The COVID-19 crisis offers a unique opportunity for updates and innovations
Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an automated machine learning platform to enable predictive analytics for business users.
Machine learning AI-powered tools used in response to COVID-19 may improve certain human activities and provide key insights necessary to make certain personal or professional decisions. However, they also highlight some ubiquitous challenges that both machines and the people who create them face.
Still, advances in AI / machine learning before and during the COVID-19 pandemic cannot be ignored. This global economic and health crisis presents a unique opportunity for updates and innovation in modeling, provided certain underlying principles are adhered to.
Here are four industry truths (note: this is not an exhaustive list). My colleagues and I have found this to matter in any design climate, especially in a global pandemic.
Some success can be attributed to chance rather than thinking
When a large group of people are working together on a problem, success may become more likely. In historical examples such as the 2008 global financial crisis, several analysts have been credited with predicting the crisis. To some, this may seem a miracle, until you consider that Wall Street had more than 200,000 people, each making their own predictions. It then becomes less of a miracle than a statistically probable result. With so many people working on modeling and forecasting at the same time, it was very likely that someone just happened to get it right.
Similarly, COVID-19 involves many people, from statistical modelers and data scientists to vaccine specialists, and there is an overwhelming drive to find solutions and concrete data-driven answers. Following reasonable statistical accuracy coupled with machine learning and AI can improve these models and reduce the likelihood of false predictions resulting from too many predictions.
Automation, if used wisely, can help maintain productivity
Time management is essential in a crisis. Automation technology can be used not only as part of crisis resolution, but also as a tool to monitor the productivity and contributions of team members working on the solution. When modeling, automation can also greatly improve the speed of the results. Every second that software can automate for a model allows a data scientist (or even a medical scientist) to perform other more important tasks. User-friendly platforms in the market now give more people such as business analysts access to predictions from custom machine learning models.