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​​Speech-to-Retrieval (S2R): A new approach to voice search

Google Research Blog · research.google · October 7, 2025

Machine Intelligence

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MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

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Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8
#artificial intelligence Review Apr 2023

Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey

This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design and analyzes how efficiency constraints reshaping model design choices in practice affects deployability, robustness, and safety.

J. Zhong, Zheng Liu, Xiangshan Chen · 21 citations
#artificial intelligence Open access Oct 2022

Adaptive surrogate modeling for high-dimensional spatio-temporal output

An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.

B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al. · 17 citations

Time for AI (Ethics) Maturity Model Is Now

There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.

Ville Vakkuri, Marianna Jantunen, Erika Halme et al. · 17 citations · ⚡1