Recent innovations in GenAI and agentic AI
AI can significantly amplify the impact of organizations working in the nonprofit sector by automating routine tasks, enhancing data-driven decision-making, and optimizing resource allocation. Neural networks and deep learning led to the development of GenAI. AI evolved from analytical, task-specific optimization to more novel, adaptive abilities.
Traditional AI automates and optimizes specific tasks. Because it relies on machine learning models with predefined rules and algorithms, traditional AI is most widely used for repetitive tasks where efficiency and precision are crucial, such as in manufacturing or data processing.
Predictive AI forecasts outcomes by analyzing historical data. It analyzes past behaviors, detects patterns, and predicts future outcomes with high accuracy. Predictive AI is foundational in finance, healthcare, manufacturing, and marketing.
Conversational AI powers chatbots and virtual assistants that facilitate natural language interactions between humans and machines through text or voice interfaces. Conversational AI relies on machine learning models and natural language processing to comprehend natural language and generate human-like responses.
Generative and agentic AI
Generative AI (GenAI) is a subset of AI that uses deep learning that handles complex tasks and large datasets to create new content in response to simple natural language prompts. It performs human-like tasks by using machine learning models to identify and mimic patterns in the data it collects. Through continuous feedback loops, the system or machine gradually improves its performance.
Unlike traditional AI, which primarily focuses on recognizing patterns and making predictions, Generative AI uses complex algorithms and models to generate novel outputs. This technology relies heavily on deep learning techniques, particularly neural networks like generative pre-trained transformer (GPT) and generative adversarial networks (GANs).
Agentic AI builds on GenAI but adds the ability to take actions, pursue goals, and operate autonomously within defined boundaries. Agents plan, make decisions, use tools, call APIs, search the web, operate in software systems, and execute multi-step tasks. Agentic AI is action-oriented rather than output-oriented.
Big data
When discussing AI and machine learning, you may hear the term big data. Big data refers to a collection of large, complex, and diverse data often characterized by the three Vs: volume, variety and velocity.
- Volume: The amount of data from multiple sources.
- Variety: The types of data (structured, unstructured and semi-structured).
- Velocity: The speed at which data is being generated and processed.
Data is the foundation of any AI or machine learning solution. Therefore, its quality and availability determine the accuracy of any AI or machine learning output. Big data is complementary to AI/ML applications. Big data technologies enable AI/ML by providing the infrastructure to manage large datasets used for analysis, pattern recognition, and prediction, at scale.
Well-known applications of AI
One of the most well-known applications of GenAI is in natural language processing, which includes models like:
- Generative pre-trained transformer (GPT), which generates human-like text based on a given prompt. GPTs predict the next word in a sentence based on its extensive training on diverse text data. GPT models are highly versatile and perform a wide range of natural language processing tasks. These models are capable of writing essays, composing emails, and answering questions.
- Visual generative models: In the visual domain, GenAI creates realistic images and videos, generates artwork, and enhances photos. These systems rely on vision-based neural networks and diffusion models, such as generative adversarial networks (GANs), that learn intricate patterns and structures from vast datasets and generate new, realistic-looking images, sounds, or other content. They also use GPT-like transformers for understanding and reasoning.
GenAI's ability to create and reduce repetition in generated content opens numerous opportunities to support the nonprofit sector in optimizing resources. This optimization comes from time and cost savings in processes such as creating communications materials or documents from scratch.