Customer-obsessed science
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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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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EMNLP 2020 Workshop on Eval4NLP2020In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test-sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model’s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision
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EMNLP 2020 Workshop on Noisy User-Generated Text (W-NUT)2020True-casing, the task of restoring proper case to (generally) lower case input, is important in downstream tasks and for screen display. In this paper, we investigate truecasing as an intrinsic task and present several experiments on noisy user queries to a voice-controlled dialog system. In particular, we compare a rule-based, an n-gram language model (LM) and a recurrent neural network (RNN) approaches
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EMNLP 2020 Workshop on PrivateNLP2020Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy-preserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distribution, and then projected back to the discrete
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EMNLP 20202020In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations
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EMNLP 20202020While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. We evaluate models on their generalizability to out-of-domain examples, responses
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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.
