What is the Difference Between AI and Machine Learning

Understanding the definitions and differences of ML and AI can be daunting, so I’m always on the look-out for well written content. This post from codementor.io does a really nice job, and I have attempted to further summarize below. Check out their site for the full post.

“AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data.

Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy.

The Four types of Artificial Intelligence are:

  • Reactive AI
    • Lacks historical data
    • Completely reacts to a certain action
    • Reinforcement learning where a prize is awarded for any successful action and penalized vice versa
  • Limited Memory
    • Past data is kept adding to the memory

    Theory of Mind

    • Yet to be built as it involves dealing with human emotions, and psychology


    • The future advancement of AI
    • Machines could be conscious, and super-intelligent

    Three of the most common usage of AI

    1. Computer Vision such as Face Recognition
    2. Natural Language Processing like Amazon’s Alexa or Apple’s Siri
    3. Self-driving cars

  • What is Machine Learning?
  • Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions.
  • In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves.
  • Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success.
  • A sub-field of Machine Learning is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain.
  • Machine Learning could be subdivided into three categories –
  • Supervised Learning
    • Both the input feature and the corresponding target variable is present
  • Unsupervised Learning
    • Only the input features are present
      The algorithms need to find patterns
  • Reinforcement Learning
    • Rewarded with a prize for every correct move and penalized for every incorrect move”


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