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
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Zheng Xu
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
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Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions. We contribute two large language model (LLM) modules and a code interpreter as part of our framework. The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration. Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workflow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.
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PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 21410-21429
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This work studies algorithms for learning from aggregate responses. We focus on the construction of aggregation sets (called \emph{bags} in the literature) for event-level loss functions. We prove for linear regression and generalized linear models (GLMs) that the optimal bagging problem reduces to one-dimensional size-constrained $k$-means clustering. Further, we theoretically quantify the advantage of using curated bags over random bags. We propose the \texttt{PriorBoost} algorithm, which iteratively forms increasingly homogenous bags with respect to (unseen) individual responses to improve model quality. We also explore label differential privacy for aggregate learning, and provide extensive experiments that demonstrate that \PriorBoost regularly achieves optimal quality, in contrast to non-adaptive algorithms for aggregate learning.
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MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization
Han-Byul Kim
Joo Hyung Lee
Sungjoo Yoo
Hong-Seok Kim
Proc. The 38th Annual AAAI Conference on Artificial Intelligence (AAAI) (2024)
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Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a novel method called MetaMix which consists of bit selection and weight training phases. The bit selection phase iterates two steps, (1) the mixed-precision-aware weight update, and (2) the bit-search training with the fixed mixed-precision-aware weights, both of which combined reduce activation instability in mixed-precision quantization and contribute to fast and high-quality bit selection. The weight training phase exploits the weights and step sizes trained in the bit selection phase and fine-tunes them thereby offering fast training. Our experiments with efficient and hard-to-quantize networks, i.e., MobileNet v2 and v3, and ResNet-18 on ImageNet show that our proposed method pushes the boundary of mixed-precision quantization, in terms of accuracy vs. operations, by outperforming both mixed- and single-precision SOTA methods.
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Slow concept drift is a ubiquitous, yet under-studied problem in practical machine learning systems. Although recent data is more indicative of future data in these settings, naively prioritizing these instances runs the risk of losing valuable information from the past. We propose an optimization-driven approach towards balancing instance importance over large training windows. First, we model instance relevance using a mixture of multiple timescales of decay, allowing us to capture rich temporal trends. Second, we learn an auxiliary \textit{scorer model} that recovers the appropriate mixture of timescales as a function of the instance itself. Finally, we propose a nested optimization objective for learning the scorer, by which it maximizes forward transfer for the learned model. Experiments on a large real-world dataset of 39M photos over a 9 year period show upto 15\% relative gains in accuracy compared to other robust learning baselines. We replicate our gains on two collections of real-world datasets for non-stationary learning, and extend our work to continual learning settings where, too, we beat SOTA methods by large margins.
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TextMesh: Generation of Realistic 3D Meshes From Text Prompts
Christina Tsalicoglou
Fabian Manhardt
Michael Niemeyer
3DV 2024 (2024)
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The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion models. Naturally, the question arises if this can be also achieved in the generation of 3D content from such text prompts. To this end, a new line of methods recently emerged trying to harness diffusion models, trained on 2D images, for supervision of 3D model generation using view dependent prompts. While achieving impressive results, these methods, however, have two major drawbacks. First, rather than commonly used 3D meshes, they instead generate neural radiance fields (NeRFs), making them impractical for most real applications. Second, these approaches tend to produce over-saturated models, giving the output a cartoonish looking effect. Therefore, in this work we propose a novel method for generation of highly realistic-looking 3D meshes. To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D mesh extraction. In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.
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Efficient Language Model Architectures for Differentially Private Federated Learning
Zheng Xu
Yanxiang Zhang
Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024 (2024) (to appear)
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Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data ever leaving the devices.
SGD is the standard client optimizer for on device training in cross-device FL, favored for its memory and computational efficiency.
However, in centralized training of neural language models, adaptive optimizers are preferred as they offer improved stability and performance.
In light of this, we ask if language models can be modified such that they can be efficiently trained with SGD client optimizers and answer this affirmatively.
We propose a scale-invariant \emph{Coupled Input Forget Gate} (SI CIFG) recurrent network by modifying the sigmoid and tanh activations in the recurrent cell
and show that this new model converges faster and achieves better utility than the standard CIFG recurrent model in cross-device FL in large scale experiments.
We further show that the proposed scale invariant modification also helps in federated learning of larger transformer models.
Finally, we demonstrate the scale invariant modification is also compatible with other non-adaptive algorithms.
Particularly, our results suggest an improved privacy utility trade-off in federated learning with differential privacy.
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