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AI learns human biases: In word vector space, “man is to computer programmer as woman is to homemaker,” as one paper put it. New research helps language models unlearn such prejudices.
Facebook uses automated systems to block hate speech, but hateful posts can slip through when seemingly benign words and pictures combine to create a nasty message. The social network is tackling this problem by enhancing AI’s ability to recognize context.
Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.
Conversational agents have a tough job following the zigs and zags of human conversation. They’re getting better at it — thanks to yesterday’s technology. Amazon recently improved the Alexa chatbot’s ability to identify the current topic of conversation.
Facebook uses automated systems to block hate speech, but hateful posts can slip through when seemingly benign words and pictures combine to create a nasty message. The social network is tackling this problem by enhancing AI’s ability to recognize context.
The words “big” and “large” have similar meanings, but they aren’t always interchangeable: You wouldn’t refer to an older, male sibling as your “large brother” (unless you meant to be cheeky). Choosing among words with similar meanings is critical in language tasks like translation.
Conversational agents have a tough job following the zigs and zags of human conversation. They’re getting better at it — thanks to yesterday’s technology. Amazon recently improved the Alexa chatbot’s ability to identify the current topic of conversation.
Language models can’t correct your misspellings or suggest the next word in a text without knowing what language you’re using. For instance, if you type “tac-,” are you aiming for “taco,” a hand-held meal in Spanish, or “taca,” a crown in Turkish?
The words “big” and “large” have similar meanings, but they aren’t always interchangeable: You wouldn’t refer to an older, male sibling as your “large brother” (unless you meant to be cheeky). Choosing among words with similar meanings is critical in language tasks like translation.
Language models can’t correct your misspellings or suggest the next word in a text without knowing what language you’re using. For instance, if you type “tac-,” are you aiming for “taco,” a hand-held meal in Spanish, or “taca,” a crown in Turkish?
Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.
AI learns human biases: In word vector space, “man is to computer programmer as woman is to homemaker,” as one paper put it. New research helps language models unlearn such prejudices.
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