AI Artificial Intelligence

Artificial Intelligence (AI)

Artificial intelligence techniques, though diverse, all fundamentally rely on data, algorithms, and computational power. AI systems learn and improve through exposure to vast amounts of data, identifying patterns and relationships that humans might miss. This data serves as the training material, the quality and quantity of which are crucial for the AI's performance

As mentioned earlier, AI isn't a single technology but a broad field encompassing several key areas:

Machine Learning (ML): This is a type of AI where systems learn from data to identify patterns and make predictions or decisions without direct programming. Imagine teaching a computer to recognize a bird by showing it thousands of bird pictures; it learns what a bird looks like on its own.
Deep Learning (DL): A subfield of ML, deep learning uses artificial neural networks with many layers (hence "deep") to learn from data. These networks are inspired by the structure of the human brain and are particularly good at complex tasks like image and speech recognition.
Natural Language Processing (NLP): NLP enables computers to understand, interpret, and generate human language. This is what powers voice assistants like Siri and Alexa, translation services, and chatbots.
Computer Vision: This area allows computers to "see" and interpret visual information from the world, such as images and videos. It's used in everything from facial recognition to self-driving cars.

🎥 - How AI Really Works (No Math, Just Logic)

The Future of Quantum Algorithms in Machine Learning (QML)

When will AI reach it's potential?

We may be asking this question for a long time.