12 AI and Machine Learning Terms Everyone Should Know

Discover these 10 essential terms that will help you navigate the exciting world of AI and machine learning.

Ever felt like an AI chatbot understands you a bit too well? That’s anthropomorphism in action. It’s when we attribute human-like qualities to AI, even though they’re just cleverly designed algorithms.


AI systems can sometimes have biases, just like humans. These biases can lead to inaccurate predictions or offensive responses. Let’s work together to ensure fair and unbiased AI.


Supervised Learning

Supervised learning is like teaching AI through labeled examples. It learns to make predictions based on provided data, mapping inputs to outputs. It’s the foundation of image recognition, speech processing, and more.

Reinforcement Learning

Just like how we learn from trial and error, reinforcement learning teaches AI models to improve through experience. They receive rewards or punishments based on their results, making them smarter over time.

Imagine an AI that creates its own original content – from text and images to video and code. Generative AI identifies patterns in massive amounts of data and produces something entirely new.

Generative AI

Natural Language Processing

Natural Language Processing is the magic behind AI’s understanding of human language. It involves techniques like text classification and sentiment analysis, helping machines grasp our words and context.

Large Language Model

Large language models have the power to learn from vast amounts of text, enabling them to generate language and engage in meaningful conversations. They’re like language wizards in the digital realm.

AI models can surprise us with their abilities. They can write code, compose music, and even create fictional stories. It’s amazing to witness the creativity that emerges from their learning patterns.

Emergent Behavior

Neural Network

Neural networks are the brains behind AI. Inspired by the human brain, they learn patterns and make predictions. These interconnected networks drive the intelligence of AI systems.

Sometimes AI models can provide bizarre or nonsensical answers. These “hallucinations” occur due to the limitations of their training data and structure. AI still has some room for improvement!