AI & Data Foundations

Perplexity and calibration evaluate language models' fluency and reliability, crucial for trustworthy AI in sensitive sectors like education, health, and humanitarian work.

Prompt injection is a security vulnerability in AI systems where hidden instructions in user inputs can lead to harmful outputs, posing risks especially for mission-driven organizations in sensitive sectors.

Prompting and prompt design shape how users interact with AI, enabling tailored, accurate, and ethical outputs for education, health, advocacy, and social impact across diverse contexts.

Reinforcement Learning enables dynamic decision-making through trial and error, with applications in health, education, agriculture, and logistics to optimize outcomes under uncertainty and scarcity.

Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to produce accurate, contextually grounded AI outputs tailored to local and mission-relevant knowledge.

Speech-to-Speech systems convert spoken language directly into another, enabling real-time, natural communication across linguistic barriers for health, education, and humanitarian sectors.

Speech-to-Text technology converts spoken language into text using AI, enhancing accessibility, inclusion, and efficiency across sectors like healthcare, education, and humanitarian work.

Supervised Learning trains models on labeled data to create predictive tools widely used in social innovation, healthcare, education, agriculture, and finance, enabling scalable, practical solutions for underserved communities.

Text-to-Speech technology converts written text into natural-sounding speech, enhancing accessibility across literacy, vision, and language barriers in various sectors including health, education, and humanitarian aid.

Tokens and context windows define how language models process text, impacting AI performance and applications in education, healthcare, and humanitarian efforts by determining the amount of information models can handle coherently.