We've always been interested in artificial intelligence here at MOTHandRUST. We've posted about
. When the Center for Brains, Minds and Machines based at MIT asked us to
, we were thrilled (a crop of an early presentation seen above).
In the studio (via Skype), we've been chatting about AI on and off quite a bit throughout lockdown, as multiple AI-powered projects are being used to predict, explain and manage the different scenarios caused by the health crisis.
Below are some key thoughts brought up in our casual conversations...
An early fascinating example of AI's role in spotting an outbreak:
In the New Year’s Eve of last year, the artificial intelligence platform BlueDot picked up an anomaly: a cluster of unusual pneumonia cases in Wuhan, China. BlueDot, based in Toronto, Canada, uses natural language processing and machine learning to track, locate, and report on infectious disease spread. It sends out its alerts to a variety of clients, including health care, government, business, and public health bodies. It had spotted what would come to be known as Covid-19, nine days before the World Health Organisation released its statement alerting people to the emergence of a novel coronavirus.
AI has already had many roles in the global fight against the coronavirus, as well as in healthcare in general. For example, it's well known that developing a treatment is costly. Very costly. A huge part of this cost is eaten up by the money and time spent on unsuccessful trials. But with AI, scientists can use machine learning to model thousands of variables and how their compounded effect may influence the responses of human cells. Beyond diagnosis and treatment, AI has the potential to make getting appointments, paying insurance bills, and making other medical systems and procedures more efficient and cost effective. The list of potential roles AI can play goes on and on.
Data, data, data:
A big reason for AI not being able to do even more is that we simply did not have the data to deliver the solutions. There are so many issues around data that need to be addressed: our health care systems generally don't give up information easily to train AI systems, there are the privacy regulations, the error-filled health databases, and the data gathered being organised it in a way that's not useful for machines and so on.
A fascinating fact: the amount of medical data in the world now is estimated to double every couple of months or so.
As we sort out all the issues around data, AI lags a step behind us. Yet we still imagine that it possesses more foresight than we do... However, we believe that next time round, things will be better.