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Learning from Data
AI learns from the data provided to it. During the model training phase, the AI model adjusts
itself based on data to learn how to perform tasks and make predictions. It also finds patterns in
the data. For example, AI learns to recognise cats in photos by analysing thousands of images
labelled as “cat.”
Decision Making
After learning from the data, the AI makes decisions or predictions. These decisions are based
on the patterns it detected during the training phase. For example, a recommendation system
decides which film to show you next based on your past viewing habits.
Improvement
AI systems improve over time by receiving feedback and analysing results. They retrain or update
their models using new data or corrections. For example, a self-driving car improves its driving
decisions as it encounters more traffic situations and receives updates.
Types of AI
AI encompasses various levels of intelligence and capabilities that artificial intelligence systems
may possess. These categories help us classify AI according to its advancement in learning,
thinking, and performing tasks that range from simple to highly sophisticated systems. There are
three main types of AI.
Types of AI
Narrow AI General AI Super AI
Narrow AI
Narrow AI, also known as Weak AI, is designed for specific tasks or a narrow range of tasks. It cannot
think or learn beyond its programmed functions and operates under predefined conditions. It
does not possess consciousness or true intelligence. For example, voice assistants like Alexa, spam
filters, Google Maps, facial recognition systems, chatbots and recommendation systems. Narrow AI
focuses on excelling in a particular area of expertise and is the most common type of AI used today.
General AI
General AI refers to an AI system that possesses human-level intelligence, enabling it to understand,
learn, and apply knowledge across a wide range of tasks, similar to humans. It is also known as
Strong AI.
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