Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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The logistics giant’s “deep learning models” incorporate weather and traffic conditions for more accurate projections, CEO Raj Subramaniam said at a CNBC event.
FedEx has tapped into machine learning for the past couple of years to improve estimated delivery time accuracy, and its precision is expected to improve with continued use.
The company’s interest in machine learning underscores its transformation over the past few years to become what Subramaniam called “a data-driven, digital-first company.” FedEx is particularly interested in leveraging the information it gathers from the 16 million packages it delivers daily.
“When you see a FedEx truck on the street, you see a truck with FedEx packages,” Subramaniam said. “I see logistics intelligence on that truck.”
Harnessing the power of machine learning can help FedEx maximize the value of this data. The logistics giant is currently using machine learning to develop more accurate volume forecasts in its Ground unit and provide shippers with predictive carbon emissions data through its FedEx Sustainability Insights platform, executives said on a June earnings call.
In terms of using AI-powered language models, Subramaniam said FedEx has “a head start,” with its data organized on the right platform to make the most out of the technology.
“We need to have data platforms that these models can run on so that we can create insights,” Subramaniam said.
Rival UPS is also tapping into emerging technological tools to strengthen its operations. It adjusted the flow of packages earlier this year via machine learning in response to shrinking demand. CEO Carol Tomé said in August that UPS’ network planning tools are now able to “do in an afternoon what used to take a team of engineers months to do.”
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A dynamic policy landscape and ongoing de minimis scrutiny are shaping an era of uncertainty for apparel brands and retailers.
The discount retailer has already cut 1,000 product types from its assortment to make shelf space for in-demand inventory.
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A dynamic policy landscape and ongoing de minimis scrutiny are shaping an era of uncertainty for apparel brands and retailers.
The discount retailer has already cut 1,000 product types from its assortment to make shelf space for in-demand inventory.
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