Mastering AI Jargon
For marketing and communications professionals, mastering basic AI terminology is not just about keeping up with tech jargon; it's about unlocking a deeper understanding of the AI landscape. This knowledge is crucial for identifying the right tools and strategies that can effectively advance your work, ensuring you stay ahead in a rapidly evolving digital world.
Adrienne Schafer
12/12/20231 min read
For marketing and communications professionals, mastering AI terminology is crucial for navigating the digital landscape effectively. Understanding these concepts can help you harness AI's potential for your organization as you think about creative content generation, predictive analytics or personalized customer engagement.
AI Terms Defined
Artificial Intelligence (AI)
AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. It includes anything from Google's search algorithms to self-driving cars.
Artificial General Intelligence (AGI)
AGI is a theoretical form of AI where a machine would have the ability to understand, learn, and apply its intelligence to solve any problem, much like a human being.
Deep Learning
A subset of machine learning that uses neural networks with many layers (hence “deep”) to analyze various factors in data. It's particularly good at recognizing patterns and making predictions.
Generative AI
This refers to a type of artificial intelligence that can generate new content, like text, images, or music, based on the data it has been trained on. It can create original outputs that didn't exist before.
Large Language Model (LLM)
These are advanced machine learning models that understand and generate human-like text based on the huge amounts of text data they have been trained on. Examples include OpenAI's GPT series.
Machine Learning (ML)
This is a subset of AI where machines are given access to data and learn for themselves. It's about creating algorithms that can modify themselves without human intervention to produce desired outputs.
Neural Network
This is a type of machine learning model designed to operate somewhat like a human brain. It consists of interconnected nodes (like neurons) and can learn to perform tasks by considering examples, generally without task-specific programming.
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