Creativity that AI cannot reshape

A landmark ruling in Munich has put renewed pressure on AI developers, following a German court’s finding that OpenAI is liable for reproducing copyrighted song lyrics in outputs generated by GPT-4 and GPT-4o. The judges rejected OpenAI’s argument that the system merely predicts text without storing training data, stressing the long-established EU principle of technological neutrality that, regardless of the medium, vinyl, MP3, or AI output, the unauthorised reproduction of protected works remains infringement.

Because the models produced lyrics nearly identical to the originals, the court concluded that they had memorised and therefore stored copyrighted content. The ruling dismantled OpenAI’s attempt to shift responsibility to users by claiming that any copying occurs only at the output stage.

Judges found this implausible, noting that simple prompts could not have ‘accidentally’ produced full, complex song verses without the model retaining them internally. Arguments around coincidence, probability, or so-called ‘hallucinations’ were dismissed, with the court highlighting that even partially altered lyrics remain protected if their creative structure survives.

As Anita Lamprecht explains in her blog, the judgement reinforces that AI systems are not neutral tools like tape recorders but active presenters of content shaped by their architecture and training data.

A deeper issue lies beneath the legal reasoning, the nature of creativity itself. The court inferred that highly original works, which are statistically unique, force AI systems into a kind of memorisation because such material cannot be reliably reproduced through generalisation alone.

That suggests that when models encounter high-entropy, creative texts during training, they must internalise them to mimic their structure, making infringement difficult to avoid. Even if this memorisation is a technical necessity, the judges stressed that it falls outside the EU’s text and data mining exemptions.

The case signals a turning point for AI regulation. It exposes contradictions between what companies claim in court and what their internal guidelines acknowledge. OpenAI’s own model specifications describe the output of lyrics as ‘reproduction’.

As Lamprecht notes, the ruling demonstrates that traditional legal principles remain resilient even as technology shifts from physical formats to vector space. It also hints at a future where regulation must reach inside AI systems themselves, requiring architectures that are legible to the law and laws that can be enforced directly within the models.

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Greece accelerates AI training for teachers

A national push to bring AI into public schools has moved ahead in Greece after the launch of an intensive training programme for secondary teachers.

Staff in selected institutions will receive guidance on a custom version of ChatGPT designed for academic use, with a wider rollout planned for January.

The government aims to prepare educators for an era in which AI tools support lesson planning, research and personalised teaching instead of remaining outside daily classroom practice.

Officials view the initiative as part of a broader ambition to position Greece as a technological centre, supported by partnerships with major AI firms and new infrastructure projects in Athens. Students will gain access to the system next spring under tight supervision.

Supporters argue that generative tools could help teachers reduce administrative workload and make learning more adaptive.

Concerns remain strong among pupils and educators who fear that AI may deepen an already exam-driven culture.

Many students say they worry about losing autonomy and creativity, while teachers’ unions warn that reliance on automated assistance could erode critical thinking. Others point to the risk of increased screen use in a country preparing to block social media for younger teenagers.

Teacher representatives also argue that school buildings require urgent attention instead of high-profile digital reforms. Poor heating, unreliable electricity and decades of underinvestment complicate adoption of new technologies.

Educators who support AI stress that meaningful progress depends on using such systems as tools to broaden creativity rather than as shortcuts that reinforce rote learning.

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Popular Python AI library compromised to deliver malware

Security researchers have confirmed that the Ultralytics YOLO library was hijacked in a supply-chain attack, where attackers injected malicious code into the PyPI-published versions 8.3.41 and 8.3.42. When installed, these versions deployed the XMRig cryptominer.

The compromise stemmed from Ultralytics’ continuous-integration workflow: by exploiting GitHub Actions, the attackers manipulated the automated build process, bypassing review and injecting cryptocurrency mining malware.

The maintainers quickly removed the malicious versions and released a clean build (8.3.43); however, newer reports suggest that further suspicious versions may have appeared.

This incident illustrates the growing risk in AI library supply chains. As open-source AI frameworks become more widely used, attackers increasingly target their build systems to deliver malware, particularly cryptominers.

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Smarter AI processing could lead to cleaner air, say UCR engineers

As AI continues to scale rapidly, the environmental cost of powering massive data centres is becoming increasingly urgent. Machines require substantial amounts of electricity and water to stay cool, and a significant portion of this energy comes from fossil-fuel sources.

Scientists at UC Riverside’s Bourns College of Engineering, led by Professors Mihri and Cengiz Ozkan, have proposed a novel solution called Federated Carbon Intelligence (FCI). Their system doesn’t just prioritise low-carbon energy; it also monitors the health of servers in real-time to decide where and when AI tasks should be run.

Using simulations, the team found that FCI could reduce carbon dioxide emissions by up to 45 percent over five years and extend the operational life of hardware by about 1.6 years.

Their model takes into account server temperature, age and physical wear, and dynamically routes computing workloads to optimise both environmental and machine-health outcomes.

Unlike other approaches that only shift workloads to regions with cleaner energy, FCI also addresses the embodied emissions of manufacturing new servers. Keeping current hardware running longer and more efficiently helps reduce the carbon footprint associated with production.

If adopted by cloud providers, this adaptive system could mark a significant milestone in the sustainable development of AI infrastructure, one that aligns compute demand with both performance and ecological goals. The researchers are now calling for pilots in real data centres.

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Gemini 3 struggles to accept the year 2025

Google’s new AI model, Gemini 3, was left temporarily confused when it refused to accept that the year was 2025 during early testing by AI researcher Andrej Karpathy.

The model, pre-trained on data only through 2024 and initially disconnected from the internet, accused Karpathy of trickery and gaslighting before finally recognising the correct date.

Once Gemini 3 accessed real-time information, it expressed astonishment and apologised for its previous behaviour, demonstrating the model’s quirky but sophisticated reasoning capabilities. The interaction went viral online, drawing attention to both the humour and unpredictability of advanced AI systems.

Experts note that incidents like this illustrate the limitations of LLMs, which, despite their reasoning power, cannot inherently perceive reality and rely entirely on pre-training data and connected tools.

Observers emphasise that AI remains a powerful human aid rather than a replacement, and understanding its quirks is essential for practical use.

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AI teaching leaves Staffordshire students frustrated

Students at the University of Staffordshire in the UK have criticised a coding course after discovering much of the teaching was delivered through AI-generated slides and voiceovers.

Participants in the government-funded apprenticeship programme said they felt deprived of knowledge and frustrated that the course relied heavily on automated materials.

Concerns arose when learners noticed inconsistencies in language, suspicious file names, and abrupt changes in voiceover accents during lessons.

Students reported raising these issues with university staff, but the institution maintained the use of AI, asserting it supported academic standards while remaining ethical and responsible.

Critics argue that AI teaching diminishes engagement and reduces the opportunity to acquire practical skills needed for career development.

Experts suggest students supplement AI-driven courses with hands-on learning and critical thinking to ensure the experience remains valuable and relevant to their professional goals.

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Entry-level jobs vanish as AI rises

Generative AI is reshaping the job market by reducing the need for entry-level positions, particularly in white-collar industries. Analysts warn that young workers are losing the opportunity to acquire skills through traditional on-the-job experience, which has historically paved the way for promotions.

Employers are drawn to AI for its efficiency, as it can complete tasks in a fraction of the time it once took human teams. This shift poses a threat to the traditional career ladder, resulting in a shortage of trained candidates for senior and managerial roles in the years to come.

Young professionals can counter these trends by acquiring practical AI skills, even outside of technology sectors. Combining human strengths, such as strategic thinking, with AI proficiency may help early-career workers remain competitive and adapt to evolving workplace demands.

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New NVIDIA model drives breakthroughs in conservation biology

Researchers have introduced a biology foundation model that can recognise over a million species and understand relationships across the animal and plant kingdoms.

BioCLIP 2 was trained on one of the most extensive biological datasets ever compiled, allowing it to identify traits, cluster organisms and reveal patterns that support conservation efforts.

A model that relies on NVIDIA accelerated computing instead of traditional methods and demonstrates what large-scale biological learning can achieve.

Training drew on more than two hundred million images that cover hundreds of thousands of taxonomic classes. The AI model learned how species fit within wider biological hierarchies and how traits differ across age, gender and related groups without explicit guidance.

It even separated diseased leaves from healthy samples, offering a route to improved monitoring of ecosystems and agricultural resilience.

Scientists now plan to expand the project by utilising wildlife digital twins that simulate ecological systems in controlled environments.

Researchers will be able to study species interactions and test scenarios instead of disturbing natural habitats. The approach opens possibilities for richer ecological research and could offer the public immersive ways to view biodiversity from the perspective of different animals.

BioCLIP 2 is available as open-source software and has already attracted strong global interest. Its capabilities indicate a shift toward more advanced biological modelling powered by accelerated computing, providing conservationists and educators with new tools to address long-standing knowledge gaps.

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GPT‑5 expands research speed and idea generation for scientists

AI technology is increasingly helping scientists accelerate research across fields including biology, mathematics, physics, and computer science. Early GPT‑5 studies show it can synthesise information, propose experiments, and aid in solving long-standing mathematical problems.

Experts note the technology expands the range of ideas researchers can explore and shortens the time to validate results.

Case studies demonstrate tangible benefits: in biology, GPT‑5 helped identify mechanisms in human immune cells within minutes, suggesting experiments that confirmed the results.

In mathematics, GPT‑5 suggested new approaches, and in optimisation, it identified improved solutions later verified by researchers.

These advances reinforce human-led research rather than replacing it.

OpenAI for Science emphasises collaboration between AI and experts. GPT‑5 excels at conceptual literature review, exploring connections across disciplines, and proposing hypotheses for experimental testing.

Its greatest impact comes when researchers guide the process, breaking down problems, critiquing suggestions, and validating outcomes.

Researchers caution that AI does not replace human expertise. Current models aid speed, idea generation, and breadth, but expert oversight is essential to ensure reliable and meaningful scientific contributions.

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AI data centre boom drives global spike in memory chip prices

The rapid expansion of AI data centres is pushing up memory chip prices and straining an already tight supply chain. DRAM costs are rising as manufacturers prioritise high-bandwidth memory for AI systems, leaving fewer components available for consumer devices.

The shift is squeezing supply across sectors that depend on standard DRAM, from PCs and smartphones to cars and medical equipment. Analysts say the imbalance is driving up component prices quickly, with Samsung reportedly raising some memory prices by as much as 60%.

Rising demand for HBM reflects the needs of AI clusters, which rely on vast memory pools alongside GPUs, CPUs and storage. But with only a handful of major suppliers, including Samsung, SK Hynix, and Micron, the surge is pushing prices across the market higher.

Industry researchers warn that rising memory costs will likely be passed on to consumers, especially in lower-priced laptops and embedded systems. Makers may switch to cheaper parts or push suppliers for concessions, but the overall price trend remains upward.

While memory is known for cyclical booms and busts, analysts say the global race to build AI data centres makes it difficult to predict when supply will stabilise. Until then, higher memory prices look set to remain a feature of the market.

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