The use of artificial intelligence in the world of business is exploding. AI is expected to boost global corporate profits by $4.4 trillion every year.
Along with new technology comes a constant stream of new AI terminology.
But even when AI terms are “explained” to you, they can feel like a different language.
Not with this AI glossary.
We’re here to demystify the world of AI. We’ve created the ultimate list of AI terminology every professional should know.
Also important to mention is that there are many terms we didn’t include. But you probably don’t need to know the difference between a convolutional neural network and a recurrent one just to run your business.
What we have here is a curated, customized glossary of AI terms for busy professionals.
The essential AI glossary for business professionals
We want this glossary to be usable and informative. So, we’ve broken up the various terms into helpful categories. We’ve also put them in alphabetical order so you can go back and find them easily if you need to.
Fundamental AI concepts
Fundamental AI concepts are the core terms you need to know to understand what AI is and how it’s commonly discussed. Think of them as the building blocks that’ll form the foundation for the rest of this glossary.
An algorithm is a set of rules or instructions given to a computer. It tells the computer step-by-step how to do tasks or solve problems.
Think of an algorithm as a recipe that guides the computer on how to accomplish something.
Artificial general intelligence
Artificial general intelligence (AGI) refers to a machine’s ability to understand, learn, and apply its intelligence.
It goes beyond algorithms, using its intelligence to solve any problem — much like a human brain does. AGI adapts to new tasks without any prior training.
Artificial intelligence (AI) is the science of making machines perform tasks that typically require human intelligence.
These tasks include recognizing speech, making decisions, and translating.
Augmented intelligence refers to using AI to make human work easier.
Rather than replacing humans, augmented intelligence works alongside them to provide insights for making better decisions and ensuring better outcomes.
Blockchain and AI
Blockchain securely and transparently records transactions. Together with AI, it can enhance data security and decision-making.
This fusion offers solutions for various industries, particularly finance and cybersecurity.
Computer vision refers to training computers to see and understand the visual world in the same way a human can.
It uses digital images from cameras and videos to recognize objects, classify them, and react to what it “sees.”
Conversational AI refers to technologies like chatbots and virtual assistants that can engage in human-like dialogue, understanding and responding to spoken or written prompts.
Examples include smart systems that can “chat” with you, such as Apple’s Siri or Amazon’s Alexa.
Data mining is the action of digging through large sets of data to discover patterns, trends, and relationships — ultimately informing decision-making.
Deep learning is a subset of machine learning. It uses layers of neural networks to analyze data.
Deep learning is especially good at recognizing patterns in images, sound, and text. As such, it’s vital for identifying faces and understanding speech.
Edge AI is AI that works right on your device, such as your smartphone, instead of over the Internet. It makes things faster and more private.
Because Edge AI can work without an Internet connection, it’s particularly useful for applications like self-driving cars.
Federated learning is a privacy-friendly way to improve AI. It learns from data spread across many devices without actually sharing the data.
Federated learning enhances privacy and reduces the need to centralize data.
Neural networks are networks of algorithms inspired by the human brain. They mimic the way humans learn, gradually improving their accuracy in tasks like speech and image recognition.
Quantum computing and AI
Quantum computing and AI refer to using very powerful and fast computers to make AI even smarter and faster than before. It leverages the principles of quantum mechanics to process information at incredible speeds.
Quantum computing has the potential to revolutionize AI by making it exponentially more powerful.
Prompt engineering is the art of crafting inputs — either questions or commands — to an AI tool in a way that produces the best possible output.
The quality of the prompts determines the results.
Reinforcement learning is an AI technique where a machine learns to make decisions by trial and error. It’s rewarded when it’s right and corrected when it’s wrong.
Think of reinforcement learning as being similar to teaching a pet new tricks.
Robotics refers to building and using robots to do tasks, often via AI, to make them smart or autonomous.
This branch of technology deals with designing, building, and operating these robots.
AI technologies and tools
This section focuses on the practical application of AI — specifically, AI’s relationship with specific tools and uses.
AI in customer relationship management (CRM)
AI helps businesses better understand their customers by analyzing data. It can improve sales, customer service, or marketing strategies.
AI in the Internet of Things (IoT)
AI enhances devices connected to the Internet, such as thermostats or security cameras, by making them “smarter.”
These devices can then make decisions or take actions on their own.
Augmented reality (AR) and virtual reality (VR) in AI
AI improves AR and VR experiences, making them more interactive and realistic. Examples include video games and virtual training simulations.
Automation tools are software that uses AI to take over repetitive tasks from humans, such as scheduling appointments or sending emails.
This automation makes workflows more efficient.
Business intelligence tools
Business intelligence tools analyze complex business data via AI to provide insights.
They help companies make informed decisions based on trends and patterns.
A chatbot is a computer program powered by AI that talks to users. It’s often used for customer service or retrieving information on websites.
ChatGPT, developed by OpenAI, is currently the most popular AI chatbot. It can “chat,” answer texts, and even write text in a human-like way.
Cloud computing in AI
Cloud computing refers to using the internet to store and process AI data. It lets businesses use powerful AI without expensive computers or advanced hardware on-site.
Generative AI can create new content that didn’t exist before, including images, videos, and text. It learns from numerous examples in large datasets.
Image and speech recognition
Image and speech recognition refers to AI that can recognize and understand pictures and spoken words.
These technologies are used in photo tagging and voice-activated assistants.
Large language model (LLM)
LLM refers to big AI systems that understand and generate text by learning from a huge amount of written material.
These tools support applications in many ways, from searching for information to creating content.
ChatGPT is the best-known example of a large language model.
Machine learning (ML)
ML is a way for computers to learn and make decisions from data. These computers get better at making decisions and predictions as they’re fed more data.
Natural language processing (NLP)
NLP refers to AI that helps computers understand, interpret, and respond to human language. It is essential for tasks like translation or sentiment analysis.
Predictive analytics means using AI to analyze data and predict future events or trends. It’s helpful in fields like marketing, finance, and healthcare, where you may need to guess what will happen in the future.
Recommender systems suggest products, services, or information to users based on what those users have liked in the past.
Examples include music and movie streaming platforms.
Sentiment analysis refers to AI’s ability to analyze text and determine the user’s sentiment.
It’s useful for monitoring social media or customer feedback to learn what customers think about your business.
Text analytics refers to using AI to extract meaningful information from text. It can find key phrases, topics, or sentiments, helping in data analysis and decision-making.
Transformer AI is very effective at dealing with language, helping to translate and summarize text. It handles sequences of data and focuses on relationships between its elements.
Ethical and societal considerations
AI is a powerful technology. With that power comes tricky dynamics around ethics and society. Here are some important terms around these complex issues:
AI and employment
AI can automate tasks once done by humans. But it also creates new jobs and transforms existing ones.
The effects of AI on employment are both a challenge and an opportunity for workers and businesses.
AI and sustainability
AI aids in environmental efforts, helping us optimize our energy and resource use. But it also raises concerns about the power needed to run its systems, as well as the e-waste left behind.
AI ethics refers to making sure AI is used fairly and ethically.
AI intersects with important moral issues, such as bias, privacy, and accountability. The focus is on creating AI that acts in the best interests of society, respecting human rights and values.
AI governance refers to setting up rules and guidelines to make sure AI is used safely and in ways that don’t cause harm. It involves governments, companies, and international bodies overseeing AI development and use.
Anthropomorphism means talking about AI as if it’s human, such as saying it “thinks” or “feels.”
While anthropomorphism makes AI more relatable, it’s also misleading about AI's true nature and capabilities.
Bias in AI
Bias in AI occurs when AI doesn’t treat everyone equally due to unintended prejudices in its training data.
Correcting and preventing biased data is crucial for AI to be fair and accurate.
Data privacy and security in AI
Data privacy and security in AI refers to keeping the information AI uses safe and private to ensure personal or sensitive info isn’t misused or stolen.
Explainable AI (XAI)
XAI refers to making AI's decision-making processes clear and understandable to humans. It helps us trust this technology and verify that it’s doing what it should.
Human in the loop (HITL)
HITL refers to keeping humans involved in AI’s decision-making processes, especially when it comes to particularly important matters. It’s crucial for accuracy and fairness in sensitive applications, like healthcare or law.
Machine learning bias
Similar to AI bias, machine learning bias refers to prejudice built into the machine learning model. The AI that learns from examples isn’t fair because the examples aren’t balanced, leading to unfair or wrong conclusions.
Transparency refers to openness about how AI works and makes decisions so that people can understand, trust, and, if necessary, question it.
AI applications in business
AI is reshaping business practices and revolutionizing industries. Here’s how it can show up in various business functions:
AI in finance and accounting
AI makes financial tasks quicker and more accurate. For instance, it can spot errors and predict future financial trends. This makes it great for financial decision-making, detecting fraud, and automating data analysis for budgets.
AI in human resources
AI can help HR find the right people for the job.
It makes the hiring process more efficient by automating resume screening, predicting employee success, and personalizing training programs.
AI in marketing and sales
AI personalizes marketing to individual customers and helps sales teams meet their goals.
It predicts sales trends, optimizes advertising, and identifies potential leads.
AI in product development
AI accelerates product design by analyzing customer feedback to give customers what they want.
It also helps to innovate and improve products, making development faster and more customer-focused.
AI in project management
AI keeps projects on track as it figures out how to best use available time and resources. It aids in planning and tracking projects, optimizing timelines through predictive analytics, and improving team collaboration.
AI in risk management
AI helps businesses spot and prevent potential problems. It analyzes vast amounts of data to identify risks, predict scenarios, and recommend actions to minimize threats.
AI in supply chain management
AI predicts what will be needed and when. It optimizes supply chains by forecasting demand, managing inventory, and enhancing logistics, helping ensure that products are delivered efficiently.
AI-powered customer service
AI provides 24/7 customer support through chatbots and virtual assistants. It handles inquiries and solves problems quickly to improve customer satisfaction without the customer having to wait for a human to help them.
Transform your workday with AI-powered Motion
We hope you’ve found our AI glossary helpful and informative. It’s a good idea to bookmark this page for later reference.
Now that you better understand AI in the business world, it’s time to harness its potential.
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In fact, Motion’s platform is so powerful that it frees up two hours a day for each user. Imagine what you could do with an extra month each year.
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