Overview of AI and ML

Artificial intelligence (AI) has come a long way since its inception. The concept of AI dates back to the 1950s when Alan Turing, a British mathematician, proposed the idea of a machine that could simulate human intelligence. Over the decades, AI has evolved from simple rule-based systems to complex neural networks that can learn, adapt, and solve problems in ways that mimic human behavior.

Machine learning (ML) emerged as a powerful approach in the 1980s with the advancements in algorithms that enable machines to learn from data rather than relying on explicit programming. Deep learning, which builds on neural networks with multiple layers, has revolutionized the field in recent years, leading to significant breakthroughs in areas such as image and speech recognition.

Learning objectives

In this chapter, we will explore the core concepts of AI and ML:

• Introduction of neural networks, which form the backbone of many modern AI systems.

• The relationship between AI, ML, Deep Learning, and Generative AI and explore key areas such as computer vision, natural language processing (NLP), and large language models (LLMs).

• Different data types of AI models used to build effective AI systems.

• Machine learning techniques, such as supervised, unsupervised, and reinforcement learning,

• Phases of building a successful ML model.

• Evaluation metrics that help measure the real-world impact of AI/ML models.

• Importance of AI/ML, when it’s the right solution for a problem and when it might not be.

By the end of this chapter, we will have a solid understanding of the key components of AI and ML, how to apply them to solve problems, and how to evaluate the effectiveness of the models we create.

Here’s a brief outline of this chapter: