Analogy in Artificial Intelligence
Mark Chang · 2025
This chapter explores the evolution of artificial intelligence (AI) architectures through the lens of analogy, highlighting their innovative designs and applications. It examines foundational machine learning paradigms, including supervised, unsupervised, reinforcement, swarm intelligence, and evolutionary learning, outlining their distinct approaches to problem-solving. Special attention is given to neural network architectures such as feedforward networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, generative adversarial networks, and autoencoders. These architectures, inspired by human cognition and biological systems, have transformed fields like image recognition, natural language processing, and generative modeling. Key advancements, including transformers and generative pre-trained transformers, are discussed for their revolutionary impact on sequence modeling and language generation. This chapter also delves into AI diffusion models, which employ probabilistic frameworks to iteratively transform noise into structured data, enabling applications in image synthesis, video creation, and drug discovery. By framing these developments through analogy, this chapter underscores how human-inspired insights drive the scalability, adaptability, and creativity of AI systems, offering a roadmap for future innovations.