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AI Part 1: the basics

What is AI

First of all, let’s say what AI stands for Artificial Intelligence.

AI indicates the ability of a computer or a machine to perform tasks that are usually done by humans, such as reasoning, learning, problem-solving, and so on.

AI can be classified into different types, such as machine learningdeep learningnatural language processing, and computer vision.

How does it work?

AI works by using algorithms and data to learn how to perform tasks that normally require human intelligence. Each type of AI has its own methods and applications, but they all rely on data and algorithms to achieve their goals.

Machine learning

It is a branch of AI that enables machines to learn from data and improve their performance without explicit programming. Machine learning algorithms can be divided into supervised learningunsupervised learning, and reinforcement learning.

Supervised learning

It is when the machine learns from labeled data, such as images with captions or emails with spam or not-spam labels.

The machine tries to find patterns in the data and make predictions based on them.

Unsupervised learning

It is when the machine learns from unlabeled data, such as text documents or customer transactions.

The machine tries to find hidden structures or patterns in the data without any guidance. For example, an unsupervised learning algorithm can learn to cluster similar documents or segment customers based on their behavior.

Reinforcement learning

It is when the machine learns from its own actions and feedback from the environment.

The machine tries to find the best actions to take in order to maximize a reward or minimize a penalty. For example, a reinforcement learning algorithm can learn to play a video game or control a robot.

Deep learning

It is a subset of machine learning that uses neural networks to learn from complex and high-dimensional data.

Neural networks are composed of layers of interconnected nodes that process information and pass it on to the next layer. The nodes can learn to extract features and representations from the data that are useful for the task at hand. For example, a deep learning algorithm can learn to generate realistic images or understand natural language.

Natural language processing

It is a branch of AI that deals with the interaction between machines and human languages.

Natural language processing algorithms can perform tasks such as speech recognitiontext analysismachine translationquestion answering, and text generation.

For example, a natural language processing algorithm can learn to transcribe speech into text or generate summaries of articles.

Computer vision

It is a branch of AI that deals with the analysis and understanding of visual information.

Computer vision algorithms can perform tasks such as face detectionobject recognitionscene segmentationoptical character recognition, and image synthesis.

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