Artificial intelligence

AI-generated media must now carry labels in China

China has introduced a sweeping new law that requires all AI-generated content online to carry labels. The measure, which came into effect on 1 September, aims to tackle misinformation, fraud and copyright infringement by ensuring greater transparency in digital media.

The law, first announced in March by the Cyberspace Administration of China, mandates that all AI-created text, images, video and audio must carry explicit and implicit markings.

These include visible labels and embedded metadata such as watermarks in files. Authorities argue that the rules will help safeguard users while reinforcing Beijing’s tightening grip over online spaces.

Major platforms such as WeChat, Douyin, Weibo and RedNote moved quickly to comply, rolling out new features and notifications for their users. The regulations also form part of the Qinglang campaign, a broader effort by Chinese authorities to clean up online activity with a strong focus on AI oversight.

While Google and other US companies are experimenting with content authentication tools, China has enacted legally binding rules nationwide.

Observers suggest that other governments may soon follow, as global concern about the risks of unlabelled AI-generated material grows.

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ChatGPT safety checks may trigger police action

OpenAI has confirmed that ChatGPT conversations signalling a risk of serious harm to others can be reviewed by human moderators and may even reach the police.

The company explained these measures in a blog post, stressing that its system is designed to balance user privacy with public safety.

The safeguards treat self-harm differently from threats to others. When a user expresses suicidal intent, ChatGPT directs them to professional resources instead of contacting law enforcement.

By contrast, conversations showing intent to harm someone else are escalated to trained moderators, and if they identify an imminent risk, OpenAI may alert authorities and suspend accounts.

The company admitted its safety measures work better in short conversations than in lengthy or repeated ones, where safeguards can weaken.

OpenAI is working to strengthen consistency across interactions and developing parental controls, new interventions for risky behaviour, and potential connections to professional help before crises worsen.

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AI oversight and audits at core of Pakistan’s security plan

Pakistan plans to roll out AI-driven cybersecurity systems to monitor and respond to attacks on critical infrastructure and sensitive data in real time. Documents from the Ministry for Information Technology outline a framework to integrate AI into every stage of security operations.

The initiative will enforce protocols like secure data storage, sandbox testing, and collaborative intelligence sharing. Human oversight will remain mandatory, with public sector AI deployments registered and subject to transparency requirements.

Audits and impact assessments will ensure compliance with evolving standards, backed by legal penalties for breaches. A national policy on data security will define authentication, auditing, and layered defence strategies across network, host, and application levels.

New governance measures include identity management policies with multi-factor authentication, role-based controls, and secure frameworks for open-source AI. AI-powered simulations will help anticipate threats, while regulatory guidelines address risks from disinformation and generative AI.

Regulatory sandboxes will allow enterprises in Pakistan to test systems under controlled conditions, with at least 20 firms expected to benefit by 2027. Officials say the measures will balance innovation with security, safeguarding infrastructure and citizens.

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Meta under fire over AI deepfake celebrity chatbots

Meta faces scrutiny after a Reuters investigation found its AI tools created deepfake chatbots and images of celebrities without consent. Some bots made flirtatious advances, encouraged meet-ups, and generated photorealistic sexualised images.

The affected celebrities include Taylor Swift, Scarlett Johansson, Anne Hathaway, and Selena Gomez.

The probe also uncovered a chatbot of 16-year-old actor Walker Scobell producing inappropriate images, raising serious child safety concerns. Meta admitted policy enforcement failures and deleted around a dozen bots shortly before publishing the report.

A spokesperson acknowledged that intimate depictions of adult celebrities and any sexualised content involving minors should not have been generated.

Following the revelations, Meta announced new safeguards to protect teenagers, including restricting access to certain AI characters and retraining models to reduce inappropriate content.

California Attorney General Rob Bonta called exposing children to sexualised content ‘indefensible,’ and experts warned Meta could face legal challenges over intellectual property and publicity laws.

The case highlights broader concerns about AI safety and ethical boundaries. It also raises questions about regulatory oversight as social media platforms deploy tools that can create realistic deepfake content without proper guardrails.

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Apple creates Asa chatbot for staff training

Apple is moving forward with its integrated approach to AI by testing an internal chatbot designed for retail training. The company focuses on embedding AI into existing services rather than launching a consumer-facing chatbot like Google’s Gemini or ChatGPT.

The new tool, Asa, is being tested within Apple’s SEED app, which offers training resources for store employees and authorised resellers. Asa is expected to improve learning by allowing staff to ask open-ended questions and receive tailored responses.

Screenshots shared by analyst Aaron Perris show Asa handling queries about device features, comparisons, and use cases. Although still in testing, the chatbot is expected to expand across Apple’s retail network in the coming weeks.

The development occurs amid broader AI tensions, as Elon Musk’s xAI sued Apple and OpenAI for allegedly colluding to limit competition. Apple’s focus on internal AI tools like Asa contrasts with Musk’s legal action, highlighting disputes over AI market dominance and platform integration.

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Walmart rolls out AI agents to transform shopping and operations

Walmart has unveiled four AI agents to ease the workloads of shoppers, employees, and suppliers. The tools, revealed at the company’s Retail Rewired event, include Marty for suppliers, Sparky for customers, an Associate Agent for staff, and a Developer Agent.

The retailer is leaning on AI as inflation, tariffs, and policy pressures weigh on consumer spending. Its agents cover payroll, time-off requests, merchandising, and personalised shopping recommendations.

Sparky is set to eventually handle automatic reordering of staples, aiming to simplify everyday restocking for households.

Walmart is also investing in ‘digital twins,’ virtual replicas of stores that allow early detection of operational issues. The company says this technology cut emergency alerts by 30% last year and reduced refrigeration maintenance costs by nearly a fifth.

Machine learning is further being applied to improve delivery-time predictions, helping to boost efficiency and customer satisfaction.

Rival retailers are making similar moves. Amazon reported a surge in generative AI use during its Prime Day sales, while Google Cloud AI has partnered with Lush to cut training costs.

Analysts suggest such tools could reshape the retail experience as companies search for ways to hold margins in a tighter economy.

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Beijing seeks to curb excess AI investment while sustaining growth

China has pledged to rein in excessive competition in AI, signalling Beijing’s desire to avoid wasteful investment while keeping the technology central to its economic strategy.

The National Development and Reform Commission stated that provinces should develop AI in a coordinated manner, leveraging local strengths to prevent duplication and overlap. Officials in China emphasised the importance of orderly flows of talent, capital, and resources.

The move follows President Xi Jinping’s warnings about unchecked local investment. Authorities aim to prevent overcapacity problems, such as those seen in electric vehicles, which have fueled deflationary pressures in other industries.

While global investment in data centres has surged, Beijing is adopting a calibrated approach. The state also vowed stronger national planning and support for private firms, aiming to nurture new domestic leaders in AI.

At the same time, policymakers are pushing to attract private capital into traditional sectors, while considering more central spending on social projects to ease local government debt burdens and stimulate long-term consumption.

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Meta faces turmoil as AI hiring spree backfires

Mark Zuckerberg’s ambitious plan to assemble a dream team of AI researchers at Meta has instead created internal instability.

High-profile recruits poached from rival firms have begun leaving within weeks of joining, citing cultural clashes and frustration with the company’s working style. Their departures have disrupted projects and unsettled long-time executives.

Meta had hoped its aggressive hiring spree would help the company rival OpenAI, Google, and Anthropic in developing advanced AI systems.

Instead of strengthening the company’s position, the strategy has led to delays in projects and uncertainty about whether Meta can deliver on its promises of achieving superintelligence.

The new arrivals were given extensive autonomy, fuelling tensions with existing teams and creating leadership friction. Some staff viewed the hires as destabilising, while others expressed concern about the direction of the AI division.

The resulting turnover has left Meta struggling to maintain momentum in its most critical area of research.

As Meta faces mounting pressure to demonstrate progress in AI, the setbacks highlight the difficulty of retaining elite talent in a fiercely competitive field.

Zuckerberg’s recruitment drive, rather than propelling Meta ahead, risks slowing down the company’s ability to compete at the highest level of AI development.

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Stethoscope with AI identifies heart issues in seconds

A new stethoscope powered by AI could enable doctors to identify three serious heart conditions in just seconds, according to UK researchers.

The device replaces the traditional chest piece with a small sensor that records both electrical signals from the heart and the sound of blood flow, which are then analysed in the cloud by AI trained on large datasets.

The AI tool has shown strong results in trials across more than 200 GP practices, with patients tested using the stethoscope being more than twice as likely to be diagnosed with heart failure within 12 months compared with those assessed through usual care.

It was also 3.45 times more likely to detect atrial fibrillation and almost twice as likely to identify heart valve disease.

Researchers from Imperial College London and Imperial College Healthcare NHS Trust said the technology could help doctors provide treatment at an earlier stage instead of waiting until patients present in hospital with advanced symptoms.

The findings, known as Tricorder, will be presented at the European Society of Cardiology Congress in Madrid.

The project, supported by the National Institute for Health and Care Research, is now preparing for further rollouts in Wales, south London and Sussex. Experts described the innovation as a significant step in updating a medical tool that has remained largely unchanged for over 200 years.

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How local LLMs are changing AI access

As AI adoption rises, more users explore running large language models (LLMs) locally instead of relying on cloud providers.

Local deployment gives individuals control over data, reduces costs, and avoids limits imposed by AI-as-a-service companies. Users can now experiment with AI on their own hardware thanks to software and hardware capabilities.

Concerns over privacy and data sovereignty are driving interest. Many cloud AI services retain user data for years, even when privacy assurances are offered.

By running models locally, companies and hobbyists can ensure compliance with GDPR and maintain control over sensitive information while leveraging high-performance AI tools.

Hardware considerations like GPU memory and processing power are central to local LLM performance. Quantisation techniques allow models to run efficiently with reduced precision, enabling use on consumer-grade machines or enterprise hardware.

Software frameworks like llama.cpp, Jan, and LM Studio simplify deployment, making local AI accessible to non-engineers and professionals across industries.

Local models are suitable for personalised tasks, learning, coding assistance, and experimentation, although cloud models remain stronger for large-scale enterprise applications.

As tools and model quality improve, running AI on personal devices may become a standard alternative, giving users more control over cost, privacy, and performance.

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