11 Nov Machine Learning behind the scenes: Playing with synthetic data
Earlier this year, meldCX launched Concept SALi, a game-changing end-to-end AI solution by meldCX.
Commissioned by Australia Post and built in collaboration with technology partners AOPEN, Intel and Google, SALi is a self-service kiosk created to make the process of parcel lodgment seamless and contactless. It uses powerful machine learning and computer vision technology to scan and detect each parcel — verifying its size, weight, handwritten information, the sender’s identity, and shipping cost automatically.
David McNamara, General Manager Post Office Network, Australia Post says the kiosks aim to help transform customer experience. Outcomes of the project include:
- Easy customer experience with parcel delivery lodged in less than 2 minutes
- Queuing time reduced from 15 minutes to 5 minutes in peak times
- More accurate address recognition
- Elimination of paper forms
- Insight into customer behaviour through automatically gathered data analytics
“We want to keep delivering outstanding customer experiences to support this growth which is why innovative, intelligent technology like Concept SALi makes it much easier and faster for customers to lodge parcels, and provides more features that customers want, such as bulk lodgement,” said Mr. McNamara.
Introducing, SALi Game Engine (SAGE), a data engine model based on synthetic data.
As Peter Drucker famously said, “The best way to predict the future is to create it.”
Using synthetic datasets in a virtual world allows for deep learning across a variety of scenarios, many of which a treasure trove of real data isn’t available, are against privacy compliance, or are costly to gather.
Stay tuned for another article we have coming up about synthetic data, and its ethical and privacy compliance implications.
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