Introduction To Artificial Intelligence, Machine Learning, Deep Learning and Gen-AI

In a world where everyone is talking about AI and using various AI tools to ease and complete their daily tasks. We are using it too much now in our daily life, whether it is ChatGPT, gemini, perplexity, or any other AI tool or model.

And we all have seen the birth of AI and AI apps and models with our eyes, and then using it for our daily tasks, I think this is the best time to learn about AI and its fundamentals. So let’s get started.

Artificial Intelligence (AI)

AI is a technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity, and autonomy (self-governance = the ability to choose one’s own path)

Applications and devices equipped with AI can see and identify objects, understand and respond to human language, learn from new information and experience, make more detailed recommendations to users and experts, and act independently, replacing the need for human intelligence or intervention ( a classic example is a self-driving car).

Before we deep dive into more, it is essential to understand the technologies on which Gen-AI tools are built: ML and DL.

Machine Learning

A simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years.

Directly underneath AI, we have machine learning, which involves creating models by training on algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.

There are many types of machine learning techniques or algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), K-nearest neighbor (KNN), clustering, and more. Each approach is suited to different kinds of problems and data.

But one of the most popular types of machine learning algorithm is called a neural network. Neural networks are modeled after the human brain’s structure and function. A neural network consists of interconnected layers of nodes that work together to process and analyze complex data.

Neural networks are best for tasks that involve identifying complex patterns and relationships in large amounts of data.

Deep Learning

Deep learning is a subset of ML that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Deep neural networks include an input layer, at least three but usually hundereds of hidden layers, and an output layer, unlike neural networks used in classic ML models, which usually have only one or two hidden layers.

Because deep learning doesn’t require human intervention, it enables ML at a tremendous scale. It is well suited for Natural language processing (NLP), computer vision and other tasks that involve the fast, accurate identification complex patterns and relationships in large amount of data. Some form of DL powers most of the AI applications in our lives today.

In deep neural network, multiple layer of nodes can extract meaning and relationships from large volumes of unstructured, unlabelled data.

Generative AI

Generative AI sometimes called as “Gen AI” refers to deep learning models that can create complex original content - such as long form text, high quality images, realistic videos or audio and more - in response to a user prompt or request.

At a high level, generative models encode a simplified representation of their training, and then draw from that representation to creat new works that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincised with the emergence of three sophisticated deep learning model types.

• Variational Encoders (VAEs)

• Diffusion Models

• Transformers

This is the general difference between AI, ML, DL and Gen-AI. In the next blog of this series I have explained more about How Gen-AI Works - In which we coverd Traning, Tuning and Generation, so make sure to read that blog too for a better and deeper understanding.

I hope you like this blog, so make sure to like, comment and share it with AI enthusiast friends and in the communities. And if not please do leave a feedback in comment below.

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