Artificial Intelligence

This week, we studied the XOR challenge, a significant question in artificial intelligence AI that first baffled early models of the technology. Simple in theory but difficult in practice, the exclusive OR XOR logic gate outputs true only when the inputs differ. Because it is non-linear, early neural networks were unable to solve it for a while, indicating a significant early AI restriction. The solution to this problem required reconsidering neural network architecture. These networks made significant progress in AI's capacity to resolve non-linear issues when they were able to comprehend the complexity of the XOR by incorporating additional layers and using a variety of activation functions.

Our real-world study includes using a neural network to programme an Arduino Uno to solve the XOR problem. The network was first configured with layers for input, hidden processing, and output. It was then trained using the relevant data. The training procedure is crucial: via a series of cycles, the network improves its prediction accuracy after each round by modifying its internal weights through a process known as backpropagation. Particularly, there were variations in the quantity of training cycles. The amount of time needed for each training session to achieve effective learning varied because initial weights and biases set random beginning places. This randomness emphasises the iterative process of learning and adapting and is a natural feature of neural network training.

lastly, the network successfully completed the XOR task, demonstrating AI's aptitude for handling challenging issues. This not only shows how AI may be used to solve complex problems, but also how important iterative improvement and an experimental mindset are to the advancement of AI technology.

The solution to the XOR problem sheds light on a significant period in AI history and demonstrates how neural networks have developed to solve challenging problems by being creative and always learning. It highlights the value of training cycles in the AI development process and the contribution of both theoretical understanding and real-world experimentation to the field's advancement.

An input portion, a hidden section, and an output section make up the three sections of our basic neural network. This configuration functions similarly to a digital display, which shows the precise number entered into the display on the screen between 0 and 9.