Abstract Most machine translation systems operate on the sentence-level while humans write and translate within a given context. Operating on individual sentences forces error-prone sentence segmentation into the machine translation pipeline. This limits the upper-bound[…]

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Abstract As artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g., clinical decision support, administrative tasks, and public health surveillance – it is perhaps more important than ever to reflect on[…]

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Abstract I discuss the application of Foundation Models in Astronomy through the collaborative efforts of the UniverseTBD consortium with a mission to democratize Science for everyone. One of our key objectives is to overcome the[…]

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Abstract We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dynamically segments input streams to create compressed anchor representations, achieving nearly lossless compression (12x)[…]

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Abstract Over the past three decades, the fields of automatic speech recognition (ASR) and machine translation (MT) have witnessed remarkable advancements, leading to exciting research directions such as speech-to-text translation (ST). This talk will delve[…]

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Abstract There is an enormous data gap between how AI systems and children learn language: The best LLMs now learn language from text with a word count in the trillions, whereas it would take a[…]

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