Test your knowledge of AI marketing terms

If your AI knowledge starts and ends with the phrase “ChatGPT,” we’re here to help. This list of must-know AI terminology will help you gain fluency before your next inevitable conversation about AI.

There’s no time like the present to brush up on your understanding of AI terminology. Organizations using AI are reporting returns in the business areas where AI is applied, and most expect to increase their investments over the next three years. Its use in marketing is also poised to grow in 2024.

First, it’s important to understand the difference between key terms.

Artificial intelligence (or traditional AI) is the overarching practice of machines mimicking human intelligence to learn, make decisions, and solve problems. It’s a broad term that can belie the nuances of the field. Subcategories include generative AI and natural language processing — more on those below.

Machine learning is a branch of AI that uses computers (machines) and algorithms to learn from data and predict outcomes. Some real-world examples include facial recognition, product recommendations, mobile voice to text, predictive text, and social media optimization.

Deep learning is a specialized version of machine learning that imitates the way the human brain processes information using neural networks. Similarly to how we learn from experience, deep learning algorithms perform repeated tasks, learning and tweaking each time to improve outcomes. Some real-world examples include chatbots, virtual assistants, translators, and autonomous vehicles.

Data science is the study of data using an interdisciplinary approach to extract meaningful insights. It overlaps with AI, machine learning, and deep learning (while also combining principles and practices from mathematics, statistics, and computer engineering). Basically, it enables organizations to analyze and strategize based on large amounts of disparate data.

Got those key terms down? Here are a few more you should know.

Model: An algorithm or program that can be trained to identify specific patterns based on data.

Algorithm: A series of rules or tasks given to a computer to solve a problem.

Data mining: The process of sorting through large data sets to identify patterns that can improve models or solve problems.

Generative AI: A type of AI that creates something (e.g., images, text, video, or code). ChatGPT is the best-known example of generative AI.

Large language model (LLM): An AI model that has been trained on a large amount of text to understand and generate human-like text.

Chatbots: A software application that is designed to imitate human conversation through text or voice commands. Chatbots are often used in customer service settings.

Natural language processing: A type of AI that allows computers to understand spoken and written human language.

Predictive analytics: Technical analysis that uses historical data and patterns to predict a future outcome in a specific timeframe.

Prescriptive analytics: Technical analysis that uses possible scenarios, past and current performance, and other inputs to aid in strategic business decision-making.

Still want more? Here are some terms that you might come across when discussing AI in a business setting.

AI ethics: The study of the ethical implication of artificial intelligence. AI ethics include fairness, transparency, safety, and accountability.

Bias: Assumptions made by the algorithm or model to simplify learning and complete an assigned task.

Hallucination: An incorrect or false response that is presented as factual by the AI system.

Model evaluation: The process of using different evaluation metrics to understand a machine learning model's performance (both strengths and weaknesses).

Prompt: The input of the person into an LLM to generate the desired outcome.

Prompt engineering: The strategic design and optimization of prompts or input queries to guide the output of LLMs to most effectively produce the desired output.

Sentiment analysis: The process of categorizing and analyzing text for feelings and attitudes the writer had at the time of creation.

Media mix modeling: A statistical method for analyzing the allocation of budget across different advertising channels to maximize effectiveness and ROI.

Multi-touch attribution: A process that assigns value to each touchpoint in a customer's journey, providing insights into the impact of various marketing channels and interactions on conversions or sales.

Have more questions? Let us know.

This is a short list of key AI terms to get you started, but if you’re motivated to learn more about this fast-evolving field, check out all the content in our thought leadership program Practical AI for Marketers or email our AI experts at [email protected].