Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains
Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains
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Google launches zero-trust, aggregated private analytics technology, integrating cryptography with a trusted execution environment.
Google’s research team has unveiled a new private analytics solution that leverages an innovative single-round lattice-based cryptographic aggregation protocol, working in tandem with a Trusted Execution Environment (TEE), to enable data aggregation without relying on any single trusted entity. This approach ensures that user data remains securely stored on the device while delivering group-level anonymized insights.
Physics AI research that’s shaping the industry.
Published breakthroughs pushing the state of the art.
Harness, Scaffold, and the AI Agent Terms Worth Getting Right
Harness, Scaffold, and the AI Agent Terms Worth Getting Right
Improving the academic workflow: Introducing two AI agents for better figures and peer review
Generative AI
Building better AI benchmarks: How many raters are enough?
Algorithms & Theory
TurboQuant: Redefining AI efficiency with extreme compression
Algorithms & Theory
Identifying Interactions at Scale for LLMs
Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features driving a prediction (Lundberg & Lee, 2017; Ribeiro et al., 2022); data attribution, which links model behaviors to influential training examples (Koh & Liang, 2017; Ilyas et al., 2022); and mechanistic interpretability, which dissects the functions of internal components (Conmy et al., 2023; Sharkey et al.
Protecting cities with AI-driven flash flood forecasting
Climate & Sustainability
Exploring the feasibility of conversational diagnostic AI in a real-world clinical study
Generative AI
Where wild things roam: Identifying wildlife with SpeciesNet
Climate & Sustainability
Teaching AI to read a map
Machine Perception
Beyond one-on-one: Authoring, simulating, and testing dynamic human-AI group conversations
Human-Computer Interaction and Visualization
How AI trained on birds is surfacing underwater mysteries
Climate & Sustainability