Amazon has launched a brand new generative AI device that creates copy listings for customers promoting gadgets on the corporate’s e-commerce platform.
Designed to simplify the promoting course of, the brand new device reduces the necessity for sellers to enter many items of particular product information when producing product descriptions. As an alternative, customers can now enter a short description of the product they’re itemizing on the market – Amazon mentioned this generally is a few phrases or sentences – and the device will generate the mandatory copy, which sellers can then evaluate and refine earlier than importing their merchandise to the Amazon catalog.
“These new capabilities will assist sellers create high-quality listings with much less effort and current clients with extra full, constant, and interesting product info”, Amazon mentioned in a weblog submit asserting the device.
The brand new generative AI device is fueled by a big language mannequin (LLM) that Amazon has been growing internally, as revealed by CEO Andy Jassy in the course of the firm’s first-quarter earnings name in April. Initially constructed to assist its good assistant, Alexa, Jassy informed analysts on the decision that Amazon’s LLM mannequin contained “a few hundred million endpoints” that have been getting used throughout leisure, purchasing, and good houses.
That very same month, Amazon’s cloud computing division, AWS, launched Bedrock, a basis mannequin API service that permits small corporations who lack the mandatory individuals energy to develop their very own LLMs to entry pre-trained fashions, together with these constructed by AI21 Labs, Anthropic, and Stability AI.
“With our new generative AI fashions, we will infer, enhance, and enrich product data at an unprecedented scale and with dramatic enchancment in high quality, efficiency, and effectivity,” mentioned Robert Tekiela, vp of Amazon choice and catalog techniques, in feedback posted alongside the announcement.
“Our fashions study to deduce product info by means of the various sources of data, latent data, and logical reasoning that they study. For instance, they’ll infer a desk is spherical if specs listing a diameter or infer the collar type of a shirt from its picture,” he mentioned.
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