Artificial Intelligence, Machine Learning and Deep Learning – but what is the difference?

The terms AI, ML, and DL are often used interchangeably, but have you ever wondered whether a chatbot is an example of AI, ML, or DL? Let’s explore this as we read on.

The discussion on AI began as early as 1950s, and after years of research and development, we are now in an era where we use it daily. When we hear the word AI, the first image that often comes to mind is that of a robot, a portrayal frequently seen in movies like “The Matrix,” “Terminator,” “Star Wars,” and many others.

AI will eventually look back at humans the way we look at fossils!

Artificial Intelligence: It Does Not Think, It Calculates!

  • AI does not replace human decisions; instead, it adds value to human judgment.
  • The model is fed with data, and AI uses that data to fulfill our requirements.
  • An example of AI is the auto-correct feature in our keyboard. If I mistakenly type “Alkohol,” the AI goes through its dictionary and corrects the spelling to “alcohol.”

Machine Learning: It Keeps Learning!

  • It is a subset of AI.
  • ML does not require extensive datasets; it learns through trial and error, continuously improving as it acquires new data.
  • It performs repetitive calculations in split seconds.
  • Netflix uses machine learning algorithms to analyze user data, including viewing history, ratings, and interactions, to identify patterns and predict which shows or movies to recommend.

Deep Learning: Machine Learning on a Grand Scale

  • Deep learning mimics the neural arrangement of the human brain, performing calculations in a neuron-like structure.
  • Unlike traditional machine learning, deep learning involves multiple layers of computation.
  • As data volumes grow, the demand on resources escalates, necessitating deep learning models to manage the extensive data and resource requirements efficiently.

A Chatbot can be an example of AI, ML or even DL:

  • A basic chatbot represents the foundational principles of AI, operating on predefined rules and responses.
  • A chatbot that learns from interactions demonstrates the application of machine learning, evolving its responses based on user engagement and data patterns.
  • An advanced chatbot with sophisticated language understanding leverages deep learning techniques to interpret complex language nuances and context, enabling more natural and human-like interactions.
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