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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MDPI Sensors Journal2023Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network
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IEEE ISM 20232023In this paper, we explore the intriguing effects of JPEG compression as a pre-processing technique for image classification tasks. Building upon the findings of a previous study by Friedland et al., which demonstrated that substantial JPEG compression does not significantly degrade classification accuracy, we investigate the potential benefits and limitations of this approach when applied to various classifiers
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CVPR 2023 Workshop on New Frontiers for Zero-Shot Image Captioning Evaluation2023In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using
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EMNLP 2023 Eighth Conference on Machine Translation (WMT23)2023Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood. In this paper, we perform a controlled experiment and compare a baseline metric that has not been trained on human evaluations (Prism) to a trained version of the same metric (Prism+FT). Surprisingly, we find that Prism+FT becomes more robust to machinetranslated references
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NeurIPS 2023 Workshop on Optimization for Machine Learning (OPT2023)2023Contrastive Language-Image Pre-training (CLIP) has shown remarkable success in the field of multimodal learning by enabling joint understanding of text and images. In this paper, we introduce a novel method called Multi-head CLIP, inspired by Stein Variational Gradient Descent (SVGD) and Sharpness-aware Minimization (SAM). Our approach aims to enhance CLIP’s learning capability by encouraging the model
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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.
