The Ultimate Glossary of Future Skills
Your definitive guide to the essential terms in AI, Machine Learning, Data Science, and more. Use this glossary to demystify the buzzwords and build a strong foundational vocabulary for the future of work.
A
Agile Leadership
A leadership style focused on adaptability, collaboration, and speed. Agile leaders empower teams to self-organize and respond quickly to change, a core tenet of modern project management. See our guide on Agile Leadership.
Algorithm
A set of step-by-step instructions or rules that a computer follows to perform a task or solve a problem. In AI, algorithms are what enable a machine to learn from data.
Artificial Intelligence (AI)
A broad area of computer science focused on creating machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Explore our full guide: What is AI?
AutoML
Stands for Automated Machine Learning. It’s the process of automating the complex, end-to-end task of applying machine learning to real-world problems, from data preparation to model deployment.
B
Bias (in Machine Learning)
A systematic error in a machine learning model that results in unfair outcomes or prejudiced results against certain groups. It often originates from incomplete or flawed training data that reflects historical human biases. Learn more in our guide to AI bias.
Big Data
Extremely large and complex datasets that cannot be easily managed or processed with traditional data-processing application software. It is often characterized by the “Five V’s”: Volume, Velocity, Variety, Veracity, and Value.
Blockchain
A decentralized, distributed, and immutable digital ledger used to record transactions across many computers so that any involved record cannot be altered retroactively without the alteration of all subsequent blocks. Explore our full guide to Blockchain and Web3.
C
Chatbot
An AI program designed to simulate conversation with human users, especially over the internet. Modern chatbots use Natural Language Processing (NLP) to understand and respond to user queries in a human-like manner.
Cloud Computing
The delivery of on-demand computing services—including servers, storage, databases, networking, software, and analytics—over the Internet (“the cloud”). Major providers include AWS, Google Cloud, and Microsoft Azure. See our Cloud Computing skills guide.
Computer Vision
A field of AI that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”
Convolutional Neural Network (CNN)
A class of deep neural networks, most commonly applied to analyzing visual imagery. They are the core technology behind image recognition and computer vision tasks. See the full definition in our ML glossary.
D
Data Science
An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines statistics, computer science, and domain expertise. Explore the Data Science career path.
Deep Learning
A specialized subset of machine learning based on artificial neural networks with many layers (hence “deep”). Deep learning is the technology behind today’s most advanced AI, including large language models like ChatGPT and sophisticated image recognition systems.
E
Emerging Skills
A set of competencies and capabilities that are growing in importance and demand in the job market, often driven by technological advancements, economic shifts, or new societal priorities. Learn more on our Emerging Skills hub.
ESG (Environmental, Social, and Governance)
A set of standards for a company’s operations that socially conscious investors use to screen potential investments. Environmental criteria consider how a company performs as a steward of nature. Social criteria examine how it manages relationships with employees, suppliers, customers, and the communities where it operates. Governance deals with a company’s leadership, executive pay, audits, and internal controls.
F
Feature (in Machine Learning)
An individual measurable property or characteristic of a phenomenon being observed. In a dataset, a feature is a column. For example, in a real estate dataset, features might include “square footage,” “number of bedrooms,” and “year built.”
Fine-Tuning
The process of taking a pre-trained language model (like GPT-3) and further training it on a smaller, specific dataset to adapt it for a particular task or to align its responses with a specific style or domain.
G
Generative AI
A class of artificial intelligence models that can generate new, original content, including text, images, audio, and code, based on the patterns and structures it learned from its training data. ChatGPT is a famous example.
Green Skills
The knowledge, abilities, values, and attitudes needed to live in, develop, and support a sustainable and resource-efficient society. These skills are critical for jobs in the growing green economy. Learn more in our Green Skills hub.
I
Internet of Things (IoT)
A network of physical objects (“things”) that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. Explore our IoT Skills guide.
L
Large Language Model (LLM)
A type of advanced deep learning model that is pre-trained on vast amounts of text data to understand and generate human-like language. LLMs are the core technology behind generative AI tools like ChatGPT, Gemini, and Claude.
LEED Certification
Stands for Leadership in Energy and Environmental Design. It is the most widely used green building rating system in the world and provides a framework for healthy, efficient, and cost-saving green buildings.
M
Machine Learning (ML)
A subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves. Explore our full ML glossary.
Model (in Machine Learning)
A mathematical representation of a real-world process. In machine learning, a model is the output of a training algorithm; it’s the “brain” that has learned the patterns from the training data and can make predictions on new data.
N
Natural Language Processing (NLP)
A branch of AI that helps computers understand, interpret, and manipulate human language. NLP enables technologies like chatbots, translation services, and sentiment analysis.
Neural Network
A computer system modeled on the human brain and nervous system. Neural networks consist of interconnected nodes (neurons) that process information, learning to perform tasks by considering examples without being programmed with task-specific rules.
O
Overfitting
A common error in machine learning where a model learns the training data too well, including its noise and random fluctuations. An overfit model performs exceptionally well on training data but fails to generalize to new, unseen data. Learn more about overfitting here.
P
Prompt Engineering
The art and science of crafting effective inputs (prompts) to guide a generative AI model toward a desired output. It is a critical skill for working with tools like ChatGPT. Learn how in our Prompt Engineering Guide.
Python
A high-level, general-purpose programming language. Its simple syntax and vast collection of specialized libraries (like TensorFlow, PyTorch, and Pandas) have made it the de facto language for AI and Data Science.
R
Resilience
The psychological capacity to adapt to stress, adversity, trauma, or tragedy. In a professional context, it’s the ability to bounce back from setbacks and navigate change effectively. Build this skill with our Resilience Training guide.
Robotic Process Automation (RPA)
A form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence /digital workers. It’s often used for automating routine, rules-based tasks in enterprise systems. Explore our RPA skills guide.
S
Supervised Learning
The most common type of machine learning, where an algorithm learns from a dataset that has been labeled with the correct outcomes. The model’s goal is to learn a mapping function that can predict the output variable (label) from the input data (features).
U
Unsupervised Learning
A type of machine learning where the model works with unlabeled data. The goal is to find hidden patterns, structures, or clusters within the data without any pre-existing outcomes to guide it.
Upskilling
The process of learning new skills or teaching workers new skills to enhance their capabilities and adaptability in a current role or industry. Upskilling helps employees stay relevant as job requirements evolve.
V
Vector Search
A modern search method that works by representing data (text, images, etc.) as mathematical vectors. It finds results based on conceptual meaning and semantic similarity rather than just matching keywords. Learn more in our Vector Search guide.
W
Web3
A new vision for the World Wide Web that incorporates concepts such as decentralization, blockchain technologies, and token-based economics. It aims to create a more user-centric, democratized internet.
Z
Zero-Shot Learning
An ability of some AI models to perform a task without having received any specific training examples for it. For instance, a language model performing a translation task it has never seen before.
Frequently Asked Questions
What is the difference between AI, Machine Learning, and Deep Learning?
Think of them as nested concepts. Artificial Intelligence (AI) is the broad field of creating smart machines. Machine Learning (ML) is a subset of AI that focuses on training machines to learn patterns from data. Deep Learning is a specialized subset of ML that uses complex, multi-layered neural networks to solve advanced problems like image recognition. Our guide on AI types explains this in detail.
Where should a complete beginner start?
A beginner should start with foundational, non-technical terms. Focus on understanding concepts like “Algorithm,” “AI,” “Cloud Computing,” and “Data” before diving into more complex areas like “Deep Learning” or “Neural Networks.” Our Novice Learning Path is designed for this purpose.
Why are there different difficulty tags (Beginner, Intermediate, Advanced)?
The tags are designed to help you navigate your learning. Beginner terms are high-level concepts that everyone should know. Intermediate terms often require some foundational knowledge to fully grasp. Advanced terms are typically specialized concepts within a subfield of AI that are most relevant to practitioners.
Ready to Build Your Foundational Knowledge?
Mastering the vocabulary of modern technology is the first step to mastering the skills. Use this glossary as your companion as you explore new career paths.
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