Customer-obsessed science
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April 27, 20264 min readA new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations.
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April 15, 20268 min read
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April 7, 202613 min read
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April 1, 20265 min read
Featured news
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Nature Communications2024While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance
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2024We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challenge of centering the object within the robot’s camera view. Our method builds a visual language pose graph (VLPG) that functions as a spatial map of VL embeddings
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KDD 2024 Workshop on GenAI Evaluation2024Large language models (LLMs) can be prone to hallucinations —generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent. In this work, we address several challenges for post-hoc hallucination detection in production settings. Our pipeline for hallucination detection entails: first, producing a confidence score representing the likelihood that a generated
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American Journal of Qualitative Research2024Qualitative sampling in the age of Big Data requires tactful negotiation. Although qualitative research aims to explore the depth as opposed to breadth of experiences, opinions, or beliefs of individuals regarding a unique phenomenon, stakeholders or sponsors might not always be convinced that small sample sizes can yield big results. Intimate population awareness, identification of attributes of importance
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Big Data Infrastructure Technologies for Data Analytics2024This chapter discusses SageMaker - a fully managed machine learning (ML) service provided by Amazon Web Services (AWS). Being a fully managed service, means that a user does not have to deal with hardware setup, patching, management, backups etc. All this is taken care of by the service provider. The user can choose from a wide variety of computing instance types that are optimized for different tasks,
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