Artificial intelligence. We all know it, some love it. One thing we can all agree on is how much information there is about it—and with that wealth of information comes an equally as daunting list of new terms to add to our vocabulary. From machine learning to natural language processing, we’re breaking down complex AI jargon into digestible concepts.
Here is Zen’s ultimate guide to understanding the AI terms and phrases you may come across (and some you might have never heard of!).
Related Reading: The Pros and Cons of AI in Marketing
The Ultimate AI Glossary for B2Bs
Algorithm (AI)
Algorithms are like recipes for computers—step-by-step guides that tell them how to solve problems. They act with sets of rules to be followed in calculations or problem-solving operations. Whether it’s tracking data or sensing information, algorithms are the secret to a high-functioning AI.
Anomaly Detection
Anomaly detection identifies outliers that don’t conform to an expected pattern in a dataset. It helps AI recognize when something’s fishy, whether it’s credit card fraud, network glitches, or even unusual patterns in your heartbeat.
Anthropomorphism
Anthropomorphism gives human traits to non-human things. In the world of AI, it’s about making machines seem more human-like, though they’re just super-smart bits of code (even if internet trolls want to convince us they’ll turn sentient).
Artificial Intelligence (AI)
Imagine having a sidekick right at your fingertips—that’s AI! This branch of computer science aims to build machines capable of performing tasks that would usually require manpower. It’s like teaching computers to think and learn so they can do tasks that would normally need human intelligence. From answering basic questions to helping you achieve your marketing goals (and truly everything in between), AI is quickly defining industries and making our world smarter and more exciting.
AI Language Models
AI language models are designed to understand, generate, and improve human language. They can write stories, answer questions, and even compose poetry within seconds. Some examples include ChatGPT, Bing, Bard, and Ernie.
Bias in AI
If AI is a sponge that soaks up info from the world, sometimes, the info it soaks up won’t be totally fair or balanced. That’s bias in AI. This systemic error is introduced in the AI model due to the biases present in the training data (e.g., the internet). These biases can lead to skewed or inaccurate outputs and can be really harmful to marginalized communities.
Big Data
Big data is exactly what it sounds like. It’s what experts call large and complex data sets that traditional data-processing application software can’t adequately process. It’s like a gigantic puzzle made of information pieces from everywhere: your phone, the internet… you name it. With the right tools, we can piece together valuable insights and solve problems we never thought possible.
Chatbot
Meet your digital BFF. Chatbots are AI software designed to interact with humans in their natural languages and are just as cool as their name. Typically used in customer service applications, they act as a virtual assistant that chats with you, helps you find info, or redirects you to a live professional.
Related Reading: How to Detect AI-Written Content and What it Means for Your B2B
ChatGPT
ChatGPT is a modern large language model chatbot that uses internet data to respond to prompts and commands (limited to September 2021). Developed by OpenAI and available to the public in November 2022, this AI has withstood overwhelming demand and a fairly positive reception.
Here’s how ChatGPT defines itself:
“ChatGPT is an AI language model created by OpenAI that can have text-based conversations. It generates human-like responses based on the input it receives, making it useful for chatbots, virtual assistants, and more. It’s trained on a lot of internet text, so it can produce coherent and contextually relevant replies, although it doesn’t truly understand like a human.”
Related Reading: The New Chatbot on the Scene: A Conversation with ChatGPT
Cognitive Computing
Cognitive computing simulates human thought processes through self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works. It gives machines a taste of human intelligence, enabling them to think, reason, and learn like humans, making decisions, solving problems, and learning from experience.
Computer Vision
Ever wished your computer could see and understand the world like you do? That’s computer vision. It’s an AI field that trains computers to interpret and understand the visual world similar to humans. It gives computers the power to recognize faces, identify objects, and even drive cars, all by analyzing images and videos.
Data Mining
No, you don’t need a hammer and a shovel for this kind of mining. Data mining is how computers discover patterns in large data sets, with methods at the intersection of machine learning, statistics, and database systems.
Deep Learning
What if your brain had layers and layers of super-focused brain cells? Well, it kinda does. Deep learning is a type of machine learning inspired by the structure of the human brain that is used to process large amounts of data and create patterns for decision-making. It’s like training computers to recognize the tiniest details in any given content.
Eliza
Eliza walked so ChatGPT could run. Widely considered the first chatbot in the history of computer science, Eliza is like the grandma of chatbots and dates back to the 1960s. Though she’s not as agile as today’s AI, she paved the way for the chatty machines we have now.
Emergent Behavior
Emergent behavior refers to unexpected or novel habits that AI models exhibit as a result of their learning processes, which were not explicitly programmed. It’s like an AI-choreographed dance routine without the help of a dance instructor.
Generative AI
This is a type of AI that is capable of creating new content (text, images, video, and code) that mimics patterns in the training data. Picture a magical AI artist that can create art, music, or even new ideas.
Related Reading: The Beginners Guide to Understanding Generative AI
Generative Adversarial Networks (GANs)
GANs create some healthy competition in the world of AI. They work together to make AI-generated stuff the best it can be, whether it’s art, music, or even virtual worlds.
Hallucination in AI
Sometimes, AI gets a little too imaginative and starts seeing things that aren’t there— that’s a hallucination in AI. This term refers to instances where AI provides factually incorrect, irrelevant, or nonsensical outputs due to the limitations of its training data or architecture.
Input Data
Input data is the “command” for your AI. It’s the info you give to AI, like text, images, or sounds, that it uses to produce its output. Just like a chef needs ingredients to cook, AI needs input data to create its digital wonders.
Large Language Model (LLM)
An LLM is a type of AI model that learns to generate text, engage in conversations, and write code by analyzing the internet. They often surprise their developers with unanticipated, language-savvy skills, chatting, answering questions, and even telling jokes.
Machine Learning (ML)
Think of machine learning as a clever pet that gets better with experience. It’s about training computers to improve at tasks by feeding them loads of examples, but without being explicitly programmed. Just like you learn from practice, ML algorithms learn patterns from data and become your digital buddies, making predictions, recommendations, and even art!
Martech Stack
A marketing tech (martech) stack is a business’s set of software tools that marketers use to organize and execute marketing processes. The stack can include some or all of the following: CRM, analytics, email marketing, social media management, web design tools, and more. A company’s marketing tech stack is as unique as its customers and goals.
Natural Language Processing (NLP)
Ever used Siri for the weather? NLP is an AI’s method of communicating with intelligent systems using a “natural” (read: human) language. Think about it like chatting with a computer as you do with friends. It helps machines understand and talk like humans.
Related Reading: AI in B2B Marketing: Where Human Intelligence Meets Martech Intelligence
Neural Networks
Inspired by biological neurons, neural networks are complex mathematical models that mimic the human brain’s structure, allowing AI systems to learn from patterns in data. They are the foundations of deep learning, where complex patterns are learned from data.
Parameters in AI
Parameters are like the settings on your digital camera— they are numerical values that shape how AI works. It’s like tweaking knobs to help AI learn faster, work better, and become a superstar problem solver.
Post-processing modules
After the pre-processing modules complete cleaning up, they send the data to the post-processing modules to finish the job. Here the final touch-ups are completed, refining the AI output to ensure it’s polished, accurate, and ready to impress.
Predictive Analytics
Predictive analysis uses data, ML techniques, and statistical algorithms to act like a personal fortune teller. It’s like using AI to peek into the future by crunching numbers from the past. From guessing which movie you’ll love next to helping businesses make smart decisions, predictive analytics is the crystal ball of the digital age, predicting future outcomes based on historical data.
Pre-processing modules
If you had a group of digital assistants that tidy up your data before it goes to work, they’d be pre-processing modules. They’re like the clean-up crew, getting rid of noise and making sure your data is spick and span for AI to use.
Reinforcement Learning
Reinforcement learning is a type of machine learning where an AI model learns to make decisions in an environment to achieve the maximum reward. It’s AI trial and error, training computers to make decisions by giving them points for doing things right and helping them learn from their mistakes.
Robotic Process Automation (RPA)
RPA is the use of software with AI and ML capabilities to handle high-volume, repeatable tasks. It allows you to train software robots to take care of tedious, repetitive (and, let’s face it, boring) jobs, freeing up humans to do more exciting and creative stuff and saving your company money and manpower.
Sentiment Analysis
Sentiment analysis includes the use of natural language processing to identify and extract subjective information from source materials. It’s like a mood ring, but using AI to figure out if people are feeling happy, sad, or somewhere in between by analyzing their words and tones online.
TensorFlow
Think of TensorFlow as the canvas where you can paint your AI dreams. It’s an open-source library developed by Google that helps create and train AI models and is crucial for ML and neural network research. Whether you’re building a chatbot or teaching a computer to play chess, TensorFlow’s got your back.
Related Reading: An Overview of Google’s Search-Generative Experience With AI
Training Data
Think of training data as the teacher’s toolkit for AI. It’s like sending a baby AI to school to teach it tons of examples so it can learn and grow smarter. Whether it’s data sets for your company or facts about World War II, training data helps AI become a pro.
Transformer Model
Think of a transformer model as the multitasking superstar of AI. This is a type of AI model architecture that can analyze an entire sentence at once rather than word-by-word and can understand context, translate languages, and even write code, all by mastering the art of attention.
Turing Test
The Turing test was designed by computer scientist Alan Turing in 1950 to determine if machines can chat so naturally that you can’t tell it apart from a human. Though you need 30% to be classified as passing, the highest documented score to date is only 33%, earned by Eugene Goostman in 2014 utilizing NLP technology (rather than the deep-learning algorithms used today). Google’s LaMDA AI also passed the Turing test, as well as ChatGPT in February 2023.
Unsupervised Learning
Unsupervised learning is an ML technique where the model learns from unlabeled data instead of a selected data set. It’s about letting computers learn without specific instructions, finding hidden patterns and connections that even we humans might miss.
So there you have it, our comprehensive guide to some of the most popular AI concepts. If you ever find yourself in a lost digital wonderland, let’s talk. We’d love to be your guide in the growing use of AI in marketing.
For more information about A.I. and how it can benefit your B2B, check out our blog.
