This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.

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