Machine Intelligence

Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.

Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.

Recent Publications

InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Johnny Lee
Alex Olwal
Ram Iyengar
Na Li
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI), ACM, pp. 23
Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari
Durga Sivasubramanian
Anmol Mekala
Ganesh Ramakrishnan
WACV 2024 (2024)
Solving olympiad geometry without human demonstrations
Trieu Trinh
Yuhuai Tony Wu
He He
Nature, 625 (2024), pp. 476-482
Multimodal Modeling for Spoken Language Identification
Shikhar Bharadwaj
Sriram (Sri) Ganapathy
Sid Dalmia
Wei Han
Yu Zhang
Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024) (2024)
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks
Marina Neseem
Conor McCullough
Randy Hsin
Chas Leichner
Shan Li
In Suk Chong
Andrew Howard
Lukasz Lew
Sherief Reda
Ville-Mikko Rautio
Daniele Moro
Conference on Computer Vision and Pattern Recognition (2024) (to appear)