Essential Terms in Generative AI You Must Know

Artificial intelligence is a topic of widespread discussion today, with everyone from professionals to the general public talking about its potential impact on our lives and jobs. With so much conversation, we frequently encounter many terms such as machine learning, NLP, generative AI, prompt, large language models, etc.

Generative AI space is filled with several technical terms; one may feel lost when these terms pop up. If you are one of them, then this article is for you. We have curated a list of technical terms related to generative AI that frequently appear when we learn about generative AI.  These are terms you'll likely encounter today or in the near future. 

This article will list essential terms to help you understand AI terminology better. Whether you're a business owner or an enthusiast eager to learn about artificial intelligence, this article will improve your understanding.

Understanding AI Terminology: A Beginner's Guide

AI has existed since the 1950s; however, many people were unaware of this transformative technology until the 2020s. It was not widespread until the launch of ChatGPT, which took the Internet by storm by reaching millions of users in just five days, a feat that took Instagram, Netflix, and Spotify months and years.
Now, several AI-powered tools are available for content marketers, designers, and business owners, taking humans' productivity and creativity to the next level. With their growing popularity, netizens are getting confused. 

Generative AI Terms to Know in 2024 

Generative AI terms are popping up everywhere with the emergence of popular tools such as ChatGPT, Bard, etc. This landscape could be exciting and confusing for beginners, as several terms are interchangeably used. Though the list is comprehensive with hundreds of words, we will be jotting down some of the most popular Gen AI terms here that every professional should know.  

1. Artificial intelligence

Artificial intelligence (AI) is a broad field focused on creating machines and programs to perform tasks that typically require human intelligence, although not as often depicted in fiction. An AI-powered machine and program can perform tasks such as learning, reasoning, problem-solving, understanding natural language, and perception. AI was conceptualized in the 1940s and 1950s, with the aim of enabling machines to think and operate autonomously.

Over the decades, AI has developed into various subsets, each focusing on different aspects of intelligent behavior. One subset is generative AI, which includes technologies that can create content autonomously. Generative AI includes models capable of generating text, images, music, and other forms of content by understanding and mimicking patterns found in the data they are trained on. 

2. Neural networks

Neural networks are inspired by neurons that make decisions in a manner similar to the human brain. Neurons are cells in the brain that transmit data throughout the body. A neural network consists of interconnected nodes that process and learn from data.

There are different types of artificial neural networks exist today: 

• Feedforward neural networks (FF), one of the oldest forms of neural networks.
• Recurrent neural networks (RNN), used for speech recognition, translation, and to caption images.
• Long/short-term memory (LSTM)
• Convolutional neural networks (CNN)
• Generative adversarial networks (GAN)

3. GPT

GPT stands for generative pre-trained transformer. It is developed by OpenAI, the company behind the popular ChatGPT tool. ChatGPT contains GPT, so now you might be wondering exactly what a generative pre-trained transformer(GPT) is. 
Apart from ChatGPT, millions of other generative AI models (or applications) surfacing across the Internet are built upon GPT. This means that GPT operates with some custom modifications behind the scenes. Major chatbots write in similar tones because they, in the end, have the same brain or program (or GPT model). The model can be considered the brain of your program. 

4. NLP

NLP stands for Natural Language Processing. It refers to processing natural languages like those we speak and write. Nowadays, machines are capable of understanding and processing our natural language. You can communicate with them using your everyday language, and they can grasp your intent.
NLP is a subset of artificial intelligence that enables interactions between machines and humans. When a system, machine, or program has NLP ability, you can interact with it using your own language. For example, AI Chatbots can understand sentiments like humans and respond appropriately. Have you ever wondered how they do it? The NLP technology behind them powers them to understand our sentiments.

5. GAN

GAN stands for Generative Adversarial Network, a type of neural network model. In this model, two neural networks compete against each other to generate authentic results. One network generates new data, while the other tries to distinguish if it's real or fake. They continue improving until the second network can't determine fake from real anymore. GAN includes a generator and a discriminator, two neural networks that work in tandem to generate content.

6. Discriminator 

When someone criticizes something you create, it allows for improvement, and this iterative pattern is also applied in machine learning.

The discriminator is a type of neural network that competes against a generator in a GAN (Generative Adversarial Network) to help the generator produce data indistinguishable from real data. Artificial intelligence is indeed trained through a process similar to how humans learn. 

7. LLM

LLM stands for large language models. In AI, a large language model refers to a computer program that is trained on massive amounts of text data from the Internet, books, articles, and more—thousands or millions of gigabytes' worth of text. Based on its learning, it can understand written language, write essays, answer questions, and even hold conversations. 

Famous examples of large language models (LLMs):

• GPT (Generative Pre-trained Transformer) series by OpenAI
• BERT (Bidirectional Encoder Representations from Transformers) by Google
• T5 (Text-To-Text Transfer Transformer) by Google
• XLNet by Google Brain 
• CTRL (Conditional Transformer Language Model) by Salesforce
• Megatron by Nvidia
• Turing-NLG by Microsoft

8. Deep Learning

In the field of AI, different methods are used to train AI models. One prominent approach involves neural networks with many layers (hence "deep") to model complex patterns in data. Deep learning has revolutionized many fields within artificial int lligence. However, it's important to note that deep learning is just one approach among several in machine learning.

9. Model 

AI models, or artificial intelligence models, are computer programs that find patterns in large data sets. They can take in information, analyze it, and then make decisions based on what they learn. As we have learned, ChatGPT and Google's Gemini are AI models, specifically large models.

10. Supervised learning

Several machine learning models utilize supervised learning, a subset of machine learning that uses labeled datasets to train algorithms to recognize patterns. Data labeling in machine learning is identifying and labeling raw data. 
For example, a label may indicate if the object is a bird or a car. Labelers may assign tags by simply saying yes/no. In supervised learning, the ML model uses human-provided labels to learn the underlying patterns in a process called "model training."

11. Unsupervised learning 

In unsupervised learning, machines learn without human supervision. In this learning, the machine receives raw data to discern patterns and insights without explicit guidance or instruction. 

12. Multi-model AI 

Multi-model AI programs are gaining traction in 2024 as they develop advanced capabilities to process a variety of inputs, including text, images, audio, and video, and convert these inputs into different formats. Google's GEMINI is a popular multi-model AI program that can read and extract data from images. 

13. Reinforcement learning

Reinforcement learning (RL) is a type of machine learning in which software learns to make decisions through trial and error. It mimics how humans learn by trying different actions and remembering what works. Good actions are rewarded, and bad ones are ignored. 
RL algorithms use rewards and punishments to learn the best way to achieve their goals. They can even handle situations where they need to make short-term sacrifices for long-term benefits. This makes RL a powerful tool for training AI to perform well in new and unpredictable situations.

14. Prompt

A prompt in AI is a command written in natural human language that describes the task the AI model should perform. A prompt can be text, images, or any other data. The quality and specificity of the prompt can significantly influence the quality and relevance of the generated output. 

15. Token

In A, a token is a basic unit of data that algorithms process, especially in natural language processing (NLP) and machine learning. Tokens are part of a larger dataset and can be words, characters, or phrases.
For instance, when handling text, a sentence is split into tokens, where each word or punctuation mark is a separate token. This step, tokenization, is essential for preparing data for AI models.
Tokens are not limited to text. They can represent different data types, and are vital for AI to understand and learn from them. In computer vision, a token might be an image segment, like a group of pi els or a single pixel. In audio processing, a token could be a short snippet of sound. This versatility makes tokens crucial for AI to interpret and learn from various forms of data.

16. Hallucinations

In AI, hallucinations refer to instances where a model generates an output that seems plausible but is incorrect or nonsensical. This is common in natural language processing (NLP) when an AI system produces text that looks coherent but is factually wrong or misleading.
For example, a chatbot might confidently provide a made-up answer to a question, or a text generation model might invent details that were not present in the original data. Hallucinations occur because the model predicts based on patterns it learned during training rather than verifying the accuracy of the information.

17. Generative models

Generative mode s are a type of AI that creates new data similar to the data they were trained on. They learn the underlying patterns and structures of the training data and use this knowledge to produce new, similar instances. For example, a generative model trained on text data can make new sentences or paragraphs that mimic the style and content of the original text.
Similarly, in image processing, a generative model can create new images that resemble the training images. These models are widely used in applications like text generation and image synthesis.