What is AI without Workflow?

Workflow turns AI predictions into business decisions

Photo by Jordan Madrid on Unsplash

If a tree falls in a forest and no one is around to hear it, does it make a sound?” — George Berkeley

This well-known philosophical experiment poses the question of observation and perception. Sound is an observation, but does it exist if no one is there to perceive or receive it?

AI Without Workflow

If a self-driving car can detect a human in the crosswalk, does it mean anything if the car cannot automatically apply the breaks to stop? In other words, if the person in the crosswalk is the observation, does it serve a purpose if no one, or in this case no thing, is there to receive and act on it?

https://medium.com/analytics-vidhya/yolov3-object-detection-in-tensorflow-2-x-8a1a104c46a8

Machine learning is a powerful tool capable of a variety of tasks. But in order to unlock the value of these predictions, the predictions need to turn into business decisions. This is done through workflow.

In the case of the car, the AI is processing video in real-time to make a prediction as to whether it sees an object in the road. If this prediction is part of a larger workflow you can logically program the car to say “if person in road, then stop”.

The Elipsa platform enables users to quickly and easily build predictive models without the technical jargon. Users can then stream future data against these models to continuously make predictions. Our integrations with partners, such as Losant, enable the predictions to plug into existing IoT workflows.

The combination of these two technologies is what enables users to turn predictions into automated business decisions.

AI for IoT

IoT sensors and machine data generate a wealth of information for AI to extract new insights from. One such use case is predictive maintenance.

In the area of predictive maintenance, users can build an outlier detection model to monitor the health of devices looking for abnormalities that are indicative of future failures.

Using our API-driven approach, these predictions can be connected into a workflow in the Losant platform. With these two systems combined, users can stream their real-time data from Losant to Elipsa, returning predictions as to the future health of the machine. These predictions can then be utilized by additional workflow capabilities to enable logical decisions such as “if outlier, then alert me” or in a more automated fashion “if outlier, then safely shutdown machine” or “if outlier, then adjust setting”.

Elipsa unlocks predictive analytics for users to implement AI without the need for a data scientist. Our integration into IoT partners such as Losant allows those predictions to be put to use in order to fully solve business problems with an end-to-end solution.

To get started, set up a Demo today to see how Elipsa is building Approachable AI in order to help unlock what’s possible.

Machine Learning enthusiast. CEO @ elipsa.ai

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