A brief overview of generative AI technologies and their use for social good initiatives, including cultural training, medical image generation, drug design, and public health.

1. Generative
AI for Social
Good
Colleen Farrelly, Post Urban

2. What is generative AI?
• Deep learning frameworks that can produce new data based on
input prompts and large training datasets
• Can have any/all of these steps in the framework:
• Encoder-decoder structure
• Training sample matches
• Random noise and blending components
• Comparison steps to ensure realism

3. Examples
ChatGPT
DALL-E
Stable Diffusion
Large Language Models on
Hugging Face
Custom Generative Adversarial
Networks for other data types

4. Text Generators
• Massive training datasets
• Typically scraped and
possibly quality controlled
• Mostly in English
• Deep learning frameworks with
billions of parameters to train
• Can be modified by fine-tuning
• Specific examples relevant to
text generation task at hand
• LoRA as quicker way to train

5. Image Generators
• Many types
• Encoder-decoder steps in some
• Pull up related images
• Blend images
• Add random noise to back-fill
• Image generators plus comparison steps
• Two competing generators with one a
few training steps ahead of the other
• Comparison step to benchmark
against real dataset
• Some rely heavily on topology

6. Case Studies

7. Case 1: Medical
Image Generation
• Medical imaging data issues:
• Small sample sizes
• Sample imbalance (rare diseases…)
• Issues when augmenting small samples
or imbalanced samples:
• Biological structure fidelity in
generation (ex: ventricles in brain)
• Image variety in generation

8. TopoGAN
• Solution involves a generative
adversarial network with
topological awareness
• Topology
• Betti number
introduction
• Advantages:
• Preserves structures
like branching and
loops
• Generates large
number of images
close to target images

9. Case 2: Human Resource Diversity
Training
• Mindbloom
• Addresses training needs by providing synthetic people with whom to discuss
several types of conversations
• Employee reporting sexual harassment
• Addressing cultural mismatch of new employee
• Policy changes that impact employees
• Misgendering in the workplace
• Conversation and voice generation with proprietary generative algorithms
• Demo

10. Automated Reporting on Skill Improvement

11. Case 3: Protein
Generation
• Designing and testing new drugs takes a lot of
time and money.
• Not good for new pandemics in urgent need
of treatment
• Increased drug costs for consumers
• Many types of proteins/molecules in venom of
different animals
• Metalloproteinases, three finger toxins,
phospholipidase A2, disintigrins…
• Varies by geography and species
• Slight modifications of toxins as good
initial drug designs

12. Graph Generators
• Approach to protein/molecule-specific generative models:
• Translate protein/molecule to graph form
• Define properties of interest (solubility, for instance) or binding score
• Create generative model to work on generating similar graphs
• GAN trials generate new proteins/molecules with:
• Better target properties
• More variety
• Less time/cost to generation than other models/human generation

13. Case 4: Public Health Campaigns
• Many recent infectious diseases that can be spread from person to
person:
• Ebola
• COVID-19
• HIV
• Issues with traditional generation of video and poster messaging to
address behaviors contributing to spread
• Time to create script, image, and translations for local populations
• Lives lost in delays

14. Coupling Generators
• Generate culturally-relevant
images
• Generate text
• Translate text to local languages

15. Ethical Considerations

16. Potential Bad
Behaviors
• Deep fakes
• Fake news
• Biased data
• Hallucinations and jailbreaks
• Manipulation of algorithm by
text engineering

17. Open-Source Resources
• https://huggingface.co/models
• https://www.craiyon.com/
• https://github.com/TopoXLab/TopoGAN-ECCV2020
• https://github.com/Biomatter-Designs/ProteinGAN