Future Outlook: Synergies Between Advanced AI and Cryptographic Research

1. Introduction

In the rapidly evolving field of technology, AI and cryptography are a good case for such interdisciplinary cooperation. The impact of AI and cryptography combined As well as being fascinating reading in their own right, the developments described here will have many interesting outcomes.

Figure 1.

Fundamental building block of an artificial neural network is the perceptron

The twenty-first century has witnessed unprecedented progress in AI and cryptography, and their convergence represents a pivotal moment in technological history. Cryptography is a perfect fit for artificial intelligence because to AI's pattern recognition, intelligent decision-making, and capacity to analyze massive volumes of data. Cryptography, on the other hand, has long been the guardian of data privacy and security, ensuring that sensitive information remains shielded from prying eyes. Artificial Intelligence (AI) is a specialized field within computer science focused on creating rational agents. These agents are designed to perceive their environment, process this information internally, and then select the most suitable action from various possibilities. Improving the agent's function of objective is the goal of this decision-making procedure (N. Hussain, A. A. J. . Pazhani, and A. K. . N, 2023). Once the agent decides what to do, it modifies its internal model of the environment and goes to work. Some of the many kinds of agents that fall under the umbrella of artificial intelligence are learning agents, logical agents, planning agents, antagonistic search agents, and search agents. Learning agents stand out because of their data-driven decision-making capabilities and use of machine learning methods. A set of data pairs is used to infer these activities.

Figure 2.

Multi-layer perceptron

At the heart of artificial intelligence (AI) lie Artificial Neural Networks (ANNs). ANNs are defined by their structure, which includes the amount of cells or perceptual neurons in each layer, the specific activation processes used by the neurons, the cost function for training, and the number and setup of densely interrelated hidden layers (Figure 1). The weights of the interconnections are adjusted during ANN training using gradient descent and backpropagation, which are based on the slope of the cost function within the weight space (as shown in Figure 2). The three most important areas of interest for artificial neural networks (ANNs) are input categorization, sequence learning, and function approximation. An important AI tool, ANNs can easily represent complex nonlinear functions thanks to their use of the nonlinear activation functions such as Sigmoid, Tanh and RELU.