The document discusses ChatGPT, an AI model developed by OpenAI that is capable of generating human-like text. It provides details on ChatGPT such as its architecture, training method, applications, and statistics on its usage. The document also discusses how cardiologists tested ChatGPT by asking it cardiology-related questions and grading its responses.

1. ChatGPT
A revolution in the future of
information production and dissemination
Mustafa Ghaderzadeh

2. Presentation Section
1: Introduction to ChatGPT
2: Application of ChatGPT
3: Register to OpenAI
4:Alternative to ChatGPT
5. How to Get Started with ChatGPT
6. Application of ChatGPT in Research
7. Application of ChatGPT in Coding

3. OpenAI
OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all
of humanity.

4. About
OpenAI
OpenAI is an artificial intelligence research laboratory
consisting of the for-profit OpenAI LP and its parent company,
the non-profit OpenAI Inc.

5. OpenAI Founded
The company was founded with the goal of developing and promoting
friendly AI in a responsible way, with a focus on transparency and open
research.
Elon
Musk
Sam
Altman
John
Schulman
Ilya
Sutskever
Greg
Brockman
Wojciech
Zaremba
The goals of OpenAI are to develop and promote friendly AI in a way that benefits all
of humanity. They conduct research in a variety of areas related to AI, including
machine learning, computer vision, natural language processing, and robotics.

6. Why?
100,000,000 Gigabyte data and information
• An interaction platform for different users
• A evolution platform for other ecosystems
• An engine for creation knowledge
• Making existing technologies smarter
• A interface engine with unlimited tools

7. and
• ChatGPT
• DALL.E: Image Generation
• CodexAI: Programming in GitHub
ChatGPT via Search Engine

8. ChatGPT
• ChatGPT is a deep learning language model developed by OpenAI, which is capable of
generating human-like text based on the input provided.
• State of the art(Cutting Edge) Based LLM
• LLM is A language model that is a probability distribution over sequences of words. Given any sequence of words
of length m, a language model assigns a probability.
• Is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (transfer learning) using
both supervised and reinforcement learning.
Ver Architecture Parameter
count
Training data
GPT-1 12-level, 12-headed Transformer decoder (no encoder),
followed by linear-softmax.
0.12 billion BookCorpus:[8] 4.5 GB of text, from 7000
unpublished books of various genres.
GPT-2 GPT-1, but with modified normalization 1.5 billion WebText: 40 GB of text, 8 million documents, from
45 million webpages upvoted on Reddit.
GPT-3 GPT-2, but with modification to allow larger scaling. 175 billion 570 GB plaintext, 0.4 trillion tokens. Mostly
CommonCrawl, WebText, English Wikipedia,
and two books corpora (Books1 and Books2).

9. Main feature of ChatGPT
• Generative Pre-trained Transformer (GPT)
• Large-scale training
• Contextual understanding:
• Multiple use cases
• Customization
• API-based deployment

10. ChatGPT Stats Overview
Number of Users
100 million in January 2023 (Crossed 1
million users in first 5 days of launch)
Daily Visitors 25 million
Total Website Visits 619 million (Source: Similarweb)
Training Method
Reinforcement Learning from Human
Feedback (RLHF)
Model Fine-tuned GPT-3.5 (text-DaVinci-003)
Knowledgebase Up to 2021
Dataset Size
300 bn words, 570 GB (crawled web,
books, Wikipedia)
Queries Per Day 10 Million (estimate)
Monthly Cost to Run 3 Million USD (estimate)
Predicted Revenue
USD 200 million by end of 2023, USD 1
billion by end of 2024

11. ChatGPT Applications
• Create a Profile in the Social Medica App
• Content Generation
• Code Generation
• Academic essay creation
• Knowledge producing
• Search Engine Core
• Automatic answering (LinkedIn,..)
• Game with ChatGPT
• Software and Application Generation
• Expert assistance
• Automatic Physician adviser

12. Next Generation of ChatGPT
ChatGPT-4
ChatGPT-3 param:175(bli)
Text based
ChatGPT-4 param:100 (tril)
Multimedia
Numerical data
Sound
Images
Video Function

13. • Input Embeddings
Positional Encoding
Encoder
Self-Attention Mechanism
Feed-Forward Neural Network
Decoder Layers
Multi-Head Attention
Layer Normalization
Output Layers
•BERT
•GPT-3
•LaMDA
•PaLM
•Bard
Transformer-Based LLM Model Architectures

14. Registration and Using
• OpenAI signup
• Verify Email
• Phone Number Verification
• OpenAI and signin
• Try ChatGPT in OpenAI

15. Cardiologists ask the popular AI model ChatGPT to answer
questions about cardiology
• 25 cardiology-related questions created that addressed
“fundamental preventive concepts.
• Each question was asked three times.
• Each set of three answers was graded as being “appropriate,”
“inappropriate” or “unreliable”
• This study qualitatively evaluated the appropriateness of AI
model responses to simple, fundamental CVD prevention
questions.”
• Reviewing their findings, the authors noted that ChatGPT
showed potential as a way to “assist clinical workflows by
augmenting patient education and patient-clinician
communication around CVD prevention queries.” They also
detailed the limitations of their analysis, including the fact that
CVD is too big of a topic to accurately summarize in just 25
questions.
Example 1

16. Success
• Wharton MBA Exam
• Success of ChatGPT In taking the French language
version of the European Board of Ophthalmology exam
• Passing the medical licensing exam and the bar exam

17. Practical examples
Any Question?