Customer-obsessed science
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
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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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COLING 20202020We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots
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Physical Review Research2020We compare the BFGS optimizer, ADAM and NatGrad in the context of VQES. We systematically analyze their performance on the QAQA ansatz for the transverse field Ising and the XXZ model as well as on overparametrized circuits with the ability to break the symmetry of the Hamiltonian. The BFGS algorithm is frequently unable to find a global minimum for systems beyond about 20 spins and ADAM easily gets trapped
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NeurIPS 2020 Workshop on KR2ML2020The predominant approach to visual question answering (VQA) relies on encoding the image and question with a “black-box” neural encoder and decoding a single token as the answer like “yes” or “no”. Despite this approach’s strong quantitative results, it struggles to come up with intuitive, human-readable forms of justification for the prediction process. To address this insufficiency, we reformulate VQA
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NeurIPS 2020 Workshop on Human in the Loop Dialogue Systems2020Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions. Motivated by how children learn their first language interacting with adults, this paper describes a new teachable AI system that is capable of learning new language nuggets called concepts, directly from end users using
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NeurIPS 2020 The Preregistration Workshop2020We propose a new framework for object detection that guides the model to explicitly reason about translation and rotation invariant object keypoints to boost model robustness. The model first predicts keypoints for each object in the image and then derives bounding-box predictions from the keypoints. While object classification and box regression are supervised, keypoints are learned through self-supervision
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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.
