What transcends the Algorithm - A Critique of the Notions of a Postdigital and a Subsymbolic

Computational logic has, although since its breakthrough with the emergence of digital computers there has always been doubt, mostly been seen as something very different from human thinking; one can e.g. refer to Dreyfus’ famous criticism about what computers can’t do. Facing statistical machine learning as a new paradigm of computing, many seem to think that these lines are getting somewhat blurry. Learning algorithms, their functions not longer explicitly coded, but acquired via optimization methods, are seen as a kind of third mode, located somewhere between classical computational paradigms and human thinking. This view seems to manifest itself in the notions of postdigital and subsymbolic computing. I will argue that this view is mistaken, and machine learning does not soften boundaries posed by the digital and the symbolic, as they were already in effect regarding classical computational logic.

Surface Media: McLuhan, the Bauhaus and the Tactile Values of TV

Marshall McLuhan understood television (TV) as a tactile medium. This understanding implied what Bruno Latour might call a ‘symmetrical’ conception of tactility. According to McLuhan, not only human actors are endowed with the sense of touch. In addition, TV, digital computers and other ‘electric media’ use light beams and similar scanning techniques for ceaselessly ‘caressing the contours’ of their surroundings. This notion of tactility was crucially shaped by the holistic aesthetics of the early Bauhaus. To get at the specific features of the TV image, McLuhan relied on the writings of László Moholy-Nagy and Sigfried Giedion, in particular their use of photography for capturing and highlighting the ‘texture’ of surfaces. However, he hardly reflected the social and political factors that, in the age of electric media, contribute to the ‘symmetricization’ of touch.

Numerical Methods I

Textbook of several universities. 2nd edition. The color edition is also available at Glasstree Bookstore. It is recommended for students. The series of books Numerical Methods is written primarily for students at technical universities, but also as a useful handbook for engineers, PhD students and scientists. This volume introduces the reader into numeral systems and representation of numbers in digital computers. Possibly the most important part of this book are descriptions of differences between constant and random variables, related types of errors and error propagation. These topics are supplemented with various types of regression analyses. Finally, direct and iterative methods for finding roots of polynomials are explained. Practical application is supported by 77 examples and 13 algorithms. For reasons of simplicity, algorithms are written in pseudo-code, so they can easily be included in any computer program.

Computing Machinery, Surprise and Originality

Lady Lovelace’s notes on Babbage’s Analytical Engine (1843) never refer to the concept of surprise. Having some pretension to ‘originate’ something—unlike the Analytical Engine—is neither necessary nor sufficient to being able to surprise someone. Turing nevertheless translates Lovelace’s ‘this machine is incapable of originating something’ in terms of a hypothetical ‘computers cannot take us by surprise’ objection to the idea that machines may be deemed capable of thinking. To understand the contemporary significance of what is missed in Turing’s ‘surprise’ translation of Lovelace’s insight, one needs to distinguish between trivial surprises (which stem from our limited ability to store data and process it) and those events, propositions or encounters that lead us to question our understanding of ourselves or what surrounds us. Only some of these non-trivial surprises are the product of originality endeavours. Not only is it uncommon for surprises to track such endeavours, the type of autonomy that would be required on the part of ‘digital computers’ for originality and surprise to intersect in that way goes far beyond the operational autonomy that can be achieved by ‘learning machines’. This paper argues that a salient translation of Lovelace’s originality insight—for contemporary and future ‘learning machines’—is an upside-down version of Turing’s surprise question: can computers be surprised by us in a non-trivial, ‘co-produced’ way?

Digital Life: Creative and Open-ended

Digitization? What does the term mean; and why does it matter? One can occasionally read in news or social media that the United States and the Netherlands were among the first countries in the world to be digitized, while most other countries stayed behind, much to their own disadvantage. However, such statements only reflect a partial truth. They only reflect how digital computers and the Internet swept the globe. Yet, digital computers and the Internet are only the latest wave of digitization. To understand what is happening in the long run, let us put this latest wave of digitization into its broader context. We will understand that almost all life is fundamentally digital. The consequences are of hallmark importance: Everywhere we look (into cities, geography, culture, virtual spaces, language, ecosystems, epidemics, virus infections, etc.) life can be studied with the same mathematical tools; and almost everywhere the same theories apply.

Using artificial intelligence technologies for sustainable development

Artificial intelligence as a simulator of human behavior and thinking emerged as a result of machine learning. Through AI, they recognize and interpret data, on the basis of which programs of various types of activities are subsequently built. The rapid introduction of artificial intelligence-based technologies into the economic and social spheres of the international community has not been left out of the United Nations’ view from the point of view of using the capabilities of digital computers to solve problems at the level of intelligent beings in order to achieve the goals of sustainable development. The article discusses the specific aspects of I, the application of which will make the process of achieving the SDGs more effective and of high-quality.

Review of Artificial Intelligence

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.