Understanding AI

AI Can Drive Your Nonprofit's Success

Artificial Intelligence (AI) can play a crucial role in modern fundraising, allowing nonprofit organizations to enhance their strategies and increase efficiency by leveraging data, automation, and personalized communication. Utilizing AI can help nonprofits better engage prospects and donors, developing and implementing more successful fundraising campaigns.

By integrating various AI capabilities in fundraising, nonprofits are better positioned to achieve their financial goals and further advance their mission.

Timeline of the Development of AI

A Brief History of AI

Human brainpower has fascinated us for a long time, and we have tried to emulate it using various gadgets. Modern computers supercharged the pursuit to mimic the human brain’s analysis and insight generation process. Because of decades of experimentation, AI has become more powerful and accessible than ever.

Since late 2022, we’ve seen the democratization of Large-Language Models (LLMs) through ChatGPT. The simple interface allows anyone to make requests in simple, natural language instead of computer code. At the same time, excellent image generation via simple text prompts also became possible, fueling the growth of StableDiffusion, MidJourney, and OpenAI’s DALL-E. We recently saw a demo of AI-generated sophisticated videos via simple text prompts. These developments indicate that tools will offer a one-stop shop experience from script generation to video creation, all with simple text commands.

Although the field of AI is only about eighty years old, given the current excitement over AI, it can be divided into two groups: Traditional AI and Modern AI.

What was your earliest experience with AI-driven applications?

Computer programming
Video games
Microsoft Clippy
Amazon shopping experience
Roomba

_{^{Image generated by ChatGPT}}

Traditional AI

Traditional AI is comprised of algorithms and techniques that group or separate objects, transactions, or entities via clustering, classification, or regression models. A familiar example is the prediction of whether someone would purchase something or at what price. “Based on your history, you may like these [things],” as seen on Amazon or Netflix, falls into this category using association rules or recommender systems.

A few examples of Traditional AI include machine learning classification techniques (e.g., fraudulent vs. non-fraudulent transactions), numeric prediction (e.g., following amount of next purchase), and segmentation (e.g., a group of customers likely to purchase the same item).

Can you think of ways AI has improved your online experiences or made life easier?

Not at all
To a moderate extent
To a great extent

Modern AI

“Modern” or generative AI writes complete essays and articles, creates stunning images, and builds believable videos by providing simple instruction in the natural language rather than waiting for algorithms to predict a transactional value.

Generative (Gen) AI can help with bulk text generation, such as large batches of personalized email messages, or specific purposes, such as a detailed report. Users can also now create images, audio, and video with a few prompts. In addition, LLMs can help with text summarization, entity extraction, sentiment analysis, and document classification. Combining different outputs and controlling the layouts can create complicated documents with text and images.

Which aspect of generative AI excites you the most for your professional use?

Generating personalized content like emails, messages, and documents
Creating images, audio, or videos from simple text prompts