AI Glossary for Business Owners

Computer systems designed to perform tasks that typically require human intelligence, such as recognizing speech, making decisions, or solving problems. In business, AI helps automate processes and gain insights from data.

A subset of AI where computers learn patterns from data without being explicitly programmed for each task. The system improves its performance as it processes more information.

AI systems trained on vast amounts of text data to understand and generate human-like language. These models can write, summarize, translate, and answer questions.

A machine learning technique using artificial neural networks with multiple layers to recognize complex patterns. It's particularly effective for processing images, speech, and text.

Computing systems inspired by biological brain networks. They consist of interconnected nodes that process information and learn patterns, forming the basis of deep learning.

AI technology that enables computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding.

AI capability that enables machines to interpret and understand visual information from images and videos, similar to human sight.

The practice of crafting effective instructions or questions for AI systems to get desired outputs. Essential skill for maximizing AI tool effectiveness.

Using AI and technology to perform tasks without human intervention. Reduces manual work and increases efficiency in business processes.

Hypothetical AI that matches or exceeds human cognitive abilities across all domains. Unlike current AI that excels at specific tasks, AGI would be truly general-purpose.

A set of rules or instructions that computers follow to solve problems or complete tasks. In AI, algorithms enable machines to learn from data and make decisions.

The information used to teach AI systems how to perform tasks. Quality and quantity of training data directly impacts AI performance.

A way for different software applications to communicate. AI APIs allow businesses to integrate AI capabilities into their existing systems without building from scratch.

Machine learning approach where the AI learns from examples with known correct answers. Like teaching with answer sheets to help the system learn patterns.

Machine learning where AI finds hidden patterns in data without being given specific examples of what to look for. Useful for discovering unknown insights.

A metric measuring the efficiency of an investment. In AI context, it compares the cost of implementing AI solutions against the benefits gained.

AI-powered software that conducts conversations with users through text or voice. Modern chatbots can handle complex customer service interactions.

Using AI to analyze current and historical data to make predictions about future events. Helps businesses make data-driven decisions.

Unfair preferences or prejudices in AI systems, often reflecting biases present in training data. Can lead to discriminatory outcomes in business applications.

Development and use of AI systems that are fair, transparent, accountable, and respect human rights. Increasingly important for business reputation and compliance.

When AI systems generate false or misleading information presented as fact. Important to understand when using AI for business content creation.

AI systems designed to provide clear explanations for their decisions and recommendations. Critical for business applications requiring transparency.

Protecting sensitive information when using AI systems. Includes securing customer data and complying with regulations like GDPR.

Technique where AI models trained on one task are adapted for related tasks. Reduces time and resources needed for implementation.

Running AI algorithms locally on devices rather than in the cloud. Provides faster responses and better privacy for sensitive business data.