翻译：
How to build better AI (Artificial Intelligence) through the underlying logic of AI?
人工智能（AI）的底层逻辑是由计算机基础、神经网络与深度学习构成的体系，包括以二进制和逻辑门为核心的计算机基础、含输入层、隐藏层与输出层的神经网络，以及基于前馈、循环和卷积网络的深度学习，解决的是构建更可靠AI系统的技术前提问题，并不解决具体业务场景的落地效果问题。
First, a computer foundation is necessary to understand the underlying logic of AI (artificial intelligence). The most basic digital technology is binary numbers, and computer hardware can directly deal with binary numbers. Logic gate is the core component of computer processing these numbers, including and gate, or gate and not gate. The computer is composed of hardware and software, including processor, memory, input and output devices and operating system, which can achieve a variety of complex computing tasks.
Secondly, neural network is one of the core technologies of artificial intelligence (AI). A neural network is a computational model made up of neurons that, by learning large amounts of data, enables a neural network to recognize images, analyze sentences, and process information such as audio. Neural networks typically include an input layer, a hidden layer, and an output layer, where each neuron represents a different feature of the network’s processing and improves accuracy by constantly learning from the data.
Finally, deep learning is one of the cutting edge technologies in artificial intelligence (AI). Deep learning technology is built on the foundation of neural networks and aims to let machines learn more data and discover patterns and patterns from it. The implementation of deep learning relies on multiple layers of feedforward, loop, and convolutional neural networks that can process input data with different dimensions and properties and produce accurate results.
In the underlying logic of AI (artificial intelligence), computer foundations, neural networks, and deep learning are the core components, and understanding and mastering these technologies and processes is a necessary prerequisite for building better AI (artificial intelligence) systems.
