Artificial intelligence
Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
Inside AI
Artificial intelligence is the field of computational methods that perform tasks associated with human intelligence, such as learning, reasoning, perception, and decision-making.
- How it is used
- Frames AI as a tool for defined tasks and business decisions rather than a general replacement for human judgment.
- How deep it goes
- Defines AI, contrasts two learning approaches, and examines applications and deployment limits without deriving algorithms or providing code.
- Examples
- Uses labeled fruit, online fraud, pizza automation, and deepfakes to distinguish useful applications from operational failure and misuse.
- What you can do after
- Evaluate/understand it by matching a proposed AI application to a defined task, realistic expectations, risks, and testing needs.
WordPress Power Toolkit: Harness AI to build next-level websites
Artificial intelligence is the field of building computational systems that perform tasks associated with human intelligence, such as reasoning, language understanding, and content generation.
- How it is used
- Supports website planning, creation, critique, audience work, monetization, and search-oriented optimization.
- How deep it goes
- Defines practical prompting and major AI domains, while emphasizing hands-on use rather than technical implementation.
- Examples
- Uses assistant roles, a homepage comparison, flawed potato imagery, recipes, decluttering, and prompt-generation scenarios.
- What you can do after
- Apply it via AI systems to generate, critique, revise, and fact-check website work through iterative prompts.
AI for Everyday IT
Artificial intelligence is the field of creating computational systems that perform tasks associated with human intelligence, such as perception, reasoning, language processing, and decision-making.
- How it is used
- Supports IT work by accelerating routine tasks, analyzing information, improving communication, and assisting decisions across technical and management workflows.
- How deep it goes
- Defines core AI categories and demonstrates practical workplace applications, while leaving model internals and formal theory aside.
- Examples
- Uses document extraction, log analysis, phishing detection, Teams summaries, crisis planning, performance reviews, and model-response comparisons.
- What you can do after
- Apply it via ChatGPT, Claude, Gemini, Copilot, or other approved tools while validating results and protecting sensitive information.
Read how AI for Everyday IT explains artificial intelligence →
Effective Data Analysis
Artificial intelligence is the field of creating computational systems that perform tasks associated with human intelligence, such as reasoning, perception, language use, and generation.
- How it is used
- Positions AI as a way to automate work and support faster decisions while requiring scrutiny of its effects.
- How deep it goes
- Defines AI and distinguishes its approaches from rules-based automation, but does not develop model internals or a build procedure.
- Examples
- Uses customer-support ticket categorization and a biased hiring system to illustrate automation’s benefits and risks.
- What you can do after
- Evaluate/understand AI as an option for workplace decisions without assuming automated outputs are fair or accurate.
Read how Effective Data Analysis explains artificial intelligence →
Data-Driven Cybersecurity
Artificial intelligence is the field of computing concerned with building systems that perform tasks commonly associated with human intelligence, such as inference and generation.
- How it is used
- Uses AI to detect threats, anticipate risks, support response, and turn security observations into decisions.
- How deep it goes
- Defines AI, distinguishes traditional from generative approaches, and outlines a security workflow without a deep dive into AI or statistics.
- Examples
- Illustrates AI through a healthcare threat-detection investment, automated security responses, and a synthetic-data analysis pipeline.
- What you can do after
- Evaluate/understand AI-assisted security results by comparing detection and response gains with false alarms, data quality, and human oversight.
Read how Data-Driven Cybersecurity explains artificial intelligence →
"Looks Good to Me"
The field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence.
- How it is used
- Deploys AI as a fast, consistent first-pass assistant while humans make final code-review decisions.
- How deep it goes
- Explains benefits, limitations, and adoption guidance but stops short of specifying a particular implementation or evaluation protocol.
- Examples
- Uses AICodeReview, CodeRabbit, PR feedback, security warnings, recurring code smells, and hallucinated code fixes as examples.
- What you can do after
- Apply AI-assisted reviews cautiously, validate findings, preserve independent review, and focus human effort on higher-stakes concerns.
Read how "Looks Good to Me" explains artificial intelligence →
Deep Learning with R, Third Edition
Artificial intelligence is the field of building systems that perform tasks associated with human intelligence, such as reasoning, perception, language use, and decision-making.
- How it is used
- Frames machine learning and deep learning as approaches within the wider effort to automate intellectual tasks.
- How deep it goes
- Defines AI, distinguishes rule-based and learned approaches, and traces expectations historically; stops short of an AI implementation procedure.
- Examples
- Uses a rule-based chess program, chatbots, autonomous driving, and protein-structure prediction to illustrate AI’s range.
- What you can do after
- Evaluate/understand whether an approach learns from data and distinguish deployed capabilities from predictions of human-level AI.
Read how Deep Learning with R, Third Edition explains artificial intelligence →
The Complete Obsolete Guide to Generative AI
The field concerned with computational systems that perform tasks associated with human intelligence, such as generating language or making inferences.
- How it is used
- Frames AI as a productivity and decision-support tool for research, business operations, investing, and hiring.
- How deep it goes
- Surveys practical applications, economic effects, and governance concerns rather than model internals or implementation steps.
- Examples
- Uses laptop research, customer-support chatbots, hiring assessments, investment platforms, and AI-driven startups as concrete cases.
- What you can do after
- Evaluate/understand where AI can accelerate work while recognizing verification, bias, transparency, and accountability requirements.
Read how The Complete Obsolete Guide to Generative AI explains artificial intelligence →
Acing the CCNA Exam: Volume 2 Advanced Networking and Security
Artificial intelligence is the field of computational methods that perform tasks requiring capabilities associated with human intelligence, such as learning, prediction, and reasoning.
- How it is used
- Frames artificial intelligence as a foundation for machine learning, predictive analysis, content generation, and network automation.
- How deep it goes
- Defines AI, ML, learning types, and predictive or generative applications, but stops short of algorithmic internals.
- Examples
- Uses labeled cat-and-dog images, traffic forecasting, predictive maintenance, capacity planning, and security threat prediction.
- What you can do after
- Evaluate AI categories and apply their network-operation use cases to forecasting, maintenance, capacity, security, and automation.
First-Party Data Activation
Artificial intelligence is the field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence.
- How it is used
- Uses AI to turn governed customer data into marketing predictions, recommendations, and audience-targeting decisions.
- How deep it goes
- Explains predictive models through feature-rich churn targeting and outlines solution-selection considerations without detailing model implementation.
- Examples
- Illustrates churn prediction, product recommendations, campaign timing, and rule-based versus model-driven audience selection.
- What you can do after
- Evaluate/understand it by comparing first-party data solutions’ model integration, generative AI capabilities, and fit with marketing workflows.
Read how First-Party Data Activation explains artificial intelligence →
Lead Developer Career Guide
The field of computing concerned with systems that perform tasks requiring capabilities such as language understanding, generation, or reasoning.
- How it is used
- Applies artificial intelligence to technical documentation, development workflows, user support, and career exploration.
- How deep it goes
- Describes workplace applications and named tools but stops short of explaining AI algorithms, model internals, or implementation.
- Examples
- Names Grammarly, ChatGPT, Inkeep, and WriterAI as tools for writing, explanation, search, and documentation assistance.
- What you can do after
- Apply artificial intelligence through Grammarly, ChatGPT, Inkeep, or WriterAI for writing and documentation tasks.
Read how Lead Developer Career Guide explains artificial intelligence →
Deep Learning with Python, Third Edition
Computer systems that perform tasks associated with human intelligence, used here in a broad modern sense that the text argues is often closer to automation.
- How it is used
- Frames AI as the umbrella field for automating human intellectual tasks and organizing machine learning, deep learning, and other approaches.
- How deep it goes
- Defines AI historically and comparatively, but stops short of detailed implementation of individual AI methods.
- Examples
- Uses hardcoded chess programs, spreadsheets, chatbots, autonomous driving, and protein-structure prediction as examples.
- What you can do after
- Evaluate/understand whether a system belongs to AI and distinguish rule-based automation from learning-based methods.
Read how Deep Learning with Python, Third Edition explains artificial intelligence →
Troubleshooting Java, Second Edition
Artificial intelligence is the field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence.
- How it is used
- Uses AI tools to generate troubleshooting starting points and accelerate investigation of Java application problems.
- How deep it goes
- Explains prompting and diagnostic use, but does not examine AI algorithms or model internals.
- Examples
- Uses ChatGPT prompts, a legacy bug scenario, and thread-dump evidence to illustrate investigation.
- What you can do after
- Apply it via ChatGPT or similar tools to find troubleshooting starting points and interpret application behavior faster.
Read how Troubleshooting Java, Second Edition explains artificial intelligence →
AI-Powered Search
Artificial intelligence is the capability of a computer program to perform tasks that previously required human intelligence.
- How it is used
- Defines computer capabilities that support search, including matching documents, ranking relevance, and improving query understanding.
- How deep it goes
- Defines AI broadly and distinguishes machine-learning from rules-based systems, but stops short of detailed algorithmic implementation.
- Examples
- Uses self-driving cars, autonomous robots, human-like feedback, web search, product search, enterprise search, and generative AI context retrieval.
Read how AI-Powered Search explains artificial intelligence →
Grokking Statistics
The field concerned with building systems that perform tasks associated with human intelligence, such as reasoning, perception, and learning.
- How it is used
- Supports model building, statistical analysis, Python generation, and career work when used deliberately and verified.
- How deep it goes
- Distinguishes generative AI tools and practical uses but stops short of explaining model internals.
- Examples
- Uses Gemini misidentification and failed AI reverse-image search to demonstrate why verification matters.
- What you can do after
- Apply it via AI assistants for code and analysis, then verify outputs and label uncertain claims as hypotheses.
Read how Grokking Statistics explains artificial intelligence →
Data Without Labels
The field of computing concerned with creating systems that perform tasks associated with human intelligence, such as reasoning, perception, language use, and learning.
- How it is used
- Positions AI as a practical computing field for building systems that emulate intelligence and improve decisions, efficiency, and communication.
- How deep it goes
- Defines AI, traces its shift from symbolic to data-driven methods, and stops short of detailed algorithm implementation.
- Examples
- Uses self-driving cars, chatbots, translation, speech-to-text, virtual assistants, recommendation systems, and prompt-based content generation.
- What you can do after
- Apply it via Python, TensorFlow, and Keras to understand data-driven systems and evaluate practical AI applications.
Read how Data Without Labels explains artificial intelligence →
The Art of AI Product Development
Artificial intelligence is the field of computational methods that perform tasks associated with human intelligence, such as reasoning, prediction, and language understanding.
- How it is used
- Frames AI as a product capability for solving customer problems, improving operations, and creating measurable business value.
- How deep it goes
- Explains AI through product applications, probabilistic behavior, system interdependencies, opportunity selection, and safeguards rather than technical derivations.
- Examples
- Uses marketing refinement, automated support, smart search, agentic chatbots, new app-building services, and chatbot failure scenarios.
- What you can do after
- Evaluate AI opportunities, prototype suitable features, communicate limitations, and plan safeguards and iterative improvement.
Read how The Art of AI Product Development explains artificial intelligence →
Data Storytelling with Altair and AI
Artificial intelligence is the field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence.
- How it is used
- Positions AI as the conceptual foundation for using generative tools in data storytelling.
- How deep it goes
- Defines intelligent agents and maps AI’s subfields, but stops short of detailed AI fundamentals or implementation internals.
- Examples
- Uses ChatGPT for text, DALL-E for images, and GitHub Copilot for code within data-storytelling work.
- What you can do after
- Apply it by situating generative AI tools within the wider AI hierarchy and data-storytelling workflow.
Read how Data Storytelling with Altair and AI explains artificial intelligence →
Investing for Programmers
AI is the field of creating computational systems capable of tasks associated with human intelligence, such as learning and reasoning.
- How it is used
- Uses computational analysis to generate financial-asset insights for investment and trading decisions.
- 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 you can do after
- Evaluate financial information with AI-generated insights while retaining responsibility for investment decisions.
Read how Investing for Programmers explains artificial intelligence →
Grokking Machine Learning
Artificial intelligence is the field concerned with computer systems making decisions or solving problems autonomously.
- How it is used
- Frames autonomous computer decision-making as the broad category that contains machine learning.
- How deep it goes
- Defines AI in plain language, distinguishes it from machine learning, and illustrates its scope with practical applications.
- Examples
- Uses autonomous driving, route finding, patient diagnosis, movie recommendation, and car-buying decisions as examples.
- What you can do after
- Evaluate/understand how artificial intelligence differs from machine learning and recognize AI in everyday computer systems.
Read how Grokking Machine Learning explains artificial intelligence →
Grokking AI Algorithms, Second Edition
The field concerned with creating computational systems that perform tasks associated with human intelligence, such as reasoning, learning, and autonomous decision-making.
- How it is used
- Frames AI as a practical toolkit for solving complex problems autonomously across software, perception, language, and decision-making.
- How deep it goes
- Builds intuition about definitions, data, problem types, algorithms, and historical progression without detailing individual implementations.
- Examples
- Uses games, medical image analysis, artwork generation, autonomous driving, chatbots, and historical milestones as concrete examples.
- What you can do after
- Evaluate AI systems by their autonomous usefulness in solving complex problems rather than by one rigid definition.
Read how Grokking AI Algorithms, Second Edition explains artificial intelligence →
Authorization in Action
Artificial intelligence is the field of computing concerned with systems that perform tasks requiring capabilities associated with human intelligence, such as pattern analysis and language processing.
- How it is used
- Supports authorization-policy design, runtime analysis, and governance without replacing deterministic enforcement.
- How deep it goes
- Explains alternative policy expressions and runtime comparison, but stops short of making authorization decisions or governance judgments.
- Examples
- Uses a document-access exception and comparisons between intended policies and production decision logs.
- What you can do after
- Diagnose authorization mismatches and explore policy alternatives while retaining human control over design and governance.
Read how Authorization in Action explains artificial intelligence →
Financial AI in Practice
Artificial intelligence is the capability of computational systems to perform tasks that typically require human intelligence.
- How it is used
- Frames AI as a dual-use capability in financial fraud: institutions use it for detection and prevention, while adversaries use it to scale attacks.
- How deep it goes
- Positions AI as a fraud-defense opportunity and threat, but stops before detailing models or implementation.
- Examples
- Names AI-generated phishing emails, synthetic identities, and complex fraud schemes as adversarial uses.
- What you can do after
- Understand how AI can help protect financial systems and how adversaries can exploit it.
Read how Financial AI in Practice explains artificial intelligence →
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.
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