Demystifying AI, ML, and DL: Unraveling the Intricacies of Modern Intelligence

Demystifying AI, ML, and DL: Unraveling the Intricacies of Modern Intelligence

AI vs. ML vs. DL: Key Differences Explained

AI (Artificial Intelligence)

  • AI = Artificial + Intelligence.

  • Artificial = Man-made or created by someone.

  • Intelligence = common sense or generalized algorithm etc. which acts like a human.

  • It was started in the 1950s by "Alan Turing".

  • The first AI was "Logic-based AI" (1950 - 1960).

  • The Second was "Rule-based AI" (1960 - 1970).

  • At this time there was a boom in technological advancement, which led to "Symbolic AI".

  • It is built with if else conditions.

  • Symbolic AI resulted in the emergence of Expert systems.

  • Symbolic AI when used in a specific domain is called Expert System.

  • These emulate the decision-making of a human expert.

examples are Chess players etc.

Expert System structure

  • Flaws in Expert Systems

  • Focused only on specific closed problems, like lung detection. chess players etc.,

  • It won't help in Fuzzy Logic, like predicting a dog in a picture.

So, expert systems got left behind. Later, ML came into the picture.


ML (Machine Learning)

  • ML = Machine + Learning

  • Machine = Robot or any other computer.

  • Learning = the process of training a computer program to make predictions based on input data.

  • It is a branch of "AI".

  • It uses statistical methods to find patterns in Input data.

  • No need to explicitly program.

  • "ML" is better than "Symbolic AI".

  • Needs lots of data.

  • learning = recognition of patterns in data.

  • No need to write rules like SymbolicAI just input lots of data, it will generate its own rules by the data.

  • ML is inspired from statistics.

If Machine Learning is doing great. Why need for Deep Learning?


DL (Deep Learning)

  • DL= Deep + Learning

  • Deep = Having many steps of calculations.

  • Learning = Recognizing patterns in data.

  • DL is also Machine Learning.

  • But algorithms are different.

  • DL is inspired by human biology but DL won't work as the human brain.

Why Deep Learning?

  • ML cannot do some things well enough.

  • In, ML Input features are provided.

  • But DL detects/extracts features on its own from the data.

  • In fuzzy logic, we don't know the features, but DL helps you to predict.

  • example: Predicting placement is done(Yes or No) from the data(Resume).

  • More layers increase the accuracy or efficiency of the model gradually.

  • More data = more performance of the model for DL. But ML stabilizes after a certain point.

DL is not used everywhere. It is used I working with large data.

If data = Images or videos or sequences. DL is used.

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