Intro to Generative-AI(Gen AI Study Jams GDGC ZHCET)

Event: Introduction to Generative AI 💡

Curious about Generative AI? Join our online speaker session designed for beginners and explore the fundamentals of AI and its innovative applications!

What we’ll cover:

- Intro to AI 🤖: What is AI and how it's shaping the future.

- ML vs. DL 🧠: The difference between Machine Learning and Deep Learning.

- Intro to Langchain 🛠️: A powerful tool for AI development.

- Generative AI Frameworks 🧑‍💻: Tools that drive generative models.

- Autonomous Agents 🤖: AI systems making independent decisions.

- Traditional vs. Generative AI 🔄: How AI has evolved from traditional models to generative ones.

This is perfect for anyone starting their AI journey. Don’t miss out!

1. Intro to:
Speaker Talk | Build With AI
GENERATIVE-AI

2. Let’s Connect
Hey There! ??????
I’m Ayan Khan , 4th year BTech
student from GGSIPU.
A guy immersed in the exciting
world of technology.

3. Checklist for today
Intro to AI
Timeline
Train Your Own Model
Traditional AI vs Generative AI
Frameworks For Generative AI
Intro to LangChain & What it does
Exploring Autonomous Agents

4. INTRODUCTION T0 AI
AI is the simulation of human intelligence in
machines.
Which involves tasks like learning, reasoning,
and adapting.
Applications of AI:
Healthcare: AI helps in diagnosis, drug discovery,
and personalized treatments.
Education: Supports adaptive learning,
personalized tutoring, and educational tools.
Industry: Enhances automation, productivity, and
supply chain optimization.

5. Scan
AI
If A.I is being used to
recognize people’s
emotion in pictures
M.L algorithm would
input thousand of
faces from those
pictures by face
recognition
D.L would help to
recognize patterns
in those faces and
emotions they share
ML DL

7. Training Your Own Model

8. Traditional AI
Task-Specific: Focuses on performing
specific tasks, such as classification,
prediction, or decision-making.
Learning from Data: Learns patterns
from input data but does not create
new content.
Feature Engineering: Often requires
manual feature selection and
engineering by humans.
Examples:
Spam filters.
Fraud detection.
Recommendation systems.
Generative AI
Vs
Content Creation: Generates new data, such
as text, images, music, or video, from learned
patterns.
Learning from Patterns: Learns to generate
data by understanding underlying data
structures (e.g., GPT-3 for text, DALL-E for
images).
Automatic Feature Extraction: Uses
advanced models like neural networks to
automatically extract features without manual
intervention.
Examples:
Text generation (e.g., GPT-3).
Image generation (e.g., DALLE- Midjourney).
Music and video creation tools.

9. Frameworks for
Generative AI
LangChain is an open-source framework that makes it easier to build
applications using LLMs.
LlamaIndex connects large language models (LLMs) to external data sources
for efficient indexing and retrieval, improving tasks like question answering.
Ollama : Software/python package for running local LLMs.
Hugging Face is a multifaceted platform that plays a crucial role in the
landscape of artificial intelligence, particularly in the field of natural language
processing (NLP) and generative AI.
The Diffusers package is a library from Hugging Face that makes it easy to use
diffusion models for generating images and audio.

10. Intro To
Langchain
Langchain is a development framework designed to create
applications powered by Large Language Models (LLMs),
like GPT-3 or GPT-4.
It simplifies building AI-powered text-processing systems by
connecting various components, such as prompts, language
models, and APIs.

11. What it Does ?
Modular Approach: Allows developers to build
complex workflows by chaining multiple
components together.
Chains: Provides a structured way to link different
tasks (e.g., query language models, extract data,
interact with APIs) to form end-to-end AI solutions.
Memory: Enables applications to store past
interactions, allowing context-aware, conversational
AI experiences.
Integrations: Easily integrates with other tools, data
sources, and APIs to build versatile AI applications.

13. Understanding the difference between
Autonomous Agent and LLM ?
Can complete a given task on it’s
own by reasoning and acting.
Needs human assistance
for acting.
“With stand-alone large language models, you have access
to a powerful brain; autonomous agents add arms and legs”.

14. Algorithm used in Agents

16. Thank You!
Don’t Forget to connect over our socials!