4 things to remember when adapting AI/ML learning models during a pandemic
The COVID-19 crisis brings a unique opportunity for updates and innovation
Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an automated machine learning platform to empower business users with predictive analytics.
The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them.
Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed.
Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate.
Some success can be attributed to chance, rather than reasoning
When a big group of people is collectively working on a problem, success may become more likely. Looking at historic examples like the 2008 Global Financial Crisis, there were several analysts credited with predicting the crisis. This may seem miraculous to some until you consider that more than 200,000 people were working in Wall Street, each of them making their own predictions. It then becomes less of a miracle and more of a statistically probable outcome. With this many individuals simultaneously working on modeling and predictions, it was highly likely someone would get it right by chance.
Similarly, with COVID-19 there are a lot of people involved, from statistical modelers and data scientists to vaccine specialists, and there is also an overwhelming eagerness to find solutions and concrete data-based answers. Following appropriate statistical rigor, coupled with machine learning and AI, can improve these models and decrease the chances of false predictions that arrive from too many predictions being made.
Automation can help in maintaining productivity if used wisely
During a crisis, time-management is essential. Automation technology can be used not only as part of the crisis solution, but also as a tool for monitoring productivity and contributions of team members working on the solution. For modeling, automation can also greatly improve the speed of results. Every second a piece of software can perform automation for a model, it allows a data scientist (or even a medical scientist) to conduct other more important tasks. User-friendly platforms in the market now give more people, like business analysts, access to predictions from custom machine learning models.