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Colleen Farrelly, profile picture
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Generative AI, WiDS 2023.pptx

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.

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GENERATIVE AI: AN INTRODUCTION Colleen M. Farrelly
WHO AM I? • Data science lead/advisor • Author of The Shape of Data • AI artist • Miami creative
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?
WHAT IS GENERATIVE AI? • Set of algorithms that generate: • Images • Text samples • Videos • Audio content • Guided by: • Training sample • User specifications
USE CASE 1: PUBLIC HEALTH CAMPAIGNS South Africa AIDS education example (2006- 2007) COVID public health messaging campaigns
USE CASE 2: CHATBOTS Educational chatbot example Built while working at Jenzabar Automates student support Ethical considerations of chatbots
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
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!
HOW DO THESE ALGORITHMS WORK?
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
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)
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
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
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
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/
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