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”


    Take A Deeper Look at Deep Learning

    Image: Pixabay

    1. Deep learning is useful only for complex problems.

    2. Deep learning solves the feature engineering problem.

    3. People fear the ‘black box.’

    4. Deep learning technology has the ability to transfer learning

    5. Deep learning is good at logistic regression.

    6. Most deep learning projects involve gradient descent.

    7. Deep learning comes in many forms.

    8. Convolutional neural networks are one of most common types of neural networks.

    9. Deep learning is now better than humans at many tasks.


    3 things we learned from Facebook’s AI chief about the future of artificial intelligence

    Facebook’s chief AI scientist Yann LeCun

    “The International Data Corporation indicates global spending on AI systems is expected to hit $US77.6 billion in 2022, more than tripling the $US24 billion forecast for 2018.

    A key element in advancing the field of artificial intelligence, particularly when it comes to deep learning, will be ensuring that there’s hardware capable of supporting it.

    1. Machines have to get much better at power consumption in order for AI to improve.
    2. We’ll continue seeing AI advancements in smartphones in the near term before improvements appear elsewhere.
    3. Giving machines “common sense” will be a big focus for AI research in the next decade.”


    The State of Machine Intelligence 3.0

    Looking Forward

    “We see all this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks; there will be more data; there will be more people interested in learning these methods; and there will always be problems worth solving. We still need ways of explaining the difference between machine intelligence and traditional software, and we’re working on that. The value of code is different from data, but what about the value of the model that code improves based on that data?

    Once we understand machine intelligence deeply, we might look back on the era of traditional software and think it was just a prologue to what’s happening now. We look forward to seeing what the next year brings.”