Deploying at Scale
How can organizations deploy machine learning more rapidly and at greater scale? Learn from the experts. Make it simple for experts to share what they see and what they know. An expert can be a health worker, a surgeon, and someone who works in a warehouse: all have insights and information that when properly analyzed, can guide MACRO-EYES technology that learns to address problems before they occur.
The US Department of Defense has a strategy for operational intelligence built on the belief that “every soldier is a sensor” – a guiding concept for MACRO-EYES that we extend across domains. When every health worker, everyone who is part of a supply chain, every soldier becomes a sensor – existing infrastructure rapidly becomes resilient. Learn from the people who know. Learn from the people who are there now. There is also a phenomenology of AI to consider: use the system, and you understand it. Contribute to the system and you can learn its logic by tracing your input to its output.
The most radical element of our implementations of expert-in-the-loop machine learning is the capability to build systems for bio-surveillance / information infrastructure in months, rather than years at fraction of the conventional cost. Imagine being able to deploy, anywhere in the world – and within a few months - infrastructure to understand populations, the burden of disease, and the context for care. Satellites image every quadrant of the earth in increasing granularity and at decreasing cost. There are 4B smart phones on earth (and 5.7B adults); smart(ish) phones are present everywhere. The combination of expert-in-the-loop + insight machine learned from satellite imagery is extremely scalable. The ‘satellite + mobile’ dyad for learning at scale makes it easier to transition existing infrastructure to predictive systems, capable of anticipating risk and identifying improvements in care with time to prepare.