JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 9, 2020 Towards the Post Digital

Vijendra Singh Bramhe1, Rani Astya2, Hoor Fatima3

1,2,3Dept. of Computer Science and Engineering,Sharda University, Greater Noida, U.P. Email Id- [email protected], [email protected], [email protected]

Received:26 January 2020 Revised and Accepted: 11 April 2020

ABSTRACT: This paper sketches on recent works of researchers on complexity theory, Artificial Intelligence, algorithm capitalism, cybernetics, quantum computing and deep learning, and these thoughts are united to build up critical philosophy of post digital. Quantum computing depends on the quantum mechanics and provides a drastically unique methodology from classical computing dependent on the classical mechanics. Complexity theory and cybernetics give knowledge into the frameworks that are too perplexing to even consider predicting its future. Deep learning and Artificial Intelligence are promising the last phase of automation that isn't perfect with welfare state dependent on the full employment. It has along these lines landed into the algorithm capitalism period and its present stage, biologization of digital explanation is a separate phenomenon which is on an early new structure that springs from digital reason to the biology application and biologization of digital forms. A critical post digital philosophy is rationalistically interrelated with the speculations, for e.g., complexity theory and cybernetics, and furthermore procedures, for e.g., complexity science, deep learning and quantum computing. These procedures comprise the developing techno science worldwide framework, algorithm capitalism and provide an opportunity to the techno-social change. KEYWORDS: Critical philosophy, Complexity theory, Cybernetics, Post digital.

I. INTRODUCTION Post digital doesn't portray a condition or , situation after digital. It's anything but a critical philosophy that asks into digital world, critiquing and examining its theoretical orientation, its consequences and its constitution. Specifically, it tends to the states of ideology and digitally of digitalism, the possibility that everything can be comprehended without loss of which means in terms of digital. It consider this critique of digital explained that has the application not just as far as theory of hyper control and social theory yet in addition in music, and art where it is worried to the humanize digital technologies[1]. There are two elements of critique of digital explained: first is that mathematic technical control frameworks that are a piece of the rising worldwide digital infrastructure inside which it presently exists and second is political economy of all these frameworks like their ownership, acquisition and structure. It additionally alludes to the marriage and convergence of two prevailing world historical powers of biological and digital frameworks also, the manners by which together it comprise the phenomenal horizon for existence furthermore, turning out to be the species evolution of life and homo sapiens when all is said in done, and colonization of space[2]. The post digital esthetics is a term which has a specific currency after collection of post digital esthetics art, design, same title and commutation by researchers, on a new resistance aesthetic against digital and return to old media and . Researchers in its joint editorial for a Special Issue on the post digital research in a Peer surveyed journal which is Ban open-get to research journal which addresses ever moving thematic systems of digital culture give a normal working meaning of the post digital: Post digital when comprehended as critical impression of the digital aesthetic immaterialism, presently portrays the paradoxical and messy condition of media and art after revolutions of digital technology. Post-digital neither perceives the differentiation among new and old media nor ideological insistence of one or another. It combines new and old frequently applying network social experimentation to the analog technologies that it re-researches and re-uses.It will in general spotlight on experiential as opposed to the conceptual[3]. It searches for the agency of DIY outside ideology of totalitarian innovation and for the systems administration off the big data capitalism. At a similar time, it as of now has gotten commercialized. It upholds a post digital philosophy based on the extreme cooperation of the informationalism and biology that it alludes to as the bio informational capitalism and incorporates 3 configurations of complexity theory, deep learning, quantum computing and algorithmic capitalism. This paper draws on the work from currently papers on the cybernetics, algorithmic capitalism and

1453

JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 9, 2020

post digital and uniting the thoughts here with the new material toward the start and end of article. Truly these perspectives are some portion of interconnected and broader perspective[4].

II. QUANTUM COMPUTING Quantum computing employs the processes and laws of the quantum mechanics to the process data. While conventional computers work through guidelines that employ a binary framework represented by 0 and 1 numbers, quantum computers employ qubits or quantum bits to encode the data as the 0 s, 1 s, or the both simultaneously representing the flow control of through circuit. Superposition of the states in the quantum computing simultaneously with both tunnelling and entanglement enables quantum computers for manipulating tremendous mixes of the states at any minute. Quantum theory is endeavour to portray the behaviour of energy and matter on this subatomic scale. Trials in early twentieth century proposed that little particles like electrons and photons can carry on either like a particle or like a wave under various conditions, and there are exact points of confinement with which amounts can be referred to[5]. Theory of quantum has no completely good clarification. The Copenhagen interpretation originally proposed "Weiner Heisenberg" and "Niels Bohr" holds that quantum mechanics nature is probabilistic and would never supplanted by a deterministic hypothesis in this manner undermining old idea physical systems causality and idea of . By applying quantum hypothesis, quantum computing plays out certain computational errands exponentially quicker than old style computing. The report refers to researcher that: Quantum computers are in a general sense not quite the same as old style computers on the grounds that the quantum information is likewise the possibility physics. Memories of classical computer are obliged to exist at some random time as a straightforward rundown of 0's & 1's. Conversely, in a solitary quantum memory some such mixes even all possible rundown of 0's & 1's would all be able to exist at the same time. While quantum algorithm, symphony of possibilities merge and split in the end blending around one solution. The capability of the quantum computing to the process various calculations at the same time makes it especially appropriate to the absolute most complex issues looked by programmers involving the optimization issue and the machine learning from recognizing patterns in enormous datasets. These are at edge of the post digital in the quantum computing that is altogether different from old style computing with various new uses dependent on fundamental contrasts and an on a very fundamental level diverse perception of world[6]. There is small doubt that quantum data science holds for the next generation processing and computing. Most exertion has gone in saddling improvement at level of applications for business in worldwide competitive economy. Some if any idea has gone in the more extensive philosophy and manners by which quantum data science will on a very fundamental level change conditions of society.

Complexity Theory: Cybernetics is additionally extensively identified with frameworks theory and philosophy, both the function as metalanguage of models and concepts for the trans disciplinarian use, yet presently evolving and a long way from being stabilized. Researcher gives a point by point history of cybernetics and systemic as far as an arrangement historical stages. To start with, Precursors Prehistory of the Systemic Cybernetic Language returning to the Descartes and Greeks in modern world and extending over disciplines with significant work in psychology, physiology, mathematics, chemistry, linguistics, biology and so forth[7]. Second, from the precursors to pioneers, starting with researcher who objective to address the issue of control and prediction and the significance of feedback for the corrective guiding and mentioning the communication mathematical theory, a blueprint of general framework theory, Spaceship Earth theory, automata theory. Researcher properly commits space to researcher on fundamental and its escape from thermodynamic models assumptions towards understanding the dissipative structures in complicated frameworks[8]. It has an enthusiasm for time got from scholar, and then later from physicists, where it built up a theorem on the instances of frameworks which were exceptionally irreversible and organized and applied to energetics of embryological development. This work in theory of irreversible phenomena drove him likewise to rethink its addition into quantum dynamics and classical and to the issue of the establishments of the statistical mechanics. Researcher theory which manages the non-straight deterministic frameworks which has been employed in numerous disciplines yet has been extremely effective in ecology for clarifying chaotic dynamics.

1454

JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 9, 2020

Researcher gives a nontechnical record of the complexity theory that Chaos theory and Complexity Theory studies frameworks that are excessively complicated to precisely foresee its future, yet all things considered show fundamental patterns that can assist us with adapting in an inexorably complicated world. Complexity is worried about theoretical establishments of the computer science being worried about the investigation of intrinsic complexity of the computational assignments and lays on comprehension the focal job of randomness[9].

Deep Learning and AI: Researcher distinguish 3 deep learning development: deep learning called as cybernetics that showed up with learning biological theories; deep learning called as connectionism that employed back propagation for the train neutral network with various layers and present resurgence under name deep learning and just showing up in book form[10]. It contends that the present approach of deep learning for artificial intelligence goes past the neuroscientific point of view applying the machine learning systems which are most certainly not neutrally inspired. Deep learning at that point, is a kind of machine learning, strategy that enables PC frameworks to improve with data and experience. Researcher composing a visitor publication for the "IEEE Transactions on the Automation Science" what's more, Engineering report on exceptional take-off of the AI and with resurgence additionally return of machinery question presented just about 200 years’ prior in the Industrial Revolution context. It note upbeat investigation of mainstream press and document a few UK and US reports publication that recommend that AI has arrived as well as provides huge potential for additional effective and efficient government and business[11]. The business analysts refer to invite AI for efficiency gains. It solicits what set off this astounding resurgence from AI? Also, it answer: All the proof focuses to an intriguing convergence of ongoing advances with regards to machine learning (ML), GPUs which is graphics processing units and big data[12]. A specific part of ML known as deep learning employing artificial neural networks got a hardware help a couple of years’ prior from GPUs, that made supervised learning from a lot of visual information practical. A dark scenario and a working theory is that during a time of deep learning which is the last phase of automation, welfare state dependent on the full employment, may appear to be an invention of a curious and sentimental past when work together with privilege to pull back one's work and work politics, all normally went together and it had some power in industrial age. By and large and from the viewpoint of algorithmic capitalism going all out, the full employment and welfare state may appear like a simple historical abnormality. Without surrendering to the technological , given present evidence and trends it appears that deep learning as the type of AI will proceed apace the automation process and while it will make a few new openings, it will do quite a lot more gradually than jobs it disestablishes[13].

Algorithm Capitalism: Progressively, cybernetics and its related hypotheses has gotten focal in understanding the idea of distributed systems and networks in politics, knowledge and energy and furthermore are huge in conceptualizing information dependent economy. Economics itself like discipline has become to perceive the significance of understanding frameworks instead of the rational agents performing alone and unadulterated rationality models of monetary behaviour are being enhanced by economic hypotheses that utilization complexity theory to model and predict transactions. Progressively basic records of globalization underline a new type of worldwide capitalism. The capitalism financialization is a procedure that appears to have accompanied globalization and neoliberalism, depicting to a move from production to the financial services, multiplication of monopolistic multinational enterprises and financialization of capital amassing procedure. Cybernetic capitalism is the framework which has been moulded by the powers of aestheticization, formalization and mathematization starting in mid-twentieth century and related with advancements in , biology, and mathematical theory. Its new structures currently show themselves in the types of informationalism, learning economy, finance capitalism and knowledge capitalism with open knowledge and creative economies. The critical inquiry in wake of the breakdown of worldwide finance framework and the approaching eco-emergency concerns whether the capitalism can advance types of ecological, economic and social sustainability.

1455

JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 9, 2020

III. PROPOSED THEORY OF POST DIGITAL A critical philosophy begins from the dismissal of an absolutely mechanistic universe, the universe of the classical mechanics that is resounded by a customary deterministic computing. The transition to something new for a philosophy that underlines nondeterministic states, likewise may discover comfort in the idea of philosophy and as procedure or an interrelated processes web which was published as Reality and Process. Researcher occasion based procedure philosophy offers a relational and ecological approach to deal with a wide scope of studies too as filling in as the shared ground for cultural traditions and Western and Eastern religious. In fact, philosophy was the disciplines which was first to build up procedure thought with "Claremont School of Philosophy" and foundation of Centre for the Process Studies. was translated as a central challenge to the scientific and perspective on reality not completely determined by casual determinism or classical mechanics: creativity is a principle of the existence and it has a scope of originality in the manner by which entities reacts to different entities. At any rate from the glance of philosophy appears to be consonant with the quantum physics and post digital as it has depicted it. It is a form of events and every event in this structure has its position and its own quality or peculiar character. In a lesson of Concept of Nature, it clearly dismisses what it calls bifurcation of nature and scrutinizes the idea of issue as substance whose qualities perceive contending that character of spatiotemporal structure of the events can be completely communicated regarding relations between these increasingly conceptual events – particles[14]. It proceeds to clarify that every event particle lying only one snapshot of given time-framework and it’s portrayed by its extraneous character, its position and its intrinsic character. In this manner, all the entities are temporal, these are the events of experience and anything doesn't exist in segregation by just its relations.

IV. CONCLUSION Philosophy process gives us with what Whitehead known as a philosophy of organism- it is the type of theoretical which benefits the process and event well beyond substance with result that these are discharged from deterministic universe, mechanistic that is a result of traditional physics. It is likewise a clear dismissal of the scientific realism introducing a connection procedure philosophy which focuses towards an in deterministic universe on the level of sub-atomic and a type of quantum philosophy dependent on computing characterizing a time and quantum mechanics these are simply entering. It would be dynamic, transformative, framework development philosophy altogether different from comprehension of digital, which has just got in progress. A philosophy of post digital must have the option to comprehend the procedures of complexity science, deep learning and quantum computing as it comprises the developing techno-science worldwide framework and its place inside an industrialist framework that itself is changed by these improvements. It depicts the critical philosophy which is used by numerous researchers for showing the . It concludes all the terms such as quantum computing, deep learning, artificial intelligence and complexity theory.

V. REFERENCES [1] M. A. Peters and P. Jandrić, “, open and bio-digital becoming: Response to Luciano Floridi’s Onlife Manifesto,” Educ. Philos. Theory, 2018. [2] S. Arndt et al., “Between the Blabbering Noise of Individuals or the Silent Dialogue of Many: a Collective Response to ‵Post digital Science and Education′ (Jandrić et al. 2018),” Post digital Sci. Educ., 2019. [3] L. Wang et al., “Temporal segment networks: Towards good practices for deep action recognition,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. [4] D. Oñoro-Rubio and R. J. López-Sastre, “Towards perspective-free object counting with deep learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. [5] M. Simonovsky and N. Komodakis, “GraphVAE: Towards generation of small graphs using variational autoencoders,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. [6] L. Muñoz-González et al., “Towards poisoning of deep learning algorithms with back-gradient optimization,” in AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2017, 2017.

1456

JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 9, 2020

[7] Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao, “SpiderCNN: Deep learning on point sets with parameterized convolutional filters,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. [8] M. Simonovsky, B. Gutiérrez-Becker, D. Mateus, N. Navab, and N. Komodakis, “A deep metric for multimodal registration,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. [9] T. Kowaliw, N. Bredeche, and R. Doursat, Growing Adaptive Machines. 2014. [10] N. Passalis and A. Tefas, “Learning Deep Representations with Probabilistic Knowledge Transfer,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. [11] T. Dreossi, S. Jha, and S. A. Seshia, “Semantic adversarial deep learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. [12] G. Petrucci, C. Ghidini, and M. Rospocher, “Ontology learning in the deep,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. [13] C. Chen, F. Tung, N. Vedula, and G. Mori, “Constraint-aware deep neural network compression,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. [14] P. Luo, “Eigennet: Towards fast and structural learning of deep neural networks,” in IJCAI International Joint Conference on Artificial Intelligence, 2017.

1457