📖 AI 101: Learn the Lingo

With AI evolving at lightning speed, it can feel like there’s a new buzzword every other week. From chatbots that sound like your best friend to tools creating art out of thin air, it’s a lot to keep up with.

But don’t worry—we’ve got you covered. Whether you’re a seasoned pro or just dipping your toes into this brave new world, consider this glossary your cheat sheet to staying informed, confident, and ahead of the curve.

Adversarial AI
Systems designed to exploit vulnerabilities in other AI systems. This can include tricking an AI model into making errors, such as confusing facial recognition software with subtly altered images. It’s often used in cybersecurity to test and strengthen defenses or, more maliciously, to manipulate outcomes.

Algorithm
A precise set of rules or instructions that a computer follows to solve problems or complete tasks. Algorithms are the foundation of AI, enabling machines to sort data, recognize patterns, and make predictions. Different types include classification, regression, and clustering algorithms.

AI (Artificial Intelligence)
The simulation of human-like intelligence in machines. It encompasses tasks such as learning, reasoning, problem-solving, and even creativity. AI is used in various fields, from virtual assistants like Siri to advanced applications like autonomous vehicles.

AI Ethics
The moral principles and guidelines that govern how AI is developed and used. This includes ensuring AI is fair, unbiased, safe, and respects user privacy. Developers, policymakers, and organizations collaborate to create frameworks that address ethical concerns.

Application Programming Interface (API)
An API is like a translator for software. It defines how two programs interact with each other, making it easier for developers to integrate different systems. APIs enable functionality such as embedding Google Maps into apps or allowing payment processing through third-party services.

Autonomous Systems
Autonomous systems are machines or programs that can operate independently, making decisions without human intervention. These systems often rely on AI to perceive their environment, analyze data, and act accordingly.
Examples: Self-driving cars, robotic vacuum cleaners.

Backward Chaining
Backward chaining is a problem-solving approach that starts with the desired outcome and works backward to find the supporting data or steps needed to achieve it. It’s often used in rule-based AI systems.

Big Data
Big data refers to extremely large datasets that traditional tools can’t easily process. These datasets are analyzed to uncover patterns, trends, and insights for decision-making. Big data powers applications like recommendation systems and predictive analytics.
Examples: Hadoop, Redshift

Black Box
A black box refers to an AI system whose decision-making process is not transparent or easily interpretable. While the system produces accurate results, understanding how it arrives at them can be challenging, especially in deep learning models.

Chatbot
A chatbot is an AI-powered program that mimics human conversation. It can answer questions, provide recommendations, and perform tasks through text or voice interactions. Some chatbots are simple and rule-based, while others, like ChatGPT, are highly advanced.
Examples: ChatGPT, Microsoft Copilot

Clustering
A machine learning technique used to group similar data points together. Unlike classification, clustering doesn’t require labeled data, making it useful for exploratory data analysis.
Examples: Market segmentation, customer profiling

Convolutional Neural Networks (CNN)
A specialized deep neural network designed to process structured grid-like data, such as images or video. CNNs employ convolutional layers that apply filters to input data, enabling them to learn visual features and hierarchies of patterns automatically. They have been widely used in systems like self-driving cars and face-detect applications.
Example: Image classification, facial recognition.

Cognitive Computing
AI systems designed to simulate human thought processes, such as reasoning, learning, and problem-solving. It’s often used in business applications to enhance decision-making.
Examples: IBM Watson, Salesforce Einstein

Computer Vision
A field of AI that enables machines to interpret and process visual information from the world, such as images and videos. It’s used in applications ranging from facial recognition to autonomous vehicles.
Examples: OpenCV, Google Vision AI

Data Augmentation
Creating modified versions of existing data to expand a dataset. It’s commonly used in AI training to improve model performance and reduce overfitting.

Deepfake
AI-generated videos, images, or audio that convincingly mimic real people or events. While they can be fun for creative projects, they also pose risks for misinformation and deception.
Examples: DeepFaceLab, DeepShot, FaceApp

Deep Learning
This is a branch of machine learning that’s all about handling massive amounts of data to find patterns humans might miss. It’s used for tasks like understanding speech, spotting fraud, or recommending movies you’ll love.
Examples: Keras, PyTorch

Deep Neural Networks (DNN)
A type of artificial neural network with multiple hidden layers, which makes them more complex and resource-intensive compared to conventional neural networks.
Example: Speech recognition

Federated Learning
Federated learning is a privacy-preserving AI technique where models are trained locally on devices instead of centralized servers. This means user data stays on their device while contributing to the overall model improvement.
Examples: Google’s keyboard predictions, Apple’s Siri suggestions.

Generative AI
Generative AI is the creative one in the family. Based on patterns it has learned from data, it can produce text, music, videos, or even art that looks like it was made by a person. Perfect for brainstorming or content creation.
Examples: DeepAI, ElevenLabs, Suno

Generative Adversarial Networks (GANs)
GANs detect and enhance imperfections in a deepfake various times so that it becomes more difficult for people to detect that it is a fake. GANs consist of two neural networks, a generator that generates an impersonation, and a discriminator that detects those fakes. The generator will continuously generate fakes to improve the fake’s quality until it is convincing enough.

Generative Pre-trained Transformers (GPTs)
These are the engines behind AI text generators. They’ve been trained on a ton of data to predict the next word in a sentence, which makes them great for writing essays, summarizing content, or answering questions.
Examples: ChatGPT, Claude, Perplexity

Hallucination
When an AI system confidently gives you an answer that’s totally wrong. This can result from gaps in training data or limitations in reasoning.

Hyperparameter Tuning
This involves adjusting the settings of an AI model (like learning rate or batch size) to optimize its performance. It’s a critical step in building effective machine learning systems.
Examples: Grid Search, Random Search

Large Language Models (LLMs)
Advanced AI systems trained on massive amounts of text data to generate human-like responses and understand context. They understand language well enough to generate essays, hold conversations, or even write code. Think of it like a turbo-charged auto-complete.
Examples: ChatGPT, Gemini, Llama

Machine Learning (ML)
A subset of artificial intelligence where systems learn patterns and improve over time through data analysis rather than explicit programming. It’s the foundation of most modern AI systems.
Examples: MLFlow, SageMaker, Scikit-learn, TensorFlow

Overfitting
Overfitting happens when an AI model learns too much from training data, including irrelevant details, making it perform poorly on new data.

Predictive AI
This type of AI uses data to extrapolate and make predictions from previous trends, and is used heavily in finance to make trades on the stock market, or in science to analyze large amounts of data.

Red-Teaming
A process of testing an AI system to identify risks, weaknesses, and potential misuse by simulating adversarial interactions. This can involve human testers, automated tools, or a combination of both. The goal is to improve the system’s safety, reliability, and alignment with ethical standards before deployment.

Reinforcement Learning (RL)
Reinforcement learning teaches AI through trial and error, rewarding good decisions and penalizing bad ones. It’s often used in robotics and gaming.
Examples: AlphaGo, OpenAI Gym

Text-to-Image Generation
This is when AI creates pictures based on what you describe. Want a painting of a purple dragon eating ice cream? AI can make it happen.
Examples: Adobe Firefly, Dall-E 2, Generative AI by iStock, Ideogram, MidJourney

Turing Test
Named after AI pioneer Alan Turing, this is the ultimate challenge for AI: Can it fool a human into thinking it’s a person? If it does, it passes the test.
Read more here.

Zero-shot Learning
Zero-shot learning is when an AI model can recognize categories or perform tasks it hasn’t been explicitly trained on, using general knowledge instead. For example, recognizing new animals based on descriptions alone.