Tag: ai/ml

Generative AI (gen AI) promises to usher in an era of transformation for quality, accessibility, efficiency, and compliance in the financial services industry. As with any new technology, it also introduces new complexities and risks. Striking a balance between harnessing its potential and mitigating its risks will be crucial for the adoption of gen AI …

Read more “Adapting model risk management for financial institutions in the generative AI era”

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Pretraining robust vision or multimodal foundation models (e.g., CLIP) relies on large-scale datasets that may be noisy, potentially misaligned, and have long-tail distributions. Previous works have shown promising results in augmenting datasets by generating synthetic samples. However, they only support domain-specific ad hoc use cases (e.g., either image or text only, but not both), and …

Read more “CtrlSynth: Controllable Image-Text Synthesis for Data-Efficient Multimodal Learning”

To stay competitive, businesses across industries use foundation models (FMs) to transform their applications. Although FMs offer impressive out-of-the-box capabilities, achieving a true competitive edge often requires deep model customization through pre-training or fine-tuning. However, these approaches demand advanced AI expertise, high performance compute, fast storage access and can be prohibitively expensive for many organizations. …

Read more “Generative AI foundation model training on Amazon SageMaker”

At Google Cloud, we’ve taken an open approach in building our Vertex AI platform — to provide the most powerful AI tools available along with unparalleled choice and flexibility. That’s why Vertex AI delivers access to over 160 models — including first-party, open-source, and third-party models — so you can build solutions specifically tailored to …

Read more “Announcing Anthropic’s upgraded Claude 3.5 Sonnet on Vertex AI”

The growing demand for personalized and private on-device applications highlights the importance of source-free unsupervised domain adaptation (SFDA) methods, especially for time-series data, where individual differences produce large domain shifts. As sensor-embedded mobile devices become ubiquitous, optimizing SFDA methods for parameter utilization and data-sample efficiency in time-series contexts becomes crucial. Personalization in time series is …

Read more “Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement”