Generative AI creates new content (images, text, music) based on learned patterns. It learns from vast examples and can produce original, unseen works. Capable of blending learned elements to generate unique outputs. Can produce customized creations based on specific prompts. Improves and refines its output over time with more data and feedback.

1. Future of Generative AI
https://abacus.ai/

2. Table of contents
01
04
02
05
03
06
Understanding
Generative AI
Evolution AI Now
Future of Generative AI Problem of Gen-AI Conclusion

3. Generative AI ?
1. Generative AI creates new content (images, text,
music) based on learned patterns.
2. It learns from vast examples and can produce
original, unseen works.
3. Capable of blending learned elements to generate
unique outputs.
4. Can produce customized creations based on
specific prompts.
5. Improves and refines its output over time with
more data and feedback.

4. At the core of Generative AI, we basically find two main types of models
GAN(Generative Adversarial Network):
1. Experts in generating Image from both text as
well as image input
2. Has 2 neural network(Generator and
Discriminator)
3. Generator generates “fake” content from input
vectors
4. Discriminator distinguishes between real sample
from domain and fake image generated by
generator

5. 1. Deep Learning model that rely on self-attention
mechanism to process sequential data
2. Unlike traditional models, transformers can
handle data in parallel reducing training time and
improving efficiency
3. Backbone of today’s LLM models(GPT, Gemini,
BERT)
Transformers

6. Some tweets regarding Generative AI

7. AI Progression in
the Past

8. Growth in AI models

9. Time Series from Regression to Large
Language Models
1. Regression – Came in 1805. It generally has 10 to 60 inputs with similar weights to optimize
2. ARIMA(Auto Regressive Integrated Moving Average) – Came in 1970s. Inputs are past time and average
3. Backpropagation – basic neural net – Came in 1970, usually fully connected layers which can range from
500 to 500k weights
4. RNN (Recurrent Neural Networks) – Came in 1980 – Output is fed as input. Didn’t work well in time series.
“Vanishing Gradient” problem
5. LSTM(Long Short Term Memory) – came in 1997 – added “memory state”. May have 20M weights
6. Attention Networks – Came in 2017 (transformers)
7. BERT(Bidirectional Transformers) – Came in 2018(~100 m weights)
8. GPT 3.5 – Launched in 2022(175B parameters). Launched by OpenAI
9. Llama model – Launched in Feb 2023. It has 3 variants(7B, 13B, 70B weights). Came as a competitor for
OpenAI by Meta
10. GPT 4.0 – Launched in March 2023(1.76T parameters). Launched by openAI
11. Gemini model – Launched in December 2023(1.5T parameters). It has 3 variants(Gemini Nano, Pro and
Ultra). Updated version of BARD model of Google

10. Lots of Evolution in LLMs in short time
https://arxiv.org/pdf/2304.13712.pdf

11. The Expansion of
Generative AI in
today’s
Landscape

12. https://marketoonist.com/2023/01/ai-tidal-wave.html

13. Few Leading LLM models
OpenAI models

14. What are Transformers?

15. What are Transformers?
1. Boom on generative AI started with
transformers
2. Family of deep learning neural network
architecture(2017)
3. Architecture contains
. Word2Vec Embeddings
. Positional Encoding
. Self Attention
. Feed Forward Neural Network
4. Primary Application: Translation

16. GPT(Generative Pretrained Transformers)
GPT is a pretrained model build for OpenAI
for natural language Processing
Generative refers to the term of the model
to generate natural language.
Pretrained has 2 phases
1. Pretraining: The model is trained over
large corpus of data to predict next
word in a sentence
2. Fine-Tuning: Once the model is
trained it can be fine tuned to perform
specific task with supervised learning

17. GPT3 Training Data and Parameters
https://arxiv.org/pdf/2005.14165.pdf

18. GPT3 Effectiveness
https://arxiv.org/pdf/2005.14165.pdf

19. GPT3 through RLHF(Reinforcement Learning through Human Feedback)
1. RLHF refers to making the model learn from human feedback without the need of labelled data.
2. Due to the training data being scraped from internet might contain inappropriate or false information, it
must be aligned using RLHF to make it user appropriate
https://huyenchip.com/2023/05/02/rlhf.html

20. Llama 2 by Meta
Open Source – even for commercial
use !!
Model sizes, in Billions of weights
1. 70B, 34B(not released), 13B,
7B
2. both regular versions and chat
versions.
Llama model also used RLHF
technique
https://arxiv.org/pdf/2307.09288.pdf

21. Llama Details
Comparison with
ChatGPT 3.0(by OpenAI)
PaLM, in the Bison size,
Falcon 40B
Vicuna by UC Berkeley
MPT 7B
https://arxiv.org/pdf/2307.09288.pdf

22. Diffusion models
Diffusion models are another form of Generative models which works by adding noise to the images in the training
data by a process called forward diffusion process and then reversing the process to recover the original image
using reverse diffusion.
Forward Process(Diffusion): The model starts with data samples and gradually adds noise to these samples over a
series of steps until the data is completely transformed into random noise
Reverse Process(Denoising): The model learns to reverse the diffusion process by starting with the noise and
gradually removing it across many steps to reconstruct the original data samples. This is the main process where
model’s generative capabilities come into play as it learns to transform noise to high quality images
Stable Diffusion, Dalle, Midjourney are few examples of diffusion models

23. Stable Diffusion
Stable Diffusion is a text-to-image
state of the art model. It has four
important key components
1. Diffusion Probabilistic Model
2. U-Net Architecture
3. Latent Text Encoding
4. Classifier-Free Guidance
https://github.com/Stability-AI/stablediffusion

24. Dalle by OpenAI

25. MidJourney

26. SORA model by OpenAI
https://openai.com/sora

27. Future of
Generative AI

28. https://marketoonist.com/2023/06/ai-and-productivity.html
Maybe AI will help you work. But more likely, you’ll be working for AI

29. Talking to AI might
be the most
important skill of the
century
The Atlantic

30. Will AI Take my Job ?
Probably Not.
But new Job would be created.

31. But !!!
Jobs like data entry, customer support, content creation might be replaced by AI.
Task Category Most Likely to be Replaced by
Generative AI
Data Entry and Analysis Data input and transcription
Customer Support Basic support via chatbots
Content Creation Basic copywriting and image
generation
Design and Art Simple graphic and pattern design
Programming and Development Code generation for repetitive tasks

32. GPT5 ?
1. GPT-5 is expected to continue improving on
the logical reasoning and broader knowledge
bases demonstrated by GPT-4.
2. Anticipated enhancements include a
significant increase in parameter size,
enriching its processing and output
capabilities.
3. It might introduce advanced multimodal
functionalities, potentially incorporating video,
for a more integrated AI experience.
4. The release timeline remains speculative,
following OpenAI's historical pattern of
delivering gradual, impactful updates
https://www.datacamp.com/blog/everything-we-know-about-gpt-5

33. Impacts on Economy
Mandeep Singh, Senior Technology
Analyst at Bloomberg Intelligence and lead
author of the report said, “The world is
poised to see an explosion of growth in
the generative AI sector over the next
ten years that promises to
fundamentally change the way the
technology sector operates. The
technology is set to become an
increasingly essential part of IT
spending, ad spending, and
cybersecurity as it develops.”
https://synthedia.substack.com/p/generative-ai-to-reach-13-trillion

34. Risks associated with Generative AI

35. https://marketoonist.com/2023/06/impact-of-chatgpt.html

37. Things to keep in mind
Generating harmful content
Bias
Fake news and disinformation
“hallucination” – faking things
Privacy and data protection
The unpredictability

38. Conclusion
The future of Generative AI promises to be transformative, building on its current
capabilities of producing highly original and customized content across various media.
It is expected to further advance logical reasoning and integrate multimodal functions,
which may include handling video content, potentially revolutionizing how AI is
experienced. This evolution will likely influence IT, advertising, and cybersecurity
spending significantly, creating new job roles while automating others. With continued
innovation, Generative AI is poised to become an even more essential tool in the
technology landscape, necessitating a critical understanding of AI interaction as a
pivotal skill set for future generations.

39. References
1. Chip, H. (2023, May 2). RLHF. Retrieved from https://huyenchip.com/2023/05/02/rlhf.html
2. Touvron, H., Almahairi, A., ..., Scialom, T. (2023). Llama2: Open Foundation and Fine-Tuned Chat
Models. arXiv:2307.09288. Retrieved from https://arxiv.org/pdf/2307.09288.pdf
3. UCD Professional Academy. (n.d.). How will AI play a part in HR in 2024 and beyond? Retrieved
from https://www.ucd.ie/professionalacademy/resources/how-will-ai-play-a-part-in-hr-in-2024-and-
beyond/
4. DataCamp. (n.d.). Everything we know about GPT-5. Retrieved from
https://www.datacamp.com/blog/everything-we-know-about-gpt-5
5. Quillen, R. (n.d.). Large Language Models (LLM) - Immediate Future. Retrieved from
https://www.linkedin.com/pulse/large-language-models-llm-immediate-future-reinhold-quillen/
6. Statista. (n.d.). Artificial Intelligence - worldwide. Retrieved from
https://www.statista.com/outlook/tmo/artificial-intelligence/generative-ai/worldwide
7. Synthedia. (n.d.). Generative AI to reach $1.3 trillion. Retrieved from
https://synthedia.substack.com/p/generative-ai-to-reach-13-trillion
8. Touvron, H., Lavril, T., ..., Izacard, G. (2023). LLaMA: Open and Efficient Foundation Language
Models. arXiv:2302.13971. Retrieved from https://arxiv.org/pdf/2302.13971.pdf
9. Ouyang, L., Zhang, C., ..., Agarwal, S. (2022). Training language models to follow instructions with
human feedback. arXiv:2203.02155. Retrieved from https://arxiv.org/pdf/2203.02155.pdf
10. Gates, B. [Bill Gates]. (n.d.). Episode 6: Sam Altman. [Video]. YouTube. Retrieved from
https://www.youtube.com/watch?v=PkXELH6Y2lM

40. Thank you