WDIS AI-ML Series: Module 1 Lesson 2: Introduction to AI, ML, DL, and Generative AI

In the last lesson, we discussed the definition of AI

Artificial Intelligence (AI) is giving machines the ability to think (and act) like humans.

But what about many other terms floating around — Machine Learning, Deep Learning, Generative AI, etc? So let us try to understand these and also look at some examples.

Artificial Intelligence

A broad field of computer science that is focused on creating thinking systems that can perform tasks that typically require human intelligence. The range of such tasks could be reasoning, problem-solving, perception, learning, language understanding, and decision-making. So in that way AI is a catch all phrase of everything — Rule Based Program (If Else conventional programming), Expert Systems, Machine Learning, Deep learning, Generative AI, Natural Language Processing (Giving Machines an ability to understand Language), Computer Vision (Giving Machines an ability to see), Robotics (Giving Machines an ability to act).

Broadly Artificial Intelligence can be achieved in two main ways.

1. Rule Based : The first approach is to build a set of rules/ conditions or hand-made models to mimic intelligence. These rules/ models are built by humans using their experience. The machines behave like intelligent being because of the knowledge transfer (Stockfish chess engine or Computer vision-based tracking etc)
2. Machine Learning: The second approach or ML approach is data-driven. We take a flexible model and automatically fine-tune its parameters using the data. The model learns from data on its own and behaves in an intelligent way (Alpha Zero chess engine)

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Machine Learning (ML)

Machine Learning (ML) can be considered a class of learning techniques used to achieve Artificial Intelligence (AI) goal. ML focuses on creating a thinking machine by feeding machines data. Instead of relying on explicit instructions, as in Rule Based Program, ML Models learn patterns and relationships from labeled or unlabeled data, enabling them to make predictions or decisions based on new data. Examples of machine learning techniques include decision trees, support vector machines, and neural networks.

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Deep Learning (DL)

A subfield of machine learning that focuses on training artificial neural networks with multiple layers, hence called Deep neural networks to learn complex representations of data. Deep learning has gained widespread attention and popularity due to its ability to automatically learn hierarchical features from raw data, such as images, text, and audio. Deep learning has achieved remarkable success in tasks such as image recognition, natural language processing, and speech recognition, often surpassing human performance in certain domains.

Note In Machine Learning, Feature Extraction is done by humans while in Deep Learning Feature Extraction is done by Machines themselves? The feature what? Feature extraction. Let me explain with a business application.

Suppose I want to predict at what Price a house in market will sell at.

What I do I need to be able to answer this?

1. Configuration of the house such as #Bed Rooms, #Bathrooms, Sq Ft etc.
2. Prices of similar house (Comps)

#1 — Configuration or attribute of a house in the above example, in Machine learning is called ‘Feature’ by Data Scientists. Identifying which features are important is called ‘Feature Extraction’.

In Machine Learning, It is Humans (Data Scientists / Business Experts) who selects feature. While in DL, Feature Extraction is done by machine.

Fig 1: Machine Learning — Feature Extraction is done by Human

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Fig 2: Deep Learning — Feature Extraction is done by Neural Network

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‍But if Machines can do Feature selection or Feature Engineering then why let Humans do this?

‍We will answer that later. But before we need to understand a few more differences between ML and DL models.

So coming back to the question ‘if Machines can do Feature selection or Feature Engineering then why let Humans do this?’

It is because DL requires a really large amount of data as compared to ML, hence much more time intense. The DL technique is also ‘Black Box’ approach i.e. we do not know what features Machine is selecting and why?

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Generative AI (Gen AI)

Generative AI is a subset of Deep Learning (DL). Generative AI refers to AI systems that can generate new data that are similar to, but not the same as, the training data. For example — Letting Machine write Texts, generate Images, Create Music etc.‍

This is why most textbooks use this Venn diagram to explain the relationship between AI, ML, DL, and Generative AI. Now this all makes sense.

Fig 3: Gen AI is a subset of DL. DL is a subset of ML. ML is a subset of AI.

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Glossary of Terms used

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