scholarly journals Artificial nervous systems—A new paradigm for artificial intelligence

Patterns ◽  
2021 ◽  
Vol 2 (6) ◽  
pp. 100265
Author(s):  
Fredric Narcross
Keyword(s):  
Artificial Intelligence ◽  
New Paradigm ◽  
Nervous Systems

Future Education within Disruptive Technologies Developments

2020 ◽  
Vol 26 (2) ◽  
pp. 288-293
Author(s):  
Codrin-Leonard Herţanu
Keyword(s):  
Artificial Intelligence ◽  
New Paradigm ◽  
Contemporary World ◽  
Disruptive Technologies ◽  
Educational Domain ◽  
Blockchain Technology ◽  
How People Learn ◽  
Future Education ◽  
Present World ◽  
Technology Domain

AbstractOur contemporary world is on the verge of crucial changes of an unparalleled pace. The ‘technological changeover’ is the new paradigm caused by the unprecedented evolution of the disruptive technologies. The present world has the tendency to evolve at least exponential, therefore future educational environment is fairly different than its present layout. An entire array of nowadays studies widely recognizes that the progress of the disruptive technologies will pose a meaningful impact over the educational system evolution. Among the most spectacular technologies with disruptive features we should encounter Artificial Intelligence, Blockchain Technology, Cloud Computing, and the like. In an era of technological disruption the education is seen as the new currency. With the help of Artificial Intelligence, for instance, the education system could track how people learn from kindergarten to retirement. Besides, the technology domain will move the centre of gravity from the institutional area to that of the education’s beneficiaries, as we might expect that they will recruit and employ the needed teacher staff, not the institutions. Moreover, the education’s recipients will be the main creators of tomorrow’s professions and within their community the overarching events will happen and the main decisions will be taken in the educational domain.

scholarly journals In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

2021 ◽  
Vol 4 ◽  
Author(s):  
Mustafa Y. Topaloglu ◽  
Elisabeth M. Morrell ◽  
Suraj Rajendran ◽  
Umit Topaloglu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dice Coefficient ◽  
New Paradigm ◽  
Underrepresented Groups ◽  
Patient Protection ◽  
Shapley Values ◽  
Geographically Distributed ◽  
Computationally Expensive ◽  
Improved Performance

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.

Disrupting Agriculture

2021 ◽  
pp. 771-812
Author(s):  
Omar F. El-Gayar ◽  
Martinson Q. Ofori
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Big Data ◽  
New Paradigm ◽  
Agricultural Industry ◽  
Decision Systems ◽  
Food And Agriculture ◽  
Key Stakeholders ◽  
Smart Agriculture ◽  
Key Aspects

The United Nations (UN) Food and Agriculture (FAO) estimates that farmers will need to produce about 70% more food by 2050. To accommodate the growing demand, the agricultural industry has grown from labor-intensive to smart agriculture, or Agriculture 4.0, which includes farm equipment that are enhanced using autonomous unmanned decision systems (robotics), big data, and artificial intelligence. In this chapter, the authors conduct a systematic review focusing on big data and artificial intelligence in agriculture. To further guide the literature review process and organize the findings, they devise a framework based on extant literature. The framework is aimed to capture key aspects of agricultural processes, supporting supply chain, key stakeholders with a particular emphasis on the potential, drivers, and challenges of big data and artificial intelligence. They discuss how this new paradigm may be shaped differently depending on context, namely developed and developing countries.

scholarly journals Automation of management processes

2019 ◽  
Vol 132 ◽  
pp. 01020
Author(s):  
Luis Ochoa Siguencia ◽  
Piotr Halemba
Keyword(s):  
Artificial Intelligence ◽  
Service Management ◽  
Environmental Changes ◽  
Technology Development ◽  
New Paradigm ◽  
Convenience Sample ◽  
Related Services ◽  
Tourism Management ◽  
Information Technology Development ◽  
Management Students

Artificial intelligence and service automation are the key to these kinds of new, product-related services. They increasingly penetrate the traditional mechanical and plant engineering sector and open up potentials for innovative services. Tourism services providers are going through rapid changes and the role of Information and Communication Technology, artificial intelligence and service automation is increasing in all spheres of the service management system. When the organization is threatened by environmental changes such as crises or competition as a result of information technology development or increased customer demands, the need for communication increases. This paper presents the first step of an ongoing investigation that focuses on the tourist services experiences and construction of management knowledge on undergraduate tourism management students. We report and discuss the result of a survey conducted involving the students of Tourism management at The Jerzy Kukuczka Physical Education Academy in Katowice - Poland. Structured questionnaires based on a 12-item importance scale were administered to a convenience sample of respondents. The authors present a new paradigm that emerges as a response to polarisation and treats communication as more receiver-centered, stakeholder ⚟ based, relationship ⚟ building ⚟ oriented and of strategic importance.

scholarly journals Processing Information in the Clouds

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 25
Author(s):  
Mark Burgin ◽  
Eugene Eberbach ◽  
Rao Mikkilineni
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Human Brain ◽  
Incremental Learning ◽  
Computational Models ◽  
Higher Order ◽  
Turing Machines ◽  
New Paradigm ◽  
New Model ◽  
Processing Information

Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

Software Agents

2011 ◽  
pp. 798-806
Author(s):  
Stanislaw Stanek ◽  
Maciej Gawinecki ◽  
Malgorzata Pankowska ◽  
Shahram Rahimi
Keyword(s):  
Artificial Intelligence ◽  
Support Systems ◽  
Internal State ◽  
Software Agents ◽  
Software Agent ◽  
The Other ◽  
Multi Agent Systems ◽  
New Paradigm ◽  
Institute Of Technology ◽  
Intelligent Tools

The origins of the software agent concept are often traced back to the pioneers of artificial intelligence—John Mc Carthy, the creator of LISP programming language, and Carl Hewitt, the father of distributed artificial intelligence (DAI). Kay (1984, p. 84) states that: …the idea of an agent originated with John McCarthy in the mid-1950s, and the term was coined by Oliver G. Selfridge a few years later, when they were both at the Massachusetts Institute of Technology. They had in view a system that, when given a goal, could carry out the details of the appropriate computer operations and could ask for and receive advice, offered in human terms, when it was stuck. An agent would be a ‘soft robot’ living and doing its business within the computer’s world. Nwana (1996, p. 205), on the other hand, claims that: …software agents have evolved from multi-agent systems (MAS), which in turn form one of three broad areas which fall under DAI, the other two being Distributed Problem Solving (DPS) and Parallel Artificial Intelligence (PAI). (…) The concept of an agent (…) can be traced back to the early days of research into DAI in the 1970s – indeed, to Carl Hewitt’s concurrent Actor model. In this model, Hewitt proposed the concept of a self-contained, interactive and concurrently-executing object which he termed ‘Actor’. This object had some encapsulated internal state and could respond to messages from other similar objects1. The software agent concept meant, in the first place, replacing the idea of an expert, which was at the core of earlier support systems, with the metaphor of an assistant. Until 1990s, decision support systems (DSS) were typically built around databases, models, expert systems, rules, simulators, and so forth. Although they could offer considerable support to the rational manager, whose decision making style would rely on quantitative terms, they had little to offer to managers who were guided by intuition. Software agents promised a new paradigm in which DSS designers would aim to augment the capabilities of individuals and organizations by deploying intelligent tools and autonomous assistants. The concept thus heralded a pivotal change in the way computer support is devised. For one thing, it called for a certain degree of intelligence on the part of the computerized tool; for another, it shifted emphasis from the delivery of expert advice toward providing support for the user’s creativity (King, 1993).

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