This chapter provides a concise survey of key Artificial Intelligence (AI) methodologies, from early rule-based systems to modern machine learning and deep learning techniques. It also compares these methods in terms of their strengths, limitations, and alignment with biological principles, particularly those discussed in earlier chapters on the brain and neocortex. The chapter begins by outlining AI’s interdisciplinary foundations and how intelligent agents interact with different environments. It covers symbolic AI approaches using logic-based reasoning, followed by search algorithms used in structured problem solving. It then introduces core machine learning categories such as supervised, unsupervised, and reinforcement learning, along with widely used algorithms like decision trees, ensemble models and support vector machines. Deep learning models, including feedforward and convolutional networks, are explored for their capabilities and constraints. Sequential learning methods are addressed next, with a focus on RNNs, LSTMs, GRUs, and Seq2Seq models for tasks involving temporal dependencies. This leads to an in-depth look at the Transformer architecture and the emergence of Large Language Models (LLMs), highlighting how these advanced models have revolutionized sequence modeling and language understanding through mechanisms like self-attention and multi-head attention, while also providing details about their architecture and the development of LLMs. Reinforcement learning is also revisited as a trial-and-error approach for dynamic and uncertain environments. The chapter concludes with a critical view of current AI methods, highlighting challenges such as sensitivity to noise, limited contextual understanding, and weak task generalization. By integrating insights from neuroscience and computation, the chapter lays the groundwork for developing more biologically inspired AI systems.