Tech Corner

What is Generative AI? The Ultimate guide (2024)

Taneshqa S.
.
last edited on
.
March 12, 2024

Table of contents

Automate your business at $5/day with Engati

REQUEST A DEMO
Switch to Engati: Smarter choice for WhatsApp Campaigns 🚀
TRY NOW
generative-ai-guide

What is generative AI?

Generative AI is an artificial intelligence technology that creates ‘new’ content by replicating patterns learnt from comprehending massive datasets.

If you’ve been on the internet for the past few months, there’s a good chance you’ve already come across Midjourney art.

And in case you haven’t, here you go:

Mona Lisa imagined as an influencer by Midjourney, an AI image generative model(Source: Midjourney,Reddit)

Amazing right? . Well we’re just scratching the potential with generative AI.

To better understand how Generative AI is used and why it is, let's learn its origin story.

The rise of Generative AI

Before the viral Midjourney and ChatGPT prompts, existed the funny and trendy face filters.

Remember the trending Snapchat filters like the rainbow vomit and dog face? This was the consequence of the trending Computer Vision field which occupied centre stage before NLP, Conversational and Generative AI stepped in.

Remember the trending Snapchat filters (source: Medium) ?

The hype surrounding tech terms ML i.e. Machine Learning and AI i.e. Artificial Intelligence may be new, but these concepts certainly aren’t.

Coined by pioneers of the Computer Science field in the 1950s, ML and AI have been studied ever since. It was only around 60 years later, with major breakthroughs in Deep learning that the still ongoing AI boom began. This subsequently led to significant development in all the sub fields of AI, such as Computer Vision (CV), Natural Language Processing (NLP), Speech Recognition and many many more, including the topic at hand- Generative AI.

Source: The 2010s: Our Decade of Deep Learning

Propelled by the progress in Natural Language Processing, Conversational AI and the work of OpenAI in developing GPT-2, the time for Generative AI to shine had finally arrived. And it hasn’t left the spotlight ever since.

Generative AI examples : DALL-E, nightcafe ai, ChatGPT and more

Generative AI can be seen in many shapes and forms. Here are just some examples that demonstrate how Generative AI can be utilized to generate content in various formats.

1. Text Generation

  • Chatbots such as ChatGPT, BARD and auto complete tools generate text that could assist with creative and content writing, grammar checks or even translate text from a language to multiple others.

2. Image and Visual Generation

  • Art and Design: DALL-E, Midjourney, Nightcafe and many more image generative ai examples can create digital artwork, including paintings, illustrations, and graphic designs.
  • Deep Dream: Google's Deep Dream generates surreal and artistic images based on existing ones.
  • Photo Enhancement: AI-powered Photoshop can extend and enhance images based on the given image.
  • Deepfake Technology: AI can create convincing videos by superimposing one person's face onto another's in a video clip.

3. Content Recommendation

  • Platforms like Netflix and Spotify use generative algorithms to recommend movies, shows, music, and playlists. Other examples of personalized content recommendations include relevant news pieces, books and other media, products on e-commerce platforms to name a few.

4. Medical Imaging

  • Image Enhancement: Generative AI enhances medical images for diagnosis and treatment planning.
  • Disease Simulation: AI simulates disease progression for research and training.

5.  Gaming and Entertainment

  • Character and Level Generation: AI generates characters, levels, and scenarios in video games.
  • Natural World Simulation: AI simulates ecosystems and landscapes in game environments.
  • World Building and Computer-Generated Imagery (CGI): Generative AI assists in generating realistic visual effects and animations in movies and games.

How does generative AI work?

generative-ai-ml-ai
How Generative models fit in reference to other domains (Source: Wikipedia )

Generative AI makes things look effortless. But underneath the surface lie multiple technologies that give it its ability to generate new and unique content.

At the base of Generative AI like many other AI concepts, lies Machine learning (ML). Machine learning involves a system that processes and learns from a dataset to perform a certain function, which also improves its performance over time.

This ML foundation is combined with the Complex Neural network architecture, an architecture that utilizes a network made of multiple layers containing many nodes that enhances the system’s ability, enabling it to grasp intricate patterns and relationships present within data, which can be seen in CNN and RNN.

Now that we’re familiar with the basics of Generative AI, let’s explore its two most popular models-   Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs).

Generative Adversarial Networks (GANs) Variational AutoEncoders (VAEs)
• GANs are a specific type of generative AI model designed for creating new data instances, like images or text. • VAEs are another type of generative AI model, primarily used for learning meaningful latent representations of data.
• GANs consist of a generator and a discriminator network that compete against each other. • VAEs consist of an encoder and a decoder network.
The generator tries to create realistic data, while the discriminator tries to distinguish real data from generated data. The encoder maps data into a lower-dimensional latent space, and the decoder maps points in the latent space back to the original data space.
• GANs learn through an adversarial process where the generator improves its output by fooling the discriminator. • VAEs aim to capture the underlying structure of the data and can be used for tasks like data compression, denoising, and generating new data instances.
• GANs excel in generating high-quality data that closely resembles the training data, making them useful for tasks like image generation and style transfer • VAEs introduce randomness to the latent space, allowing them to generate diverse outputs while maintaining continuity in the data space.

Where can generative AI be utilized / Industries / Generative AI industry examples

examples-of-generative-ai-in-different-industries

1. Travel, Tourism, and Hospitality:

  • Travel Planning: Can generate personalized travel itineraries and suggests activities.
  • Language Translation: AI-powered translation services enhances communication for tourists.

2. Real Estate:

  • Virtual Property Tours: AI-generated virtual tours provide immersive property viewing experiences.
  • Personalized Recommendations: Chatbot can suggest properties based on user preferences and budget.

3. BFSI (Banking, Financial Services, and Insurance):

  • Risk Assessment: AI can analyze financial data to assess risks and recommend investment strategies.
  • Chatbots: Generative AI-powered chatbots offer personalized financial advice and assist with inquiries.
  • Fraud Detection: AI can identify fraudulent activities in real-time by analyzing transaction data.

4. E-commerce:

  • Personalized experience: AI generates product recommendations based on browsing and purchase history.
  • Assistant: Customers can track discounts thanks to chatbots delivering apt messages, and even use the chatbot’s help when it comes to checking out their cart
  • Lead generation and ensuring purchases: Chatbots apart from assisting consumers, also prove to be of great help to business owners by generating leads, reducing cart abandonment and providing consumer support at all times.

5. Education:

  • Interactive Learning: Generative AI creates interactive educational content and simulations.
  • Language Learning: Can assist in language practice and conversation simulations.

6. Healthcare:

  • Patient Engagement: AI-generated content educates patients about medical conditions and treatment options.
  • Booking and Scheduling Assistance: Chatbots can assist patients, doctors and staff alike with managing medication, appointments and other details.

7. Gaming and Entertainment:

  • Design, Ideation and Assistance: AI serves as the ultimate assistant, helping with scripts, character narratives, world building, visuals and anything needed to build a more compelling and immersive gaming experience.
  • Personalized Recommendations: AI suggests movies, music, and news based on user preferences.

Benefits of Generative AI

Generative AI has 3 core  which work together to provide a world of applications.

1. Generation

  1. Content Generation: Generative AI can create a wide range of content such as text, images, audio, and even videos, that can be used for a wide range of applications such as creative endeavors, content production, and marketing.
  2. Data Augmentation: Generative AI can create synthetic data, which is useful for training machine learning models, especially in cases where obtaining large, real datasets is challenging.

2. Personalization

Generative AI can generate content tailored to individual preferences, leading to more personalized user experiences in various applications, from recommendations to marketing.

3. Innovation

Generative AI fosters innovation by providing novel content and insights, pushing the boundaries of what is possible in various domains.

  • Creativity Enhancement: It can assist creative professionals by generating ideas, designs, and suggestions, amplifying human creativity and innovation.
  • Problem Solving: It can be applied to problem-solving tasks, such as generating solutions or optimizing processes, leading to improved decision-making and efficiency.

The overall effect of utilizing Generative AI

Automation and Cost Savings

It enables the automation of tasks involving content creation, data synthesis, and even decision-making thereby streamlining operations, reducing manual labor, operational costs, and resources needed while also increasing efficiency.

Scalability

It enables the generation of vast amounts of content or solutions quickly and consistently, making it suitable for applications that require scalability.

Enhanced User Engagement and Experience

Combined with the engagement offered by applications like chatbots and virtual assistants such as Alexa, Siri and others, and Personalisation i.e. generating plans, recommendations suited to particular groups or individuals, utilizing Generative AI ultimately elevates user experience.

Challenges of using Generative AI

1. Misleading content generation

Generative AI is capable of producing misleading, harmful, or offensive content, raising concerns about responsible use and potential negative impacts on society.

2. Bias and Fairness

Inherent biases in training data can lead to biased outputs, perpetuating stereotypes and discrimination in generated content.

3. Quality Control and Authenticity

Maintaining consistent content quality and verifying the authenticity of AI-generated content are essential for user satisfaction and trust.

4. Privacy and Security

The need to gather large datasets for training poses challenges to preserving data privacy and preventing unauthorized access.

5. Regulation and Over-reliance

Rapid advancement of generative AI requires balancing regulatory frameworks with innovation, and avoiding over-reliance on AI for creativity while maintaining human contribution.

What’s next for Generative AI

Apart from the obvious need to overcome challenges to ensure ethical and responsible usage, Generative AI’s future shows great promise and potential.

More personalization, More formats.

Generative AI will create personalized content, from text to music, enhancing engagement and experiences.

Need for Regulation and ethical guidelines

There’s a dire need to address the bias, ensure privacy and responsible use, which can be achieved through developing ethical guidelines and regulatory frameworks.

Developing ethical guidelines and regulatory frameworks will address bias, privacy, and responsible use.

Transforming Industries

Further collaboration between AI and humans will propagate progress, spark innovation and heighten creativity. This combined with generative AI’s potential to streamline process in domains, and to eventually reshape industries.

Taneshqa S.

Taneshqa J. Singh is a content writer at Engati who believes in repeated revision, frequent feedback complete with a generous dose of empathy are the key ingredients to good content and ensuring a delightful reading experience. Apart from the usual writing, frequent book or movie discussions and occasional coffee indulgence, Taneshqa enjoys keeping up with content trends, design patterns and the latest in tech.

Close Icon
Request a Demo!
Get started on Engati with the help of a personalised demo.
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
*only for sharing demo link on WhatsApp
Thanks for the information.
We will be shortly getting in touch with you.
Oops! something went wrong!
For any query reach out to us on contact@engati.com
Close Icon
Congratulations! Your demo is recorded.

Select an option on how Engati can help you.

I am looking for a conversational AI engagement solution for the web and other channels.

I would like for a conversational AI engagement solution for WhatsApp as the primary channel

I am an e-commerce store with Shopify. I am looking for a conversational AI engagement solution for my business

I am looking to partner with Engati to build conversational AI solutions for other businesses

continue
Finish
Close Icon
You're a step away from building your Al chatbot

How many customers do you expect to engage in a month?

Less Than 2000

2000-5000

More than 5000

Finish
Close Icon
Thanks for the information.

We will be shortly getting in touch with you.

Close Icon

Contact Us

Please fill in your details and we will contact you shortly.

This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
This is some text inside of a div block.
Thanks for the information.
We will be shortly getting in touch with you.
Oops! Looks like there is a problem.
Never mind, drop us a mail at contact@engati.com