As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
How do you match AI warnings about future events with what actually happens? A KDD test-of-time award recognizes a 2014 approach that modern systems still need.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.
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February 2, 202610 min readEvery NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed. -
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December 29, 20256 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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RecSys 20232023Being able to measure the success of online shopping interactions is crucial in order to evaluate and optimize the performance of e-commerce systems. It is especially challenging in the domain of voice shopping, typically supported by voice-based AI assistants. Unlike Web shopping, which offers a rich amount of behavioral signals such as clicks, in voice shopping a non-negligible amount of shopping interactions
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Voice conversion for Lombard speaking style with implicit and explicit acoustic feature conditioningInterspeech 2023 Workshop on Machine Learning Challenges for Hearing Aids2023Text-to-Speech (TTS) systems in Lombard speaking style can im-prove the overall intelligibility of speech, useful for hearing loss and noisy conditions. However, training those models requires a large amount of data and the Lombard effect is challenging to record due to speaker and noise variability and tiring recording conditions. Voice conversion (VC) has been shown to be a useful augmentation technique
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CIKM 20232023In e-commerce sites, customer questions on the product detail page express the customers’ information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey
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SIGIR 20232023Immersive technologies such as virtual reality (VR) and head-mounted displays (HMD) have seen increased adoption in recent years. In this work, we study two factors that influence users’ experience when shopping in VR through voice queries: (1) context alignment of the search environment and (2) the level of detail on the Search Engine Results Page (SERP). To this end, we developed a search system for VR
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SIGIR 20232023Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods in the literature primarily focus on disentangling multi-party conversations involving three or more speakers, which enables their models to explicitly or implicitly incorporate speaker-related feature signals while disentangling. Most existing models require a large amount of human
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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.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
