Tag Archives: Artificial Neural Network

Will artificial intelligent devices ever become “sentient?”

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Many technology experts are convinced that it is unavoidable that machines will become sentient, which means that artificial intelligences (AIs) will show typical human characteristics, such as self-awareness, the ability to set goals, and a sense of creativity.

Developments in AI tools, fuzzy logic and neuronal networks show first successful directions in creating sentient technology, though machines still are far away from the human capability to recognize pictures and understand language and even further away from making decisions based on this inputs. Continue reading “Will artificial intelligent devices ever become “sentient?”” »

Biological Inspired Computing – Logic by Nature

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Biological organisms and whole biological systems show the characteristics of adaption, reactivity and dispersion. Principles to that degrees of perfection are rarely used in human-engineered technologies.

The multi-disciplinary field of biological computing (also called bio-inspired computing or biocomputing) is based on theoretical natural science and system biology, mathematics, cognitive science, logic and complexity, computer science, informatics, robotics and cybernetics.
Biocomputing aims to solve complex problems and system architectural questions by developing computational models following Continue reading “Biological Inspired Computing – Logic by Nature” »

Virtual Humans – Be Interactive with your App

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There is continuous growth in computational speed and power, software apps controlling methods and intelligent algorithms allowing the creation of 3D virtual humans for interactive applications of machine and human.
Such computer characters, called Virtual Humans, are more than just fantasy figures such as the ‘Star Trek holodeck characters’ or the librarian in the movie ‘Time Machine’ but they are becoming real-life applications using artificial intelligence (AI) and computer graphics technology. Continue reading “Virtual Humans – Be Interactive with your App” »

Genetic Algorithms in Medicine

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Genetic Algorithms (GAs) are non-linear adaptive optimization methods that mimic natural evolution processes via non-exhaustive searches among randomly generated solutions. Genetic Algorithms are highly operative for searching through large or complex data structures seeking the optimal solution for decision, classification, optimization and simulation. Continue reading “Genetic Algorithms in Medicine” »

Neural Networks IV – Medical Innovation of Tomorrow

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Artificial Neural Networks (ANNs) are one of the “hot” topics in creating innovative medical diagnosis and treatment software for patient-centered medicine.
Neural networks are a class of pattern recognition methods to model the biological neuron to process non-linear or chaotic information where no algorithmic solution is possible or the solution is too complex for traditional techniques.

Recent ANN applications include the modeling of the human body recognizing patterns in scans such as MRI, CAT scans or X-rays but are also used for analyzing brain maturation, ultrasound pictures or cardiograms. They are applied to resolve different diagnostic problems such as detection and classification of cancer, cardiovascular diseases and the processing of EEG signals. Continue reading “Neural Networks IV – Medical Innovation of Tomorrow” »

Neuronal Networks in Business and Marketing

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Artificial neural networks (ANN) imitate the principles of the biological brain and are progressively more applied across research, medical, engineering, social science and other fields. They also deliver substantial benefits in business applications.

Today’s world is multifaceted and there are countless inter-related variables that make predicting business outcomes very difficult. ANNs are the modern computational tools to study data and develop models to identify patterns and structures in data that offer Continue reading “Neuronal Networks in Business and Marketing” »

Artificial Neural Networks – Learning Smart

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Both, the learning of human neurons and artificial neural networks (ANNs) occur in a training phase and an operation phase. Feed-forward ANNs are very steady and may range over multiple units without having any feedback connections present whereas recurrent ANNs, that do contain feedback connections and provide dynamic network components, that allow developing a dynamic behavior of output patterns of the neural network.
An ANN learns “off-line” if the learning and operation phases are separate and it learns “on-line” if learning and operating are performed at the same time Continue reading “Artificial Neural Networks – Learning Smart” »

Neural Networks I – Artificial Neural Networks

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Artificial neural networks (ANNs) are an attempt to simulate the human brain within a computer system.

Both, biological and artificial neuronal networks are built from atomic modules – the ‘neurons’. The human brain has up to 150 billion neurons with hundreds of different classified subtypes. Each neuron usually has one axon, which expands off from a part of the cell body to conduct electrical signals to other cells. Thousands of interconnected neurons build separate groups that are called networks.
Biological neurons can be classified into ‘sensory neurons’ that provide all information for perception and motor coordination, ‘motor neurons’ which transfer signals to muscles and glands and ‘inter-neuronal neurons’ that are needed to relay information, protect other neurons or connect different parts of the brain. These neurons have linear and non-linear relationships to obtain and transfer information as well as to build memory. Continue reading “Neural Networks I – Artificial Neural Networks” »