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Amazon is bringing its generative AI listing smarts to more sellers, revealing today<\/a> that those in France, Germany, Italy, Spain, and the U.K. can now access tools designed to improve product listings by generating product descriptions, titles, and associated details.<\/p>\n Additionally, sellers can also \u201cenrich\u201d existing product listings by automatically adding missing information. <\/p>\n The announcement comes nine months after Amazon first revealed plans<\/a> to bring generative AI technology to sellers. The company hasn\u2019t been overly forthcoming with its availability on a market-by-market basis, but presumably it has largely been limited to the U.S. so far, though the company did quietly launch the tools in the U.K. earlier this month, according to an Amazon forum<\/a> post. And in its blog post<\/a> today, the company said that it rolled out this feature in the U.K. and some EU markets \u201ca few weeks ago,\u201d with more than 30,000 of its sellers apparently using at least of these AI-enabled listing tools in the intervening timeframe.<\/p>\n Amazon pitches these new tools as a way to enable sellers to list goods more quickly. The seller heads to their List Your Products<\/em> page as usual, where they can begin by entering some relevant keywords that describe their product and simply hit the Create button to formulate the basis of a new listing. The seller can also generate a listing by uploading a photo via the Product image tab.<\/p>\n Amazon will then magic up a product title, bullet point, and description which can be left as is, or edited by the seller. However, given the propensity for large language models (LLMs) to hallucinate<\/a>, it wouldn\u2019t be prudent to post a listing unchecked \u2014 a point that Amazon acknowledges by recommending that the seller reviews the copy \u201cthoroughly\u201d to ensure everything is correct. <\/p>\n \u201cOur generative AI tools are constantly learning and evolving,\u201d the company announced<\/a> in its U.K. forum two weeks back. \u201cWe\u2019re actively developing powerful new capabilities to make generated listings more effective, and make it even easier for you to list products.\u201d<\/p>\n Earlier this year, Amazon also launched a new tool that allows sellers to generate product listings by posting a URL<\/a> to their existing website. It\u2019s not clear when, or if, Amazon will be extending this functionality to Europe or other markets outside the U.S.<\/p>\n While Amazon is no stranger to AI and machine learning across its vast e-commerce empire, bringing any form of AI to European markets raises some potential issues around regulation. There\u2019s GDPR<\/a> on the data privacy side for starters, not to mention the Digital Services Act (DSA) on the algorithmic risk side, with Amazon\u2019s online store designated as a<\/a> Very Large Online Platform (VLOP) for the purposes of ensuring transparency in the application of AI.<\/p>\n For context, Meta last week was forced to pause plans<\/a> to train its AI on European users\u2019 public posts following regulatory pressure. And Amazon itself has faced the wrath of EU regulators in the past over its mis-use of merchant data<\/a>, when it was alleged that Amazon tapped non-public data from third-party sellers to benefit its own competing business as a retailer. And just this month, U.K. retailers hit Amazon with \u00a31.1 billion lawsuit<\/a> over similar accusations.<\/p>\n While Amazon\u2019s latest foray into generative AI is a different proposition, it will have had to have trained its LLMs on some sort of data \u2014 what data this is, precisely, isn\u2019t clear. In its initial announcement last September<\/a>, Amazon shared a quote from its VP of Selection and Catalog Systems, Robert Tekiela<\/a>, which referred to \u201cdiverse sources of information.\u201d<\/p>\n With our new generative AI models, we can infer, improve, and enrich product knowledge at an unprecedented scale and with dramatic improvement in quality, performance, and efficiency. Our models learn to infer product information through the diverse sources of information, latent knowledge, and logical reasoning that they learn. For example, they can infer a table is round if specifications list a diameter or infer the collar style of a shirt from its image<\/em>.<\/p>\n Robert Tekiela, VP of Amazon Selection and Catalog Systems<\/p>\n<\/blockquote>\n TechCrunch has reached out to Amazon for comment on these various issues, and will update when \u2014 or if \u2014 we hear back. <\/p>\n<\/div>\nThe data question<\/h2>\n
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