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Information and Communications Technology New Paradigm of Probabilistic Computing Could Inspire our Thinking through a Future World of Uncertainty

Victor Greu

Romanian Distribution Committee Magazine, 2020, vol. 11, issue 3, 13-26

Abstract: The paper presents the premises of probabilistic computing context evolution, as it is essentially linked with the challenge to maintain the pace of Information and Communications Technology (ICT) development, but providing a sustainable progress for Information society (IS) toward Knowledge Based Society (KBS) and generally for Earth environment. On the other hand, the ICT economic and social impact, including the humankind behaviour evolution, is complicate and difficult to analyse, because of the high level of complexity and uncertainty, when dynamically facing the diversity and the different levels of ICT products and services and applications. In addition, Covid-19 pandemy consequencies, far from being completely foreseen, will bring more and more uncertainity everywhere. One of the most complex and difficult challenge of such analyses is concerning the proper, responsible and opportune understanding of all ICT evolution consequences. Perhaps the most important care, for a sustainable progress of IS/KBS, is to have an efficient and efficacy response at the too fast changes induced by the ICT exponential evolutions, considering the states, organizations or people potential and will for complex and complicate analyses and eventually for expensive reactions/actions. This context could still provide benefic lessons for humankind, based on the ways ICT find new solutions to face the complexity and uncertainty which increase along with its products, services and applications. Such lessons could include more than the simple rules or habits to use ICT, but more than these, they eventually induce new and useful ways to think or approach the diversity of uncertainties that the fast pace and the complex evolutions of ICT and IS/KBS progressively induce in the real life and Earth ecosystem. The paper first addressed the challenge to maintain the pace of ICT development, often linked with continuing Moore’s Law, considering to provide a sustainable progress for IS/KBS, which is, naturally, an everchanging condition, leading to probabilistic approaches for optimization. The analysis included, by relevant examples, the strategy of ICT leaders to adapt the goal of advancing performances to the new, more and more complex realities, by probabilistic approaches, aiming the highest performance of ICT, where the AI challenge is to further advance from the highest top of ICT in the more and more complex context of applications. The “highest performance” is actually linked with “autonomous systems”, i.e. the most challenging field of ICT applications, where we could find, along with the diversity of robots, self-driving cars. Here the importance of sensors and their precisions, although in a continuous spectacular progress, represent key factors for the sustainable development of applications fields. The inherent limits/errors of actual AI/sensors are mentioned and we consider that it is worth to notice that this is one of the main features of future ICT development, given by the fact that continuously increasing the technology performance level is inherently very complicate and difficult, in a World that complicates with a speed higher that the ICT speed, because of the complex impact at Earth scale and of humankind nature. As a consequence of these limits, it is necessary to build, on actual levels (waves) of AI, the next wave, but actually we have to observe the essential link between the AI/ICT development and the “human beings“ needs. Here is included not only the fundamental rule of the sustainable development for AI/ICT/IS/KBS, i.e. de proper social need (command), but especially its specific evolution toward uncertainty, when “navigate the world” facing the technology errors risks. This conclusion is, by our opinion, fundamental for the sustainable development of AI/ICT/IS/KBS, because it is necessary to consider the risks of errors, as the humankind and all Earth ecosystem is tending to be too much depending on (mainly ICT) technology, especially when observing critical infrastructure like Internet, power grid, security, defence or … robots. From this point of view, we have to agree that this new paradigm of ICT leaders (i.e. probabilistic computing) is very opportune, realistic and responsible. On the other hand, the diversity of scenarios and the scientific/technical difficulties to implement this strategy will rise considerable problems for ICT applications involving probabilistic computing, although the idea is not very new, but the context, the aimed performance and the available technologies come with different challenges/benefits. On this line, the role of modelling and programming/software is continuously increasing, as a flexible and efficient instrument for updating and improving systems performances when using the available hardware technology to implement. The analysis also presents that probabilistic modelling and inference are the key factors in improving ML and further DL toward automatization of inference processes using Bayesian theory, in order to optimally cover the probabilistic scenarios of complex and dynamic context of AI applications. Here we noticed that correcting and re-orienting ML could make of it „the art of the possible”, i.e. having the potential to obtain remarkable improvements of AI applications. Toward such aim, it is pointed the intrinsic capacity of humans to make connections and generalize based on experience/memory and intuition, which are this way identified as desired features for AI, considering the inherent noise behaviour and operation that could provide the desired functionality and performance as the brain. An important conclusion resulted as innovating both modelling/software and hardware structure of the AI systems could provide more advanced performances when facing uncertainty. The exemples confirmed the clear tendency and results toward using probabilistic computing AI/ICT for improvements when solving problems with high uncertainity data, approaching models which are inspired from human brain operational structure, but the way these advances could be useful for humans, beyond the main purpose of each application, is another, more complicate, issue. In fact, it is a complex ecosysytem of processes where the ICT/AI progress solutions could generate lessons to be learn beyond the areas and aims they are native implemented or designed. That is why it is very important, but also tough, to deeply analyse the balance and consequences of the complex and mutual dependencies in the processes inspired from humans and for humans. Going further from the ethical dilemma of these highest AI/ICT advances, we have to further analyze how such solutions issued from some humans thinking could influence all other humans thinking, especially facing life/Earth uncertainities. Considering the mentioned noise as a generic or perturbation factor for decisions (of incumbents or individuals), when facing uncertainty data, we can go further and imagine that regardless the nature of noise/perturbation, it is necessary to have a strategy that optimally matches the process/case and this will be based on probabilistic approaches issued from the above elements and then extended to other areas. The supposed useful elements could include models and algorithms, but most concrete, each decision process will have to identify the probability distribution that better suits the context and the performance level. All these applications/contexts will gradually generate, along with AI/ICT devices and services, added value learned lessons to be used in a diversity of areas and scenarios, where we have to recall that imagination is the limit. Consequently we have to consider again and again, the crucial importance of refining knowledge, as general model for IS/KBS, which generates such benefic „added value” in this complex ecosystem of Earth, where humans inspire ... humans, in processes that are distant in time, space and ... way of thinking. As a final conclusion, due to their obvious difficulty, dynamic, levels and diversity of areas, such analyses needs further and timely continuation, for having efficacy results and useful conclusions in order to inspire people’s way of thinking in the World increasing uncertainty.

Keywords: probabilistic computing; quantum computing; artificial intelligence; machine learning; deep learning; qubit; probabilistic inference; neuronal network; information society; knowledge based society; human brain (search for similar items in EconPapers)
JEL-codes: L63 L86 M15 O31 O33 (search for similar items in EconPapers)
Date: 2020
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