Defining the terminology
For the true beginner, let’s start with defining some key terms: AI, ML, and GenAI. You will come across these terms repeatedly in this book, so it helps to have a strong conceptual foundation of these terms:
- Artificial intelligence (AI) refers to the ability of machines to perform tasks that would normally require human intelligence. This includes tasks such as perception, reasoning, learning, and decision making. The journey of AI has evolved significantly from early speculative ideas to the sophisticated technologies of today. Figure 1.1 shows a timeline of the development of AI.
Figure 1.1: A timeline of AI
- Machine learning (ML) is a subset of AI that involves the use of algorithms to automatically learn from data and improve over time. Essentially, it’s a way for machines to learn and adapt without being explicitly programmed. Most often used in fields that require advanced analysis of thousands of data points, ML is most useful in medical diagnostics, market analysis, and military intelligence. Effectively, ML identifies hidden or complex patterns in data that would be impossible for a human to see and then can make suggestions for the next steps or actions.
- Generative AI (GenAI) is the ability to create text, images, audio, video, and other content in response to a user prompt. It powers chatbots, virtual assistants, language translators, and other similar services. These systems use algorithms trained on vast amounts of data, such as text and images from the internet, to learn patterns and relationships. This enables them to generate new content that is similar but not identical to the underlying training data. For instance, large language models (LLMs) use training data to learn patterns in written language. GenAI can then use these models to emulate a human writing style.
