Artificial intelligence

EU split widens over ban on AI nudification apps

European lawmakers remain divided over whether AI tools that generate non-consensual sexual images should face an explicit ban in the EU legislation.

The split emerged as debate intensified over the AI simplification package, which is moving through Parliament and the Council rather than remaining confined to earlier negotiations.

Concerns escalated after Grok was used to create images that digitally undressed women and children.

The EU regulators responded by launching an investigation under the Digital Services Act, and the Commission described the behaviour as illegal under existing European rules. Several lawmakers argue that the AI Act should name pornification apps directly instead of relying on broader legal provisions.

Lead MEPs did not include a ban in their initial draft of the Parliament’s position, prompting other groups to consider adding amendments. Negotiations continue as parties explore how such a restriction could be framed without creating inconsistencies within the broader AI framework.

The Commission appears open to strengthening the law and has hinted that the AI omnibus could be an appropriate moment to act. Lawmakers now have a limited time to decide whether an explicit prohibition can secure political agreement before the amendment deadline passes.

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MIT researchers unveil EnCompass for AI agent search

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Asari AI have introduced EnCompass, a framework designed to enhance how AI agents interact with large language models.

The system improves agent performance by automatically backtracking when errors occur and running multiple execution paths in parallel to identify the most effective outcome.

Programming AI agents traditionally requires extensive additional code to handle model mistakes. EnCompass removes that burden by embedding retry and search logic directly into execution.

Developers annotate key decision points, allowing the framework to explore alternative reasoning paths while preserving the agent’s original workflow structure.

Efficiency gains appear significant. Trials show coding effort for search implementation reduced by as much as 80%, while accuracy in code translation tasks improved between 15% and 40%.

Researchers demonstrated the framework’s ability to optimise repository translation and rule discovery across complex digital systems.

Future applications extend to large-scale software maintenance, scientific experimentation, and engineering design. Presented at NeurIPS, EnCompass positions structured search as key to advancing reliable, high-performance AI agent systems.

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Claude Opus 4.6 sets new benchmark for enterprise AI

Anthropic has released Claude Opus 4.6, its most advanced AI model to date, introducing significant improvements in coding performance, reasoning depth, and long-context comprehension.

Engineering workflows stand to benefit from stronger debugging, code review, and better large-scale repository management, while agentic task execution now runs for more extended periods with greater reliability.

The model’s 1M token context window, now in beta, enables sustained reasoning across vast datasets and extended conversations. Performance gains span multiple benchmarks, leading in agentic coding, multidisciplinary reasoning, and high-value knowledge work.

Information retrieval in long documents has also improved, addressing persistent industry concerns around context degradation.

Operational capabilities extend beyond software development into enterprise productivity. Financial analysis, research, and document workflows gain direct support, with spreadsheet and presentation integrations enhancing daily business use.

Within Claude Code, newly introduced agent teams allow multiple AI agents to collaborate autonomously on complex workloads.

Safety remains central, with expanded evaluations showing low misalignment risk backed by interpretability research and cybersecurity safeguards. Additional tools- adaptive thinking, effort scaling, and context compaction- add flexibility for deploying long-running AI systems at scale.

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Frontier and GPT-5.3-Codex mark major OpenAI expansion

OpenAI has unveiled Frontier, a new enterprise platform designed to help organisations build, deploy, and manage AI agents capable of executing real operational work.

The launch reflects accelerating enterprise adoption, with businesses reporting measurable productivity gains across manufacturing, finance, sales, and energy operations through agent deployment.

Frontier addresses a growing gap between AI model capability and real-world implementation. The platform equips AI agents with shared organisational context, system access, governance permissions, and feedback learning mechanisms.

By integrating across existing cloud infrastructure, enterprise software, and data environments, Frontier enables AI coworkers to operate across workflows rather than within isolated tools.

Alongside the platform release, OpenAI introduced GPT-5.3-Codex, its most advanced agentic coding model to date. The system combines the coding strength of earlier Codex iterations with expanded reasoning and professional task execution.

Benchmark performance leads across SWE-Bench Pro, Terminal-Bench, OSWorld, and GDPval, reflecting gains in software engineering, computer use, and knowledge work automation.

Cybersecurity capabilities also advance with the release. GPT-5.3-Codex includes enhanced vulnerability detection training and operates under strengthened safeguards designed to support defensive research while mitigating misuse.

Together, Frontier and GPT-5.3-Codex position AI agents as scalable digital coworkers capable of executing complex technical and enterprise workloads end-to-end.

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AI predicts brain age and cancer survival

Researchers at Mass General Brigham have unveiled BrainIAC, an artificial intelligence model capable of analysing brain MRI scans to predict age, dementia risk, tumour mutations, and cancer survival. The model demonstrates remarkable flexibility, handling a wide variety of medical tasks with high accuracy.

BrainIAC employs self-supervised learning to identify features from unlabeled MRI datasets, allowing it to adapt to numerous clinical applications without requiring extensive annotated data. Its performance surpasses that of conventional task-specific AI frameworks.

Tests on nearly 49,000 MRI scans across seven different tasks revealed the model’s ability to generalise across both healthy and abnormal images. It successfully tackled both straightforward tasks, such as scan classification, and complex challenges, including tumour mutation detection.

The team highlights BrainIAC’s potential to accelerate biomarker discovery, improve diagnostic tools, and personalise patient care. While results are promising, researchers note that further studies on additional imaging techniques and larger datasets are necessary to validate its broader clinical use.

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AI set to redefine Indian industry and economy

Artificial intelligence is becoming a cornerstone of India’s economic and industrial growth. The upcoming AI summit highlights the goal of building AI as national infrastructure, reflecting India’s languages, values, and knowledge systems.

Indian IT and service industries are moving beyond software maintenance to providing AI infrastructure and intelligent systems. Such a transformation can automate workflows, boost productivity, and create new opportunities domestically and globally.

Industrial AI is set to transform manufacturing, enabling next-generation factories through virtual twin technologies. AI grounded in physics and industrial knowledge allows faster prototyping, efficient resource use, and greater competitiveness for large enterprises and MSMEs.

Collaborations between NVIDIA and Dassault Systèmes showcase AI-driven factories and industrial intelligence. India’s talent, scale, and digital ecosystem position it to lead in industrial and generative AI, setting global technological and economic benchmarks.

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Claude AI will remain ad-free to preserve user trust and deep reasoning

Anthropic’s official announcement emphasises that Claude will not carry advertising or ad-influenced content within conversations, positioning the AI assistant as a trusted and distraction-free ‘space to think’ for tasks ranging from deep thinking and research to work and personal problem-solving.

The company argues that AI interactions differ fundamentally from search or social media, as users often share context-rich, sensitive information where commercial incentives could conflict with genuinely helpful responses.

In the post, Anthropic explains that while ads have a clear place in many digital products, introducing them into conversational AI would compromise usefulness and trust.

Instead, the company plans to generate revenue through enterprise contracts and paid subscriptions, continuing to invest in product improvements, integrations with third-party tools (e.g., Figma, Asana), and broader access initiatives, all without monetising attention or engagement directly.

The statement also notes that Claude’s conversation data is kept private and anonymous, and that ads could skew model incentives toward engagement metrics rather than solving user problems effectively.

Anthropic positions this approach as central to preserving Claude’s role as a dedicated thinking and productivity assistant.

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AI drives bold change with five technologies at Milano Cortina 2026

AI and digital technologies are set to play a central role at the Milano-Cortina 2026 Winter Olympic Games, positioning the event as a global showcase for large-scale AI deployment. Organisers aim to demonstrate how advanced technologies can transform major international events across media, operations, and public engagement.

Audience experience will be significantly reshaped through AI-powered broadcasting and digital platforms. Innovations include first-person drones following athletes in real time and AI-assisted replays that generate multi-angle freeze frames and performance data. In parallel, AI-driven tools on the official Olympic website and social media platforms will offer personalised highlights, summaries, and interactive content.

At the same time, technology will support athletes both on and off the field. AI systems will monitor and flag abusive online content, while dedicated digital applications will assist with training, injury prevention, and communication with family members during competitions.

Beyond digital innovation, the Games will also highlight sustainability through design. Transparent, reusable Olympic torches powered by biofuel and made from recycled materials will showcase how technology can support environmental responsibility alongside sporting tradition.

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AI demand squeezes memory supply, putting mid-range phones at risk

The rapid expansion of AI infrastructure, especially high-bandwidth memory (HBM) used in data centres and AI accelerators, is causing memory chip makers to prioritise production capacity for high-margin AI-related products, squeezing the supply of traditional DRAM and NAND used in consumer devices like smartphones, tablets and PCs.

Industry leaders, including Samsung, SK Hynix and Micron, are shifting wafer capacity toward AI-grade memory, and major cloud and hyperscale buyers (e.g., Nvidia, AWS, Google) are securing supply through long-term contracts, which reduces available inventory for mid-tier device manufacturers.

As a result, memory pricing has climbed sharply, forcing consumer electronics makers to raise retail prices, cut specs or downgrade other components to maintain margins.

Analysts warn the mid-range smartphone segment, typically priced between roughly $400–$600, faces a particular squeeze, with fewer compelling devices expected and slower spec improvements, as memory becomes a dominant cost driver and supply constraints persist.

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New AI system predicts iceberg movements to aid maritime safety

Scientists are applying AI to enhance iceberg tracking and movement prediction, using machine learning models that analyse historical drift patterns, ocean currents and weather data.

These AI systems aim to identify how icebergs behave over time and improve forecasts for their positions, which can help maritime operators and climate researchers anticipate hazards and plan safe routes.

Traditional methods for tracking icebergs, relying on satellite imagery and manual analysis, are limited by coverage gaps and delays. The new AI techniques can fill these gaps by generating continuous trajectory predictions, enabling more proactive monitoring over remote polar waters.

Researchers suggest that this approach could support shipping safety, offshore operations, and environmental management as climate change alters iceberg calving and drift behaviours.

This work reflects broader trends in using AI for environmental modelling, where machine learning augments physical models to better understand complex natural systems influenced by changing climate conditions.

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