Artificial Intelligence and machine learning being an extensive field come with enhanced scope for learning and career advancement! As a professional machine learning engineer or an AI enthusiast software engineer/manager, acquiring commendable knowledge about the major generative AI glossary can always keep you at the upper hand in your tech career.

In this article, we have curated the most important generative AI glossary that you should stay aware of from A to Z!

Also read: Applied GenAI Explained, Benefits, Examples

50+ Generative AI Glossary: From A to Z

To fully take advantage of the potential of genAI, it is important that your familiarize yourself with the key generative AI terms and concepts underpinning this technology.

Understanding the generative AI glossary will provide you with the knowledge to navigate and leverage genAI effectively. Learning these key terms can be especially useful for genAI beginners.

Letâ€™s look at some of the key genAI terms below:

A

1. Agents

Agents can be described as independently acting software robots within a designated environment to accomplish tasks assigned to them. Agents can be utilized to achieve goals related to the virtual world.

2. AGI

AGI stands for Artificial General Intelligence. It is a generative AI term that means a hypothetical AI capable of accomplishing intellectual tasks that are performed by humans such as composing poetry, diagnosing diseases, etc. This AI model can function across various domains.

3. Alignment

Alignment refers to the set of protocols assigned to achieve human-compatible AI goals without creating any unfavorable consequences. It can be further described as a set of doâ€™s and don’ts.

4. Attention Mechanism

The performance of AI models is enhanced using the attention mechanism by addressing distinct parts of the input data and weighing various elements. With this mechanism, large and complex datasets can be efficiently processed, allowing image recognition and sequential prediction tasks.

5. Autoencoders

It refers to the reconstruction of data compressed from neural networks into its original form.

B

6. Backpropagation

This generative AI term can be described as a feedback learning mechanism designated to make the AI model function better by taking note of its faults. An algorithm enables neural networks to adjust to their internal connections depending on how accurately they perform their tasks.

7. BERT

It is an extensively designed language AI model used as a bidirectional transformer model to examine texts from all contexts.

8. Bias

The unintentional and discriminatory outcomes resulting from assumptions integrated into AI models are stated as bias.

9. BigGan

It is an important term in the generative AI glossary. It means a type of GAN, efficient in generating images, that are immensely realistic and of top-notch resolution. More specifically, it can provide users with intensely realistic virtual experiences.

C

10. Capsule Networks

Capturing spatial relations and distinct components of objects through networks using capsules instead of neurons are termed Capsule Networks.

11. Chain Of Thought

It is a dedicated approach designed for AI models to elaborate reasoning processes before deriving a conclusion or decision. To understand this better, you can think of the steps involved in solving a mathematical problem and deriving an answer.

12. Chatbot

Chatbot is an important term in the generative AI glossary that can induce friendly and informative virtual conversations with humans regarding all their doubts and queries with 100% accuracy.

13. ChatGPT

It is an extensively designed large language model capable of conversing with humans with utmost fluency and comprehension. Humans can engage in witty and knowledge chat sessions with ChatGPT.

14. CNN (Convolutional Neural Network)

These are specialized models designed to process data. The data arranged in grids, images, etc can be identified by the convolutional neural network by analyzing patterns and features integrated within them.

15. CycleGAN

This AI model is designed to translate images independently into various styles without taking information from paired references. To understand this term better, you can think of a mechanism capable of transforming a watercolor painting into a sketch.

D

16. Data Augmentation

It enables an AI model to be more robust and generalizable, thereby incredibly comprehending a more diverse set of training data.

17. Data Imputation

Data imputation is an important term in the genAI glossary. It can be classified as a method used by AI models to retain the maximum information of the dataset and substitute the missing data with another value instead of forgetting it completely.

18. DeepSpeed

This system is attributed to providing training to extensive language models on various distributed systems. Zero-offloading, Megatron-Turing NLG, and mixed precision training are some of the techniques used in enhancing the scalability of the training procedure.

19. Diffusion Models

This is one of the most important and newly developed gen AI terms. It is attributed to generating data by adding and later reversing noise.

20. Double Descent

It is a phenomenon that describes how the complexity of an AI model can intensify before its gradual improvement. To understand more precisely, you can think of a rollercoaster ride filled with frequent ups and downs, before commencing the final climb.

E

21. Emergence/Emergent Behavior

This generative AI term can be classified as completely unexpected and complicated AI behavior arising from AI models following individual rules in a system.

22. Expert Systems

These are AI applications designed to accumulate extensive and accurate knowledge on particular domains such as technology, medical databases, etc.

Also read: How Generative AI is Transforming the Job Market: Skills in Demand

F

23. Few-Shot Learning

This training mechanism enables AI models to acquire knowledge in a short time even with limited amounts of data available. Therefore, it enables the AI models to accomplish and adapt to new tasks with minimum information or data.

24. Fine-tuning

This generative AI term refers to the practice of making a previously trained AI model to accomplish a designated task based on limited amounts of data. It can also be defined as a customization mechanism.

25. Foundation Model

Almost all specialized applications require a base model for further development. A foundation model is an extensively designed versatile AI model providing a privileged platform to specialized applications.

G

26. GAN (General Adversarial Network)

The general adversarial network is an important in the generative AI glossary. It is a category including two AI models. One of which is involved in generating data and the other one functions to distinguish it from real data. As a result, it leads to the generation of realistic outputs.

27. Generative AI

Machine AI models efficient in curating new content autonomously can be defined as generative AI. Enroll with us for Advanced Generative AI Course and boost your professional career.

28. GPT

It is an Open AI-developed language model that comes integrated with the functionality of producing human-compatible text. It can also translate multiple languages and lead to unique content creation.

H

29. Hallucination

Various biases, data limitations, and other similar factors can make the AI models generate unrealistic outcomes. This phenomenon is termed a hallucination.

30. Hidden Layer

Often layers in a neural network are not connected to the input or output prominently leading to the complexity in data transformation, capturing hidden patterns.

31. Hyperparameter Tuning

The parameters such as learning rate, number of hidden layers, or regularization when regulated in an AI model, are stated as Hyperparameter Tuning.

I

32. Instruction Tuning

This is one of the most prominent terms in gen AI glossary that an AI enthusiast must learn, defining the phenomenon of imparting training to a previously trained AI model through limited datasets.

L

33. Large Language Model

A large language model is defined as an extensively designed language training model based on massive data including both text and code. A LLM is perfectly capable of generating all sorts of human-readable content.

34. Latent Space

The representation of the low-dimensional learning perceived by a machine learning model is attributed to latent space.

35. Latent diffusion

This technicality allows the latent representation of data by adding noise. More specifically, a diffusion procedure is used for data representation.

36. Long Short-Term Memory (LSTM)

This is an important term in the GenAI glossary. It can be defined as a type of neural network. It formulates machines to learn, retain and process data over long periods.

M

37. Mixture of Experts

A method comprising multiple predictions of specialized data sub-models for enhancement of functionality and performance of AI models is referred to as mixture of experts.

38. Multimodal AI

Multimodal AI is one of the most prominent gen AI terms referring to the procedure of data generation from various modalities, contributing to a much more comprehensive response.

N

39. NeRF

The comprehensive procedure of formulating 3D scenes from 2D images is referred to as Neural Radiance Fields (NeRF). It is a valuable term in the generative AI glossary. The color and space passing through each point in space are comprehended through this procedure. Therefore, generating realistic images from all points is possible with this term.

O

40. Objective Function

Objective function refers to the maximization or minimization of the machine learning model through its training period. It assists in illustrating the objectives of the AI model along with the results generated.

41. One-Shot Learning

This is a learning approach dedicated to making the AI model learn only from a single learning session. More specifically, it refers to the machine learning procedure only through one example per class. So, even if the data is limited, the AI model can efficiently make realistic predictions and identify patterns without requiring another instance.

P

42. PLEFT

The performance and functionality of large language models can be considerably improved through the procedure of Prompt Engineering Fine-Tuning. PLEFT, falling under the category of generative AI glossary, includes both crafting instructions and fine-tuning models based on datasets.

43. Pre-training

Pre-training can be defined as a kindergarten approach to AI models. The AI models develop all their fundamental skills through this procedure such as identifying similar patterns or extracting features from images.

44. Prompt

To kickstart the task of an AI model, a set of questions or riddles, referred to as prompt is an important term in generative AI glossary. It provides the model with the direction and context, allowing more accuracy in generating results.

45. Policy Gradient Methods

These are a set of learning techniques designed to directly optimize the policy according to the objective function.

Q

46. QLoRA

This is the only genAI term starting with the letter â€˜qâ€™. The Quantized definition of LoRA is termed as QLoRA. Through this technicality, data can be easily deployed even with limited information on edge devices and mobile phones.

Also read: Navigating the Ethics of Generative AI in Data Engineering and Science

R

47. Regularization

Regularization is efficient in enabling AI models to prevent over-retention of training data. By implementing procedures such as the addition of noise, diffusion, and constraints, it becomes much easier to increase the flexibility and versatility of the machine learning models.

48. Recurrent Neural Network (RNN)

The recurrent neural network is an important term in the GenAI glossary. It is an extensively designed deep learning AI model that functions to produce specific sequential data output by processing sequential data input.

49. Reinforcement Learning

Reinforcement learning refers to the maximization of a reward signal by making the agents interact freely within a distinct environment.

S

50. Self-Supervised Learning

SSL refers to the procedure of generating unlabeled data through inherent structures or patterns. Procedures such as contrastive learning or inpainting are integrated into self-supervised learning.

51. Sequence-to-Sequence Models

Sequence-to-sequence models enable the swift transformation of one sequence into another. This technicality is essential in aspects of language translation, speech recognition, and other similar tasks, thereby making it a vital term of the generative AI glossary.

52. StyleGAN

It can be described as a group of GANs specially attributed to producing human faces that are of utmost realism. It also enables easy customization of the generated results. First, the facial features are well-captured and then refined for further alteration.

T

53. Text-to-Speech

This is one of the most unique terms in the gen AI glossary capable of generating spoken voice output through written text. This technicality involves statistical parametric synthesis and deep learning techniques to produce speech with intense realism and vocal expressions. It is widely used in applications such as voice assistants, screen readers, etc.

54. Transformer Architecture

This term can be classified as an architecture of neural networks designed to input a text sequence and generate another one as an output.

55. Transfer Learning

It is a technique in which current complications are solved by deriving references or data from pre-trained AI models. This training approach considerably saves a lot of time and effort.

V

56. Variational Autoencoders

This is a classification of generative AI models used for image generation, anomaly detection, and other similar tasks by generating or encoding low-dimensional data from neural networks.

57. Vector Databases

The databases specially designed to store high-dimensional vectors such as texts, images, etc are termed vector databases.

X

58. Explainable AI (XAI)

To make AI models much more interpretable, an extensive research field has been generated. It greatly facilitates the overall procedure of result generation, decision-making, trust-building, etc.

Z

59. Zero-shot Learning

This is another important term in the generative AI glossary dedicated to referring to AI models capable of handling and accomplishing tasks without any formal training at all.

Also read: Generative AI vs Predictive AI: Everything You Need to Know

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FAQs: Generative AI Glossary

Q1. What are the Primary AI Generative Classes?

The main classes of generative AI include generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based Models, Autoregressive Models and Recurrent Neural Networks (RNNs).

Q2. Can Google be Considered as a Generative AI?

Google is considered a generative AI since it uses the search function to brainstorm ideas the user is looking for and produces completely organized results.

Q3. What is the Main Concept Behind Generative AI?

The key concept behind generative AI is to perform multiple tasks without the requirement of coding or computing skills.

Q4. How Many Techniques are Used for Gen AI?

Multiple learning techniques are used by generative AI to produce human-compatible content. Some of the most popular and efficient techniques used for gen AI terms are deep learning, neural networks, and machine learning.

Q5. What is the Architecture of Generative AI?

According to the data provided in a dataset, the AI machine model can generate similar data including text, images, videos, music, etc.

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Generative AI Glossary – 50+ Terms Every GenAI Enthusiast Must Know!