How to Make a Class

Qui Parle ◽  
2021 ◽  
Vol 30 (1) ◽  
pp. 159-184
Author(s):  
Matteo Pasquinelli

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.

ITNOW ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 56-57
Author(s):  
Grace Lindsay

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.


Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce

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.


Author(s):  
Malihe Molaie ◽  
Razieh Falahian ◽  
Shahriar Gharibzadeh ◽  
Sajad Jafari ◽  
Julien C. Sprott

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron

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.


2021 ◽  
Vol 4 ◽  
Author(s):  
Sergio Ledesma ◽  
Mario-Alberto Ibarra-Manzano ◽  
Dora-Luz Almanza-Ojeda ◽  
Pascal Fallavollita ◽  
Jason Steffener

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.


Sign in / Sign up

Export Citation Format

Share Document