TIMELINE PIECE 1: ARTIFICIAL INTELLIGENCE IN ITS DIFFERENT FORMS

You use Artificial Intelligence (AI) whenever you ask Siri, Alexa, or Bixby to carry out a task or do something on your device. It is that basic.

However, in its current state, AI has transcended the idea and early forms of computer algorithms that laid the foundation for programming to develop into different categories – being progressive.

Artificial Intelligence (AI) can be categorized into various types based on its capabilities and functionalities. These categories represent the diversity of AI applications, each addressing specific challenges and tasks. Advances in AI continue to drive innovation across various domains, making AI a transformative force in technology and industry. Common categories of AI include:

1.???? Narrow or Weak AI

Narrow AI systems are designed and trained for a specific task or a narrow set of tasks.

E.g., Virtual personal assistants like Apple's Siri, Amazon's Alexa, or chatbots are used in customer service. These systems excel in specific domains but lack general intelligence.

2.???? General or Strong AI

These are AI systems with the ability to understand, learn, and apply knowledge across diverse tasks, similar to human intelligence.

True general AI does not yet exist. Current AI systems are primarily narrow AI, designed for specific applications. Achieving true general AI is a long-term goal in AI research.

3.???? Machine Learning (ML)

A subset of AI that enables systems to learn and improve from experience without explicit programming.

E.g., Image recognition algorithms, recommendation systems (like Netflix or Spotify recommendations), and natural language processing (NLP) applications.

4.???? Deep Learning

Deep learning involves a subset of machine learning that involves neural networks with multiple layers (deep neural networks), enabling the model to learn complex features and representations.

E.g., Deep neural networks are used in image and speech recognition, language translation, and playing strategic games like Go (e.g., AlphaGo).

5.???? Supervised Learning

This is a machine-learning approach where the model is trained on a labeled dataset, with input-output pairs provided during training.

E.g., Handwriting recognition, email filtering, and image classification.

6.???? Unsupervised Learning

This is a machine-learning approach where the model is trained on unlabeled data, allowing it to find patterns and relationships on its own.

E.g., Clustering algorithms, dimensionality reduction, and some types of anomaly detection.

7.???? Reinforcement Learning

Machine learning paradigm where an agent learns by interacting with its environment and receiving feedback in the form of rewards or penalties.

E.g., Training a computer program to play games, such as AlphaGo, or reinforcement learning algorithms used in robotics.

8.???? ?Natural Language Processing (NLP)

A field of AI focused on enabling computers to understand, interpret, and generate human language.

E.g., Language translation services (Google Translate), sentiment analysis, chatbots, and virtual assistants.

9.???? Computer Vision

This is a Field of AI that enables machines to interpret and understand visual information from the world, such as images and videos.

E.g., Facial recognition systems, object detection in images, and medical image analysis.

10.? Expert Systems

These are AI systems designed to emulate the decision-making abilities of a human expert in a specific domain.

E.g., Diagnostic systems in healthcare, financial forecasting systems, and expert systems used in various industries for decision support.

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