What do ‘AI for Social Good’ projects need? Here are 7 key components.
By Anna Bethke – At their core, ‘AI for Social Good’ projects use artificial intelligence (AI) hardware and software technologies to positively impact the well-being of people, animals or the planet – and they span most, if not all, of the United Nations Sustainable Development Goals (SDGs).
The range of potential projects continues to grow as the AI community advances our technology capability and better understands the problems being faced.
Our team of AI researchers at Intel achieved success by working with partners to understand the problems, collecting the appropriate data, retraining algorithms, and molding them into a practical solution.
At their core, an AI for Social Good project requires the following elements:
- A problem to solve, such as improving water quality, tracking endangered species, or diagnosing tumors.
- Partners to work together in defining the most complete view of the challenges and possible solutions.
- Data with features that represent the problem, accurately labeled, with privacy maintained.
- Compute power that scales for both training and inference, no matter the size and type of data, or where it lives. An example of hardware choice is at ai.intel.com/hardware.
- Algorithm development, which is the fun part! There are many ways to solve a problem, from a simple logistic regression algorithm to complex neural networks. Algorithms match the problem, type of data, implementation method, and more.
- Testing to ensure the system works in every way we think it should, like driving a car in rain, snow, or sleet over a variety of paved and unpaved surfaces. We want to test for every scenario to prevent unanticipated failures.
- Real-world deployment, which is a critical and complicated step that should be considered right from the start. Tested solutions need a scalable implementation system in the real world, or risk its benefits not seeing the light of day.