AI for Good
The last few months have been a great learning experience in completing the AI for Good Specialisation from DeepLearning.AI through Coursera.
Created in collaboration with Microsoft AI for Good Lab, the course covered an overall framework for AI projects that can be applied across any number of scenarios. The three courses in this specialisation covered three projects focused on monitoring and reporting air quality showing the use of AI for public health, Applying AI for managing climate change by analysing data for wind energy forecasting, protecting wildlife species with biodiversity monitoring, and finally how AI plays a role in disaster management.
What I really liked about this course overall was the real-world data used throughout each module, with hands on labs specific to each case study. It wasn’t the easiest of courses to complete as there was plenty of technical concepts to understand when it came to AI technologies being applied for data analysis and prediction but was thoroughly enjoyable.
The modules and examples presented were anchored on the “Do no harm” principle – meaning that in general terms the use of AI does not disadvantage a group or the use of AI does not actively promote harm.
Highlights
Throughout the course the “AI for Good” theme was thoughtfully applied to how AI potentially can benefit how we approach future public health outcomes, mitigating or adapting to climate change and responding to natural disasters.
Very useful framing of the four phases of the disaster management cycle and the actions involved at each phase.
Ethical considerations and leadership guidelines when working with communities affected and what to do when applying AI technology in the field.
Thanks to Robert Monarch who instructed the course and of course Andrew Ng plus the many other experts who contributed.
Check out the work of AI for Good Lab Here: