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4 Ways Nonprofits Can Start Using AI in 2024

Nonprofit Tech for Good

The nonprofit sector, often stretched for resources and expertise, is uniquely positioned to benefit from the efficiencies and opportunities offered by Artificial Intelligence (AI). Additionally, nonprofits can create their own custom-trained GPT chatbot with their custom data. The ChatGPT Plus Plan offers immense value.

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Preparing Your Association for AI

Association Analytics

Types of AI Tools To start, it’s important to know about some of the models already available to the public. The most popular model today is called a Large Language Model (LLM) , which is trained on massive text datasets. LLMs are meant to produce conversational human language responses.

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How to Enhance Your Nonprofit’s Written Content with Artificial Intelligence

Nonprofit Tech for Good

” Its response neatly explained the nitty-gritty: “ ChatGPT is a large language model (LLM) developed by OpenAI. It is trained on a massive dataset of text and code, and it can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

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RAG-Enhanced Conversational AI: A Comprehensive Guide

Forum One

The introduction of Retrieval-Augmented Generation, or RAG, now allows AI applications to enhance the capabilities of machine learning models by integrating them with a retrieval component. Each type of solution has benefits and drawbacks, so it’s important to understand the landscape and map the right solution to the use case at hand.

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If Racial Equity is Our North Star, How Can We Navigate Racial Bias in AI/LLMs?

John Kenyon

For nonprofits and grantmaking organizations committed to racial equity, using artificial intelligence (AI) and large language models (LLMs) raises important concerns. However, they also offer intriguing potential benefits. The Bias Issue AI systems like LLMs are trained on vast datasets of text, primarily from the internet.

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#DontTrainOnMe: Are you Polluting your LLM Brand?

Whole Whale

There is a massive risk with using LLMs that train on your data, and part of that risk is that we don’t fully understand the scale of that risk as it gets bigger. This is a potent metaphor as LLMs (Large Language Models) are described as being trained on oceans of web and other data. Bigger is different.

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Top Ten Data Challenges (And Solutions) for Associations

Association Analytics

Older models tended to be AMS-centric, leading to siloed data, static reports, and that trapped feeling. It can be a challenge to get everyone on board with one language when things have been done differently in the past. You may also consider training for all employees to become more familiar with how your association uses data.