Introduction

Artificial intelligence is rapidly changing the world around us. It affects everything from how we communicate to how we conduct business. This comprehensive glossary is designed to give you a clear understanding of key terms and concepts that define the field of AI. Whether you are an experienced professional or just starting your AI journey, this resource will provide you with the knowledge you need to navigate this exciting and complex landscape. Let’s get started!

Popular Artificial Intelligence Terms You Need to Know

The benefits of this artificial intelligence glossary include the creation of awareness of AI concepts and support to shape future practices. If you are starting your career in AI or switching from another field completely, you need to be familiar with the following terms.

Adapter

An adapter is a framework that helps transfer learning to new AI models by stitching layers onto existing models. The aim is to switch a model into a new task without starting from scratch. Adapter modules save time, money, and storage space by reusing pre-trained models for tasks like talking to a computer, translating common basic languages ​​into new languages, or driving a robot.

AI Algorithm

AI algorithms refer to specific programming that tells machines how to act autonomously. This includes step-by-step instructions or rules that allow AI systems ​​to process raw data, make decisions, and learn from it. Are you impressed by AI’s ability to understand various languages, facial recognition, playing chess, or even driving? Algorithms are the brains behind these activities!

AI Ethics

AI ethics is a broad field. It covers a wide range of concepts to ensure that our brilliant artificial intelligence systems are not only smart but also well-behaved and courteous even in unfamiliar environments. The goal is to reduce risks such as unreliable results, unintended consequences, and the potential loss of humanity.

Artificial Intelligence

It is a simulation of human intellectual processes by machines or computer systems. AI can imitate human abilities such as communication, learning, and decision-making. It is a technology that allows computers and machines to simulate human learning, understanding, problem solving, decision making, creativity, and independence.

Automation

Automation means using AI technology to run tedious tasks and business processes on autopilot. The focus is on productivity and reducing manual errors.

Black Box AI

Black box AI, or BAI, refers to AI models that are not very transparent about how they make decisions. Users and designers strive to understand or explain the inner workings or decision-making processes. This is different from the white box model that is easy to understand. This lack of openness can raise ethical questions, responsibility questions, and the potential for biases, making BAI unsuitable for use in high-stakes fields, such as the military or health care.

Big Data

Big data refers to large data sets that can be studied to reveal patterns and trends to support business decision-making. It is called “big” data because organizations can now store vast amounts of complex data using data collection tools and systems.

Chatbot

A smart computer program that is always available to chat, answer questions, and help with specific tasks—that’s an AI chatbot. It is the unsung hero of customer support and data discovery. This is because its main purpose is to engage with human language. So, next time you enjoy reading recommendations from a streaming service or Amazon, you are most likely witnessing the magic of AI-powered bots!

Conversational AI

Conversational AI refers to the technology that powers machines, such as chatbots, virtual assistants, and apps that use similar words to hold human-like conversations. This AI uses natural language processing (NLP) or high processing power in a variety of contexts and languages ​​to do a variety of tasks, such as finding the perfect song for the moment or ordering your favourite bagel.

Computer Vision

Computer vision is an interdisciplinary field of science and technology that focuses on how computers can make sense of images and videos. For AI engineers, computer vision allows them to automate tasks that the human visual system normally does.

Data Augmentation

Data augmentation is the efficient management and expansion of existing data. This practice is a cornerstone of machine learning and AI because it promotes the amount and variety of training data for models. The aim is to increase the capabilities of the algorithm by providing a variety of examples to learn from.

Data Science

Data science is an interdisciplinary technology field that uses algorithms and processes to collect and analyze large amounts of data to find patterns and insights that inform business decisions.

Deep Learning

Deep learning is the brain behind the AI ​​revolution. It is a subset of machine learning systems that aim to mimic the structure of the human brain by using multiple layers of artificial neural networks to process enormous amounts of data. Deep learning models can recognize patterns, predict, and learn complex tasks. It has helped in revolutionizing areas such as automatic driving and facial and speech recognition.

Generative AI

Generative AI, or GenAI, refers to AI models that generate new content such as images or text. It reflects the style and format derived from training data. From imaginative art to informative writing, GenAI tools can produce a variety of results without writing codes, which is why these tools serve as productivity partners for many professionals.

Guardrails

Guardrails refer to the constraints and rules placed on AI systems to ensure that the system handles data appropriately and does not create unethical content.

Hallucination

In the case of AI, hallucinations occur when the system generates incorrect, inconsistent or nonsensical data, often due to its errors or limitations in training, understanding, or processing ability. It is considered a hiccup that makes the AI ​​system unreliable.

Image Recognition

Image recognition is the process of identifying objects, people, places, or text in images or videos.

Internet of Things—IoT

The Internet of Things, or IoT, acts as a network of digital objects embedded in our physical world. It is a network of smart devices. From everyday items like thermostats and smartwatches to industrial equipment that can store, exchange, and work with information when used in conjunction with AI.

Large Language Model—LLM

The linguistic giant in the world of AI is the Large Language Model Development, or LLM, a powerful artificial intelligence system built from extensive data and complex algorithms. It helps to understand, create, and manipulate human language with outstanding abilities.

Limited Memory

Limited memory is a type of AI system that acquires knowledge from real-time events and stores it in a database to make better predictions.

Machine Learning

Machine learning trains algorithms on data to recognize patterns and make decisions. As the algorithm finds more information, its discerning process will be better, which makes it more efficient at the intended task. It’s like teaching a computer to learn and adapt on its own.

Natural Language Processing

Natural language processing, or NLP, bridges the gap between humans and machines. It helps computers understand, interpret, and respond to human language. It uses advanced concepts like sentiment analysis to improve interpretation.

Neural Network

Artificial neural networks are computer systems inspired by the human brain. It consists of layers of interconnected nodes that work together to analyze and process data.

Overfitting

Overfitting of machine learning occurs when an algorithm can only work on specific examples within trained data. An AI model performing a specific task must be able to infer patterns in the data to handle the new task.

Pattern Recognition

Pattern recognition is a method of using computer algorithms to analyze, detect, and label regularities in data. The information obtained then shows how to classify data into different categories.

Predictive Analytics

Predictive analytics is a type of analysis that uses technology to predict what will happen in a given time frame based on past data and patterns.

Prescriptive Analytics

Prescriptive analytics is a type of analysis that uses technology to analyze information about situations, past and present performance, and other factors such as resources to help organizations make better strategic decisions.

Prompt

Prompts are data or questions that AI models use to produce meaningful, contextually relevant results. They range from simple questions like “How do I translate this sentence into French?” to more complex requests, such as writing a short story about an adventurer who locates an island full of gold. Using the right prompts is critical to getting useful feedback from AI. You need to phrase them appropriately to suit your system’s resource profile.

Reinforcement Learning

Reinforcement learning is like teaching a dog new tricks, but instead of predicting treats, AI ​​learns through rewards and punishments. The AI ​​agent explores its environment, and when it moves well, it gets a virtual pat on the back—a reward—and when it gets messy, it will get a digital slap on the head—a punishment (over time).

Sentiment Analysis

Sentiment analysis is an AI technique used to interpret the tonal value of speech. It can be positive, negative, or neutral. Businesses often use it to analyze social media posts, reviews, or news articles and collect hidden opinions about their products.

Structured Data

Structured data is data that has a standardized format for efficient access by software and humans. They typically have rows and columns that clearly define data attributes. Computers can efficiently process structured data for investigations due to its quantitative nature.

Supervised Learning

Supervised learning is a form of machine learning that uses classified output data to train machines and create accurate algorithms.

Token

Tokens are the basic units of text that LLM uses to understand and construct language. Tokens can be whole words or parts of words.

Training Data

Trained data are samples that AI systems are ​​provided with so they can learn, find patterns, and create new content.

Transfer Learning

This is a technique where an AI model leverages knowledge gained on one task to excel in another. Instead of starting from scratch every time, they can build on what they already know. In the world of AI, transfer learning is used to make models smarter and more efficient during their intended tasks.

Turing Test

The Turing Test was created by computer scientist Alan Turing to assess the ability of machines to exhibit intelligence similar to humans. This is especially true in the areas of language and behavior. If the evaluator cannot distinguish between the responses, the machine is said to have passed the Turing test.

Unstructured Data

Unstructured data is data that does not have a predefined structure or format. This makes it difficult to analyze, sort, and search. It is usually heavy text, but it can contain numbers, dates, and facts too.

Unsupervised Learning

Unsupervised learning is a form of machine learning in which algorithms are trained with unclassified and unlabeled data to be able to operate without supervision.

Voice Recognition

Speech recognition, also known as voice recognition, is a method of human-computer interaction where a computer listens to and interprets human commands (speech), creating a written or spoken output. An example is Amazon’s Alexa, which helps make requests and perform tasks.

Ready to Unlock the Power of AI in Your Career?

The world of artificial intelligence is vast and constantly evolving. The foundation of this glossary is understanding the key concepts and terminology that shape this transformative technology. Whether you’re a seasoned professional or just starting your AI journey, we hope this resource has empowered you to navigate this exciting landscape.

We encourage you to explore available resources and continue learning about the transformative potential of AI, and we are more than happy to guide you through this exciting journey. Together we can build your AI-powered future today!