Information Processing Machines Research Articles

A quantum information processing machine for computing by observables

A quantum machine that accepts an input and processes it in parallel is described. The logic variables of the machine are not wavefunctions (qubits) but observables (i.e.,operators) and its operation is described in the Heisenberg picture. The active core is a solid-state assembly of small nanosized colloidal quantum dots (QDs) or dimers of dots. The size dispersion of the QDs that causes fluctuations in their discrete electronic energies is a limiting factor. The input to the machine is provided by a train of very brief laser pulses, at least four in number. The coherent band width of each ultrashort pulse needs to span at least several and preferably all the single electron excited states of the dots. The spectrum of the QD assembly is measured as a function of the time delays between the input laser pulses. The dependence of the spectrum on the time delays can be Fourier transformed to a frequency spectrum. This spectrum of a finite range in time is made up of discrete pixels. These are the visible, raw, basic logic variables. Thespectrum is analyzed to determine a possibly smaller number of principal components. ALie-algebraic point of view is used to explore the use of the machine to emulate the dynamics of other quantum systems. An explicit example demonstrates the considerable quantum advantage of our scheme.

Human–machine hybrid intelligence for the generation of car frontal forms

With the acceleration of the upgrading of the automobile consumption market, artificial intelligence has become an increasingly effective means of enhancing the creative design of automobile appearance modeling. However, when artificial intelligence processes specific design tasks, creativity is primarily based on data drive, resulting in machine-generated design schemes that do not match human-specific psychological intentions. Due to the absence of design knowledge in the process of machine design, there is a data gap between human cognitive thought and machine information processing. This paper aims to structure the human's complex cognitive knowledge of car frontal form, establish the consistency between human and machine cognitive structures, and reduce communication barriers in the process of human–machine hybrid creative design. To achieve this objective, a human–machine hybrid intelligence methodology – a combination of human cognitive mental model, human–machine shared knowledge base, and Generative Adversarial Networks (GAN) – was developed to generate a large number of car frontal forms that are consistent with the design intent. First, we constructeda mental model of human cognition based on three dimensions: design intent, drawing behavior, and functional structure. Second, we created a shared human–machine knowledge base with design Knowledge. This knowledge base contains 12,560 images of car frontal form designs with corresponding morphological semantic labels and 3,140 sketches of car frontal forms drawn by hand. Human–machine shared knowledge base datawasutilized in a machine learning training network. In addition, a conditional cross-domain generative adversarial network was developed to investigate the implicit relationship between sketch characteristics, morphological semantics, and image visual effects. Using the suggested method, a large number of images with the specified morphological semantic category and resembling the hand-drawn sketch of a car frontal form can be generated rapidly. In terms of the quality of car frontal form generation, our research is superior to the baseline model according to qualitative and quantitative assessments. In comparison to the designer's output, the human–machine hybrid intelligent generation also demonstrates excellent creative performance.

A nanopore interface for higher bandwidth DNA computing

DNA has emerged as a powerful substrate for programming information processing machines at the nanoscale. Among the DNA computing primitives used today, DNA strand displacement (DSD) is arguably the most popular, with DSD-based circuit applications ranging from disease diagnostics to molecular artificial neural networks. The outputs of DSD circuits are generally read using fluorescence spectroscopy. However, due to the spectral overlap of typical small-molecule fluorescent reporters, the number of unique outputs that can be detected in parallel is limited, requiring complex optical setups or spatial isolation of reactions to make output bandwidths scalable. Here, we present a multiplexable sequencing-free readout method that enables real-time, kinetic measurement of DSD circuit activity through highly parallel, direct detection of barcoded output strands using nanopore sensor array technology (Oxford Nanopore Technologies’ MinION device). These results increase DSD output bandwidth by an order of magnitude over what is currently feasible with fluorescence spectroscopy.

Evolution of Society in the Light of the Philosophy of Technology

The article provides the general opinion of philosophers, scientists, and engineers heading institutes and centers of Samara National Research University regarding the issues of scientific and technological progress, social management problems under the condition of digital reality, human functions in new artificial environments. The technology is classically understood as satisfaction of human needs through the ability to apply knowledge of the laws of universe or nature in the broad sense. With advances in technology, the artificial human environment, the metacosmos, emerges. It is formed by technologies: by energy conversion machines, information processing machines, and presently by artificial intelligence. The evolution of the artificial environment, caused by scientific and technological development, entails social transformations, changes in the goals and methods of management system in human communities. Nowadays, the accumulated amount of contradictions of the information society testifies to the need of the search for a new form of organization of social structure, for new goals that would include human development, its creative potential growth, increase of the role of man in scientific and technological development. The authors discuss what the artificial intelligence technologies creating a new artificial human environment should be like to meet those goals. The article analyzes the impact of the fourth industrial revolution on the transformation of society. The question is raised from what philosophical positions one should understand man, society, technology, social development, and its goals. Analyzing the fundament for the philosophy of technology concepts of human, space, evolution, technology, artificial nature, the principles of the society 5.0 are introduced, which were formulated by Noritsugu Uemura. The authors conclude that the development of the artificial intelligence technologies provides opportunities for consistent unlocking the human potential, for advancement in personal growth, and finally for overcoming the crisis of the imbalance between the technological and the human, which characterizes contemporary information society.

Machine learning and quantum devices

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforce-ment learning (for discovering control strategies), recurrent neural networks (for analyz-ing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.

Photonics for artificial intelligence and neuromorphic computing

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research

PurposeLooking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.Design/methodology/approachThis article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.FindingsAI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.Research limitations/implicationsFuture research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.Practical implicationsAI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.Originality/valueThe research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

A universal model describing the structure and functions of living systems

ABSTRACT Can Life be explained based on the fundamental Laws of Nature? This question is central in Science since its answer could unify Physics and Biology and open new routes for Medicine. The present study introduces a clear and well-documented hypothesis addressing the unified description of all living systems. The proposed universal model is based on two established characteristics of Life. First, the concept of Functional Self-similarity (FSS) is introduced. As shown by several authors, all living systems can be classified in a multi-level hierarchy of increasing complexity. Systems in all hierarchical levels are characterized by a small set of the same attributes defining Life. This observation implies the existence of an elementary living system (i.e., a quantum of Life) having all the necessary functionalities of living systems. Secondly, the non-equilibrium nature of living systems implies that they should be able to process information since such a function is required for reducing entropy. Therefore, all living systems necessarily perform logical operations similar to electronic circuits. This conclusion, which is based on the requirement to overcome the constraints of the Second Law of Thermodynamics, indicates a close correspondence between living systems and information processing machines, namely computers. Consequently, important theoretical principles and concepts regarding computer design may also apply in the study of living systems. The above considerations lead to the Hypothesis of a Universal Architecture (UAH).

The physical basis of memory

Neuroscientists are searching for the engram within the conceptual framework established by John Locke's theory of mind. This framework was elaborated before the development of information theory, before the development of information processing machines and the science of computation, before the discovery that molecules carry hereditary information, before the discovery of the codon code and the molecular machinery for editing the messages written in this code and translating it into transcription factors that mark abstract features of organic structure such as anterior and distal. The search for the engram needs to abandon Locke's conceptual framework and work within a framework informed by these developments. The engram is the medium by which information extracted from past experience is transmitted to the computations that inform future behavior. The information-conveying symbols in the engram are rapidly generated in the course of computations, which implies that they are molecules.

Primer on silicon neuromorphic photonic processors: architecture and compiler

AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales.Neuromorphic photonicsaims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

On the Variety of Cognitive Temperatures and Their Symmetry-Breaking Dynamics

The asymptotic limit theorems of information and control theories permit exploration of a surprising number of temperature-like measures and symmetry-breaking dynamics associated with cognition. Each of several markedly different perspectives produces a distinct temperature-analog, capturing a rich and highly-punctuated behavioral landscape across the complex, hierarchical cognitive phenomena that characterize life at every scale and level of organization. Theories of cognition may be confronted by canonical conundrums similar to those plaguing the study of consciousness and its regulation. In short, there may be a spectrum of interacting cognitive 'temperatures' for organisms, social structures, institutions, information processing machines, and their composite entities, that varies across different systems, and between similar systems having undergone different individual developmental trajectories. The complexities of cognitive failure-leading to a vast array of pathologies-may be far stranger than generally recognized.

Granular aluminium as a superconducting material for high-impedance quantum circuits

Superconducting quantum information processing machines are predominantly based on microwave circuits with relatively low characteristic impedance, about 100 Ω, and small anharmonicity, which can limit their coherence and logic gate fidelity1,2. A promising alternative is circuits based on so-called superinductors3-6, with characteristic impedances exceeding the resistance quantum RQ = 6.4 kΩ. However, previous implementations of superinductors, consisting of mesoscopic Josephson junction arrays7,8, can introduce unintended nonlinearity or parasitic resonant modes in the qubit vicinity, degrading its coherence. Here, we present a fluxonium qubit design based on a granular aluminium superinductor strip9-11. We show that granular aluminium can form an effective junction array with high kinetic inductance and be in situ integrated with standard aluminium circuit processing. The measured qubit coherence time [Formula: see text] illustrates the potential of granular aluminium for applications ranging from protected qubit designs to quantum-limited amplifiers and detectors.

Deciding the status of controversial phonemes using frequency distributions; An application to semiconsonants in Spanish

Exploiting the fact that natural languages are complex systems, the present exploratory article proposes a direct method based on frequency distributions that may be useful when making a decision on the status of problematic phonemes, an open problem in linguistics. The main notion is that natural languages, which can be considered from a complex outlook as information processing machines, and which somehow manage to set appropriate levels of redundancy, already “made the choice” whether a linguistic unit is a phoneme or not, and this would be reflected in a greater smoothness in a frequency versus rank graph. For the particular case we chose to study, we conclude that it is reasonable to consider the Spanish semiconsonant /w/ as a separate phoneme from its vowel counterpart /u/, on the one hand, and possibly also the semiconsonant /j/ as a separate phoneme from its vowel counterpart /i/, on the other. As language has been so central a topic in the study of complexity, this discussion grants us, in addition, an opportunity to gain insight into emerging properties in the broader complex systems debate.

Blockchain government - a next form of infrastructure for the twenty-first century

Today, more than 100 blockchain projects created to transform government systems are being conducted in more than 30 countries. What leads countries rapidly initiate blockchain projects? I argue that it is because blockchain is a technology directly related to social organization; Unlike other technologies, a consensus mechanism form the core of blockchain. Traditionally, consensus is not the domain of machines but rather humankind. However, blockchain operates through a consensus algorithm with human intervention; once that consensus is made, it cannot be modified or forged. Through utilization of Lawrence Lessig’s proposition that “Code is law,” I suggest that blockchain creates “absolute law” that cannot be violated. This characteristic of blockchain makes it possible to implement social technology that can replace existing social apparatuses including bureaucracy. In addition, there are three close similarities between blockchain and bureaucracy. First, both of them are defined by the rules and execute predetermined rules. Second, both of them work as information processing machines for society. Third, both of them work as trust machines for society. Therefore, I posit that it is possible and moreover unavoidable to replace bureaucracy with blockchain systems. In conclusion, I suggest five principles that should be adhered to when we replace bureaucracy with the blockchain system: 1) introducing Blockchain Statute law; 2) transparent disclosure of data and source code; 3) implementing autonomous executing administration; 4) building a governance system based on direct democracy and 5) making Distributed Autonomous Government(DAG).

Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring

Rapid advancement in sensing, communication, and mobile technologies brings a new wave of Industrial Internet of Things (IIoT). IIoT integrates a large number of sensors for smart and connected monitoring of machine conditions. Sensor observations contain rich information on operational signatures of machines, thereby providing a great opportunity for machine condition monitoring and control. However, realizing the full potential of IIoT depends to a great extent on the development of new methodologies using big data analytics. This paper presents a new methodology for large-scale IIoT machine information processing, network modeling, condition monitoring, and fault diagnosis. First, we introduce a dynamic warping algorithm to characterize the dissimilarity of machine signatures (e.g., power profiles during operations). Second, we develop a stochastic network embedding algorithm to construct a large-scale network of IIoT machines, in which the dissimilarity between machine signatures is preserved in the network node-to-node distance. When the machine condition varies, the location of the corresponding network node changes accordingly. As such, node locations will reveal diagnostic information about machine conditions. However, the network embedding algorithm is computationally expensive in the presence of large amounts of IIoT-enabled machines. Therefore, we further develop a parallel computing scheme that harnesses the power of multiple processors for efficient network modeling of large-scale IIoT-enabled machines. Experimental results show that the developed algorithm efficiently and effectively characterizes the variations of signatures in both cycle-to-cycle and machine-to-machine scales. This new approach shows strong potentials for optimal machine scheduling and maintenance in the context of large-scale IIoT.

인지의 시대 : 인지과학이 열어가는 인문학과 인공지능의 결합

The intelligence defined by the cognitive science of the past was a kind of function, that is, the analogy of the input, processing, and output of the information processing machine. The embodied cognition is a phenomenon in which individual human beings strictly distinguish between the inside and the outside of the system by organism of 'body' as an individual unit, self-generated autopoiesis and self organizing. This defines the more advanced and radical constructivist aspects of intelligence. This means that intelligence is not merely the ability of problem solving. By overcoming the definition of intelligence based on the information processing theory of the 1950s and by extreme constructivist view that everything is alive and constantly evolving through self-generation, thinking and body, Humberto R. Maturana, Francisco J. Varela and Niklas Luhman have developed the connection between them. What is ultimately important in the cognitive science of this embodied cognition is the phenomenon of communication that communicates each system, not each of the thought, the body, and the world – environment. At the bottom of this idea is the Information Theory, which deals with entropy and information. This is because the information is a phenomenon and existence that has meaning and information volume only through communication. The essence of the Fourth Industrial Revolution, which creates space for all things to live and think, is the information and communication.

Computers assisted instruction in refining the quality of teaching and learning

real danger is not that computers will begin to think like men, but that men will begin to think like computers.Above said line by Sydney J. Harris appropriately depict that inventions of computers have brought a major technological change in our life. The teacher is responsible for introducing concepts to in class and scheduling topics for review and practice, which will work on at instructional terminals Daily reports, inform the teacher of each student's performance and progress.Accepted teaching and learning practices have undergone changes of revolutionary proportions in recent years. These changes are evident in situations as diverse as early childhood teaching, university physics teaching and workplace training. They have been underpinned by shifts in psychological and pedagogical theory, the most recent of which fit broadly under the heading of constructivism. Computer is a fast and accurate data manipulating device that accepts and stores data and produces a meaningful result under the stored set of instructions provided in the programme. It is a device which is derived from human intelligence. It is not an extremely fast information processing machine which, neither processes intelligence of its own nor has any thinking, arguing or decision taking power of its own. The development of information and technology enables the application of computers in the language learning process, which is known as Computer Assisted Learning (CAL). But there is still a question whether computers really assist second language learning. Many teachers who have never touched a computer tend to respond with an emphatic no; whereas, the overwhelming number of teachers who give computers a try find that they are indeed useful in second language learning. The responds indeed depend on the teachers' willingness to develop their teaching methods by utilizing the technology.Undoubtedly, computers make excellent teaching tools, especially in teaching languages in any aspect, such as vocabulary, grammar, composition, pronunciation, or other linguistic and pragmatic-communicative skills.The aim of this paper is to inform both educators and learners how computers are helping them and analyze the advantages of the utilization of computer in teaching and learning. When the advantages of using computer in teaching and learning understood well by teachers, learners and all stakeholders, it will be very useful to help learners improve their teaching and learning. Furthermore, there is a belief that through the use of it are going to improve various skills.The paper focuses on the advantages of using for learners and teachers. Furthermore, there is a belief that through the use of it are going to improve some skills, such as pronunciation, vocabulary and grammar, listening comprehension, and others on their own.COMPUTER AS AN EFFECTIVE INSTRUCTIONAL AIDEducators use a computer technology to create a rich environment where can show their capacity. It provides for opportunities for in active participation, exploration and research. In this context, one of the ways by which computer technology can be made useful in the classroom is i.e. Computer Assisted Instruction.Lawrence Sturlow & Daniel Davis (1965) developed a complex model of teaching in which computer can present the instruction in place of teacher. This model is known as Computer Assisted Instruction. CAI refers to the use of computer and software programmes to assist the delivery of information and students (Wright & Forcier). Thus refers to the application of computer software with the intention to fulfil the student's needs. CAI, an educational programme, is designed to serve as a teaching tool. It enables to work at their own pace. It has been found to be very effective in the teaching of basic skills.COMPUTER ASSISTED INSTRUCTION (CAI)CAI deals with flexible, rapidly changing and detailed information. …

Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition.

Quantum Process Tomography of an Optically-Controlled Kerr Non-linearity.

Any optical quantum information processing machine would be comprised of fully-characterized constituent devices for both single state manipulations and tasks involving the interaction between multiple quantum optical states. Ideally for the latter, would be an apparatus capable of deterministic optical phase shifts that operate on input quantum states with the action mediated solely by auxiliary signal fields. Here we present the complete experimental characterization of a system designed for optically controlled phase shifts acting on single-photon level probe coherent states. Our setup is based on a warm vapor of rubidium atoms under the conditions of electromagnetically induced transparency with its dispersion properties modified through the use of an optically triggered N-type Kerr non-linearity. We fully characterize the performance of our device by sending in a set of input probe states and measuring the corresponding output via time-domain homodyne tomography and subsequently performing the technique of coherent state quantum process tomography. This method provides us with the precise knowledge of how our optical phase shift will modify any arbitrary input quantum state engineered in the mode of the reconstruction.

How nanomechanical systems can minimize dissipation.

Information processing machines at the nanoscales are unavoidably affected by thermal fluctuations. Efficient design requires understanding how nanomachines can operate at minimal energy dissipation. Here we focus on mechanical systems controlled by smoothly varying potential forces. We show that optimal control equations come about in a natural way if the energy cost to manipulate the potential is taken into account. When such a cost becomes negligible, an optimal control strategy can be constructed by transparent geometrical methods which recover the solution of optimal mass transport equations in the overdamped limit. Our equations are equivalent to hierarchies of kinetic equations of a form well known in the theory of dilute gases. From our results, optimal strategies for energy efficient nanosystems may be devised by established techniques from kinetic theory.