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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
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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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EACL 20232023Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only time-consuming but also involves significant resources and access to fine-tuning data.
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EACL 20232023Information extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization aims to abstract the most salient information into a generated text summary — thus potentially encountering the technical limitations of current text generation methods (e.g.,
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EACL 20232023Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system
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EACL 20232023A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or reading user’s messages. Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users. Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature
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EACL 20232023Pre-trained neural masked language models are often used for predicting a replacement token for a given sequence position, in a cloze-like task. However, this usage is restricted to predicting a single token, from a relatively small pre-trained vocabulary. Recent Sequence2Sequence pre-trained LMs like T5 do allow predicting multi-token completions, but are more expensive to train and run. We show that pre-trained
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Gretchen Ertl
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