#africanamericanenglishaae_ — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #africanamericanenglishaae_, aggregated by home.social.
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Cornell University: Amazon’s AI assistant struggles with diverse dialects, study finds. “A new Cornell study revealed that Amazon’s AI shopping assistant, Rufus, gives vague or incorrect responses to users writing in some English dialects, such as African American English (AAE), especially when prompts contain typos.”
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Cornell University: Amazon’s AI assistant struggles with diverse dialects, study finds. “A new Cornell study revealed that Amazon’s AI shopping assistant, Rufus, gives vague or incorrect responses to users writing in some English dialects, such as African American English (AAE), especially when prompts contain typos.”
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Cornell University: Amazon’s AI assistant struggles with diverse dialects, study finds. “A new Cornell study revealed that Amazon’s AI shopping assistant, Rufus, gives vague or incorrect responses to users writing in some English dialects, such as African American English (AAE), especially when prompts contain typos.”
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Cornell University: Amazon’s AI assistant struggles with diverse dialects, study finds. “A new Cornell study revealed that Amazon’s AI shopping assistant, Rufus, gives vague or incorrect responses to users writing in some English dialects, such as African American English (AAE), especially when prompts contain typos.”
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Howard University: Howard University and Google Research Enhance A.I. Speech Recognition of African American English. “Researchers collected 600 hours of data from users of different [African American English] dialects in an effort to address implicit barriers to improving [automatic speech recognition] performance. Thirty-two states are represented in the dataset.”
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Howard University: Howard University and Google Research Enhance A.I. Speech Recognition of African American English. “Researchers collected 600 hours of data from users of different [African American English] dialects in an effort to address implicit barriers to improving [automatic speech recognition] performance. Thirty-two states are represented in the dataset.”