ChatGPT &
GenAI Landscape
- Aditya Jain
About Me:
1. BTech in CS from RCOEM, Nagpur (9+ CGPA)
2. Google DSC Campus ambassador from Nagpur
3. Only student from college to get an Internship at Amazon
4. AIR 60 in GATE - CS 2019 (first attempt, city topper)
5. Went to IIT Bombay for MTech
6. Specialised in Data Science with 9.36/10 CGPA
7. Published multiple research papers in well known conferences
8. Received Excellence in Research award from IIT Bombay
9. Worked at Dream11 as a Data Scientist
10. Mentored & Upskilled 10,000+ working professionals.
11. Founder & CEO: Next Bigg Tech
Contents:
1. Introduction
2. History & Evolution
3. Key Concepts & Terminologies
4. How ChatGPT Works?
5. Practical Use Cases of Generative AI
6. Popular Generative AI Tools & Platforms
7. Challenges & Ethical Considerations
8. The Future of Generative AI
Introduction
● AI enables machines to perform human-like tasks, including
creativity.
● Generative AI creates text, images, and more, transforming
industries.
● Engineers can use AI for automation, research, and design.
● We interact with AI daily through chatbots, voice assistants,
and recommendations.
Evolution of AI
&
Generative AI
● AI started with rule-based systems that followed strict instructions but
couldn't learn from data.
● Machine Learning (ML) introduced models that could recognize patterns in
data and improve over time.
● Deep Learning, inspired by the human brain, allowed AI to process complex
information like speech and images.
● Generative AI evolved with technologies like Transformers, enabling AI to
generate realistic content.
● The launch of models like ChatGPT, which can engage in human-like
conversations, marked a major breakthrough.
Key Concepts
&
Terminologies
● AI vs. ML vs. Deep Learning: AI is broad, ML learns patterns,
Deep Learning uses neural networks.
● Generative vs. Predictive AI: Generative creates content;
Predictive forecasts trends.
● Transformers & LLMs: Core AI models for language
processing.
● Fine-tuning: Adapting AI models for specific applications.
How ChatGPT
Works?
● ChatGPT is trained on a massive dataset of text and learns patterns in language
to generate human-like responses.
● It undergoes two major training phases: Pretraining, where it learns from a
large amount of text, and Fine-tuning, where it improves with human feedback.
● Reinforcement Learning from Human Feedback (RLHF) helps refine responses
to make them more accurate and aligned with human expectations.
● It does not "think" like a human but predicts the most likely next words based
on its training.
● The quality of responses depends on Prompt Engineering, where users give
clear and structured instructions to get better outputs.
Practical Use Cases
of Generative AI
● Text Generation: Emails, articles, summaries.
● Code Generation: AI-powered coding assistants.
● Image & Video Creation: AI-generated designs.
● Chatbots: Customer support, education, and healthcare.
● Engineering Applications: designing circuits, predicting
system failures, and automating repetitive tasks.
Popular Generative
AI Tools & Platforms
● ChatGPT: Developed by OpenAI, this is one of the most powerful AI
chatbots for conversations, research, and automation.
● Google Bard & Claude: Other AI-powered chatbots with different strengths
in reasoning and knowledge retrieval.
● Open-source LLMs (Mistral, Falcon): Models that developers can customize
and use freely.
● AI Coding Assistants (Copilot, CodeWhisperer): AI-powered tools that help
programmers write and debug code faster.
● Text-to-Image Tools (DALL·E, Midjourney, Stable Diffusion): AI that
generates high-quality images from text descriptions.
Challenges & Ethical
Considerations
● Bias in AI: AI models may reflect societal biases present in their training
data, leading to unfair or inaccurate results.
● Hallucinations: AI sometimes generates false or misleading information,
making fact-checking essential.
● Privacy & Security: AI models require large amounts of data, raising
concerns about user privacy and data protection.
● Job Impact: While AI automates tasks, it also creates new job opportunities,
requiring skill adaptation.
● AI Regulations: Governments and organizations are working on guidelines
to ensure responsible AI usage.
The Future of
Generative AI
● Multimodal AI: Combining text, images, and audio.
● AI Agents: Autonomous AI performing complex tasks.
● Engineering AI: Innovations in robotics and design
optimization.
● Personalized AI: Tailored AI experiences.
● Ethical AI: Transparency and fairness in AI.
Roadmap to be
Industry Ready
● Step 1: Learn the Basics of Coding (Python & SQL)
● Step 2: Get Comfortable with Data Analytics (NumPy,
Pandas, Data Visualization)
● Step 3: Introduction to Machine Learning & Deep Learning
● Step 4: Dive into Generative AI (LLMs, ChatGPT, Stable
Diffusion, etc.)
● Step 5: Build Projects & Apply AI in Real Life
● Code: RAISONI40 to get flat 40% off on all courses
● Get 80% refund if you don’t find the course valuable within first
48 hours
● Refer and earn credits for the future course purchases. Details
on the platform: nextbiggtech.com (App: Upskill with AJ )
● Above offers valid only for the first 250 users till 26th Feb.
● Connect with us: NBT by Aditya Jain
Thank you!
