Algorithm Defined

By Jim Moore

The word algorithm itself is derived from the 9th century mathematician, Algoritmi. A partial formalization of what would become the modern concept of algorithm began with attempts to solve the decision problem. The concept of algorithms has existed for centuries. Greek mathematicians used algorithms for finding prime numbers, and for finding the greatest common divisor of two numbers.

An algorithm can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

📖 - Jim Explains and easy to understand.

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)