Gen AI for Beginners: Understanding its Basics

With the emergence of Generative AI, the world is witnessing an evolution in how various forms of content are created. This technology is revolutionizing how content is created and utilized across industries.

This article covers all the basics of Generative AI including its definition, how it’s different from traditional AI, basic functionalities of Gen AI, tools, and platforms, and how to begin with Gen AI.

Generative AI: Definition

Generative (Gen) AI is a subset of artificial intelligence that creates content like text, photographs, images, and videos. Unlike traditional AI which recognizes patterns from available data, generative AI creates new and original content.

Gen AI works by learning from a large dataset and then predicting what’s the next piece of information in a sequence, for example the next word of a sentence or the next pixel of an image.

It’s important to learn what is Gen AI and what it has to offer over traditional AI. Generative AI is impacting the jobs market and creating new responsibilities in almost every major sector.

Readers who wish to have an in-depth understanding of Gen AI can enroll in the Applied Gen AI course offered by Interview Kickstart. In addition to providing a conceptual understanding of Generative AI, the course teaches how to train and build applications with large language models (LLMs).

Subsequently, they can opt for Advanced Gen AI course to uplevel their Gen AI skills.

How Gen AI is Different from Traditional AI

• Whereas Traditional AI analyzes large datasets to interpret patterns and provide recommendations, Generative AI creates new content like articles, musical notes, program codes, photos, graphics and videos.
• Traditional AI produces outputs on the basis of available inputs but Generative AI produces new outputs on the basis of a prompt.
• Examples of Traditional AI are recommendation systems of videos in YouTube and Netflix and products in Amazon. Examples of Generative AI are chatbots like ChatGPT4, Sora AI, and Google Gemini.

How Gen AI Works

As we know by now, Generative AI is a revolutionary type of AI that can create various types of original content. A Generative AI model works in two phases.‍

Training Phase:
Generative AI first gets trained on large data sets. For example, if it’s required to create images of airplanes, it first learns from thousands of images of airplanes it is fed with as part of the training phase.

While being trained, it looks for patterns like cockpits, wings, flaps, jet engines, and elevators of an airplane including their shapes and dimensions.‍

Generation Phase:
Once trained, AI can create images based on images and patterns of airplanes it had recognized in the training phase. The images created are new as AI makes necessary tweaks in the data it has been trained with.

Generative AI Models

One of the things that you should familiarize youself with to get started with GenAI, is to learn its models. This is because understanding these models is crucial as they form the backbone of how GenAI systems operate.

Generative Adversarial Networks (GANs)

AI models like ChatGPT4 and Sora AI are based on Generative Adversarial Networks (GANs). GANs work on deep learning architecture to generate more authentic data from a given training dataset.

GANs have two components: generator and discriminator. A generator produces new images, and a discriminator compares the new image with the image dataset it has been trained with.

The components train together and improve overtime until the generator produces a new image.

VAEs (Variational Autoencoders)

A Variational Autoencoder (VAE) learns to compress these pictures into a secret code, which is a set of numbers that describes each picture of an airplane part. This procedure of picture compression is called Encoding.‍

These numbers form a Latent Space where each point represents an airplane picture and points resembling each other are placed closer to each other.

Now, if you feed VAE with one of these secret codes, it will quickly convert the secret code into an airplane picture which is completely unique and in all likelihood, never created before. This conversion process is called Decoding.

VAE performs decoding by learning from various patterns and variations in the data. ‍

Applications of Gen AI

Generative AI is being used in a wide range of applications in different fields. It is majorly used by creators in the art and entertainment industry to generate art and royalty-free music.

In marketing, it’s proving to be a good helping hand for creating compelling ad copies.

Gen AI in Healthcare is helping with administrative tasks. Specialists just have to give patient-specific information to generate effective bookkeeping.

Also read: AI in Healthcare: Examples, Benefits, and Challenges

In Entertainment, Gen AI creates compelling movie scripts and generate enhanced special effects.

Tools and Platforms for Gen AI

Before you start working on Gen AI applications, make yourself familiar with some popular and beginner-friendly tools.

• TensorFlow: Is a free and open-source application developed by Google. It can be used in various Gen AI applications including training of neural networks.
• PyTorch: Is a flexible and easy to use open-source learning library developed by Meta primarily for research and development. ‍
• RunwayML: With the principles of machine learning at its backend, this video editing tool helps to quickly edit and export videos in a variety of formats.  ‍
• Artbreeder: Uses Gen AI in various image editing applications like combining of images, creation of hybrid images, and iteration of images.   ‍
• AlphaCode: Write codes, resolve errors and suggest optimal programming solutions to empower programmers.‍
• GPT-4: Creates texts, graphics and images with ease. All it takes is one engineered prompt for it to start working its magic.‍
• Github Copilot: Helps developers working in Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments to resolve coding-related issues.‍
• Dall E2: Create new images, upload existing ones to modify them with a targeted prompt, and do a lot more with this OpenAI application. ‍
• Type Studio: Edit videos, add subtitles and captions to existing videos, convert spoken words into text with this video editing tool.‍
• Designs.AI: Easily create and share voice-overs in 15+ languages. Also create videos, social media posts, and logos with this Gen-AI powered tool ‍

Steps to Begin With Gen AI

Follow these practical steps to commence your Gen AI journey:

Step 1: Learn the Basics
Learn the basics of Gen AI and machine learning by enrolling in Advanced Gen AI and Applied Gen AI courses offered by Interview Kickstart. Read books and watch videos to gain fundamental knowledge of Gen AI. Access online tutorials to gain an understanding of coding and model training

Step 2: Set up the Environment
Set up the environment to create work on machine learning projects with tools like Virtualenv, Docker, or Google Colab. Also, install libraries like Tenserflow and Pytorch.

Step 3: Hands-on-Practice
Start by working on simpler projects like generating images with GANs. Train and test your models with  publicly available datasets.  ‍

Step 4: Join Communities
Join online forums on Reddit and StackOverflow to seek answers to enhance your knowledge. Also take mock interviews, attend bootcamps, webinars and conferences to build your network.

FAQs: Gen AI for Beginners

Is Gen AI easy to learn?
Anyone with an interest in AI can learn Generative AI. Because of its potential use across industries like healthcare, music and entertainment it has become popular among data scientists, engineers and tech enthusiasts.‍

How to learn Gen AI?
Here are the steps you can follow to start learning Gen AI.

• Gain thorough knowledge of Gen AI and machine learning fundamentals
• Learn Python and R programming and gain knowledge of open-source libraries like Tenserflow and Pytorch.
• Gain hands-on experience with boot camps and live projects.
• Pursue a tech interview prep course that prepares candidates to job interviews with top IT companies in the world

Does Gen AI require coding?
To develop Gen AI applications one requires a thorough understanding of Python programming language, OOPS concepts, and data structures.

What is a prompt in AI?
Prompts are inputs that a user or a program gives to an LLM application like GPT-4 or Gemini. The model interprets the response and generates relevant output.

Related topics:

• ‍The Impact of Generative AI on Big Data: A Transformation in Data Science and Engineering‍
• Enhancing Data Quality and Variety through Generative AI Techniques‍
• Navigating the Ethics of Generative AI in Data Engineering and Science‍
• Top 11 Commonly Used Generative AI Tools in 2024