Mohammed bin Rashid approved establishing an Artificial Intelligence and Data Authority. This is a formal government action creating a new AI/data governance body.
The European Commission has launched a call for experts to join the RAISE High-Level Academic Advisory Board, including an AI Science expert role. Applications are open.
Singapore is consulting on personal data rules for generative AI. The record indicates an open consultation by Singapore’s data protection regulator.
UNESCO and ICOM launched a global survey on the use of artificial intelligence in museums. This is a formal multilateral initiative to collect input on AI use in the sector.
The European Parliament approved amendments to the EU AI Act, including a ban on “nudifiers.”
We develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial \emph{geometric redundancy}, and that this redundancy can be exploited in two complementary ways. First,
Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from a representation gap: continuous approaches offer analytical precision but are visually abstract, whereas discrete methods provide intuitive clarity at the expense of geometric precision. To bridge this gap, we introduc
Complex medical reasoning requires integrating heterogeneous clinical evidence across multiple inference steps. Large language models (LLMs) now approach this through two routes: internalized reasoning and externalized agent scaffolding (frameworks that decompose problems collaboratively amongst multiple LLMs). To determine whether these routes are exclusive or complementary, we introduce MedicalA
We present the first systematic Membership Inference Attack (MIA) evaluation of LALMs. Using Multi-modal Blind Baselines based on textual, spectral and prosodic features, we demonstrate that common audio datasets exhibit near-perfect train/test separability (AUC ~ 1.0) even without model inference, thus MIA may primarily detect distribution shift. We therefore introduce a blind-baseline protocol t
Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices
Capital is clustering at the very top of the market, with late-stage public-market financing dominating the desk. The signal is less breadth than concentration: one company absorbs multiple large entries, leaving little room for early-stage dispersion.
Capital is clustering at the very top of the market, with late-stage public-market financing dominating the desk. The signal is less breadth than concentration: one company absorbs multiple large entries, leaving little room for early-stage dispersion.
Capital is clustering at the very top of the market, with late-stage public-market financing dominating the desk. The signal is less breadth than concentration: one company absorbs multiple large entries, leaving little room for early-stage dispersion.
Named moves here are about control points rather than volume: a CEO departure, then two founder formations. The desk matters when leadership changes or new teams crystallize around a clear mandate.
Named moves here are about control points rather than volume: a CEO departure, then two founder formations. The desk matters when leadership changes or new teams crystallize around a clear mandate.
Named moves here are about control points rather than volume: a CEO departure, then two founder formations. The desk matters when leadership changes or new teams crystallize around a clear mandate.
The benchmark desk is tracking a narrow slice of capability, with one model surfacing across multiple academic-style evaluations. That pattern matters because repeated entries on the same system make cross-benchmark comparison easier than isolated score bumps.
The benchmark desk is tracking a narrow slice of capability, with one model surfacing across multiple academic-style evaluations. That pattern matters because repeated entries on the same system make cross-benchmark comparison easier than isolated score bumps.
The benchmark desk is tracking a narrow slice of capability, with one model surfacing across multiple academic-style evaluations. That pattern matters because repeated entries on the same system make cross-benchmark comparison easier than isolated score bumps.