Customer-obsessed science
Research areas
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July 9, 202610 min readA new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts. -
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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SIGIR 2023 Workshop on eCommerce2023Over a period of years, search engines have become adept at understanding and providing relevant results for short user generated queries for monolingual search. However, the brevity of search queries can be a limitation for cross-lingual e-commerce search. Previous studies have demonstrated that discourse-level context information can improve machine translation (MT) for document translation but there
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Winter Simulation Conference 20232023Fulfillment centers in the E-commerce industry are highly complex systems that houses inventory and fulfill customer orders. One of the key processes at these centers involves translating customer demands into trucks and yard operations. Truck yards with operational issues can create delays in customer orders. In this paper, we show how a scalable cloud-based hybrid simulation model is used to improve yard
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NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, International Journal of Forecasting – Innovations in Hierarchical Forecasting2023Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coherence in the literature. Maintaining coherence while producing accurate forecasts can be
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SIGIR 2023 Workshop on eCommerce2023Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user
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SIGIR 2023 Workshop on eCommerce2023Pre-trained language models (PLM) excel at capturing semantic similarity in language, while in e-commerce, customer shopping behavior data (e.g., clicks, add-to-cart, purchases) helps establish connections between similar queries based on behavior on products. This work addressed the challenges of using sparse behavior data to build a robust query-to-query similarity prediction model and apply it to a product
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
