required for future employment will be dramatically different and substantially higher. The emerging crisis: How will society organize itself when emotionless, rational machines and robots (with embedded synthesized biological components in them) outperform, self-improve, out-think humans, and can no longer be controlled by humans because of unshared values, interests, and contested command and control mechanisms?
The first wave of AI has already stealthily pervaded our lives via speech recognition, search engines, and image classification, and is about to do so via self-driving cars, and applications in health care. The continuing march of AI and AI-aided automation will undoubtedly transform and restructure society on a global scale. What we do not know is how the transition will pan out—who will benefit and who will be heavily marginalized, and in either case, for what duration and with what probability of bouncing back. A society dependent on indifferent AI that knows neither brutality, compassion nor any form of emotion and is essentially algorithmically driven and answerable to no human constructed institutions related to law and order, is a pristinely alien phenomenon. In this society, there will be enormous potential to create wealth through increased productivity independent of a human labor force. Currently, wealth gains derived from technology are concentrated in companies and shared by their shareholders and the C-suite. They comprise a miniscule percentage of the global population, which includes the infamous 1% highlighted by Oxfam25. The sharing of enhanced productivity generated wealth with the rest of the global population has no easy answer to the question: “What norms should we adopt that would make equitable wealth sharing possible?” What socio-economic reforms and community assistance will allow those unemployed because of AI, to contribute to society, and if necessary, to be reskilled?
The new science and technology advances in AI and robots (equipped with mimicking human sensory sensors) are now set to replace repetitive but skilled jobs hitherto performed by humans. It is not at all clear what new compensating jobs for humans can be created or even from where the jobs would come from to permit a dignified, basic life style that includes food, clothing, and shelter. All past socio-economic reforms assiduously put in place since the agricultural era are in danger of falling apart. The specter of mass employment, strife and mayhem in an era of abundance of food and automated mass manufactured luxuries may well become far too real for lack of mass purchasing power. The crisis will begin with increased inequality that hits hardest along lines of class, race and gender. Those lacking employable skills may become destitute. We have entered the era of continuous learning, just to stay employable. That may not be good news for those wishing to live a long life. These are religion-threatening, man-made developments. Calculatingly rational machines rather than rationalizing humans are on the ascendancy. It is not clear how classical human minds can divine the future or cope with it.
15 Final remarks of the paper
Turing showed that given an algorithm its execution is mechanizable and Landauer showed that computing requires a physical system (information is physical). Developing algorithms requires intelligence and a knowledge of the laws of Nature. Our present knowledge shows there is a world of difference in the way we understand Nature in terms of classical physics and quantum physics. These differences lead to fundamental differences in the way we can design and efficiently execute algorithms on physical computers. Interest in quantum computers emerged when it was shown that reversible Turing machines were possible and therefore Turing machines could be mimicked using unitary operators in the quantum world. Today, physical quantum computers exist, and the technologies needed to improve and scale them are advancing rapidly.
The algorithms described in this paper will, of course, come efficiently coded into reusable libraries for quantum computers, but there are many other novel and non-obvious algorithms waiting in the wings to emerge. It is the excitement of developing and discovering them that induces people to delve into quantum computing in the hope that phenomenally powerful algorithms beyond the reach of classical computing machines will emerge if quantum superposition, entanglement, teleportation, and state vector collapse are intelligently used in the Hilbert space. We now know that quantum computers are not just faster versions of ordinary computers, but something much stranger.26
25Oxfam (2018). “Eighty two percent of the wealth generated last year  went to the richest one percent of the global population, while the 3.7 billion people who make up the poorest half of the world saw no increase in their wealth”.
26Brandt, Yannouleas & Landman (2018).