History and Philosophy of Neural Networks

The 2011 International Joint Conference on Neural Networks, 2011

There has been important new cross-disciplinary work using neural network mathematics to unify key issues in engineering, technology, psychology and neuroscience-and many opportunities to create a discrete revolution in science by pushing this work further. This strain of research has a natural link to clinical and subjective human experience-the "first person science" of the mind. This paper discusses why and how, and gives several examples of links between neural network models and key phenomena in human experience, such as Freud's "psychic energy," the role of traumatic experience, the interpretation of dreams and creativity and the cultivation of human potential and sanity in general, and the biological foundations of language.

Neural Networks and Deep Learning A Textbook, 2018

One of the greatest mysteries of science is in the elusiveness of knowing exactly how the brain and thus the mind makes thought possible. The phenomenon of unlocking the secrets of the brain and therefore understanding its fundamental areas of function represents one of the greatest challenges of our time. According to most neural network researchers, the objective of AI is to seek an understanding of nature's capabilities for which the human race can engineer solutions to problems that cannot be solved by traditional computing. This paper takes a basic looks at the physical biological brain as the motivation behind AI.

International Journal of Creative Research Thoughts, 2017

The present study insights on the impact of artificial intelligence, coupled with neural networks. It has emerged as one of the tools for executing data science problems and performing rapid programming and processing. The neural network is recognized to be a mathematical function that converts a set of inputs into designated output components. These inputs might be the basis of computational learning. As there has been significant usage of these applications related to neural networks, the present study describes its importance in artificial intelligence.

Journal of the Society of Dyers and Colourists, 1998

Journal of Chemical Education, 1994

Handbook of Measuring System Design, 2005

Sensor Review, 1997

Arti cial neural networks (ANNs), or connectionist systems, are composed of simple non-linear processing units (`neurons') connected into networks. They may be regarded, furthermore, as systems which adapt their functionality as a result of exposure to information (`training'). They have become enormously popular for data analysis over the past decade. Applications have included speech recognition, face recognition, DNA sequence labelling, time series prediction and medical data analysis to name but a few 13, 4, 9]. There are good reasons for this popularity and for hence utilising them, but there are also good reasons for not using them : ANNs o er no`magic' solution to problems and their internal`workings' are often obscure to the user. As such, then, they would appear to be, at best, an unreliable tool and, at worst, downright misleading. Why is it then that they have achieved such notoriety that even hardened statisticians (such as 11]) have become interested in their use? To answer this question we rst consider the two basic forms of data inference, classi cation and regression (prediction is regarded as a subset within the latter).

Reviews of Modern Physics, 1999