scholarly journals Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5796 ◽  
Author(s):  
Nourah Janbi ◽  
Iyad Katib ◽  
Aiiad Albeshri ◽  
Rashid Mehmood
Keyword(s):  
Artificial Intelligence ◽  
Case Studies ◽  
High Speed ◽  
Mobile Internet ◽  
Distributed Artificial Intelligence ◽  
Distributed Ai ◽  
Domain Specific ◽  
Distributed Training ◽  
Software Modules ◽  
And Performance

Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

Microservice-Oriented Architecture in Distributed Artificial Intelligence Systems and the Language of AI in Bio-Neural Systems

2020 ◽  
Vol 10 (2) ◽  
pp. 18-27
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Circuits ◽  
Distributed Artificial Intelligence ◽  
Neural Systems ◽  
Computer Language ◽  
New Approach ◽  
Distributed Ai ◽  
Protocol Of Communication ◽  
Client Side ◽  
Artificial Intelligence Systems

This article describes the views on the architecture of distributed AI systems based on the simulated bio-neurons representing the basis for the bio-neural circuits, which represent distributed AI subsystems and serve as microservices for the AI client-side systems. The article also describes the interface and the demands to the protocol of communication with the distributed subsystems of the AI, the ways of tuning the synaptic contacts in the brand new neural circuits, which represent the distributed AI systems, and finally, the new approach to communication with such the systems based on the new computer language, which will be used in construction and tuning of such the AI systems.

Battlefield Awareness Network Research Based on Intelligence Role Division and Wireless Sensor Network

2013 ◽  
Vol 300-301 ◽  
pp. 580-584 ◽  
Author(s):  
Tian Li ◽  
Peng Yuan Liu ◽  
Yong Ke
Keyword(s):  
Artificial Intelligence ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Performance Optimization ◽  
Distributed Artificial Intelligence ◽  
Wireless Sensor ◽  
Network Technology ◽  
Technical Application ◽  
Intelligence Theory ◽  
And Performance

This article mainly describes battlefield awareness network scheme based on distributed artificial intelligence theory and intelligence wireless sensor network technology. Critical technologies are discussed, such as role-divided wireless sensor group, mission decision based on intelligence cooperation and performance optimization for battlefield circumstance. The research takes on advanced theory significance and operable technical application foreground.

scholarly journals Photonic Matrix Computing: From Fundamentals to Applications

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1683
Author(s):  
Junwei Cheng ◽  
Hailong Zhou ◽  
Jianji Dong
Keyword(s):  
Artificial Intelligence ◽  
High Speed ◽  
Optical Computing ◽  
Low Energy ◽  
Research Progress ◽  
Domain Specific ◽  
Matrix Operations ◽  
Optical Neural Networks ◽  
Low Energy Consumption ◽  
Computing Speed

In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.

Humanism and Music

2020 ◽  
Author(s):  
Christopher M. Driscoll
Keyword(s):  
Artificial Intelligence ◽  
Popular Culture ◽  
Popular Music ◽  
Case Studies ◽  
Contemporary Music ◽  
Theoretical Relationship ◽  
Rock Band ◽  
Historical Developments ◽  
The Relationship

This chapter explores the relationship between humanism and music, giving attention to important theoretical and historical developments, before focusing on four brief case studies rooted in popular culture. The first turns to rock band Modest Mouse as an example of music as a space of humanist expression. Next, the chapter explores Austin-based Rock band Quiet Company and Westcoast rapper Ras Kass and their use of music to critique religion. Last, the chapter discusses contemporary popular music created by artificial intelligence and considers what non-human production of music suggests about the category of the human and, resultantly, humanism. These case studies give attention to the historical and theoretical relationship between humanism and music, and they offer examples of that relationship as it plays out in contemporary music.

scholarly journals Spatially Resolved Experimental Modal Analysis on High-Speed Composite Rotors Using a Non-Contact, Non-Rotating Sensor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4705
Author(s):  
Julian Lich ◽  
Tino Wollmann ◽  
Angelos Filippatos ◽  
Maik Gude ◽  
Juergen Czarske ◽  
...  
Keyword(s):  
High Speed ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Fiber Reinforced ◽  
Structural Dynamic ◽  
Spatially Resolved ◽  
Glass Fiber Reinforced ◽  
Vibration Responses ◽  
And Performance

Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 μrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

2021 ◽  
pp. 016555152098549
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Cognitive Processes ◽  
Process Model ◽  
Extended Cognition ◽  
Dual Process ◽  
Personalized Recommendation ◽  
Normative Values ◽  
And Performance ◽  
Concise Guide

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

Pareto domain: an invaluable source of process information

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Geraldine Cáceres Sepúlveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Case Studies ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Process Variables ◽  
Optimal Point ◽  
Decision Variables ◽  
Visualization Tools ◽  
Pareto Domain ◽  
And Performance ◽  
Typical Process

AbstractDue to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

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