In online retailing, the most common customer complaints are slow shipping and inability to try on clothes. Amazon conceived its Prime program to address the first concern. To answer the second, it built a virtual fitting room.
From server to smartphone, devices with less processing speed and memory require smaller networks. Instead of building and training separate models to run on a variety of hardware, a new approach trains a single network that can be adapted to any device.
A growing body of literature shows that some steps in AI’s forward march may actually move sideways. A new study questions advances in metric learning.
A growing body of literature shows that some steps in AI’s forward march may actually move sideways. A new study questions advances in metric learning.
From server to smartphone, devices with less processing speed and memory require smaller networks. Instead of building and training separate models to run on a variety of hardware, a new approach trains a single network that can be adapted to any device.
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.
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.
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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