Artificial intelligence has quietly moved from tech labs to living rooms. It helps us unlock phones with our face, filters spam from email, recommends what to watch next, and even writes or draws on command. What started as machines mimicking human thinking has evolved into a powerful layer woven into everyday routines often so seamlessly that we barely notice it.
What Is Artificial Intelligence?
Artificial Intelligence, or AI, is a field of computer science that enables machines to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding language, recognising objects, and even creating content. Although AI often sounds complex, its basic principle is that it allows machines to learn from data, identify patterns, and make decisions.
Today, most AI development is focused on generative AI, a type of AI that can produce text, images, videos, or music.
How Does AI Work?
AI works by learning from data and using that knowledge to make decisions or perform tasks. At its core, AI identifies patterns in information and uses them to predict outcomes or generate new content. The process starts with data collection. AI systems need large amounts of information like text, images, numbers, or videos to understand how things work. This data trains algorithms, which are step-by-step instructions guiding the AI on how to analyse information.
Next comes learning. In machine learning, the AI studies data and adjusts its internal rules to make more accurate predictions. For instance, a program that detects cats in photos will examine thousands of images labelled “cat” and “not cat” to learn what defines a cat.
Some AI systems use deep learning, which involves multi-layered neural networks. These networks can process complex data like speech, facial recognition, or natural language. The deeper the network, the more intricate patterns it can understand.
Once trained, AI can apply its knowledge to new situations. It can answer questions, recommend products, drive a car, or even generate content, like writing a story or creating images, by combining patterns learned during training.
What AI Really Is
At its core, AI is technology that allows computers to simulate human abilities like learning, reasoning, understanding language, spotting patterns, solving problems, and making decisions. Modern AI systems can:
- Identify objects and people
- Understand and respond to natural language
- Learn from data and experience
- Make recommendations
- Act autonomously (like self-driving systems)
Much of the current global excitement centres around generative AI, which creates original text, images, video, audio, and more. Tools like ChatGPT, image generators, coding assistants, and personalised AI agents are generative AI.
Machine Learning: The First Layer
Machine learning (ML) is the idea that computers can learn patterns from data instead of being manually programmed for every task.
Popular ML techniques include:
- Linear/logistic regression
- Decision trees, random forests
- SVMs
- KNN
- Clustering
- Neural networks
Neural networks, inspired by the human brain, are especially powerful for recognising complex patterns in large datasets. These networks learn by analysing thousands or millions of examples until they can make accurate predictions on new data.
Supervised learning, where humans provide labelled examples, is the simplest form like – ‘Given this input, the correct output is this.’
Deep Learning
Deep learning extends neural networks into multi-layered, high-capacity models (often hundreds of layers). These networks extract meaning and features from massive, unstructured data (images, text, audio) without needing human labelling.
Deep learning powers:
- Speech recognition
- Image classification
- Natural language understanding
- Autonomous vehicles
It also enables modern learning methods like semi-supervised, self-supervised, and reinforcement learning.
Generative AI
Generative AI (gen AI) uses deep learning to create new content. It works by encoding patterns in the training data, then generating new, not copied, content based on that internal representation.
Three model families shaped this revolution:
- VAEs – create variations of content
- Diffusion models – power modern image generation
- Transformers – the engine behind ChatGPT, BERT, Gemini, Midjourney, etc.
How Gen AI Works
- Training the foundation model on huge unlabeled datasets.
- Tuning (fine-tuning, RLHF) for specific tasks..
- Generation + evaluation, with continuous improvement.
Today, retrieval augmented generation (RAG) and AI agents extend these models to reason, take actions, and use external tools.
The Rise Of AI Agents
AI agents are autonomous programs that can plan, take actions, and use tools on their own. They can research, compare options, make decisions, and execute tasks end-to-end.
For example- If generative AI tells you the best time to trek Everest, an AI agent books your flight, hotel, and gear automatically.
AI In Daily Life
Real-world use cases now include:
- Customer service: AI chatbots handle FAQs and support
- Fraud detection: Banks spot suspicious transactions instantly.
- Personalised marketing: Recommendation engines tailor ads and offers.
- Hiring: Automated resume screening and interview analysis.
- Coding: Gen AI writes boilerplate code or helps modernise old systems.
- Predictive maintenance: Machines schedule their own repairs.
- Healthcare: Diagnostic assistance, robotic surgery guidance.
A Short History Of AI
- 1950: Computer scientist Alan Turing asks, “Can machines think?”
- 1956: Term “artificial intelligence” coined; first AI program written.
- 1967: First neural network-based computer.
- 1980s: Backpropagation revives neural networks.
- 1997: IBM Deep Blue defeats Russian chess champion Garry Kasparov.
- 2011: IBM Watson wins Jeopardy!
- 2016: AlphaGo beats world Go champion.
- 2022: LLMs like ChatGPT explode into the mainstream.
- 2024-25: Multimodal and agentic AI reshape how we work and live.