Illinois enacted a law expanding the cyberbullying definition to include AI-generated images. This broadens the scope of conduct covered under the state’s cyberbullying framework.
The RBI proposes an AI governance framework for banks, including a mandate for human oversight of model-driven decisions. This is presented as a proposal rather than a finalized rule.
Brazos County adopted an AI policy that emphasizes human oversight. The news item indicates a formal local government policy action affecting AI use.
A bill moving in Congress would require tech companies to pay AI data center energy costs. This is a legislative proposal reported by CNBC, not a finalized law.
Rep. Sam Liccardo unveiled an AI workforce tax credit bill. This is a proposed legislative action and not yet enacted.
Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, ITU-T, and the O-RAN Alliance, increasingly characterize networks as AI-native systems in which high-level semantic reasoning layers operate above standardized control and data-plane functions. Although frontier-scale large language models (LLMs) such as Qwen2.5-7B and Olmo-3-7B demonstrate strong reasoning
This paper studies optimization for a family of problems termed $\textbf{compositional entropic risk minimization}$, in which each data's loss is formulated as a Log-Expectation-Exponential (Log-E-Exp) function. The Log-E-Exp formulation serves as an abstraction of the Log-Sum-Exponential (LogSumExp) function when the explicit summation inside the logarithm is taken over a gigantic number of items
In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks (GNNs). For efficient deployment of GNNs, we introduce a graph feature construction with $\mathrm{O}(\mathrm{nnz} + n)$ complexity, where $\mathrm{nnz}$ is the number of non-zero elements in the matrix and $n$ denotes the matrix dimension. We propose two schemes for estimating
Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether physics-based or data-driven, often emphasize temporal patterns while underexploring how multiple interacting factors jointly shape flood dynamics. To address this problem, we conduct a large-scale data-driven
Acute ischemic stroke (AIS) requires time-critical decision-making, where inaccurate interpretation of neuroimaging findings can lead to irreversible disability. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps from magnetic resonance imaging (MRI) are central to detecting acute infarction, yet generating factually reliable radiology reports directly from 3D MRI remai
Capital is concentrating in a small number of outsized transactions, with infrastructure and frontier AI still absorbing the largest checks. The pattern favors late-stage or balance-sheet-heavy financing over broad seed dispersion.
Capital is concentrating in a small number of outsized transactions, with infrastructure and frontier AI still absorbing the largest checks. The pattern favors late-stage or balance-sheet-heavy financing over broad seed dispersion.
Capital is concentrating in a small number of outsized transactions, with infrastructure and frontier AI still absorbing the largest checks. The pattern favors late-stage or balance-sheet-heavy financing over broad seed dispersion.
Named moves are still sparse, but the signal is in formation rather than churn: one hire and two founder launches. That points to early team-building around new ventures rather than major lab departures.
Named moves are still sparse, but the signal is in formation rather than churn: one hire and two founder launches. That points to early team-building around new ventures rather than major lab departures.
Named moves are still sparse, but the signal is in formation rather than churn: one hire and two founder launches. That points to early team-building around new ventures rather than major lab departures.
The benchmark desk is tracking a narrow slice of capability rather than a leaderboard shakeup. The current reads are useful as reference points, but they do not yet show a broad surface shift across tasks.
The benchmark desk is tracking a narrow slice of capability rather than a leaderboard shakeup. The current reads are useful as reference points, but they do not yet show a broad surface shift across tasks.
The benchmark desk is tracking a narrow slice of capability rather than a leaderboard shakeup. The current reads are useful as reference points, but they do not yet show a broad surface shift across tasks.