In the ever-evolving world of technology, artificial intelligence (AI) stands as a testament to human ingenuity. It's a vast, interconnected field, with concepts such as machine learning (ML), neural networks, and deep learning at its core.
AI
AI, in its broadest sense, refers to a computer's ability to mimic human intelligence and to learn and make decisions like a human being. It's a science that aims to create systems that understand the world, adapt to changes, and interact seamlessly with people. The goal of AI is not just to replicate human intelligence, but to augment our abilities, increase efficiency, and transform our lives by learning, reasoning, solving problems, and making decisions.
Machine Learning
A subset of AI, machine learning, takes this concept a step further. It involves teaching computers to recognize patterns through data and algorithms. Unlike traditional programming, which relies on explicit logic coding, machine learning trains computers to perform tasks by learning from data.
This shift in approach has led to groundbreaking applications, from recommendation systems used by Netflix and YouTube, which learn user preferences to provide personalized content, to more complex tasks like image recognition, fraud detection, language processing, and even guiding self-driving vehicles.
Deep Learning
Deep learning, a specialized technique within machine learning, takes inspiration from the human brain. It involves training artificial networks, mimicking the way our brain strengthens connections between neurons based on our experiences, knowledge, and skills.
In the realm of computer science, these connections are represented by layers and nodes in models. When these layers and nodes are combined and multiplied by billions, they form deep learning models capable of remarkable feats.
LLMs and GenAI
Two significant subsets of deep learning are Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI). Language models like GPT-3.5, GPT-4, and Google Gemini specialize in generating text, while generative AI systems like DALL-E and Midjourney produce images. Tools like GitHub Copilot, on the other hand, generate code. These models typically use "prompts" as text input to guide the model and provide context for a specific task.
The illustration above explains how LLMs and GenAI fit into AI.