Brains as naturally emerging turing machines

Abstract—It has been shown that a Developmental Network (DN) can learn any Finite Automaton (FA) [29] but FA is not a general purpose automaton by itself. This theoretical paper presents that the controller of any Turing Machine (TM) is equivalent to an FA. It further models a motivation-free brain — excluding motivation e.g., emotions — as a TM inside a grounded DN — DN with the real world. Unlike a traditional TM, the TM-in-DN uses natural encoding of input and output and uses emergent internal representations. In Artificial Intelligence (AI) there are two major schools, symbolism and connectionism. The theoretical result here implies that the connectionist school is at least as powerful as the symbolic school also in terms of the general-purpose nature of TM. Furthermore, any TM simulated by the DN is grounded and uses natural encoding so that the DN autonomously learns any TM directly from natural world without a need for a human to encode its input and output. This opens the door for the DN to fully autonomously learn any TM, from a human teacher, reading a book, or real world events. The motivated version of DN [31] further enables a DN to go beyond action-supervised learning — so as to learn based on pain-avoidance, pleasure seeking, and novelty seeking [31]. I

Aplicativo Android para reconhecimento de cédulas de real

Orientador: Prof. Dr. Razer Anthom Nizer Rojas MontañoMonografia (especialização) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Curso de Especialização em Inteligência Artificial AplicadaInclui referências: p.113-125Resumo: O objetivo central do trabalho é o desenvolvimento de uma aplicação para dispositivos móveis Android de reconhecimento de cédulas de real através da câmera do celular. A aplicação deve ser simples, visando o uso de portadores de deficiência visuais. Como forma de identificação das cédulas e comunicação ao usuário, a aplicação deve vocalizar a classe reconhecida. Para detecção das classes das imagens capturadas pela câmera do celular, foi treinada uma rede neural especializada através da plataforma gratuita de desenvolvimento colaborativo em nuvem do Google Colab. Como método neste trabalho realizou-se a coleta e treinamento de 1412 imagens de cédulas de real nos valores de R$2,00, R$ 5,00, R$10,00, R$ 20,00, R$50,00 e R$ 100,00, frente e verso rotacionadas em ângulos de 90º, 180º e 270º. Como resultado o aplicativo desenvolvido para Android é capaz de reconhecer e classificar as cédulas de real treinadas na rede neural, bem como vocalizar a classificação de saída. Como conclusão espera-se que este trabalho possa auxiliar pessoas portadoras de alguma deficiência visual através de um aplicativo de reconhecimento de cédulas de real

Neural network techniques for position and scale invariant image classification

This research is concerned with the application of neural network techniques to the problems of classifying images in a manner that is invariant to changes in position and scale. In addition to the goal of invariant classification, the network has to classify the objects in a hierarchical manner, in which complex features are constructed from simpler features, and use unsupervised learning. The resultant hierarchical structure should be able to classify the image by having an internal representation that models the structure of the image. After finding existing neural network techniques unsuitable, a new type of neural network was developed that differed from the conventional multi-layer perceptron type of architecture. This network was constructed from neurons that were grouped into feature detectors.These neurons were taught in an unsupervised manner that used a technique based on Kohonen learning.A number of novel techniques were developed to improve the learning and classification performance of the network. The network was able to retain the spatial relationship of the classified features; this inherent property resulted in the capability for position and scale invariant classification. As a consequence, an additional invariance filter was not required. In addition to achieving the invariance property, the developed techniques enabled multiple objects in an image to be classified. When the network had learned the spatial relationships between the lower level features, names could be assigned to the identified features. As part of the classification process, th e system was able to identify the positions of the classified features in all layers of the network. A software model of an artificial retina was used to test the grey scale classification performance of the network and to assess the response of the retina to changes in brightness. Like the Neocognitron, the resulting network was developed solely for image classification. Although the Neocognitron is not designed for scale or position invariance, it was chosen for comparison purposes because it has structural similarities and the ability to accommodates light changes in the image. This type of network could be used as the basis for a 2D-scene analysis neural network, in which the inherent parallelism of the neural network would provide simultaneous classification of the objects in the image

Методи та технології обчислювального інтелекту: Практикум

Навчальний посібник призначено для закріплення на практичних заняттях теоретичних знань, методів та алгоритмів обчислювального інтелекту. Надані загальні відомості з теорії машинного навчання, штучного інтелекту та сучасних архітектур штучних нейронних мереж. У навчальному посібнику врахований сучасний стан розвитку алгоритмі та мереж глибокого навчання