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
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation
Chris Donahue
Dima Kuzmin
Judith Li
Kun Su
Mauro Verzetti
Qingqing Huang
Yu Wang
Vol. 38 No. 5: AAAI-24 Technical Tracks 5, AAAI Press(2024), pp. 4952-4960
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Video-to-music generation demands both a temporally localized high-quality listening experience and globally aligned video-acoustic signatures. While recent music generation models excel at the former through advanced audio codecs, the exploration of video-acoustic signatures has been confined to specific visual scenarios. In contrast, our research confronts the challenge of learning globally aligned signatures between video and music directly from paired music and videos, without explicitly modeling domain-specific rhythmic or semantic relationships. We propose V2Meow, a video-to-music generation system capable of producing high-quality music audio for a diverse range of video input types using a multi-stage autoregressive model. Trained on 5k hours of music audio clips paired with video frames mined from in-the-wild music videos, V2Meow is competitive with previous domain-specific models when evaluated in a zero-shot manner. It synthesizes high-fidelity music audio waveforms solely by conditioning on pre-trained general purpose visual features extracted from video frames, with optional style control via text prompts. Through both qualitative and quantitative evaluations, we demonstrate that our model outperforms various existing music generation systems in terms of visual-audio correspondence and audio quality. Music samples are available at tinyurl.com/v2meow.
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Securing the AI Software Supply Chain
Isaac Hepworth
Kara Olive
Kingshuk Dasgupta
Michael Le
Mark Lodato
Mihai Maruseac
Sarah Meiklejohn
Shamik Chaudhuri
Tehila Minkus
Google, Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043(2024)
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As AI-powered features gain traction in software applications, we see many of the same problems we’ve faced with traditional software—but at an accelerated pace. The threat landscape continues to expand as AI is further integrated into everyday products, so we can expect more attacks. Given the expense of building models, there is a clear need for supply chain solutions.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
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Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan
Karolina Dziugaite
Transactions on Machine Learning Research(2024)
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The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model, without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.
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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)
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Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language identification task. Prior techniques have been constrained to a single modality; however in the case of video data there is a wealth of other metadata that may be beneficial for this task. In this work, we propose MuSeLI, a Multimodal Spoken Language Identification method, which delves into the use of various metadata sources to enhance language identification. Our study reveals that metadata such as video title, description and geographic location provide substantial information to identify the spoken language of the multimedia recording. We conduct experiments using two diverse public datasets of YouTube videos, and obtain state-of-the-art results on the language identification task. We additionally conduct an ablation study that describes the distinct contribution of each modality for language recognition.
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Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen
Berivan Isik
Albert No
Sewoong Oh
International Conference on Machine Learning (ICML)(2024)
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We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L_2$ geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation or matrix factorization), rendering them incompatible with DP-FTRL type optimizers.
In this work, we tackle these issues by introducing a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP noise. Unlike previous approaches, our accounting algorithm directly operates in $L_2$ geometry, yielding MSEs that fast converge to those of the uncompressed Gaussian mechanism. Additionally, we extend the sparsification scheme to the matrix factorization framework under streaming DP and provide a precise accountant tailored for DP-FTRL type optimizers. Empirically, our method demonstrates at least a 100x improvement of compression for DP-SGD across various FL tasks.
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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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