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.

Artificial Intelligence Based on Biological Neurons

2020 ◽  
pp. 25-53
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Next Generation ◽  
New Approach ◽  
Home Based

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home-based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.

Biological Neural Circuits as Applications in Business and Engineering as a New Approach in Artificial Intelligence

2021 ◽  
pp. 49-74
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Parallel Processing ◽  
Neural Circuits ◽  
Numerical Data ◽  
Biological Information ◽  
Next Generation ◽  
Computing Systems ◽  
New Approach ◽  
Business Applications ◽  
Elementary Basis

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and created neural circuits which represent the next generation of computing systems and artificial intelligence for business applications. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on bit-parallel processing of numerical data (synapses) for obtaining result. The proposed approach of implementation a neuron can serve as a new elementary basis for the construction of neuron-based computers with a higher processing speed of biological information and good survivability. The research demonstrates the developed nervous circuit constructor and its usage in building of the nervous circuits of biological creatures and simulation of their work and how it could be used in the next generation of the computing systems.

scholarly journals A new approach to identify dropout learners based on their performance-based behavior

2021 ◽  
Vol 27 (10) ◽  
pp. 1001-1025
Author(s):  
Rochdi Boudjehem ◽  
Yacine Lafifi
Keyword(s):  
Artificial Intelligence ◽  
Distance Learning ◽  
Learning Environments ◽  
Intelligent Systems ◽  
Dynamic Models ◽  
Distributed Artificial Intelligence ◽  
Struggling Learners ◽  
New Approach ◽  
Academic Tasks ◽  
E Learning

Distance learning environments are increasingly offering more comfort to both learners and teachers, allowing them to carry out their academic tasks remotely, especially in critical times where it is difficult, or even dangerous, to bring these actors together in one physical place. Nevertheless, These same environments are complaining about the massive dropout numbers among their learners. Therefore, designing new intelligent systems capable of reducing these numbers becomes imperative. This paper proposes a new approach capable of identifying and assisting endangered learners experiencing difficulties by monitoring and analyzing their behavior inside the e-learning environment. By building dynamic models to follow the learners’ current situation, the proposed approach could intervene autonomously to save learners identified as struggling. Relying on distributed artificial intelligence instead of humans to closely monitor learners within distance learning environments can be very effective when identifying struggling learners. Furthermore, targeting these learners with early enough and carefully designed interventions can reduce the number of dropouts.

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.

Artificial Intelligence Based on Biological Neurons

2021 ◽  
pp. 66-89
Author(s):  
Rinat Galiautdinov
Keyword(s):  
Artificial Intelligence ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Next Generation ◽  
New Approach ◽  
Home Based

The chapter describes the new approach in artificial intelligence based on simulated biological neurons and creation of the neural circuits for the sphere of IoT which represent the next generation of artificial intelligence and IoT. Unlike existing technical devices for implementing a neuron based on classical nodes oriented to binary processing, the proposed path is based on simulation of biological neurons, creation of biologically close neural circuits where every device will implement the function of either a sensor or a “muscle” in the frame of the home based live AI and IoT. The research demonstrates the developed nervous circuit constructor and its usage in building of the AI (neural circuit) for IoT.

scholarly journals Commitments and conventions: The foundation of coordination in multi-agent systems

1993 ◽  
Vol 8 (3) ◽  
pp. 223-250 ◽  
Author(s):  
Nick R. Jennings
Keyword(s):  
Artificial Intelligence ◽  
Individual Performance ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Diverse Range ◽  
Agent Systems ◽  
Course Of Action ◽  
Central Hypothesis ◽  
Multi Agent ◽  
Artificial Intelligence Systems

AbstractDistributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued thatcommitments(pledges to undertake a specific course of action) andconventions(means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do notexplicitlyinvolve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.

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