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G7 tightens AI safety guidance as SpaceX IPO dominates

19 items · 5 desks · 10 min read
Policy Highlights5
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Leaders’ call on a safer digital space for minors

G7 Leaders issue a call to governments and digital service providers to prioritize minors’ online safety, including safe-by-design measures, age assurance, parental controls, and risk management for digital services and conversational AI.

POLICY HIGHLIGHTS

Prime Minister Carney secures new partnerships in defence and critical minerals at the 2026 G7 Leaders’ Summit

Prime Minister Carney announced Canada is imposing new sanctions targeting Russia’s shadow fleet, energy revenues, defence industrial, and disinformation actors, targeting 162 individuals, entities, and vessels.

POLICY HIGHLIGHTS

Leaders’ statement for a more balanced, durable and resilient growth

G7 Leaders reaffirm commitments on global economic resilience and call for further discussions on opportunities and risks from frontier AI, including in the financial sector, and enhanced cyber information sharing. The statement also encourages dialogue on quantum preparedness.

POLICY HIGHLIGHTS

State of the Digital Decade 2026 - Closing structural gaps and mobilising investments for 2030 and beyond

The European Commission publishes the State of the Digital Decade 2026 report assessing progress toward the 2030 Digital Decade targets and highlighting gaps in areas such as computing capacity, cybersecurity, skills, and advanced digital take-up. It calls on Member States to use the next adjustment of national roadmaps to address gaps and strengthen EU-level coordination.

POLICY HIGHLIGHTS

Canada's Bill C-36 introduces privacy reforms, enforcement changes - IAPP

Bill C-36 is introduced in Canada and would implement privacy reforms, including changes to enforcement. The record is based on reporting and does not provide the bill text or official publication details.

POLICY HIGHLIGHTS

Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation

Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts. Ideally, these models provide the stability required for critical treatment planning and surgical intervention. However, standard deep learning models often suffer from miscalibration, yiel

RESEARCH

Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models

While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter-Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address

RESEARCH

Cosmos 3: Omnimodal World Models for Physical AI

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. By supporting highly flexible input-output configurations, Cosmos 3 seamlessly unifies critical modalities for Physical AI -- effectively subsuming vision-language models, video generators, worl

RESEARCH

Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information

The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision. While deep learning has shown success in controlled environments, its application to complex geological materials under conditions of incomplete information remains underexplored. This study presents an integrated framework for the

RESEARCH

From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization

Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propo

RESEARCH