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UK government urges awareness as £106m lost to romance fraud in one year

Romance fraud has surged across the United Kingdom, with new figures showing that victims lost a combined £106 million in the past financial year. Action Fraud, the UK’s national reporting centre for cybercrime, described the crime as one that causes severe financial, emotional, and social damage.

Among the victims is London banker Varun Yadav, who lost £40,000 to a scammer posing as a romantic partner on a dating app. After months of chatting online, the fraudster persuaded him to invest in a cryptocurrency platform.

When his funds became inaccessible, Yadav realised he had been deceived. ‘You see all the signs, but you are so emotionally attached,’ he said. ‘You are willing to lose the money, but not the connection.’

The Financial Conduct Authority (FCA) said banks should play a stronger role in disrupting romance scams, calling for improved detection systems and better staff training to identify vulnerable customers. It urged firms to adopt what it called ‘compassionate aftercare’ for those affected.

Romance fraud typically involves criminals creating fake online profiles to build emotional connections before manipulating victims into transferring money.

The National Cyber Security Centre (NCSC) and UK police recommend maintaining privacy on social media, avoiding financial transfers to online contacts, and speaking openly with friends or family before sending money.

The Metropolitan Police recently launched an awareness campaign featuring victim testimonies and guidance on spotting red flags. The initiative also promotes collaboration with dating apps, banks, and social platforms to identify fraud networks.

Detective Superintendent Kerry Wood, head of economic crime for the Met Police, said that romance scams remain ‘one of the most devastating’ forms of fraud. ‘It’s an abuse of trust which undermines people’s confidence and sense of self-worth. Awareness is the most powerful defence against fraud,’ she said.

Although Yadav never recovered his savings, he said sharing his story helped him rebuild his life. He urged others facing similar scams to speak up: ‘Do not isolate yourself. There is hope.’

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Meta to pull all political ads in EU ahead of new transparency law

Meta Platforms has said it will stop selling and showing political, electoral and social issue advertisements across its services in the European Union from early October 2025. The decision follows the EU’s Transparency and Targeting of Political Advertising (TTPA) regulation coming into full effect on 10 October.

Under TTPA, platforms will be required to clearly label political ads, disclose the sponsor, the election or social issue at hand, the amounts paid, and how the ads are targeted. These obligations also include strict conditions on targeting and require explicit consent for certain data use.

Meta called the requirements ‘significant operational challenges and legal uncertainties’ and labelled parts of the new rules ‘unworkable’ for advertisers and platforms. It said that personalised ads are widely used for issue-based campaigns and that limiting them might restrict how people access political or social issue-related information.

The company joins Google, which made a similar move last year citing comparable concerns about TTPA compliance.

While political ads will be banned under paid formats, Meta says organic political content (e.g. users posting or sharing political views) will still be permitted.

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AWS glitch triggers widespread outages across major apps

A major internet outage hit some of the world’s biggest apps and sites from about 9 a.m. CET Monday, with issues traced to Amazon Web Services. Tracking sites reported widespread failures across the US and beyond, disrupting consumer and enterprise services.

AWS cited ‘significant error rates’ in DynamoDB requests in the U S-EAST-1 region, impacting additional services in Northern Virginia. Engineers are mitigating while investigating root cause, and some customers couldn’t create or update Support Cases.

Outages clustered around Virginia’s dense data-centre corridor but rippled globally. Impacted brands included Amazon, Google, Snapchat, Roblox, Fortnite, Canva, Coinbase, Slack, Signal, Vodafone and the UK tax authority HMRC.

Coinbase told users ‘all funds are safe’ as platforms struggled to authenticate, fetch data and serve content tied to affected back-ends. Third-party monitors noted elevated failure rates across APIs and app logins.

The incident underscores heavy reliance on hyperscale infrastructure and the blast radius when core data services falter. Full restoration and a formal post-mortem are pending from AWS.

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Labels and Spotify align on artist-first AI safeguards

Spotify partners with major labels on artist-first AI tools, putting consent and copyright at the centre of product design. The plan aims to align new features with transparent labelling and fair compensation while addressing concerns about generative music flooding platforms.

The collaboration with Sony, Universal, Warner, and Merlin will give artists control over participation in AI experiences and how their catalogues are used. Spotify says it will prioritise consent, clearer attribution, and rights management as it builds new tools.

Early direction points to expanded labelling via DDEX, stricter controls against mass AI uploads, and protections against search and recommendation manipulation. Spotify’s AI DJ and prompt-based playlists hint at how engagement features could evolve without sidelining creators.

Future products are expected to let artists opt in, monitor usage, and manage when their music feeds AI-generated works. Rights holders and distributors would gain better tracking and payment flows as transparency improves across the ecosystem.

Industry observers say the tie-up could set a benchmark for responsible AI in music if enforcement matches ambition. By moving in step with labels, Spotify is pitching a path where innovation and artist advocacy reinforce rather than undermine each other.

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Between trips, Uber pilots paid AI data work

Uber is piloting ‘Digital Tasks’ in the US, letting select drivers and couriers earn by training AI models between trips.

Tasks include short selfie videos in any language, uploading multilingual documents, and uploading category-tagged images; each takes minutes, and pay varies by task.

Uber says demand came from US drivers seeking off-road income; participants can opt in via the Work Hub and need no extra experience.

Partners commissioning the data aren’t named. The pilot starts later this year, with potential expansion to non-drivers and wider markets.

The move diversifies beyond rides and delivery as robotaxis loom. Uber argues for more earning channels now, while autonomy scales over time.

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Music and AI unite to power new mental health innovations

MIT PhD student Kimaya Lecamwasam is blending neuroscience, artificial intelligence, and music to pioneer new approaches to mental health care. Her research explores how music impacts the brain and emotions to develop scalable non-drug therapies.

Lecamwasam combines her neuroscience background and love of music to study how performances, composition, and listening affect emotional and physical well-being. Her work validates music as a mental health tool and explores AI-generated music for therapy.

Lecamwasam studies AI- and human-composed music, exploring ethical ways to use AI in emotional health without compromising creativity. She collaborates with institutions like Carnegie Hall and Myndstream to test music-based applications in real-world settings.

Beyond research, Lecamwasam contributes to building supportive communities at MIT. Through mentoring and student initiatives, she promotes inclusion and collaboration among emerging scientists and artists who share her belief in music’s power to heal and connect.

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Lehane backs OpenAI’s Australia presence as AI copyright debate heats up

OpenAI signalled a break with Australia’s tech lobby on copyright, with global affairs chief Chris Lehane telling SXSW Sydney the company’s models are ‘going to be in Australia, one way or the other’, regardless of reforms or data-mining exemptions.

Lehane framed two global approaches: US-style fair use that enables ‘frontier’ AI, versus a tighter, historical copyright that narrows scope, saying OpenAI will work under either regime. Asked if Australia risked losing datacentres without loser laws, he replied ‘No’.

Pressed on launching and monetising Sora 2 before copyright issues are settled, Lehane argued innovation precedes adaptation and said OpenAI aims to ‘benefit everyone’. The company paused videos featuring Martin Luther King Jr.’s likeness after family complaints.

Lehane described the US-China AI rivalry as a ‘very real competition’ over values, predicting that one ecosystem will become the default. He said US-led frontier models would reflect democratic norms, while China’s would ‘probably’ align with autocratic ones.

To sustain a ‘democratic lead’, Lehane said allies must add gigawatt-scale power capacity each week to build AI infrastructure. He called Australia uniquely positioned, citing high AI usage, a 30,000-strong developer base, fibre links to Asia, Five Eyes membership, and fast-growing renewables.

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New method helps AI models locate personalised objects in scenes

MIT and the MIT-IBM Watson AI Lab have developed a training approach that enables generative vision-language models to localise personalised objects (for example, a specific cat) across new scenes, a task at which they previously performed poorly.

While vision-language models (VLMs) are good at recognising generic object categories (dogs, chairs, etc.), they struggle when asked to point out your specific dog or chair under different conditions.

To remedy this, the researchers framed a fine-tuning regime using video-tracking datasets, where the same object appears in multiple frames.

Crucially, they used pseudo-names (e.g. ‘Charlie’) instead of real object names to prevent the model from relying on memorised label associations. This encourages it to reason about context, scene layout, appearance cues, and relative position, rather than shortcut to category matches.

AI models trained with the method showed a 12% average improvement in personalised localization. In some settings, especially with pseudo-naming, gains reached 21%. Importantly, this enhanced ability did not degrade the model’s overall object recognition performance.

Potential applications include smart home cameras recognising your pet, assistive devices helping visually impaired users find items, robotics, surveillance, and ecological monitoring (e.g. tracking particular animals). The approach helps models better generalise from a few example images rather than needing full retraining for each new object.

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Adaptive optics meets AI for cellular-scale eye care

AI is moving from lab demos to frontline eye care, with clinicians using algorithms alongside routine fundus photos to spot disease before symptoms appear. The aim is simple: catch diabetic retinopathy early enough to prevent avoidable vision loss and speed referrals for treatment.

New imaging workflows pair adaptive optics with machine learning to shrink scan times from hours to minutes while preserving single-cell detail. At the US National Eye Institute, models recover retinal pigment epithelium features and clean noisy OCT data to make standard scans more informative.

Duke University’s open-source DCAOSLO goes further by combining multiplexed light signals with AI to capture cellular-scale images quickly. The approach eases patient strain and raises the odds of getting diagnostic-quality data in busy clinics.

Clinic-ready diagnostics are already changing triage. LumineticsCore, the first FDA-cleared AI to detect more-than-mild diabetic retinopathy from primary-care images, flags who needs urgent referral in seconds, enabling earlier laser or pharmacologic therapy.

Researchers also see the retina as a window on wider health, linking vascular and choroidal biomarkers to diabetes, hypertension and cardiovascular risk. Standardised AI tools promise more reproducible reads, support for trials and, ultimately, home-based monitoring that extends specialist insight beyond the clinic.

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AI system links hidden signals in patient records to improve diagnosis

Researchers at Mount Sinai and UC Irvine have developed a novel AI system, InfEHR, which creates a dynamic network of an individual’s medical events and relationships over time. The system detects disease patterns that traditional approaches often miss.

InfEHR transforms time-ordered data, visits, labs, medications, and vital signs, into a graphical network for each patient. It then learns which combinations of clues across that network tend to correlate with hidden disease states.

In testing, with only a few physician-annotated examples, the AI system identified neonatal sepsis without positive blood cultures at rates 12–16× higher than current methods, and post-operative kidney injury with 4–7× more sensitivity than baseline clinical rules.

As a safety feature, InfEHR can also respond ‘not sure’ when the record lacks enough signal, reducing the risk of overconfident errors.

Because it adapts its reasoning per patient rather than applying the same rules to all, InfEHR shows promise for personalized diagnostics across hospitals and populations, even with relatively small annotated datasets.

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