History and Introduction for Generative AI ( GenAI )

Introduction into GenAI and transformers, also coveres major histories behind them.

1. ABOUT ME : ¯\_(ツ)_/¯
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me.currentOrg = “ Settlin - Linking Spaces Technologies Pvt. Ltd.”;
me.eduQual = {clg:ACET,course:B.Tech CSE,gradYr:2022};
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2. Transforming Tomorrow:
Exploring GenAI and
Transformers
Unveiling the Power and Potential of GenAI and
Transformers.

3. Let’s Discuss
NLP
AI
ML
GPT
LLM
GenAI
Fine-tuning
Foundational Model
Transformers
Prompt engineering

4. Why AL/Ml now ?
Source: https://www.sciencedirect.com/science/article/pii/S0022435922000288

5. HOw we achieved GenAi
Source :https://www.hfsresearch.com/research/how-business-leaders-can-take-control-of-the-genai-conversation/

6. GenAI: Under the hooD
Source :https://www.hfsresearch.com/research/how-business-leaders-can-take-control-of-the-genai-conversation/

7. Natural Language Processing (NLP)
●Natural Language Processing (NLP) is a
subfield of artificial intelligence
that enables machines to understand,
interpret and manipulate human
language
●NLP allows computers to break down and
interpret human language by studying
different aspects like
syntax,semantics, pragmatics, and
morphology.
●It transforms linguistic knowledge
into rule-based, machine learning
algorithms that can solve specific
problems and perform desired tasks

8. INtRO to Transformers

9. Why Transformers ?
Recurrent Neural Networks (RNNs), including Long Short-Term
Memory Networks (LSTMs), have applied neural networks to NLP
sequence models for decades. However, recurrent
functionality reaches its limit when faced with long
sequences and large numbers of parameters. Therefore,
state-of-the-art transformer models now prevail.

RNN : processes data in sequence by step by step.
LSTMs = RNN + Memory cells.

10. Transformers = Attention Mechanism
The core concept of a
transformer can be summed up
loosely as “mixing tokens.” NLP
models first convert word
sequences into tokens. RNNs
analyze tokens in recurrent
functions. Transformers do not
analyze tokens in sequences but
relate every token to the other
tokens in a sequence

11. Attention Mechanism
A transformer is a deep learning architecture developed by
Google and based on the multi-head attention mechanism,
proposed in a 2017 paper "Attention Is All You Need".[1]
Text is converted to numerical representations called
tokens, and each token is converted into a vector via
looking up from a word embedding table.[1] At each layer,
each token is then contextualized within the scope of the
context window with other (unmasked) tokens via a parallel
multi-head attention mechanism allowing the signal for key
tokens to be amplified and less important tokens to be
diminished.

12. Attention mechanism: Overview

13. Foundation MOdel
●Pre-trained on different types of unlabeled datasets
(e.g., language and images)
●Self-supervised learning.
●Generalized data representations which can be used in
multiple downstream tasks (e.g., classification and
generation).
●The Transformer architecture is mostly used, but not
mandatory.

14. What are Generative AI models

15. THanK you