Artificial Intelligence Modelling of Complex Systems

1989 ◽  
pp. 265-290 ◽  
Author(s):  
Patrice Poyet ◽  
Pierre Haren
Keyword(s):  
Artificial Intelligence ◽  
Complex Systems

scholarly journals Latent bias and the implementation of artificial intelligence in medicine

2020 ◽  
Vol 27 (12) ◽  
pp. 2020-2023 ◽  
Author(s):  
Matthew DeCamp ◽  
Charlotta Lindvall
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Complex Systems ◽  
Widespread Implementation

Abstract Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call “latent biases.” Just as latent errors are generally described as errors “waiting to happen” in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in AI algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of AI algorithms in clinical practice.

scholarly journals Intelligence, Artificial Intelligence and Wisdom in the Global Sustainable Information Society

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 39 ◽  
Author(s):  
Wolfgang Hofkirchner
Keyword(s):  
Artificial Intelligence ◽  
Complex Systems ◽  
Information Society ◽  
Theoretical Concept ◽  
World Society ◽  
Good Society ◽  
Global Challenges ◽  
To Come ◽  
Systems Intelligence ◽  
Society Today

The Global Sustainable Information Society is a theoretical concept describing the vision of a good society in the age of global challenges. Globality, sustainability and informationality are understood in an innovative way as essential features of a world society to come that is capable of mastering the global challenges. Regarding informationality, the distinction between informedness and informatisation is made and a law of requisite information is introduced. The terms “intelligence”, “Artificial Intelligence” (AI) and “wisdom” are discussed from the perspective of complex systems. Intelligence and AI without wisdom are not deemed sufficient to meet the conditions of a good society today.

scholarly journals Intelligence, Artificial Intelligence and Wisdom in the Global Sustainable Information Society

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 39 ◽  
Author(s):  
Wolfgang Hofkirchner
Keyword(s):  
Artificial Intelligence ◽  
Complex Systems ◽  
Information Society ◽  
Theoretical Concept ◽  
World Society ◽  
Good Society ◽  
Global Challenges ◽  
To Come ◽  
Systems Intelligence ◽  
Society Today

The Global Sustainable Information Society is a theoretical concept describing the vision of a good society in the age of global challenges. Globality, sustainability and informationality are understood in an innovative way as essential features of a world society to come that is capable of mastering the global challenges. Regarding informationality, the distinction between informedness and informatisation is made and a law of requisite information is introduced. The terms “intelligence”, “Artificial Intelligence” (AI) and “wisdom” are discussed from the perspective of complex systems. Intelligence and AI without wisdom are not deemed sufficient to meet the conditions of a good society today.

Blockchain, black magic and event ontology Interview with Alexander Boldachev

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Alexander Boldachev ◽  
Pavel Baryshnikov
Keyword(s):  
Artificial Intelligence ◽  
Complex Systems ◽  
Biological Evolution ◽  
Semantic Modeling ◽  
Temporal Ontology ◽  
Web 3.0 ◽  
Russian Philosopher ◽  
Event Ontology ◽  
Black Magic ◽  
Logic System

Alexander Boldachev is a Russian philosopher, futurologist (member of the Association of Futurologists of Russia), author of books and articles on universal evolutionism, biological evolution, philosophy of artificial intelligence, temporal ontology, epistemology, and logic. System architect and analyst of blockchain applications, author of articles on the problems of trust technologies, eGovernment, web 3.0, semantic modeling of complex systems, speaker of many specialized conferences.

Frost Measuring and Prediction Systems for Demand Defrost Control

2019 ◽  
pp. 24-50
Author(s):  
Martim Lima de Aguiar ◽  
Pedro Dinis Gaspar ◽  
Pedro Dinho da Silva
Keyword(s):  
Artificial Intelligence ◽  
Pressure Drop ◽  
Complex Systems ◽  
Surface Temperature ◽  
Air Humidity ◽  
Air Velocity ◽  
Resistive Heating ◽  
Frost Formation ◽  
Refrigeration Equipment ◽  
Prediction Systems

It is widely known that the defrosting operation of evaporators of commercial refrigeration equipment is one of the main causes of inefficiency on these systems. Several defrosting methods are used nowadays, but the most commonly used are still time-controlled defrosting systems, usually by either electric resistive heating or reverse cycle. This happens because most demand defrost methods are still considered complex, expensive, or unreliable. Demand defrost can work by either predicting frost formation by processing measured conditions (fin surface temperature, air humidity, and air velocity), operative symptoms of frost accumulation (pressure drop and refrigerant properties), or directly measuring the frost formation using sensors (photoelectric, piezoelectric, capacitive, resistive, etc.). The data measured by the sensors can be directly used by the system but can also be processed either by simple algorithms or more complex systems that use artificial intelligence and predictive methods. This chapter approaches frost sensing and prediction for command of demand defrost systems.

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