Artificial intelligence

As explained in Investing for Programmers

AI is the field of creating computational systems capable of tasks associated with human intelligence, such as learning and reasoning.

Artificial intelligence creates computational insights from financial-asset data, helping investors and traders inform decisions through analytical methods such as machine learning. It supports structured analysis rather than gambling-like speculation, but does not determine whether anyone should invest. section 1.3

Use AI within a disciplined financial workflow:

• Collect information about securities from financial platforms and public sources. section 1.3
• Generate additional insights with analytical methods, including machine learning and AI. section 1.3
• Apply those insights critically while recognizing that investment decisions remain personal, time-sensitive, and uncertain. section 1.3 section 13.1

Figure 1.1 A survey of the tools and techniques covered in this book, including pointers to the chapters that focus on the relevant domains

Use the investment-process survey to locate AI alongside financial data, analytical techniques, and decision-making tools. section 1.3

How Investing for Programmers covers it

How deep it goes

Positions AI within an investment workflow but stops short of explaining models, implementation, or model evaluation.

Examples

Uses securities data, financial platforms, public information, analytical methods, and an investment-process survey.

What it assumes

Assumes familiarity with financial assets and introduces AI as one analytical method within a broader investment process.

What you can do after

Evaluate financial information with AI-generated insights while retaining responsibility for investment decisions.

How it connects

Connects AI with machine learning, financial assets, investing, trading, gambling, and critical thinking.

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Related concepts

• machine learningincludes · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• code reviewuses · Code review is the systematic examination of source code or proposed changes to identify problems and assess or improve quality.
• large language modelincludes · A large language model is a machine-learning model, typically neural, trained on extensive text data to process and generate language.
• dependencykind of · A software dependency is a component, resource, or prerequisite on which another software component or operation relies to function or proceed.
• generative modelingincludes · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

This explainer was generated by Manning's knowledge graph from the text of Investing for Programmers. The section links point to the parts of the book it draws on.