NFRA issued its first dedicated AI guidelines for banks and insurers, setting governance, data, risk management, and supervisory expectations for AI across the AI lifecycle. It includes restrictions on using personal data to train generative AI and classifies certain AI uses (e.g., credit approval) as high-risk.
Bangko Sentral ng Pilipinas (BSP) issued Memorandum M-2026-031 and a guidance paper, Governance Principles for Artificial Intelligence in Financial Services, urging financial institutions to adopt AI governance frameworks. The principles are described as non-binding and compliance is voluntary.
New York’s synthetic performer disclosure law took effect in June 2026, requiring ads that include an AI-generated person to include a conspicuous label disclosing use of a “synthetic performer.” First violations carry a $1,000 fine; repeat violations $5,000 each.
South Australia’s Premier says the state will draft and introduce new laws to regulate AI data centres, alongside a data centre strategy, citing water and energy concerns. The article describes the planned “data centre and AI infrastructure act” to be introduced to parliament.
Reports published by the National Academy of Sciences, Republic of Korea recommend securing SMR competitiveness and developing AI-based autonomous nuclear power plant operation technologies, alongside proposals for responsible AI use and R&D culture reforms.
Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness. Existing studies have verified that adaptively optimizing textual descriptions throughout the tracking process can effectively mitigate the semantic-visual mismatch induced by dynamic variations in target appearance, positi
Establishing semantic correspondence is a challenging task in computer vision, aiming to match keypoints with the same semantic information across different images. Benefiting from the rapid development of deep learning, remarkable progress has been made over the past decade. However, a comprehensive review and analysis of this task remains absent. In this paper, we present the first extensive sur
As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised. To address the threat, we propose proof-of-guardrail, a system that enables developers to provide cryptographic proof that a response is generated after a specific open-source guardrail. To gener
Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowering diverse human-computer interaction applications. However, existing models are still limited to coarse-grained emotion perception or deficient reasoning capabilities. To bridge this gap, we introduce EEmoDB, the largest image-evoked emotion understand
Accurately explaining hidden patterns in multi-aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata. However, labels and auxiliary data may be inaccurate (e.g. nonstandard, inconsistent), insufficient (e.g. static tabular metadata for time-dependent recordings), or unavailable. % We propose \fullmethod (\method), which leverages the knowledge of large lan
Capital remains concentrated in a few large bets, with infrastructure and AI platforms absorbing the biggest checks. The mix of an IPO-scale exit and private financing points to continued preference for scale, control, and strategic backers.
Capital remains concentrated in a few large bets, with infrastructure and AI platforms absorbing the biggest checks. The mix of an IPO-scale exit and private financing points to continued preference for scale, control, and strategic backers.
Capital remains concentrated in a few large bets, with infrastructure and AI platforms absorbing the biggest checks. The mix of an IPO-scale exit and private financing points to continued preference for scale, control, and strategic backers.
Senior AI appointments continue to reshape operating teams at telecom, mobility, and frontier-lab-adjacent organizations. The signal is less churn than capability stacking: companies are adding named leaders to formalize research, governance, and product execution.
Senior AI appointments continue to reshape operating teams at telecom, mobility, and frontier-lab-adjacent organizations. The signal is less churn than capability stacking: companies are adding named leaders to formalize research, governance, and product execution.
Senior AI appointments continue to reshape operating teams at telecom, mobility, and frontier-lab-adjacent organizations. The signal is less churn than capability stacking: companies are adding named leaders to formalize research, governance, and product execution.
Benchmark movement is narrow but useful: the cycle adds fresh scores for a single model across core reasoning and knowledge tests. That kind of clustered reporting matters because it shows where evaluation attention is landing, even without a leaderboard shake-up.
Benchmark movement is narrow but useful: the cycle adds fresh scores for a single model across core reasoning and knowledge tests. That kind of clustered reporting matters because it shows where evaluation attention is landing, even without a leaderboard shake-up.
Benchmark movement is narrow but useful: the cycle adds fresh scores for a single model across core reasoning and knowledge tests. That kind of clustered reporting matters because it shows where evaluation attention is landing, even without a leaderboard shake-up.