What is AI and How Does it Work?
The use of AI (artificial intelligence) has quickly become commonplace throughout many industries. With mention of AI taking over news channels and showing up regularly in work meetings, it’s easy to get confused by all the AI terms and jargon flying around; so, what AI terms do you need to know? Let’s put them in plain English.
When you start to dig into AI, it can become a flood of buzzwords that don’t always make sense. By learning a few key terms, you can be better prepared to understand what AI is, how it works, and how you can use it to be more productive at work and in your personal life.
AI Defined
AI is an abbreviation of “artificial intelligence”. Artificial intelligence is the term used to describe a computer system that was trained to simulate human intelligence to perform various tasks. To perform these tasks efficiently, AI models require proper training and access to a database they can scan for information.
With so many variables affecting the outcomes provided by AI models, you need to make sure you have a basic understanding of:
- The training techniques AI models use.
- The data types AI models utilize.
- Data storage options.
- How to properly adjust prompts.
Depending on the programming, some models can learn and adjust their responses automatically, while other systems require more manual input from the user.
How Are People Using AI?
The most common reasons companies choose to use AI are to increase productivity, automate monotonous tasks, or use it as a research tool. Depending on the needs of the user, there are multiple AI agents developed to help with those tasks.
Just like with any tool, it’s important to understand the abilities and limitations of AI. While AI may help increase productivity, there are also cases where AI models have given inaccurate or harmful outputs. By learning how AI models work, you can become more diligent in your prompts to ensure the output is accurate.
The best way to begin understanding the world of artificial intelligence is to get acquainted with some key AI terms to know. We’ve developed the terms list below to get you started.
A Non-Exhaustive Glossary of AI Terms You Should Know
AI (Artificial Intelligence): the simulation of human intelligence by computers to perform tasks like learning, reasoning, and decision-making. In a business context, AI can be used to boost productivity and quickly handle routine or repetitive tasks more efficiently.
Agent: A program that uses AI to complete tasks, answer queries, and achieve goals for the user. This software can interact with its environment, gather data, and utilize that data to meet the user’s needs.
Agent-Oriented Framework: A programming approach where software is created around the concept of software agents. These frameworks are designed to make it easier to create, launch, and manage AI Agents.
Agentic AI: An AI system created to achieve goals without human supervision. These AI models show autonomy and have the ability to adapt their processes to mimic human decision-making skills.
Agentic Automation: The process by which Agentic AI systems make decisions and take action automatically without having to follow predefined rules. Agentic AI systems adapt to their environment, changing their behaviors based on what they’ve learned automatically instead of waiting to be told what to adjust.
Agentic Orchestration: A framework designed to coordinate the collaboration between humans, AI agents, and process automation bots. This helps blend human oversight with AI capabilities and integration tools to ensure tasks are completed efficiently.
Agentic Workflow: The process an Agentic AI model uses so that it can achieve its goals without human interference. This process uses the AI agent’s reason and decision-making abilities to execute tasks. This is a complex, multi-step process that allows autonomous AI agents to make decisions, understand context, and work without needing additional instructions.
Algorithm: A set of rules or instructions used by AI systems to analyze data and make decisions. AI algorithms process large amounts of data, identify patterns, and produce outputs based on the needs of each task.
API (Application Programming Interface): A set of rules and protocols that allow different software applications to communicate and interact with each other. This is how developers can integrate AI functionalities into other apps.
Bias: Systematic errors in AI models caused by biased training data or flawed algorithms. Bias can show up in results as outputs that favor certain demographics and reinforce negative stereotypes.
Computer Vision: A field of AI that enables machines to interpret visual data. This explains how AI analyzes images and videos.
Copilot: An AI-powered assistant that helps improve a user’s productivity. Copilots automate tasks, analyze data, and offer suggestions to help streamline workflows and are trained to understand user intent to provide relevant recommendations and assistance. Many companies are engineering their own copilots, like Zendesk’s AI Copilot, Microsoft Copilot, and HubSpot’s Breeze Assistant.
Data Ingestion: The process of collecting data from multiple sources and importing that data into a system for it to be stored. From there, the AI model can process and analyze that data. The quality and amount of data ingested can impact how accurate and effective the AI model is.
Dataset: A structured collection of data used to train, validate, and test AI models. These can be structured (tables/graphs), unstructured (images/audio/text), or semi-structured (JSON/ XML).
DL (Deep Learning): A type of machine learning that processes complex data by using artificial neural networks. This type of learning is used to produce outputs for more complicated queries.
Emergent Behavior: Unintended capabilities that arise when different parts of an AI system interact with each other. This happens most commonly in large AI models, where complex data can lead to unpredictable actions that the AI model was not specifically programmed for.
Generative AI: AI systems that create new content based on learned patterns. Generative AI can create images, videos, text, or music based on the prompt given to it.
Hallucinations: Commonly known as fabrications, these are inaccurate responses an AI system provides as an output. Generative AI systems can’t tell the difference between fake information and true information, so if a model doesn’t have current data after it’s been trained, it can provide false or inaccurate outputs.
Hyperparameter: Parameters set before training an AI to control the training process. This helps define guidelines for an AI’s learning process and can affect the speed and quality of the model’s performance.
RAG (Retrieval-Augmented Generation): A technique AI uses to enhance the performance of LLMs by connecting them to external knowledge bases. This allows AI models to give more accurate responses to queries by referencing relevant and true data before generating answers.
Inference: The process of using a trained AI model to make predictions based on new input data. This is done after the AI system has been trained, and it has already identified patterns in old data.
Ingestion: The process of absorbing or bringing in data to a system for processing and storage. The ingestion process sets the foundation for AI to complete tasks like training, inference, and understanding semantics in human language.
LLM (Large Language Model): A way of teaching AI models through text and human language. This teaches AI both vocabulary and syntax, ensuring the system can understand how humans talk and produce outputs humans can understand.
LMM (Large Multimodal Model): A way of teaching AI through text and human language, as well as giving them other multimedia resources. This model learns through multiple types of data, like text, video, and audio.
MCP (Model Context Protocol): This is how AI systems and other LLMs integrate information from other sources through APIs. It acts as a bridge and translator between information, allowing AI models to access and catalog data from multiple sources.
ML (Machine Learning): This is the development of AI systems that learn from data without explicit programming. By using algorithms to analyze and discover patterns in data, AI systems can learn and adapt their answers without needing to be programmed to do so.
Model: A representation of a learning process used to make predictions or decisions based on a query or input. These AI frameworks are trained on datasets to learn patterns and relationships in order to perform tasks.
Neural Network: Inspired by the human brain, this is a model that is made up of interconnected nodes (neurons) used for data processing. This explains how AI models can process information to give accurate outputs.
NLP (Natural Language Processing): The process of enabling machines to understand and generate human language. This combines machine learning techniques to process text/speech data to better communicate with humans.
Overfitting: A scenario where a model performs well on training data but fails to adapt what it learned to new data. This usually happens when an AI model overfocuses on unnecessary details that don’t generalize to new information.
Prompt: An input provided by a user to an AI system to guide its output. This defines what you search for in an AI platform to get it to generate a response.
Prompt Engineering: The process of structuring a query (or prompt) in such a way before submitting it to an AI model to produce a better outcome. This usually involves adding more detail to your prompt to help the AI model comprehend exactly what you are asking it, allowing it to give you a more relevant output.
Reinforcement Learning: A learning method where an AI agent interacts with an environment to maximize rewards through trial and error. The model will learn through experience and receive feedback from the environment.
Supervised Learning: A machine learning method where AI models are trained on labeled data to predict outcomes. Doing this trains AI models by giving them inputs and outcomes, allowing them to identify patterns, and then letting them predict unknown data by generalizing from the patterns they learned during training.
Token: The smallest unit of text used by language models to process and generate language. This can be a word or part of a word that AI models use to process information and is most common in natural language processing. Tokenization can help AI systems analyze language and sentiment, allowing them to generate text.
Unsupervised Learning: A machine learning method where models identify patterns in unlabeled data. In this method, the AI model recognizes hidden patterns, relationships, and structures in data without human intervention.
Vector Database: A database designed to process vectorized data, which is a combination of numbers that represent points in a multi-dimensional space. Using vectorized data makes it possible for AI systems to store and process complex information more quickly.
AI Data Integration with eOne Solutions
The power of AI is only as strong as its understanding of your business and the applications you use. Your AI agent should be able to interpret your data and provide context for each query. For data privacy purposes, you’ll also want to be specific as to which datasets you give your LLM access to. This is where eOne Solutions comes in.
PopdockAI MCP Integration
At eOne Solutions, we’re committed to making it easier for you to communicate with your tech stack, which includes AI. What we have done is create our own MCP server for Popdock, making PopdockAI, so you can access your data from any platform.
PopdockAI interprets your business and provides your LLM with a clear, natural language definition. When you ask a question, it understands where and how to get the correct answer. This means PopdockAI’s MCP server can determine whether your LLM needs to search in your CRM, your ERP, or other applications’ datasets to produce the correct output.
With these new capabilities, eOne Solutions is better equipped to handle all your AI data integration needs.
Getting Started with AI
The tools are developed, and PopdockAI is ready to help you better access your data. With eOne Solutions, you’ll be able to:
- Easily connect any AI platform of your choice
- Make one simple connection to access the data your AI needs
- Get answers powered by your secure, clean, and up-to-date data
Have questions about how eOne Solutions is leading the industry in AI data integration? Read some of our other resources to learn more.