Does AI need symbols?

ITNOW ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 56-57
Author(s):  
Grace Lindsay
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
The Mind ◽  
The Brain

Abstract Inspired by the brain, artificial neural networks are core to modern artificial intelligence. Grace Lindsay, author of Models of the Mind, explains concerns over the cognitive limits of these systems.

scholarly journals Artificial Intelligence to Analyze the Cortical Thickness Through Age

2021 ◽  
Vol 4 ◽  
Author(s):  
Sergio Ledesma ◽  
Mario-Alberto Ibarra-Manzano ◽  
Dora-Luz Almanza-Ojeda ◽  
Pascal Fallavollita ◽  
Jason Steffener
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Left Hemisphere ◽  
Cortical Thickness ◽  
Right Hemisphere ◽  
Unseen Data ◽  
Artificial Neural ◽  
The Right ◽  
The Brain

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

How to Make a Class

Qui Parle ◽  
2021 ◽  
Vol 30 (1) ◽  
pp. 159-184
Author(s):  
Matteo Pasquinelli
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Artificial Neural Networks ◽  
Methodological Individualism ◽  
Similar Function ◽  
Gestalt Principles ◽  
Economic Paradigm ◽  
Artificial Neural ◽  
The Mind

Abstract It was not a cybernetician but a neoliberal economist who provided the first systematic treatise on connectionism or, as it would later be known, the paradigm of artificial neural networks. In his 1952 book The Sensory Order, Friedrich Hayek advanced a connectionist theory of the mind already far more advanced than the theory of symbolic artificial intelligence, whose birth is redundantly celebrated in 1956 with the exalted Dartmouth workshop. In this text Hayek provided a synthesis of Gestalt principles and considerations of artificial neural networks, even speculating about the possibility of a machine fulfilling a similar function of “the nervous system as an instrument of classification,” auguring what we call today a “classifier algorithm.” This article shows how Hayek’s connectionist theory of the mind was used to shore up a specific and ideological view of the market and schematically reconstructs Hayek’s line of argumentation from his economic paradigm backward to his theory of cognition. Eventually, in Hayek’s interpretation, connectionism provides a relativist cognitive paradigm that justifies the “methodological individualism” of neoliberalism.

Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

2020 ◽  
pp. 138-156
Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Input Data ◽  
Traffic Signals ◽  
Educational Materials ◽  
Ishihara Test ◽  
Artificial Neural ◽  
Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

2021 ◽  
pp. 1-31
Author(s):  
Pankaj Dadheech ◽  
Ankit Kumar ◽  
Vijander Singh ◽  
Linesh Raja ◽  
Ramesh C. Poonia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Structure ◽  
Modern Agriculture ◽  
Different Types ◽  
Pest Detection ◽  
Real Time Applications ◽  
Artificial Neural ◽  
And Control ◽  
The Brain

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

scholarly journals Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Francisco Cedron ◽  
Sara Alvarez-Gonzalez ◽  
Alejandro Pazos ◽  
Ana B. Porto-Pazos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Glial Cell ◽  
Process Information ◽  
Connectionist System ◽  
Artificial Neural ◽  
The Brain

The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.

scholarly journals Application of ANN in Milling Process: A Review

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Problems ◽  
Milling Process ◽  
Machining Processes ◽  
Traditional Methods ◽  
Artificial Neural

In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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