Predictions for the potential of artificial intelligence wax poetic — solutions from climate change to curing disease — but the everyday applications make it seem far more mundane, like a glorified clock radio.
Thankfully, the future may be closer than we think. And the miraculous feats are not happening in Silicon Valley X-Labs — in a plot twist, nonprofits are leading the charge in creating human-centered applications of the hottest AI technologies. From the simplest automated communications to contextual learnings based on analysis of deep data, these technologies have the potential to rapidly scale and improve the lives of our most underserved communities.
Take chatbots for example, a new spin on mobile messaging that has historically been human-powered. Organizations like mRelief have for years used simple mobile messaging to meet users where they’re at. Recently, tech nonprofits are taking a new approach. Raheem.ai, a Facebook Messenger bot for reporting and rating experiences with police officers, engages with users to walk them through reporting police incidents and provide follow-on support. The interactions are simple, but powerful.
Crisis Text Line still implements a human-to-human volunteer model, but the tech nonprofit has the largest open source database of youth crisis behavior in the country, and has been able to use AI to dramatically shorten response time for high-risk texters from 120 seconds to 39. Crisis Text Line leveraged machine learning to identify the term “ibuprofen” as 16 times more likely to predict the need for emergency aid than the word “suicide.” Now using AI, messages containing the word “ibuprofen” are prioritized in the queue.
Machine learning even allows you to select the energy source that powers your home appliances. WattTime creates software that enables smart hardware devices to prioritize clean energy with a simple flip of a switch. Their product relies on machine learning to detect when to tell smart devices like thermostats to pull from the power grid, based on surges in clean energy. This means your A/C may turn on five minutes earlier or later than it typically would, because the algorithms instruct your utilities to capitalize upon instances of excess clean energy from sources like windmills, thus minimizing the use of dirty power.
Quill, a free online tool that helps students measurably improve grammar and writing, discovered that natural-language processing was essential to remedy students’ struggles with sentence fragmentation. Using open source tools and online training programs, Quill’s technical team built its own fragment detection algorithm powered by a combination of machine learning and natural-language processing. Quill’s methodology is exemplary for resource-constrained tech nonprofits. It leveraged Wikipedia to amass a dataset of 100,000 high-quality sentences, integrated the natural-language processing tool Spacy.io to break the sentences down, and incorporated Tensorflow for data classification.
--edited for space from Nonprofits, not Silicon Valley startups, are creating AI apps for the greater good