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Demystifying GenAI: A Comprehensive Guide to Essential Terms

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Khushbu Raval
Khushbu Raval
Khushbu is a Senior Correspondent and a content strategist with a special foray into DataTech and MarTech. She has been a keen researcher in the tech domain and is responsible for strategizing the social media scripts to optimize the collateral creation process.

Have you heard of GenAI (generative artificial intelligence) but feel lost? You’re not alone. This rapidly evolving field uses a lot of jargon. This guide unpacks 49 key terms to equip you with a solid understanding of GenAI’s potential and complexities.

GenAI (generative artificial intelligence) is a trending buzzword these days. Though it has seen significant adoption, we are still at the start of a journey.

While it has taken the world by storm, GenAI did not arrive suddenly. According to Inc42, there are 100+ native GenAI startups in India. The GenAI global market size by 2030 will be over $552 billion. The five key growth drivers of GenAI are — digitalization and cloud computing, demand for creative content, access to structured data, new developments in AI models, and workflow automation.

As India aims to be a $7 trillion economy by 2030, automation in business operations will play a key role. GenAI promises to upend operations as we know them and unleash a productivity boost. It can transform how work is done today. It would impact the way jobs are done and the skill sets required to do them, bringing down the number of manual interventions.

Whether you are a techie, a seasoned user, or someone who has just started to get acquainted with GenAI, it is crucial to know not only its basic fundamentals but also the terminologies, acronyms, and lingos that accompany them.

From LLM and NLG to GPT and ML, we have curated a list of all essential terms related to GenAI. Dive into this comprehensive guide to decode the top 49 GenAI-related terms. 

40+ Essential GenAI Terms You Must Know

Artificial Intelligence (AI)

AI is a technology that enables machines and computers to stimulate human problem-solving capabilities and intelligence. GPS guidance, digital assistants, and autonomous vehicles are some examples of AI in daily life. With the hype around the technology taking off, talks about AI ethics have also become crucial. Read more

Big Data

Data containing greater variety, arriving in increasing volumes and with more velocity (3Vs), is known as big data. This data can be used to resolve complex business problems but cannot be managed by traditional data processing software. Big data can be fundamentally categorized into structured, semi-structured, and unstructured. Read more…

Chatbot

A chatbot is a computer program that simulates a conversation with humans via text or voice. It is often used to answer frequently asked questions or provide customer service. Chatbots are primarily of two types: rule-based and AI-powered. Customer service chatbots, shopping bots, and entertainment bots are some typical examples of chatbots. Read more…

Conversational AI

Conversational AI utilizes technologies like natural language processing (NLP) and machine learning to understand and respond to human language in a way that mimics natural interaction. Smart speakers, smartphone assistants, and customer service chatbots are some examples of conversational AI. Read more…

Ethical AI

It is the development and use of AI systems by prioritizing ethical principles. Ethical AI considerations have converged globally around five principles — transparency, justice, fairness, non-maleficence, responsibility, and privacy. It can also be concerned with the moral behavior of humans as they design, make, use, and treat AI systems. Read more…

GPT

GPT, or generative pre-trained transformer, is a language model developed by OpenAI that uses deep learning techniques to generate natural language text that closely resembles human-written text. The model is pre-trained on a massive amount of text data to learn structures and statistical patterns of natural language. Read more…

Large Language Models (LLMs)

LLMs use transformer models and are trained using massive datasets to perform several natural language processing (NLP) tasks. They can recognize, translate, predict, or generate text or other content. They can also be trained to perform diverse tasks, such as understanding protein structures, writing software code, etc. Read more….

Machine Learning (ML)

ML focuses on using data and algorithms to enable AI to imitate human learning, gradually improving its accuracy. ML models fall into three primary categories: supervised machine learning, unsupervised machine learning, and semi-supervised machine learning. Depending on the budget, the need for speed, and the precision required, each has its advantages and disadvantages. Read more….

Responsible AI

Responsible AI is a comprehensive approach to AI that considers the ethical, social, and legal implications throughout the entire AI lifecycle, from ideation and design to development, deployment, and use. Fairness and non-discrimination, transparency and explainability, accountability, privacy, and security are some of the key principles of responsible AI. Read more….

Training Data (Training Set Or Learning Set)

It is a collection of examples that a machine learning model learns from to identify patterns and make predictions. Each data point has a corresponding label or classification. By structure, training data can be classified into unstructured, structured, and semi-structured training data. Read more…

AI Alignment

Encoding human values and goals into LLMs and making them as helpful, safe, and reliable as possible is called alignment. Corporations can train AI models to follow their business rules and policies through it. Alignment aims to solve the mismatch between an LLM’s mathematical training and the soft skills humans expect in a conversational partner. Read more…

Supervised Learning

In supervised learning, machine learning algorithms are trained using labeled data. Data points have pre-defined outputs like tags or labels to guide learning. Supervised learning tackles diverse real-world challenges across various industries, including finance, healthcare, retail, and technology. Read more…

Semi-Supervised Learning

Semi-supervised learning is a powerful ML technique that combines the strengths of supervised and unsupervised learning. It leverages a small amount of labeled data (expensive and time-consuming to acquire) and a big chunk of unlabelled data to create effective models. Read more…

Unsupervised Learning

It is a type of ML that learns from data without human supervision. Unlike supervised learning, unsupervised learning algorithms are given unlabelled data and allowed to discover patterns and insights without explicit guidance. These algorithms are better suited for more complex processing tasks, such as organizing large datasets into clusters. Read more…

Artificial General Intelligence (AGI)

AGI, also known as “strong” AI, is a hypothetical intelligence that does not exist yet. It attempts to create software with human-like intelligence and the ability to self-teach. Today’s AI systems are called “weak” AI and lack the flexibility and adaptability that come with true general intelligence. Read more…

Artificial Neural Networks (ANNs)

They are machine learning algorithms that use neurons or interconnected computers to mimic the layered structure of a human brain. Each neural network consists of layers of nodes – an input layer, one or more hidden layers, and an outer layer. Each node connects to others and has its weight and threshold. Read more…

Artificial Superintelligence (ASI)

It is a hypothetical concept referring to an AI system with intellectual capabilities that surpass that of humans. Advancements in computer science, computational power, and algorithms fuel speculation about ASI. Though it is currently in the domain of science fiction, a big step towards its development would be the development of Artificial General Intelligence (AGI). Read more…

Autoregressive Model

An autoregressive model is a statistical technique that predicts future values based on past values. This technique is commonly used in time series analysis, where data is collected over time, such as website traffic, weather patterns, or stock prices. AR(p), ARMA, and ARIMA are common autoregressive models. Read more…

Bayesian Networks

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are probabilistic graphical models that use a directed acyclic graph (DAG) to represent the relationships between variables and their conditional dependencies. They perform structure learning, parameter learning, and inference. Read more…

Composite AI

Composite AI uses the varying strengths of different AI tools to address complex problems that a single technique might not be able to handle effectively. Some applications of composite AI are personalized treatment plans, drug discovery, fraud detection, and autonomous vehicles. It enhances problem-solving, improves decision-making, is more adaptable, and reduces bias. Read more…

Conditional Generation

Conditional data generation (seeding or prompting) is a technique in which a generative model is asked to generate data according to pre-specified conditioning, such as a topic or a sentiment. Some of its use cases are training a financial model to detect fraud better and filing synthetic user details for users who opted out of data collection. Read more…

Convolutional Neural Network (CNN)

A CNN is a deep learning algorithm designed to analyze visual data like images and videos. It uses 3D data for object recognition and image classification tasks. It leverages principles from linear algebra, specifically matrix multiplication, to identify patterns within an image. Read more…

Deep Belief Network (DBN)

A DBN is a sophisticated artificial neural network used in deep learning, a subset of machine learning. It is designed to discover and learn patterns within large data sets automatically. Its architecture also makes it good at unsupervised learning (understanding and labeling input data without explicit guidance). Read more…

Emotion AI

Emotion AI analyzes aspects like facial expressions, tone of voice, body language, and even text to gauge how someone feels. Most advanced emotion AI, particularly those focussing on facial expressions, achieves around 75-80% accuracy. Read more….

Encoder-Decoder Architecture

It is a fundamental framework used in diverse fields, including speech synthesis, image recognition, and natural language processing. It involves two connected neural networks—an encoder and a decoder. The encoder processes the input data and transforms it into a different representation. Subsequently, the decoder decodes it to produce the desired output. Read more…

Explainable AI

Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms by shedding light on internal processes. It can be employed in various industries, including healthcare, financial services, and criminal justice. Read more…. 

Fuzzy Logic

Going beyond the “true or false” approach of regular logic, fuzzy logic allows for degrees of truth between completely true (1) and completely false (0). It presents the uncertainty and ambiguity often present in real-world problems. Fuzzy logic finds various real-life applications – from electronics to weather forecasting. Read more…

Generative Adversarial Network (GAN)

GANs are an approach to generative modeling using deep learning methods, such as convolutional neural networks. They train the generative model by framing the problem as a supervised learning problem with two sub-models: the generator model (for generating new examples) and the discriminator model (for classifying the examples as real or fake). Read more…

Generative Model

It is a machine learning model that works on learning the underlying patterns of data to generate new, similar data. Due to its ability to create, this model has vast applicability in diverse fields, from art to science. It could generate textual content, compose music, synthesize realistic human faces, and more. Read more…

Hierarchical Models

Hierarchical models in AI can capture the hierarchical nature of real-world phenomena, enabling multi-level representations and insightful analysis. They form the core of numerous applications – from natural language processing to pattern recognition. They facilitate more informed decision-making and adaptive learning by enabling AI systems to unravel the underlying hierarchy of information in data. Read more….

Hybrid AI

Hybrid AI refers to the integration of multiple types of artificial intelligence systems, such as rule-based expert systems, machine learning models, and natural language processing, to solve complex problems. It is a rapidly growing area with potential uses in many industries, including healthcare, finance, and transportation. Read more….

Latent Space

In AI, latent space is a hidden space, often with many dimensions, that captures the vital features of a set of data. It allows an AI system to position data points based on their similarities and differences, helping the model learn the relationship between different data sets. Read more…

Markov Chain Monte Carlo (MCMC)

It is a mathematical process undergoing transitions from one step to the other. The key properties of a Markov process are that it is random and each step in the process is “memoryless”. The future depends only on the current state of the process and not the past. Read more….

Natural Language Generation (NLG)

It is a software process driven by AI that produces natural written or spoken language from structured and unstructured data. For instance, NLG can be used after analyzing computer input (such as queries to chatbots, calls to help centers and more) to respond in an easily understood way. Read more….

Natural Language Understanding (NLU)

NLU is a complex research area in AI involving techniques from various fields (including computer science, linguistics and psychology), focussing on enabling computers to understand human language the same way humans do. Chatbots, smart speakers, and customer service applications are some of its use cases. Read more…

Neural Radiance Field

A neural radiance field (NeRF) is a neural network that can reconstruct complex three-dimensional scenes from a partial set of two-dimensional images. Computer graphics and animation, medical imaging, virtual reality, satellite imagery, and planning are some of the use cases of neural variance fields. Read more….

Overfitting

An undesirable ML behavior occurs when the ML model gives accurate predictions for training data but not new data. Overfitting happens when the training data is too small or contains massive, irrelevant information. It can also occur when a model trains too long on a single sample of data or when the model complexity is high. Read more….

Predictive Analysis

Predictive analysis leverages historical data, statistical modeling techniques, and machine learning algorithms to identify patterns and relationships that can predict what might happen next. It can automate data processing and feature engineering, handle complex and unstructured data, and more. Read more….

Probabilistic Model

A probabilistic model is a statistical tool that accounts for randomness or uncertainty when predicting future events. In contrast to deterministic models (which make fixed predictions based on specific inputs), a probabilistic model incorporates probability distributions (which describe the likelihood of different events happening). Read more….

Probability Density Function (Bell Curve)

A probability density function (PDF) tells the probability of a specific outcome happening at a given time. It describes the likelihood of observing some outcome from a data-generating process. However, it does not give the probability of a single specific value. Read more….

Quantum Generative Model (QGM)

It is a type of ML algorithm that uses the principles of quantum mechanics to generate complex data distributions. These models can leverage quantum mechanics for greater power and efficiency than classical models. QGMs can be potentially useful in drug discovery, material science, financial modeling, and image and music generation. Read more….

Recurrent Neural Network (RNNs)

RNNs are a type of artificial neural network that uses sequential or time series data. They are incorporated into popular applications such as Siri, voice search, and Google Translate and are also used for image captioning, language translation, and speech recognition. Read more…

Reinforcement Learning

A subfield of machine learning, reinforcement learning is concerned with how an intelligent agent can learn through trial and error to make optimal decisions. It can be used to expand beyond supervised learning, personalize and optimize learning complex texts, and collaborate with other learning paradigms. Read more…

Technological Singularity (AI Singularity)

It is a hypothetical future where AI surpasses human intelligence and experiences rapid, uncontrollable growth. This hypothetical event can have unforeseen and potentially profound consequences for human civilization. While some futurists regard it as an inevitable fate, others are trying to prevent the creation of a digital mind beyond human oversight. Read more…

Transformer-Based Models

First introduced in the 2017 paper ‘Attention is All You Need’, transformer-based models have become the foundation for many Natural Language Processing (NLP) tasks. BERT, GPT-3 & 4, and T5 are popular transformer-based models. They can understand complex relationships in text, leading to superior performance in tasks like question answering, summarisation, and machine translation. Read more….

Underfitting

Underfitting occurs when a model is too simple, needs more training time, has more input features, or requires less regularisation. When a model is underfitted, it cannot establish the dominant trend within the data, resulting in training errors and poor performance. Read more….

Variational Autoencoders (VAEs)

VAEs are powerful generative models, with applications ranging from generating fake human faces to producing purely synthetic music. They are a class of probabilistic models that find low-dimensional representations of data. They comprise two parts – an encoder network (mapping the input data to a lower-dimensional latent space) and a decoder network (mapping the latent representation back to the original data space). Read more…..

Zero Data Retention (ZDR)

It means not storing any data intentionally after it has served its immediate purpose. ChatGPT developer OpenAI has championed this cause by rolling out a ZDR policy in its application programming interface (API) calls. ZDR is critical for enhanced security, privacy, and ethical considerations. Read more….

Zero-Shot Learning

Zero-shot learning is a challenging area of machine learning where a model is trained on data from some classes and asked to classify data from new, unseen classes. It bridges the gap between seen and unseen, learning with auxiliary information. The technique is mostly used in deep learning. Read more…

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