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Artificial Intelligence, or AI, one of the most commonly discussed technologies today – powering everything from voice assistants like Siri to self-driving cars and intelligent cameras. Let’s dwell more into this in the following chapters.

What exactly is AI?

AI or Artificial intelligence , as the name suggests, is the ability of machine to mimic human intelligence like making decisions, image recognition, understanding language etc. It uses the principle of neurons of human brain to solve problems likes humans.

Just like humans use their brains to solve problems, AI allows machines to use algorithms to “think” and solve problems on their own.

It mimics the neural connections in human brain in the form of interconnected nodes called neural networks that compute according to specific algorithms on respective data to enable human-like decision making.

Any collection of data is called a dataset.

What is ‘Data’ to AI? and why does it matter?

“If AI is the engine, then data is the fuel.”

Just like humans learn by seeing, reading, or experiencing things, AI learns from data. That means every image, sentence, sound, or number we give an AI system helps it understand how to do a task — whether it’s identifying cats, translating languages, or detecting cancer cells.

Importance:

1. Learning Patterns: AI doesn’t “think” like humans. Instead, it detects patterns from huge amounts of data.
2. Better Data = Better AI: Clean, diverse, and high-quality data makes AI smarter, fairer, and more accurate.
3. Different Tasks Need Different Data: AI for speech recognition needs audio clips, AI for translation needs sentence pairs, and AI for object detection needs labelled images.
4. Performance Improvement: The more data AI sees, the better it performs — this is what we call machine learning.

How long has AI been in the picture?

Starting from the 1950s, Alan Turing proposed the idea of how machines can “think”. 1960 saw the development of the first AI program, a chess game, by experts. In the late 90s and early 2000s, expert systems were built to emulate decision-making. This period showed the rise of big data in the industry. 2010 saw the rise of the deep learning revolution, where AI could easily beat humans in games like Go. In the 2020s, AI went live with various models like ChatGPT, DALL-E, Tesla Autopilot, and Edge AI in devices.

AI Family : AI/ML/DL

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are part of a family tree – with clear relationships between them.

1. Artificial Intelligence: AI is the broadest concept. It includes any technique that allows machines to mimic human intelligence. Its main goal is to make machines think, reason, and solve problems. It includes rule-based systems, logic, planning, learning, etc.
2. Machine Learning (ML): ML is a subset of AI. Instead of giving the machine rules, we give it data — and let it learn on its own. Its main goal is to enable machines to learn from data and improve over time. It uses algorithms like decision trees, SVMs, k-NN, etc.
3. Deep Learning (DL): DL is a subset of ML that uses neural networks (inspired by the human brain) to learn from very large datasets. Its main goal is to solve complex tasks like image recognition, natural language processing, and speech-to-text. It includes CNNs, RNNs, transformers, etc.

The Modern Faces of AI

Generative AI (GenAI)

Generative AI focuses on creation of new content – like text, images, music, code, or even video – by learning patterns from large datasets. These are used in content creation, code generation, design, education etc. Think of it as the “creative” side of AI — it doesn’t just analyze, it generates.

It uses deep learning models like transformers (e.g. GPT, BERT) to perform its tasks.

Agentic AI (AI Agents)

Agentic AI involves goal-driven, autonomous systems that can take actions, make decisions, and even trigger other systems. These are used for virtual personal assistants, robotic process automation (RPA), autonomous vehicles, etc. It acts as a virtual employee to perform automated tasks.

Agentic AI agents combine reasoning, memory, tools(APIs), and decision logics to take actions.

Edge AI

Edge AI runs AI models directly on embedded devices, without relying on cloud servers. It’s faster, more private, and energy-efficient. This type of AI is used in smart cameras, wearables, smart homes, healthcare devices, Industry 4.0, autonomous drones. Hence, Edge AI brings intelligence to the “Edge” – the physical devices around us.

In Edge AI, AI models are compressed and deployed on MCUs, MPUs, or NPUs.

Cloud AI

This is traditional, server-based AI that runs in large data centres or via APIs. Some of these examples are Google Cloud AI, AWS Sagemaker, Azure AI, etc.

Cloud AI is used in big data analytics and enterprise-scale AI solutions. It requires strong internet connectivity and high-performance computing.

This AI is powerful but centralized — perfect for heavy workloads or large-scale training.

Features / Applications of AI

1. Machine Learning (ML): The ability to learn from data without being explicitly programmed.
2. Reasoning and Problem Solving: AI can make decisions based on logic and rules.
3. Natural Language Processing (NLP): Enables machines to understand, interpret, and generate human language.
4. Computer Vision: Interpretation of visual information from the world.
5. Speech Recognition: Converts spoken language into text.
6. Perception and Sensor Fusion: The ability to interpret data from multiple sensors, essential in robotics and autonomous systems.
7. Autonomous Execution: AI can act without human intervention.

Limitations of AI

1. Data Dependency: AI systems require large, high-quality datasets for training. Poor, biased, or insufficient data leads to inaccurate or harmful outcomes.
2. Lack of Creativity and Emotional Intelligence: AI can generate content but doesn’t understand emotions, ethics, or creativity like humans do.
3. Bias and Fairness Issues: AI can unintentionally learn and reproduce human biases present in data.
4. High Cost of Development: Building and deploying AI systems is expensive in terms of hardware, talent and time.
5. Dependence on Human Input: AI needs humans for labelling data, defining goals, and monitoring performance. It’s not fully autonomous in most real-world use cases.