Gen Ai Introduction to Generative AI to the world

eading: What is Generative AI?
Text: A brief description of Generative AI, explaining how it creates new content like text, images, audio, and video by learning from patterns in data.
Key Points:
Uses machine learning models like GANs, transformers, and variational autoencoders.
Generates human-like content.
Applied in various domains such as art, music, and writing.
Image/Diagram: AI neural network generating data.
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1. G e n e r a t iv e A I S.Sachin Ramco Institute of Technology

2. G e n e r a t iv e A I G P T , G A N a n d D i ff us i o n M o d e l s A pp li c a t i o n s o f G e n e r a t iv e A I E m e r g i n g T r e n d s , L i m i t a t i o ns , P o t e n t i a l A h e a d F o r W if i u s e G r ee n lin e

3. G e n e r a t i v e A I Definition Generative AI refers to a set of artificial intelligence methodologies that can produce novel content that resembles the training data they were exposed to. F o r W if i u s e G r ee n lin e

4. G e n e r a t i v e A I G e n e r a t i v e P r e - T r a in e d T r a n s f o r m e r ( GP T ) A Generative-Pre-Trained Transformer is a kind of t r a n s f o rm e r s m o d e l d e v e l o p e d b y O p e n A I f o r n a t u r a l l a n g u a g e p r o c e ss in g t a s k s Ge ne r a t i v e r e f e r s t o t h e m o d e l ’ s a b ili t y t o g e n e r a t e text Pre-Trained refers to models training process c o n s i s t in g o f t w o s t a g e s P r e - T r a i n i n g : M o d e l is t r a in e d o n a l a r g e c o r p u s o f t e x t d a t a , w h e r e t h e o b je c t i v e is t o p r e d ic t n e x t w o r d in a s e n t e n c e F i n e - t u n i n g : O n c e t h e m o d e l is p r e - t r a in e d t h e m o d e l c a n b e f in e - t un e d o n a s p e c i f ic t a s k wi t h a t a s k - s p e c i f ic d a t a s e t wi t h s u p e r v i s e d le a r n in g https://arxiv.org/pdf/2005.14165.pdf https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf F o r W if i u s e G r ee n lin e

5. G e n e r a t i v e A I Generative Pre-Trained Transformer ( GPT ) –Transformers Architecture https://arxiv.org/pdf/1706.03762.pdf http://jalammar.github.io/illustrated-transformer/ https://cs182sp21.github.io/static/slides/lec-12.pdf Architecture S ix E n c o d e r l a ye r s s t a c k e d S ix D e c o d e r l a ye r s s t a c k e d P o s i t i o n a l E m b e dd in g s M a s k e d A tt e n t i o n ( E n c o d e r - D e c o d e r A tt e n t i o n ) Advantages B e tt e r l o n g - r a n g e c o nn e c t i o n s E a s ie r t o p a r a lle li z e C a n m a k e t h e n e t w o r k s m u c h d ee p e r ( m o r e l a ye r s ) t h a n R NN s F o r W if i u s e G r ee n lin e

6. G e n e r a t i v e A I G e n e r a t i v e P r e - T r a in e d T r a n s f o r m e r ( GP T ) – G P T 3 T r a inin g D a t a a n d P a r a m e t e r s Dataset F o r W if i u s e G r ee n lin e https://arxiv.org/pdf/2005.14165.pdf Parameters

7. G e n e r a t i v e A I G e n e r a t i v e A d v e r s a r i a l N e t w o r k s ( G A N s ) https://arxiv.org/pdf/1406.2661.pdf Imagine you have a bunch of cat images , and you want a machine learning model to create similar images. This is exactly what a GAN does. Generator : Takes in random numbers as input and generates the images of interest ( the forger ) Discriminator : Takes both the images from the generator and the real images from the data and spots the difference between them ( the detective ) Both the generator and the discriminator are trained together. And, over the duration of training, the generator gets better at creating images which look real, and the discriminator gets better at spotting fakes. Adversarial Objective : These two networks are pitted against each other where the generator creates more realistic synthetic images to fool the discriminator while the discriminator networks tries to get better at detecting fake images . This back-and-forth strategy forces both the networks to improve until the generator can create highly realistic synthetic images, that indistinguishable from real images F o r W if i u s e G r ee n lin e

8. Applications D a ll - E E x a m p le 1 I m a g e s G e n e r a t e d f o r t h is p r e s e n t a t i o n F o r W if i u s e G r ee n lin e

9. Applications D a ll - E E x a m p le 2 I m a g e s G e n e r a t e d f o r t h is p r e s e n t a t i o n F o r W if i u s e G r ee n lin e

10. Applications Midjourney I m a g e G e n e r a t e d f o r t h is p r e s e n t a t i o n F o r W if i u s e G r ee n lin e

11. Applications Midjourney Prompt : Imagine a small seed planted in the ground. It sprouts, grows into a sapling, then a small tree, and finally a large robust tree. Each year, it sprouts new branches, leaves and sometimes fruits – all from that small seed. This is how your investment grows with compounding – It branches out producing more and more just like a tree F o r W if i u s e G r ee n lin e I m a g e s G e n e r a t e d f o r t h is p r e s e n t a t i o n

12. Applications ChatGPT F o r W if i u s e G r ee n lin e I m a g e s G e n e r a t e d f o r t h is p r e s e n t a t i o n

13. G e n e r a t i v e A I F o r W if i u s e G r ee n lin e E m e r g in g T r e n d s Major breakthroughs in deep learning architectures like Transformers and Generative Adversarial Networks Availability of massive datasets and GPU/TPU compute New advances in techniques like RLHF/Prompting made it much easy to align these models Low barrier of entry due to intuitive and user-friendly interfaces and strong open-source ecosystem GenAI holds potential to create photo-realistic images, human-like speech and text and generate working code from natural language descriptions which was not possible until recently