New Delhi, February 15 Artificial intelligence is rapidly transforming industries, workplaces, and daily digital life, emerging as one of the most transformative technologies – and a focal point of global conversations.
As discussions around AI intensify ahead of the mega summit that New Delhi is set to host, here is a straightforward guide to some frequently used terms making headlines, and what they mean.
From LLMs to guardrails, decoding AI vocabulary:
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-- AI: First things first. Artificial Intelligence, or AI, refers to the simulation of human intelligence by machines. Think of it as systems designed to perform tasks that typically require human intelligence – understanding language, recognizing images, making decisions, solving problems, and increasingly, creating content such as text, music, or videos.
At its core, AI is about enabling machines to learn from data. Instead of being programmed step-by-step for every scenario – as is the case with conventional software – AI systems are trained on large volumes of information to detect patterns, make predictions, and improvise over time.
-- Large Language Model (LLM): An LLM is a type of AI model trained on vast amounts of data (books, websites, articles) to understand and generate human-like language. LLMs power chatbots, writing assistants, coding tools, and search summaries.
They work by predicting the next word in a sequence based on patterns learned from massive data sets. An LLM specialises in language.
Prominent examples include Grok, GPT-4o, Claude 4, Gemini 2.5, Llama 4, and DeepSeek-R1.
-- Generative AI: AI that can create new content – text, images, music, code, or video – in response to prompts.
It includes text generators (often powered by LLMs), as well as image models, video models, voice synthesis tools, and music generators.
These systems respond to prompts and generate outputs that resemble human-created work, from summarizing reports and writing code to composing music, designing logos, creating marketing copy, generating product descriptions, producing social media posts, building presentations, creating synthetic voices, generating realistic images and videos, and even simulating customer service conversations.
-- Use Cases: A 'use case' means how AI is applied in real-world scenarios, or simply, its practical impact. Common use cases could include fraud detection in banking, personalized recommendations on OTT platforms, AI tools in agriculture, analyzing soil and weather data, healthcare diagnostics, and drug discovery.
-- Algorithm: A set of defined rules or instructions that tells a computer how to process data and make decisions. Think of algorithms as the building blocks of AI systems.
-- AI guardrails: Safeguards woven into AI systems to ensure they operate safely, ethically, and within defined boundaries. They are designed to prevent harmful, biased, illegal, or inappropriate outputs, and to align the system's behaviour with laws, policies, and human values.
Guardrails could be around content filters, safety policies, bias mitigation, among others.
-- Bias (AI Bias): Systematic errors in AI outputs caused by skewed training data, flawed assumptions, or design limitations.
-- AI hallucination: When an AI system generates information that appears plausible and convincing but is factually incorrect or fabricated.
-- Prompt: The input or instruction given to a generative AI system to produce a response.
-- Token: A unit of text (word, sub-word, or character) that an AI model processes during training and inference.
