The Complete AI Terms Guide

AI (Artificial Intelligence)

AI (Artificial Intelligence) refers to the ability of a machine to learn patterns and make predictions. It is the simulation of human intelligence in machines that can perform tasks and learn from experience. AI is like a computer brain that can learn and make decisions. It's used to automate tasks and solve problems without needing human input every time.

Example: A virtual assistant that learns your preferences over time to suggest personalized tasks.

ANI (Artificial Narrow Intelligence)

Artificial Narrow Intelligence is focused on addressing a single task, such as predicting your next purchase or planning your day.

Examples: Siri on an iPhone, the Amazon recommendation engine, autonomous vehicles.

ABI (Artificial Broad Intelligence)

Artificial Broad Intelligence is a midpoint between Narrow and General AI. Rather than being limited to a single task, Broad AI systems are more versatile and can handle a wider range of related tasks. It is focused on integrating AI within a specific business process where companies need business- and enterprise-specific knowledge and data to train this type of system.

Examples: Systems that predict global weather, trace pandemics, and help businesses predict future trends.

AGI (Artificial General Intelligence)

AGI refers to highly autonomous systems that can perform a wide range of tasks and exhibit human-like intelligence.

ASI (Artificial Super Intelligence)

Artificial Super Intelligence refers to AI systems that surpass human intelligence in all aspects.

AI Misuse

The harmful or unintended consequences can occur when AI prompts are used inappropriately or maliciously.

Example: Using AI to generate deceptive or harmful content.

AI-Powered Applications

Specialized platforms built on top of Large Language Models (LLMs) designed to serve specific purposes.

Examples: AdCreative.ai, Customers.ai, Anyword.ai

For a comprehensive list, visit: https://theresanaiforthat.com/

AI Response Analysis

Examining and evaluating the outputs generated by an AI model in response to different prompts.

Example: Reviewing chatbot interactions to improve accuracy and user satisfaction.

Algorithm Bias

Refers to the systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.

Example: Facial recognition technologies have been found to have higher error rates for people of color than for white individuals due to the underrepresentation of these groups in the training datasets.

API (Application Programming Interface)

Helps software applications communicate with each other.

API Endpoints: Resources allowing an application to share data with others (search, add a row, update a row, etc.).

Backward Prompt Design

An approach to prompt design that begins with the desired outcomes from an AI interaction and works backward to create the prompt. This method ensures that the prompt is crafted to lead directly to the expected type of response or information.

Example: If the goal is to generate a detailed business plan, the process will start by outlining the specific sections and data points needed, such as market analysis or financial projections. Based on this, a structured prompt would be developed, possibly involving multiple questions or statements to guide the AI in producing a comprehensive document.

Big Data

Extensive data sets that can be analyzed to reveal patterns and trends. It's like having a giant library of information that can give you valuable insights.

Burstiness

A mix of short and long sentences that adds rhythm and emphasis to written or spoken communication.

Example: A customer service chatbot designed to handle inquiries about account management. The chatbot utilizes burstiness in its responses to keep the conversation engaging and not monotonous. This means it alternates between shorter, straightforward answers for simple queries and longer, more detailed explanations for complex issues.

Chain of Thought Prompting

This approach encourages the AI to "think out loud" by structuring prompts that lead the AI to process information step-by-step. It can improve problem-solving accuracy and transparency in the AI’s reasoning process.

Example: In a task where the AI is asked to calculate the environmental impact of switching from plastic to paper bags, the prompt might guide the AI to consider multiple factors sequentially:

• Assess the production emissions of both materials.
• Analyze the biodegradability and recycling efficiency.
• Calculate net environmental savings.

This structured prompt helps the AI tackle the problem step-by-step, leading to a thorough and reasoned conclusion.

Chatbot

Computer programs that can hold conversations with people. They help answer customer questions and provide information automatically. A software application used to conduct an online chat conversation via text or text-to-speech instead of giving direct contact with a live human agent, designed to convincingly simulate how a human would behave as a conversational partner.

Example: A retail website employs a chatbot to assist online customers. When a user visits the site and asks, "Do you have any running shoes in size 10?" the chatbot instantly searches the inventory, finds relevant products, and responds, "Yes, we have several running shoes available in size 10. Would you like to see them?" This interaction helps streamline the shopping experience and provides immediate assistance to the customer.

Complexity

Refers to the level of intricacy, difficulty, or sophistication in the tasks or challenges an AI is designed to handle. In AI, complexity can range from simple pattern recognition to solving complex problems that require contextual understanding and decision-making.

Example: An AI system developed for medical diagnosis might be trained to interpret X-rays to identify common fractures. However, as the complexity increases, the same AI could be further developed to identify a broader range of medical conditions from various imaging types (like CT scans and MRIs) and suggest personalized treatment plans based on the patient’s medical history and current health parameters. This advanced application requires a deeper level of algorithmic sophistication and a broader dataset for training.

Computer Vision

Is an AI field that develops algorithms and models to understand and interpret visual information, such as images and videos.

Example: Image classification, object detection.

Context Optimization

Involves ensuring that the prompt contains all necessary context for the AI to understand the question or task without ambiguity. This might include providing background information, specifying constraints, or outlining desired outcomes.

Control Codes

Using specific keywords or tokens trained into the model to steer the response's style, tone, or format. This requires knowledge of the model’s training and capabilities.

Example: Including the token <|formal|> in a prompt to an AI instructs it to generate a response in a formal tone, adhering to stylistic guidelines specified during its training.

Conversational Depth

The level of engagement and back-and-forth interaction achieved through multi-turn prompts.

Example: A dialogue system that can discuss complex topics like politics or philosophy in depth.

Creative Prompts

Prompts designed to stimulate imaginative and artistic AI outputs, such as storytelling, poetry, or music.

Example: An AI that composes original music based on mood and genre inputs.

Custom GPT

Refers to a version of the Generative Pre-trained Transformer (GPT) model that has been tailored or fine-tuned for specific tasks, industries, or applications based on unique requirements.

Example: A legal firm creates a Custom GPT to assist with drafting and reviewing legal documents. This custom model is trained extensively on a vast corpus of legal texts, including laws, case studies, and legal briefs, to ensure it understands and generates text that adheres to legal standards and terminologies. When a lawyer inputs a prompt such as "Draft a non-disclosure agreement for a tech startup," the Custom GPT uses its specialized training to produce a draft that aligns with current legal practices and language specific to technology-related confidentiality agreements.

Data

Is raw information. Facts, statistics, opinions, or any content that is recorded in some format.

Structured Data is typically categorized as quantitative data and is highly organized. It is information that can be organized in rows and columns.

Examples: Names, dates, addresses, credit card numbers, stock information.

Unstructured Data