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Shipping power of AI , but only if quality data is at the core of every algorithm used .
Scaling customer contributions
Even if a model is trained on the highestquality dataset , its knowledge is still finite . Since every larger-than-parcel item is unique , AI may successfully compile the general attributes of an item , but any dimensions generated will be ballpark figures . That ’ s why customer input is the final piece of the puzzle to creating an accurate algorithm for ugly freight shipping .
With every armoire and ottoman shipment input into an AI tool , the data used to estimate future shipment dimensions and weight will improve . Add in context inputted by the customer for each shipment ( differentiating a sleeper sofa from a couch , or a 20th-century piece from a fast-furniture special ), and an algorithm is well on its way to becoming incredibly accurate over time .
AI ’ s expansion into the logistics space has been constrained thus far by a myriad of factors , chief among them being high cost , perceived complexity , and the technology ’ s current inability to deliver on years of lofty promises . Despite the industry ’ s trepidation and muddled forecasts of the technology ’ s future impacts , however , the AI revolution is still coming to the logistics space . If ugly freight shippers embrace AI , algorithms are trained on high-quality data , and customer input is ongoing , AI can and will transform transport . ■
For a list of the sources used in this article , please contact the editor .
Jami Caruso www . uship . com
Jami Caruso is the VP of Customer Operations and Home Delivery at uShip , the world ’ s largest and most trusted online transportation marketplace . People and businesses use uShip because it ’ s easy and affordable to price , book , and ship everything from cars to cranes , freight to furniture , and households to horses — whether it ’ s going local or long distance .
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