Microsoft Technology Licensing, LLC

Google Inc.

Gagan Bansal

Yonghui Wu

Brian Strope

Timothy Youngjin Sohn

Saleema Amin Amershi

Tobias Kaufmann

Adam Fourney

Yun-hsuan Sung

Walter Sun

Benjamin N Lee

Gregory R Bullock

Paul Roland Lambert

Vijay Krishna Narayanan

Xu Chen

Matthew Vincent Dierker

Jacqueline Amy Tsay

Mayank Shrivastava

Victor Chukwuma Dibia

Debraj GuhaThakurta

Katherine Anna Evans

Cole Austin Bevis

Tobias Bosch

Yinan Wang

Kaushik Roy

Justin Tzi-jay Lu

Amita Surendra Gajewar

Do Kook Choe

Ettore Randazzo

Rami Eid Sammouf Al-Rfou'

Konstantin Golyaev

Systems and methods for directing behavior of a generative artificial intelligence (AI) system are provided. In particular, a computing device may obtain an input prompt associated with a requested task for one or more generative artificial intelligence (AI) systems, obtain one or more attributes based on the input prompt, modify the input prompt based on the one or more embedded attributes, and provide the modified input prompt to the one or more generative AI systems.

Embedded attributes for modifying behaviors of generative AI systems

A computing system is described that includes user interface components configured to receive typed user input; and one or more processors. The one or more processors are configured to: receive, by a computing system and at a first time, a first portion of text typed by a user in an electronic message being edited; predict, based on the first portion of text, a first candidate portion of text to follow the first portion of text; output, for display, the predicted first candidate portion of text for optional selection to append to the first portion of text; determine, at a second time that is after the first time, that the electronic message is directed to a sensitive topic; and responsive to determining that the electronic message is directed to a sensitive topic, refrain from outputting subsequent candidate portions of text for optional selection to append to text in the electronic message.

Selective text prediction for electronic messaging

A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.

Predictive modeling across multiple horizons combining time series and external data

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Sunnyvale, CA, United States of America

Gagan Bansal