Artificial intelligence

AI model boosts accuracy in ranking harmful genetic variants

Researchers have unveiled a new AI model that ranks genetic variants based on their severity. The approach combines deep evolutionary signals with population data to highlight clinically relevant mutations.

The popEVE system integrates protein-scale models with constraints drawn from major genomic databases. Its combined scoring separates harmful missense variants more accurately than leading diagnostic tools.

Clinical tests showed strong performance in developmental disorder cohorts, where damaging mutations clustered clearly. The model also pinpointed likely causal variants in unsolved cases without parental genomes.

Researchers identified hundreds of credible candidate genes with structural and functional support. Findings suggest that AI could accelerate rare disease diagnoses and inform precision counselling worldwide.

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Meta expands global push against online scam networks

The US tech giant, Meta, outlined an expanded strategy to limit online fraud by combining technical defences with stronger collaboration across industry and law enforcement.

The company described scams as a threat to user safety and as a direct risk to the credibility of its advertising ecosystem, which remains central to its business model.

Executives emphasised that large criminal networks continue to evolve and that a faster, coordinated response is essential instead of fragmented efforts.

Meta presented recent progress, noting that more than 134 million scam advertisements were removed in 2025 and that reports about misleading advertising fell significantly in the last fifteen months.

It also provided details about disrupted criminal networks that operated across Facebook, Instagram and WhatsApp.

Facial recognition tools played a crucial role in detecting scam content that utilised images of public figures, resulting in an increased volume of removals during testing, rather than allowing wider circulation.

Cooperation with law enforcement remains central to Meta’s approach. The company supported investigations that targeted criminal centres in Myanmar and illegal online gambling operations connected to transfers through anonymous accounts.

Information shared with financial institutions and partners in the Global Signal Exchange contributed to the removal of thousands of accounts. At the same time, legal action continued against those who used impersonation or bulk messaging to deceive users.

Meta stated that it backs bipartisan legislation designed to support a national response to online fraud. The company argued that new laws are necessary to weaken transnational groups behind large-scale scam operations and to protect users more effectively.

A broader aim is to strengthen trust across Meta’s services, rather than allowing criminal activity to undermine user confidence and advertiser investment.

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New findings reveal untrained AI can mirror human brain responses

Researchers at Johns Hopkins report that brain-inspired AI architectures can display human-like neural activity before any training. Structural design may provide stronger starting points than data-heavy methods. The findings challenge long-held views about how machine intelligence forms.

Researchers tested modified transformers, fully connected networks, and convolutional networks across multiple variants. They compared untrained model responses with neural data from humans and primates viewing identical images. The approach allowed a direct measure of architectural influence.

Transformers and fully connected networks showed limited change when scaled. Convolutional models, by contrast, produced patterns that aligned more closely with human brain activity. Architecture appears to be a decisive factor early in development.

Untrained convolutional models matched aspects of systems trained on millions of images. The results suggest brain-like structures could cut reliance on vast datasets and energy-intensive computation. The implications may reshape how advanced models are engineered.

Further research will examine simple, biologically inspired learning rules. The team plans to integrate these mechanisms into future AI frameworks. The goal is to combine architecture and biology to accelerate meaningful advances.

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Governments urged to build learning systems for the AI era

Governments are facing increased pressure to govern AI effectively, prompting calls for continuous institutional learning. Researchers argue that the public sector must develop adaptive capacity to keep pace with rapid technological change.

Past digital reforms often stalled because administrations focused on minor upgrades rather than redesigning core services. Slow adaptation now carries greater risks, as AI transforms decisions, systems and expectations across government.

Experts emphasise the need for a learning infrastructure that facilitates to reliable flow of knowledge across institutions. Singapore and the UAE have already invested heavily in large-scale capability-building programmes.

Public servants require stronger technical and institutional literacy, supported through ongoing training and open collaboration with research communities. Advocates say that states that embed learning deeply will govern AI more effectively and maintain public trust.

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Japan plans large scale investment to boost AI capability

Japan plans to increase generative AI usage to 80 percent as officials push national adoption. Current uptake remains far lower than in the United States and China.

The government intends to raise early usage to 50 percent and stimulate private investment. A trillion yen target highlights the efforts to expand infrastructure and accelerate deployment across various Japanese sectors quickly.

Guidelines stress risk reduction and stronger oversight through an enhanced AI Safety Institute. Critics argue that measures lack detail and fail to address misuse with sufficient clarity.

Authorities expect broader AI use in health care, finance and agriculture through coordinated public-private work. Annual updates will monitor progress as Japan seeks to enhance its competitiveness and strategic capabilities.

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Mistral AI unveils new open models with broader capabilities

Yesterday, Mistral AI introduced Mistral 3 as a new generation of open multimodal and multilingual models that aim to support developers and enterprises through broader access and improved efficiency.

The company presented both small dense models and a new mixture-of-experts system called Mistral Large 3, offering open-weight releases to encourage wider adoption across different sectors.

Developers are encouraged to build on models in compressed formats that reduce deployment costs, rather than relying on heavier, closed solutions.

The organisation highlighted that Large 3 was trained with extensive resources on NVIDIA hardware to improve performance in multilingual communication, image understanding and general instruction tasks.

Mistral AI underlined its cooperation with NVIDIA, Red Hat and vLLM to deliver faster inference and easier deployment, providing optimised support for data centres along with options suited for edge computing.

A partnership that introduced lower-precision execution and improved kernels to increase throughput for frontier-scale workloads.

Attention was also given to the Ministral 3 series, which includes models designed for local or edge settings in three sizes. Each version supports image understanding and multilingual tasks, with instruction and reasoning variants that aim to strike a balance between accuracy and cost efficiency.

Moreover, the company stated that these models produce fewer tokens in real-world use cases, rather than generating unnecessarily long outputs, a choice that aims to reduce operational burdens for enterprises.

Mistral AI continued by noting that all releases will be available through major platforms and cloud partners, offering both standard and custom training services. Organisations that require specialised performance are invited to adapt the models to domain-specific needs under the Apache 2.0 licence.

The company emphasised a long-term commitment to open development and encouraged developers to explore and customise the models to support new applications across different industries.

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AI helps detect congenital heart defects in unborn babies

Mount Sinai doctors in New York City are the first to utilise AI to enhance prenatal ultrasounds and detect congenital heart defects more effectively. BrightHeart’s FDA-approved technology is now used at Mount Sinai-affiliated Carnegie Imaging for Women across three Manhattan locations.

Congenital heart defects affect about 1 in 500 newborns and often require urgent intervention.

A study in Obstetrics & Gynecology found AI-assisted ultrasounds detected major defects with over 97 percent accuracy, cut reading time by 18 percent, and raised confidence scores by 19 percent.

The study reviewed 200 fetal ultrasounds from 11 centres across two countries, with and without AI assistance, by obstetricians and maternal-fetal medicine specialists.

AI improved detection, confidence, and efficiency, especially in centres without specialised fetal heart experts.

Experts say AI can level the field of prenatal diagnosis and optimise patient care. Dr Lam-Rachlin and Dr Rebarber emphasised AI’s potential to standardise detection and urged further research for routine clinical use.

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NVIDIA platform lifts leading MoE models

Frontier developers are adopting a mixture-of-experts architecture as the foundation for their most advanced open-source models. Designers now rely on specialised experts that activate only when needed instead of forcing every parameter to work on each token.

Major models, such as DeepSeek-R1, Kimi K2 Thinking, and Mistral Large 3, rise to the top of the Artificial Analysis leaderboard by utilising this pattern to combine greater capability with lower computational strain.

Scaling the architecture has always been the main obstacle. Expert parallelism requires high-speed memory access and near-instant communication between multiple GPUs, yet traditional systems often create bottlenecks that slow down training and inference.

NVIDIA has shifted toward extreme hardware and software codesign to remove those constraints.

The GB200 NVL72 rack-scale system links seventy-two Blackwell GPUs via fast shared memory and a dense NVLink fabric, enabling experts to exchange information rapidly, rather than relying on slower network layers.

Model developers report significant improvements once they deploy MoE designs on NVL72. Performance leaps of up to ten times have been recorded for frontier systems, improving latency, energy efficiency and the overall cost of running large-scale inference.

Cloud providers integrate the platform to support customers in building agentic workflows and multimodal systems that route tasks between specialised components, rather than duplicating full models for each purpose.

Industry adoption signals a shift toward a future where efficiency and intelligence evolve together. MoE has become the preferred architecture for state-of-the-art reasoning, and NVL72 offers a practical route for enterprises seeking predictable performance gains.

NVIDIA positions its roadmap, including the forthcoming Vera Rubin architecture, as the next step in expanding the scale and capability of frontier AI.

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AWS launches frontier agents to boost software development

AWS has launched frontier agents, autonomous AI tools that extend software development teams. The first three – Kiro, AWS Security Agent, and AWS DevOps Agent – enhance development, security, and operations while working independently for extended periods.

Kiro functions as a virtual developer, maintaining context, learning from feedback, and managing tasks across multiple repositories. AWS Security Agent automates code reviews, penetration testing, and enforces organisational security standards.

AWS DevOps Agent identifies root causes of incidents, reduces alerts, and provides proactive recommendations to improve system reliability.

These agents operate autonomously, scale across multiple tasks, and free teams from repetitive work, allowing focus on high-priority projects. Early users, including SmugMug and Commonwealth Bank of Australia, report quicker development, stronger security, and more efficient operations.

By integrating frontier agents into the software development lifecycle, AWS is shifting AI from task assistance to completing complex projects independently, marking a significant step forward in what AI can achieve for development teams.

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Honolulu in the US pushes for transparency in government AI use

Growing pressure from Honolulu residents in the US is prompting city leaders to consider stricter safeguards surrounding the use of AI. Calls for greater transparency have intensified as AI has quietly become part of everyday government operations.

Several city departments already rely on automated systems for tasks such as building-plan screening, customer service support and internal administrative work. Advocates now want voters to decide whether the charter should require a public registry of AI tools, human appeal rights and routine audits.

Concerns have deepened after the police department began testing AI-assisted report-writing software without broad consultation. Supporters of reform argue that stronger oversight is crucial to maintain public trust, especially if AI starts influencing high-stakes decisions that impact residents’ lives.

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