The document introduces generative AI, outlining its capabilities in creating images, text, videos, and audio guided by training samples and user specifications. It discusses various use cases, including public health campaigns, educational chatbots, and character design in video games, while highlighting the ethical considerations and the need for prompt engineering. Additionally, it mentions significant technologies like GPT-3, DALL-E 2, and Stable Diffusion, along with resources for further exploration.

1. GENERATIVE AI: AN INTRODUCTION Colleen M. Farrelly

2. WHO AM I?
• Data science
lead/advisor
• Author of The
Shape of Data
• AI artist
• Miami creative

3. MY PATH
• Told I was dumb and bad
at school
• Athlete/writer/inventor/
engineer outside of school
• Tried to drop out 1st
semester of university
• Dropped out of MD/PhD
program to enter AI
• 10+ years in R&D and AI
for social good
• Possibly PhD student in
the future?

4. WHAT IS
GENERATIVE
AI?
• Set of algorithms that
generate:
• Images
• Text samples
• Videos
• Audio content
• Guided by:
• Training sample
• User specifications

5. USE CASE 1: PUBLIC
HEALTH CAMPAIGNS
South Africa
AIDS education
example (2006-
2007)
COVID public
health
messaging
campaigns

6. USE CASE 2: CHATBOTS
Educational chatbot example
Built while working at Jenzabar
Automates student support
Ethical considerations of
chatbots

7. U SE C ASE 3: BETA
TESTIN G C ON TEN T
• New cartoon targeting
males ages 4-9 who
watch a lot of science
fiction:
• Generate lots of
potential content
• Choose promising
characters/scenes
• Do a pilot
• Create the show

8. OTHER USE CASES
• Video game character design (Tomb
Raider-type example)
• Public policy campaign content
generation
• Blog content
• Educational video generation
• Virtual therapist apps
• Personal assistant bots
• Many, many, many more!

9. HOW DO THESE ALGORITHMS WORK?

10. GPT
• Generative Pre-trained
Transformer 3
• Decoder-only transformer
network
• Gives sequence-to-sequence
decoder with long-range
memory
• Already blurring lines
between human
composition and AI

11. DALL-E 2
(OPEN AI)
• Capabilities:
• Can generate images from text
• Can insert new features or styles into that image
to modify it
• System pieces:
• Contrastive Language-Image Pre-training (CLIP)
• Prior model (build off existing repository)
• Decoder Diffusion model (inverse step)

12. STABLE
DIFFUSION
• Text-to-image technology
• Based on latent diffusion
models coupled to text
input
• Translation of text cues
• Markov chains wandering
around a latent space
• Denoise and renoise
images
• Leverages some inpainting
techniques to fill in gaps

13. PROMPT
ENGINEERING
• New field of engineering
unique to generative AI
• Leverage
• Knowledge of
technical architecture
• Good combinations of
elements to coax
system to desired
output
• Growing need for prompt
engineers in industry
• Many new career
opportunities
• Gaming systems
• Other content
generation

14. REPRESENTATION
• Languages with no or bad models
• Lingala
• Hausa
• Patwa
• Cultural contexts missing
• Burqa
• Subgroups
• Lack of worldwide access to some tools
• OpenAI and dozens of countries

15. OTHER
RESOURCES
• https://www.linkedin.com/in/c
olleenmfarrelly/
• https://stablediffusionweb.co
m/
• https://openai.com/
• https://hourone.ai/generative-
ai-video-des/
• https://midjourney.com/home/
• https://www.lesswrong.com/p
osts/fRzkDWewwyS6fHp4w/
palm-api-and-makersuite
• https://mathgpt.streamlit.app/

16. TOOLS
THAT
SHOULD
WORK IN
ANYWHERE
Speech generation:
• https://play.ht/text-to-speech-voices/egyptian-
arabic/
Text generation (OpenAI alternative, GPT-
2):
• https://huggingface.co/tasks/text-generation
Image generation:
• https://creator.nightcafe.studio/create
Hopefully OpenAI (and Google’s PaLM
API):
• DALL-E, ChatGPT, GPT-4, Whisper