Artificial intelligence Archives | Page 7 of 595

Google launches AI skills initiative to support Europe’s workforce transition

At the Future of Work Forum, Google introduced ‘AI Works for Europe’, a programme aimed at strengthening digital skills and supporting workforce adaptation to AI across the region.

Funding of $30 million will be directed through Google.org to expand training opportunities, alongside broader access to AI certification programmes designed to help individuals and businesses adopt new technologies in practical contexts.

A central focus involves preparing workers and students for labour market changes.

Partnerships with organisations such as INCO are supporting the development of targeted training programmes, particularly in sectors where demand for AI-related skills is increasing, including finance, logistics and marketing.

New educational pathways are also being introduced, including an expanded AI Professional Certificate available in multiple European languages. These initiatives aim to improve AI literacy and provide hands-on experience aligned with employer expectations.

Collaboration with local organisations and institutions remains a key element, reflecting a broader strategy to ensure access to training across different regions and communities.

Efforts to expand AI capabilities across Europe highlight the growing importance of skills development as AI becomes more integrated into economic activity.

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MIT research highlights embedded and enacted risks in AI

Generative AI offers major productivity and growth opportunities, but also brings new risks as organisations move from experiments to full deployment. MIT research highlights key risk areas, including training data, foundation models, user prompts, and system prompts.

Researchers identify two types of risk.

Embedded risks come from the technology itself, shaped by model behaviour, data quality, and vendor updates, and are mostly outside an organisation’s control.

Enacted risks arise from choices in deploying AI, from prompt design to agent permissions, and require strong governance.

Advanced uses such as retrieval-augmented generation (RAG) and autonomous AI agents increase exposure. RAG uses internal data to improve outputs, but may reveal sensitive information or control gaps. AI agents acting across multiple tools can lead to ‘autonomy creep,’ performing tasks without proper oversight.

To manage AI risk, organisations should map tools, assign ownership, track outputs, and use separate strategies for embedded and enacted risks. Vendor engagement, governance frameworks, and technical controls are essential for safe AI use.

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AI-powered MRI previews aim to reduce errors and rescans

Philips is creating AI-driven predictive MRI previews to improve scan planning and reduce operator variability. Using NVIDIA accelerated computing and foundation models, the system creates a pre-scan image to validate protocols, optimise positioning, and spot potential issues.

The technology is based on a dedicated MR foundation model trained on diverse datasets covering anatomies, field strengths, protocols, and artefacts.

When combined with NVIDIA’s NV‑Generate, NV‑Segment, and NV‑Reason models, the platform integrates image generation, segmentation, and interpretation. It creates a single intelligent workflow that supports consistent and efficient MRI procedures.

Predictive previews reduce rescans, enhance image quality, and increase technologist confidence, especially in complex exams or areas with limited expertise. Early guidance helps confirm protocols, optimise positioning, and flag issues that could affect diagnostic outcomes.

Philips envisions autonomous MRI, with AI monitoring image quality, guiding positioning, and assisting radiologists with actionable insights. Predictive imaging boosts consistency, efficiency, and access, improving patient experience and expanding MRI availability.

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xAI faces lawsuit over alleged misuse of AI image generation

Legal action has been filed against xAI in a US federal court, with plaintiffs alleging that its AI system Grok was used to generate harmful and explicitly manipulated images of minors.

The lawsuit claims that xAI failed to implement adequate safeguards to prevent the creation of such content, despite similar protections adopted by other AI developers.

According to the filing, the technology enabled the transformation of real images into explicit material without sufficient restrictions.

Plaintiffs seek to establish a class action, arguing that the company should be held accountable for both direct and third-party uses of its models. Legal arguments focus on whether responsibility extends to external applications built using the same underlying AI systems.

The case also highlights broader regulatory challenges surrounding AI-generated content, particularly the difficulty of preventing misuse when systems can modify real images. Questions around platform liability, safety standards, and enforcement are likely to shape future policy discussions.

Growing scrutiny of AI developers reflects increasing concern over how generative systems are deployed, especially in contexts involving sensitive or harmful content.

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Green light for massive UK AI tech park

North Lincolnshire Council has granted outline planning permission for the Elsham Tech Park, a proposed AI data centre campus that would rank among the largest of its kind in the UK.

At full build-out, the site would include up to 15 hyperscale data centre buildings across 176 hectares, delivering more than 1.5 million square metres of floorspace and up to 1GW of computing capacity.

The development is expected to cost between £5.5 billion and £7.5 billion to build and could attract up to £10 billion in private investment over its lifetime.

Developer Greystoke plans to begin construction in 2027, with the first phase due to open in 2029, and the full campus to be delivered in phases over approximately a decade.

The project is also required to source at least 30% of build costs from businesses within a 30-mile radius, injecting an estimated £1.65 billion to £2.25 billion into the local economy.

The scheme received over 380 letters of objection from residents and environmental groups. Critics raised concerns, including loss of privacy for neighbouring properties, around-the-clock noise and light, and the scale of carbon emissions, with one campaign group estimating the equivalent of twice the woodland of Wales would be needed to offset the development’s environmental impact.

Permission was nonetheless granted unanimously by councillors.

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NVIDIA expands physical AI ecosystem to accelerate real world robotics

Partnerships across the robotics sector are positioning NVIDIA at the centre of what is increasingly described as ‘physical AI’, a shift towards intelligent machines capable of perceiving, reasoning and acting in real environments.

A new generation of tools, including NVIDIA Cosmos world models and updated NVIDIA Isaac simulation frameworks, aims to support developers in training and validating robots before deployment.

These systems enable companies to simulate complex environments, reducing the risks and costs of real-world testing.

Industrial robotics leaders such as ABB Robotics, KUKA, and FANUC are integrating NVIDIA technologies into digital twin environments, enabling more accurate modelling of production lines and automation systems.

Advances are also extending into humanoid robotics, where companies are using AI models to develop machines capable of more flexible and adaptive behaviour.

New foundation models, including GR00T systems, are designed to give robots general-purpose capabilities instead of limiting them to specific tasks.

Healthcare and logistics represent additional areas of deployment, with robotics platforms being tested in surgical systems, warehouse automation and manufacturing environments. These applications highlight how physical AI could reshape industries requiring precision, safety and scalability.

Growing collaboration across cloud providers, manufacturers and AI developers suggests that robotics is moving toward a more integrated ecosystem, where simulation, data generation and deployment are increasingly interconnected.

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6G will make wireless networks capable of thinking for themselves

Unlike its predecessors, 6G is being designed from the ground up with AI as a core feature rather than a performance add-on.

From user devices and base stations through to the network core, AI and machine learning will enable 6G networks to self-optimise, manage interference, predict user mobility, and make real-time decisions with minimal human intervention.

One of 6G’s most distinctive capabilities will be Integrated Sensing and Communication (ISAC), which allows radio signals to simultaneously carry data and sense the surrounding environment, effectively turning the network into a vast, distributed sensor capable of detecting motion, tracking objects, and supporting applications such as predictive maintenance and autonomous vehicles.

AI plays a central role in interpreting this sensing data in real time, enabling split-second responses to real-world conditions.

Standardisation efforts are already underway, with 3GPP’s Release 20 exploring how AI and machine learning can optimise the air interface and improve tasks such as channel state information compression.

Commercial 6G deployment is expected in the early 2030s, by which point AI is projected to act as the brain and nervous system of key parts of the network, constantly learning, adapting, and optimising with little human oversight.

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Britain targets quantum leadership with £1bn investment

UK Secretary of State for Science, Innovation and Technology Liz Kendall has announced a £1bn funding package to boost UK quantum computing and retain domestic talent.

The initiative reflects growing concern over the country’s ability to compete globally, particularly after the US established dominance in AI.

Officials emphasised the need to retain British startups, engineers, and researchers who often relocate abroad in search of better funding and scaling opportunities. The UK produces top talent, but Google and OpenAI own many leading firms.

The investment will support the development of large-scale quantum computers for use across science, industry, and the public sector. Another £1bn will fund real-world use in finance, pharmaceuticals, and energy.

The government aims to build a fully operational domestic quantum system by the early 2030s.

Quantum computing uses qubits that can exist in multiple states simultaneously, enabling far greater computational power than classical systems. Fully fault-tolerant machines are still in development, but the technology could drive advances in drug discovery, materials science, and complex modelling.

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AI tool could help detect domestic violence risk years earlier

Researchers in the United States have developed an AI system designed to help doctors identify patients who may be at risk of intimate partner violence. The tool analyses hospital data to detect patterns associated with abuse, potentially enabling healthcare professionals to intervene earlier.

Intimate partner violence refers to abuse from current or former partners and can lead to serious injuries, chronic pain, and long-term mental health problems. According to the European Commission, 18 percent of women who have had a partner reported experiencing physical or sexual violence from a partner in 2021.

The study, published in the journal Nature, examined hospital records from nearly 850 women who had experienced intimate partner violence and more than 5,200 similar patients in a control group. Researchers used the data to train three different machine learning systems to detect patterns associated with abuse.

One model analysed structured hospital data, such as age and medical history. A second model examined written clinical notes, including doctors’ observations and radiology reports. A third system combined both data types and achieved the strongest results, correctly identifying risk in 88 percent of cases.

Researchers found that the system could flag potential abuse more than three years before some patients later entered hospital-based intervention programmes. By analysing large datasets, the tool can detect patterns of physical trauma linked to abuse and alert clinicians so they can approach the issue carefully and offer support.

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Human made labels emerge as industries react to AI expansion

Organisations around the world are developing certification labels designed to show that products or creative work were made by humans rather than AI. New badges such as ‘Human made’, ‘AI free’ and ‘Proudly Human’ are appearing across books, films, marketing and websites as industries respond to the rapid spread of AI tools.

At least eight initiatives are now attempting to create a label that could achieve global recognition similar to the Fair Trade mark. Experts warn that competing definitions and inconsistent certification systems could confuse consumers unless a universal standard is agreed upon.

Some schemes allow creators to download AI-free badges with little or no verification, while others use paid auditing processes that rely on analysts and AI detection tools. Researchers note that defining ‘human-made’ is increasingly difficult because AI technologies are embedded in many everyday software tools.

Creative industries are at the centre of the debate as generative AI rapidly produces books, films and music at lower cost and higher speed. Advocates of certification argue that verified human-created content may gain greater value if consumers can clearly distinguish it from AI-generated work.

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