For example there was a model that differentiated

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Artificial neural networks require enormous computing power Neural networks also need lots and lots of data to process them. They also need enormous computing power in order to process this amount of data which are capabilities that are usually not available in ordinary devices available for daily use and sometimes even not in the most advanced and powerful computers.


So the CPU and GPU required are enormous. In addition to this neural networks are also expensive when it comes to time. To process this huge amount of data even when the necessary compu Belgium WhatsApp Number Data ting power is available. we need a large amount of time which may reach months in order to train the model and the neural network and reach acceptable and reliable efficiency. The difficulty and blindness of artificial neural networks The difficulty with artificial neural networks is that they are complex and it is difficult to design new neural networks that have stronger functions and this is because they are very complex and require a brilliant understanding of mathematics and linking many things to each other and therefore the number of existing neural networks or models is almost limited.





As for blindness in neural networks it appears clearly in complex neural networks where we cot see how the neural networks process data but we can only see the results and determine whether they are correct or not and therefore we do not know how the network thinks about what it is doing which is what It could cause disaster later. between dogs and wolves and the model produced highly effective results. Then when untitled images were presented to it it made terrible errors and its effectiveness was very poor. After a lot of time and effort the research team discovered that the neural network determines whether an object is a wolf or a dog based on the background because the images of wolves presented to it in training were against a snowy background.