Fundamentals of Generative AI

In this article, we will set a strong foundation with the fundamentals of Generative AI. Generative AI is Generative Artificial intelligence, the innermost sub-field of Artificial Intelligence. The Venn diagram shows that Generative AI is the core layer residing under Deep Learning. So, we can say that Generative AI is a sub-field of Deep Learning.

What is Generative AI?

Generative AI is an artificial intelligence system which internally based on deep neural networks that focus on generating new content based on a variety of inputs. Output of Generative AI model are:

• Text
• Images
• Audio
• Video
• Music
• Synthetic Data
• Code
• 3D objects

Generative AI UseCases

Use cases of Generative AI are divided into five broad categories. These five categories are further divided into subcategories. We will learn them in detail.

1. Text Generation

Various outputs created by Generative AI under text Generation are:

3. Audio Generation

Various outputs created by Generative AI under audio Generation are:

4. Video Generation

Various outputs created by Generative AI under video Generation are:

5. 3D Generation

Various outputs created by Generative AI under 3D Generation are:

How is Generative AI possible now?

We have known Artificial Intelligence since 1956, when John McCarthy an American Computer scientist at the Dartmouth Conference adopted the word “Artificial Intelligence” for the first time. It was in the year 2006 when AI came into the business when big giants like Facebook, Google, and Amazon started leveraging Artificial intelligence to their advantage after so many up-downs. To learn more about the history of AI click here.

Generative AI started leveraging in the year 2022 and Open AI created a huge buzz across the globe with its tremendously powerful Generative AI application ‘ChatGPT‘ with exceptional outcomes.

The big question arises, How is Generative AI possible now?

There are three reasons to explain the same.

1. Large Datasets

There are large and diverse datasets available to train and learn patterns, correlations, features, and characteristics by Machine Learning models.

2. Computational Power

The Computational power has increased so much with the advanced hardware and GPU(Graphical Processing Units). Training deep learning models needs huge computation power, time, and complexity. Also, cloud computing makes it highly approachable and achievable.

3. Ingenious DL Models

There are several ingenious Deep Learning(DL) models available now that make the task easier to achieve.

• Generative Adversarial Networks (GAN)
• Reinforcement Learning from Human Feedback (RLHF)
• Transformers
• Transfer Learning (Pre-trained state-of-the-art models)

Challenges with Generative AI

Generative AI is still in its early stages and there is lots of scope for further improvement. Let’s list down some challenges Generative AI is facing in current scenarios.

• Large Computer Infrastructure
• Lack of high-quality data
• Model training and response speed
• Output Authenticity
• Information Security issues

Conclusion

This is just a brief about Generative AI fundamentals. There is a lot to discuss and elaborate on about the technology behind Gen AI which we will discuss in future articles.

Stay Tuned!!

Go through our Data Science with Python series to learn about basics of Data Science.

Data Science with Python: Basic Data Structures

Keep learning and keep implementing!!