Harnessing the Power of Generative AI for your Business By Siddharth.pdf

We'll explore AI evolution from early machine learning models to today's sophisticated algorithms and its transformative applications across business domains. Generative AI revolutionizes content generation (text, visuals, video), enhances employee support with personalized training and task automation, and transforms customer service with AI-powered chatbots and sentiment analysis. It advances enterprise search, accelerates software development through automated coding, and provides deep insights with predictive analytics in data analysis. Additionally, AI-driven tools enable rapid and collaborative design prototyping. Discover the immense potential of generative AI in shaping the future of business.

2. Siddarth Kengadaran

3. theproductguy.xyz
Who am I?
➔Product Consultant | Strategy and Design
➔Information Technology and Psychology
➔Convenor - The Product Space
➔Organizer - Google Developer Groups and Friends of Figma, Coimbatore

4. How Generative AI works?
Table of contents
The Rise of Generative AI
What is Generative AI
capable of?
Assessing Your Business
Needs
Future Trends and
Opportunities
Conclusion
01
02
03
04
05
06

5. Artificial
Intelligence (AI)
Artificial Intelligence (AI) refers to the simulation of
human intelligence in machines that are
programmed to mimic human actions and cognitive
processes.
The Rise of Generative AI

6. Logical Reasoning &
Problem-Solving
Abstract Thinking
Learning & Adaptation
Memory
Language &
Communication
Perception &
Sensory Processing
Emotional
Intelligence

7. Social Intelligence
Creativity &
Imagination
Decision-Making
Metacognition
Spatial Reasoning
Numerical &
Quantitative Skills
Practical Intelligence
Moral & Ethical
Reasoning

8. Expert systems, rule-based systems, automated reasoning,
theorem proving, constraint satisfaction algorithms.
Deep learning, neural networks, generative models (e.g.,
GANs, VAEs), reinforcement learning.
Natural language processing (NLP), natural
language understanding (NLU), natural
language generation (NLG), machine
translation, chatbots, language models (e.g.,
GPT-4).
Machine learning (supervised, unsupervised,
semi-supervised, and reinforcement learning), adaptive
systems, transfer learning, lifelong learning systems.
Knowledge graphs, semantic networks, databases,
memory-augmented neural networks, long short-term
memory (LSTM) networks.
Computer vision, speech recognition, audio
processing, sensor fusion, image and video
recognition systems.

9. Affective computing, sentiment analysis, emotion
recognition systems, empathy bots.
Social robots, conversational agents, virtual assistants,
social network analysis.
Meta-learning, self-improving AI, automated
machine learning (AutoML), reflective agents.
Generative adversarial networks (GANs), creative AI, music
composition AI, art generation AI, creative writing AI.
Decision support systems, recommendation engines,
optimization algorithms, predictive analytics.
Robotic perception, pathfinding algorithms,
spatial analytics, autonomous navigation
systems, 3D modeling.

10. Data analytics, statistical analysis software, financial
modeling AI, algorithmic trading systems.
Robotics, autonomous systems, smart appliances,
context-aware computing.
Generative adversarial networks (GANs), creative AI, music
composition AI, art generation AI, creative writing AI.
AI ethics frameworks, fairness-aware AI, explainable AI
(XAI), bias detection and mitigation tools.

11. Artificial
Intelligence[AI]
Machine
Learning [ML]
Natural Language
Processing [NLP]
Deep Learning
Vision Speech
Robotics
Planning
Expert
Systems
Neural Networks
Generative AI

12. The Rise of Generative AI
Machine Learning (ML)
Machine Learning (ML) is a subset of AI that
enables systems to automatically learn and
improve from experience without being
explicitly programmed.
Deep Learning
Deep Learning is a subset of machine
learning that uses neural networks with
multiple layers to learn hierarchical
representations of data.

13. Generative AI
Generative AI falls under the umbrella of Machine
Learning, particularly within the realm of deep
learning. It's a specialized type of model that
leverages neural networks (often very large and
complex ones) to generate new data that resembles
the data it was trained on.


The Rise of Generative AI

14. ✦Abstract Thinking
✦Language & Communication
✦Creativity & Imagination

16. 1966
2017
2023
OpenAl GPT-3
May: OpenAl releases GPT-3, the largest language model to date with 175 billion parameters.
Microsoft Introduces GPT-4
March: Microsoft debut OpenAl's GPT-4 likely a multimodal trillion parameter version of GPT-3
Introduction of Transformer Models
Transformer Models are introduced through papers like Google's Transformer: A Novel
Neural Network O Architecture for Language Understanding and Attention Is All You Need,
Vaswani et al., 2017.
2020
2024
Meta introduces LLaMA 3
June: AI model that surpasses previous versions in terms of versatility and language generation,
with better contextual understanding and reduced biases.
Statistical Language Model (N-gram model)
2022

17. Statistical Language Model (N-gram model)
An n-gram model breaks text down into chunks of n consecutive words (or
"grams") to predict the next word in a sequence. Let's use a 3-gram (trigram)
model for simplicity.

Our model has been trained on a large corpus of text, and it has learned that
after the sequence "The cat is on the", the most probable next words are
"roof", "floor", "bed", or "mat", let's say.

It knows nothing more than the statistical probability of each of these words
appearing after the input sequence based on its training data.

So, if "roof" appeared most frequently in its training data after the phrase
"The cat is on the", it would predict "roof" as the next word.

18. Neural Network Language Model (like GPT-4)
These models take a more sophisticated approach. They don't just look at
the immediate previous words, but they understand the entire context of the
input and have a notion of word meaning derived from their training data.

Now, if we had a more nuanced sentence like:

"The cat spotted a mouse. Quietly, it started to climb. The cat is on the..."

Despite the commonality of phrases like "the cat is on the floor/bed/mat", a
neural network model like GPT-4 might predict "chase" or "prowl", as it
can understand from the earlier part of the sentence that the cat is likely
pursuing the mouse, and "climb" implies an upward movement, possibly
indicating something like a table or a counter.

19. Large
Vision-Language
Models

20. Model
The result of the machine's learning process. The model holds the patterns
and insights the computer discovered from the training data, allowing it to
make predictions or take informed actions on new information.
Foundation
Model
Adapted Models
Domain-Specific
Models
Task-Specific
Models
Hybrid Models
Multimodal
Models
Explainable &
Interpretable Models
Personalized
Models

21. Foundation Model
BERT, GPT-n,
DALL-E,..
Adapted Models
BioGPT
Domain-Specific Models
BloombergGPT
Task-Specific Models
Whisper
Hybrid Models
Multimodal Models
Gemini
Explainable & Interpretable Models
Personalized Models
Apple Intelligence

22. Data
Text
Images
Audio
Structured
Data
3D Signals
Video
Foundation
Model
Tasks
Question &
Answering
Summarization
Generation
Extraction
Paraphrase
Search
Classification
Analysis
Captioning
Recognition
Translation
Rephrase
ReasoningPrediction
EnhancementSegmentation
Deciding &
Planning
Conversion

24. Generative pre-training
Fine-tuning
Retrieval-augmented
generation (RAG)
Prompt engineering
Complexity
Accuracy
Cost
Time to Implement
Domain Specificity
Flexibility

25. Prompt engineering
Complexity
AccuracyCost
Time to
Implement
Domain
Specificity
Flexibility

26. Retrieval-augmented
generation (RAG)
Complexity
AccuracyCost
Time to
Implement
Domain
Specificity
Flexibility

27. Fine-tuning
Complexity
AccuracyCost
Time to
Implement
Domain
Specificity
Flexibility

28. Generative pre-training
Complexity
AccuracyCost
Time to
Implement
Domain
Specificity
Flexibility

30. LLM OS
Agents
RAG
Chat Bot
Question & Answers
Levels of LLM Apps
Predicts answers based on patterns learned
from a vast corpus of text.
Engages in interactive dialogues by
generating contextually relevant responses.
Retrieves and incorporates information
from external knowledge sources to
enhance responses.
Executes actions in external systems based
on user requests and retrieved information.
Orchestrates multiple agents and processes,
managing complex tasks and workflows
through a unified interface.

31. ✦MAKER
Train and build custom models
✦SHAPER
Tune foundational Industry Models
✦TAKER
Use pre-trained ML API models and point to
your apps

32. Thank you!
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