Capacity development

Deepfake videos raises environmental worries

Deepfake videos powered by AI are spreading across social media at an unprecedented pace, but their popularity carries a hidden environmental cost.

Creating realistic AI videos depends on vast data centres that consume enormous amounts of electricity and use fresh water to cool powerful servers. Each clip quietly produced adds to the rising energy demand and increasing pressure on local water supplies.

Apps such as Sora have made generating these videos almost effortless, resulting in millions of downloads and a constant stream of new content. Users are being urged to consider how frequently they produce and share such media, given the heavy energy and water footprint behind every video.

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Elon Musk launches AI-powered Grokipedia to rival Wikipedia

Elon Musk has launched Grokipedia, an AI-driven online encyclopedia developed by his company xAI. The platform, described as an alternative to Wikipedia, debuted on Monday with over 885,000 articles written and verified by AI.

Musk claimed the early version already surpasses Wikipedia in quality and transparency, promising significant improvements with the release of version 1.0.

Unlike Wikipedia’s crowdsourced model, Grokipedia does not allow users to edit content directly. Instead, users can request modifications through xAI’s chatbot Grok, which decides whether to implement changes and explains its reasoning.

Musk said the project’s guiding principle is ‘the truth, the whole truth, and nothing but the truth,’ acknowledging the platform’s imperfections while pledging continuous refinement.

However, Grokipedia’s launch has raised questions about originality. Several entries contain disclaimers crediting Wikipedia under a Creative Commons licence, with some articles appearing nearly identical.

Musk confirmed awareness of the issue and stated that improvements are expected before the end of the year. The Wikimedia Foundation, which operates Wikipedia, responded calmly, noting that human-created knowledge remains at the heart of its mission.

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AI200 and AI250 set a rack-scale inference push from Qualcomm

Qualcomm unveiled AI200 and AI250 data-centre accelerators aimed at high-throughput, low-TCO generative AI inference. AI200 targets rack-level deployment with high performance per pound per watt and 768 GB LPDDR per card for large models.

AI250 introduces a near-memory architecture that boosts adequate memory bandwidth by over tenfold while lowering power draw. Qualcomm pitches the design for disaggregated serving, improving hardware utilisation across large fleets.

Both arrive as full racks with direct liquid cooling, PCIe for scale-up, Ethernet for scale-out, and confidential computing. Qualcomm quotes around 160 kW per rack for thermally efficient, dense inference.

A hyperscaler-grade software stack spans apps to system software with one-click onboarding of Hugging Face models. Support covers leading frameworks, inference engines, and optimisation techniques to simplify secure, scalable deployments.

Commercial timing splits the roadmap: AI200 in 2026 and AI250 in 2027. Qualcomm commits to an annual cadence for data-centre inference, aiming to lead in performance, energy efficiency, and total cost of ownership.

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A generative AI model helps athletes avoid injuries and recover faster

Researchers at the University of California, San Diego, have developed a generative AI model designed to prevent sports injuries and assist rehabilitation.

The system, named BIGE (Biomechanics-informed GenAI for Exercise Science), integrates data on human motion with biomechanical constraints such as muscle force limits to create realistic training guidance.

BIGE can generate video demonstrations of optimal movements that athletes can imitate to enhance performance or avoid injury. It can also produce adaptive motions suited for athletes recovering from injuries, offering a personalised approach to rehabilitation.

The model merges generative AI with accurate modelling, overcoming limitations of previous systems that produced anatomically unrealistic results or required heavy computational resources.

To train BIGE, researchers used motion-capture data of athletes performing squats, converting them into 3D skeletal models with precise force calculations. The project’s next phase will expand to other types of movements and individualised training models.

Beyond sports, researchers suggest the tool could predict fall risks among the elderly. Professor Andrew McCulloch described the technology as ‘the future of exercise science’, while co-author Professor Rose Yu said its methods could be widely applied across healthcare and fitness.

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FDA and patent law create dual hurdles for AI-enabled medical technologies

AI reshapes healthcare by powering more precise and adaptive medical devices and diagnostic systems.

Yet, innovators face two significant challenges: navigating the US Food and Drug Administration’s evolving regulatory framework and overcoming legal uncertainty under US patent law.

These two systems, although interconnected, serve different goals. The FDA protects patients, while patent law rewards invention.

The FDA’s latest guidance seeks to adapt oversight for AI-enabled medical technologies that change over time. Its framework for predetermined change control plans allows developers to update AI models without resubmitting complete applications, provided updates stay within approved limits.

An approach that promotes innovation while maintaining transparency, bias control and post-market safety. By clarifying how adaptive AI devices can evolve safely, the FDA aims to balance accountability with progress.

Patent protection remains more complex. US courts continue to exclude non-human inventors, creating tension when AI contributes to discoveries.

Legal precedents such as Thaler vs Vidal and Alice Corp. vs CLS Bank limit patent eligibility for algorithms or diagnostic methods that resemble abstract ideas or natural laws. Companies must show human-led innovation and technical improvement beyond routine computation to secure patents.

Aligning regulatory and intellectual property strategies is now essential. Developers who engage regulators early, design flexible change control plans and coordinate patent claims with development timelines can reduce risk and accelerate market entry.

Integrating these processes helps ensure AI technologies in healthcare advance safely while preserving inventors’ rights and innovation incentives.

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Google Research applies AI across cancer, quantum computing and Earth science

Google Research has outlined how it tackles three major domains where foundational AI and science research are applied for tangible global effect, under a framework the team calls the ‘magic cycle’.

The three focus areas highlighted are fighting cancer with AI, quantum computing for medicines and materials, and understanding Earth at scale with Earth AI.

One of the flagship tools is DeepSomatic, an AI system developed to detect genetic variants in cancer cells that previous techniques missed. The tool partnered with a children’s hospital to identify ten new variants in childhood leukaemia samples. Significantly, DeepSomatic was applied to a brain cancer type it had never encountered before and still flagged likely causal variants.

Google Research is exploring the frontiers with its service chip (Willow) and algorithms like Quantum Echoes to simulate molecular behaviours with precision that classical computers struggle to reach. These efforts target improved medicines, better batteries and advanced materials by capturing quantum-scale phenomena.

Aiming to model complex interconnected systems, from weather and infrastructure to population vulnerability, the Earth AI initiative seeks to bring disparate geospatial data into unified systems. For example, predicting which communities are most at risk in a storm requires combining meteorological, infrastructure and socioeconomic data.

Google Research states that across these domains, research and applied work feed each other: foundational research leads to tools, which, when deployed, reveal new challenges that drive fresh research, the ‘magic cycle’.

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AI set to improve bowel cancer screening in Ireland

Ireland’s BowelScreen has joined the EU-backed Microb-AI-ome project with Viatel to enhance AI-driven colorectal cancer screening. The initiative aims to enhance early detection, improve patient outcomes, and reduce unnecessary colonoscopies across Europe.

Bowel cancer remains the second leading cause of cancer deaths in Europe, with over 360,000 new cases and 161,000 deaths reported in 2022.

The project uses AI to analyse gut microbiome data from participants’ stool samples. Three Irish research hospitals are enrolling patients, while Viatel has developed a secure, cloud-based data management platform using Microsoft Azure.

The system anonymises sensitive information, ensuring full compliance with GDPR and Irish legislation, while enabling AI to process vast datasets to identify cancer risks accurately.

BowelScreen’s Pádraic Mac Mathúna says AI can analyse millions of data points to assess individual cancer risk. Viatel’s James Finglas calls the platform ‘game-changing,’ noting its ability to pinpoint patients needing colonoscopies and improve population screening.

The project demonstrates how AI can be applied meaningfully in healthcare, supporting earlier detection and better patient outcomes.

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AMD powers US AI factory supercomputers for national research

The US Department of Energy and AMD are joining forces to expand America’s AI and scientific computing power through two new supercomputers at Oak Ridge National Laboratory.

Named Lux and Discovery, the systems will drive the country’s sovereign AI strategy, combining public and private investment worth around $1 billion to strengthen research, innovation, and security infrastructure.

Lux, arriving in 2026, will become the nation’s first dedicated AI factory for science.

Built with AMD’s EPYC CPUs and Instinct GPUs alongside Oracle and HPE technologies, Lux will accelerate research across materials, medicine, and advanced manufacturing, supporting the US AI Action Plan and boosting the Department of Energy’s AI capacity.

Discovery, set for deployment in 2028, will deepen collaboration between the DOE, AMD, and HPE. Powered by AMD’s next-generation ‘Venice’ CPUs and MI430X GPUs, Discovery will train and deploy AI models on secure US-built systems, protecting national data and competitiveness.

It aims to deliver faster energy, biology, and national security breakthroughs while maintaining high efficiency and open standards.

AMD’s CEO, Dr Lisa Su, said the collaboration represents the best public-private partnerships, advancing the nation’s foundation for science and innovation.

US Energy Secretary Chris Wright described the initiative as proof that America leads when government and industry work together toward shared AI and scientific goals.

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Celebrity estates push back on Sora as app surges to No.1

OpenAI’s short-video app Sora topped one million downloads in under a week, then ran headlong into a likeness-rights firestorm. Celebrity families and studios demanded stricter controls. Estates for figures like Martin Luther King Jr. sought blocks on unauthorised cameos.

Users showcased hyperreal mashups that blurred satire and deception, from cartoon crossovers to dead celebrities in improbable scenes. All clips are AI-made, yet reposting across platforms spread confusion. Viewers faced a constant real-or-fake dilemma.

Rights holders pressed for consent, compensation, and veto power over characters and personas. OpenAI shifted toward opt-in for copyrighted properties and enabled estate requests to restrict cameos. Policy language on who qualifies as a public figure remains fuzzy.

Agencies and unions amplified pressure, warning of exploitation and reputational risks. Detection firms reported a surge in takedown requests for unauthorised impersonations. Watermarks exist, but removal tools undercut provenance and complicate enforcement.

Researchers warned about a growing fog of doubt as realistic fakes multiply. Every day, people are placed in deceptive scenarios, while bad actors exploit deniability. OpenAI promised stronger guardrails as Sora scales within tighter rules.

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Samsung and SoftBank team up on AI-RAN for next-gen telecom

Samsung Electronics and SoftBank Corp. have collaborated to develop advanced AI-RAN technologies to enhance next-generation telecommunications infrastructure.

The partnership will combine Samsung’s expertise in network solutions with SoftBank’s extensive operational data to advance automation and intelligence in wireless networks.

The companies plan to explore how AI can optimise network efficiency, improve real-time decision-making, and dynamically manage radio resources to deliver faster, more reliable connections. AI-RAN technologies are expected to play a key role in the evolution of 6G networks, where managing complex data flows and ensuring energy-efficient operations will become essential.

Dr Junehee Lee, Executive Vice President at Samsung Electronics, said the collaboration represents ‘an important step toward realising autonomous networks powered by AI.’ He noted that such systems can predict network conditions, self-adjust to maintain quality of service, and reduce human intervention in operations.

Junichi Miyakawa, President and CEO of SoftBank, emphasised that the project aligns with SoftBank’s long-term vision to build smarter, more resilient telecommunications infrastructure. ‘By combining our operational insights with Samsung’s technology leadership, we aim to accelerate innovation in network automation and deliver superior experiences to users,’ he said.

The two companies will begin joint trials in Japan using Samsung’s virtualised RAN (vRAN) and Open RAN solutions. These trials will focus on applying machine learning models to radio network optimisation, particularly in urban environments with high data demand.

Both firms view AI-RAN as a foundation for future communications systems that can autonomously adapt to network load and interference, setting the stage for a new generation of intelligent, energy-efficient mobile connectivity.

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