Capacity development

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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Nurses gain AI support as Microsoft evolves Dragon Copilot in healthcare

Microsoft has announced major AI upgrades to Dragon Copilot, its healthcare assistant, extending ambient and generative AI capabilities to nursing workflows and third-party partner integrations.

The update is designed to improve patient journeys, reduce administrative workloads and enhance efficiency across healthcare systems.

The new features allow partners to integrate their own AI applications directly into Dragon Copilot, helping clinicians access trusted information, automate documentation and streamline financial management without leaving their workflow.

Partnerships with Elsevier, Wolters Kluwer, Atropos Health, Canary Speech and others will provide real-time decision support, clinical insights and revenue cycle automation.

Microsoft is also introducing the first commercial ambient AI solution built for nurses, designed to reduce burnout and enhance care quality.

A technology that automatically records nurse-patient interactions and transforms them into editable documentation for electronic health records, saving time and supporting accuracy.

Nurses can also access medical content within the same interface and automate note-taking and summaries, allowing greater focus on patient care.

The company says these developments mark a new phase in its AI strategy for healthcare, strengthening its collaboration with providers and partners.

Microsoft aims to make clinical workflows more connected, reliable and human-centred, while supporting safe, evidence-based decision-making through its expanding ecosystem of AI tools.

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AI predicts how cells respond to drugs and genes

KAIST researchers have developed AI that predicts cell responses to drugs and genes, with potential to transform drug discovery, cancer therapy, and regenerative medicine. The method models cell-drug interactions in a modular ‘Lego block’ approach, enabling analysis of previously untested combinations.

The AI separates representations of cell states and drug effects in a ‘latent space’ and recombines them to forecast reactions. The system can predict gene effects on cells, providing a quantitative view of drug and genetic impacts.

Validation using real experimental data demonstrated the AI’s ability to identify molecular targets that restored colorectal cancer cells to a normal-like state.

Beyond cancer treatment, the platform is versatile, capable of predicting diverse cell-state transitions and drug responses. The technology shows how drugs work inside cells, offering a powerful tool to design therapies that guide cells toward desired outcomes.

The study, led by Professor Kwang-Hyun Cho with his KAIST team, was published in Cell Systems and supported by the National Research Foundation of Korea. Researchers highlight the AI framework’s broad use, from restoring cells to developing new therapies.

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Florida renews effort to create state crypto reserve

Florida has reintroduced its push to establish a state crypto reserve, with Representative Webster Barnaby filing House Bill 183 to permit limited investment of public funds in digital assets. The proposal comes after his earlier attempt was withdrawn in June.

Under the new bill, Florida could invest up to 10% of state and public entity funds in assets such as Bitcoin, crypto ETFs, tokenised securities and other blockchain-based products. The legislation adds stricter standards to improve oversight of digital holdings.

Unlike its predecessor, the bill broadens investment options beyond Bitcoin, aiming to provide greater flexibility for portfolio diversification. If passed, HB 183 would take effect on 1 July 2026, allowing digital assets in state pension and trust funds.

Barnaby also introduced a separate measure, HB 175, which seeks to clarify regulations for stablecoin issuers. The proposal exempts recognised payment stablecoin issuers from additional licensing, provided they maintain full collateral in dollars or treasuries and conduct monthly reserve audits.

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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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AI Infrastructure Partnership and BlackRock consortium acquire Aligned Data Centers

A consortium comprising the Artificial Intelligence Infrastructure Partnership, MGX, and BlackRock’s Global Infrastructure Partners has announced the acquisition of Aligned Data Centers for an estimated forty billion dollars.

The move marks a major step towards expanding the infrastructure underpinning global AI and cloud growth.

Aligned, headquartered in Dallas, operates more than fifty campuses and five gigawatts of capacity across the US and Latin America. The company is known for its patented air, liquid, and hybrid cooling systems that enhance efficiency and sustainability, particularly in high-density AI environments.

Under the consortium, Aligned will accelerate the development of scalable and energy-efficient data facilities to meet rising global demand.

The Artificial Intelligence Infrastructure Partnership was founded by BlackRock, GIP, MGX, Microsoft, and NVIDIA to advance large-scale AI infrastructure investment.

Backed by sovereign wealth funds from Kuwait and Singapore, it aims to mobilise thirty billion dollars in equity and up to one hundred billion, including debt.

The Aligned acquisition represents its first major investment and positions the company as a cornerstone of the group’s strategy.

Executives from BlackRock, MGX, and GIP said the deal reflects a shared commitment to building sustainable, resilient infrastructure for the AI era.

Aligned CEO Andrew Schaap added that the partnership would strengthen the company’s global reach and innovation capacity, redefining standards for digital infrastructure in an increasingly AI-driven economy.

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Oracle launches embedded AI Agent Marketplace in Fusion Applications

Oracle has announced substantial enhancements to its AI Agent Studio for Fusion Applications, introducing a native AI Agent Marketplace, broader LLM support, and advanced agent tooling and governance features.

The AI Agent Marketplace is embedded within Fusion Applications, allowing customers to browse, test and deploy partner-built, Oracle-validated agents directly within their enterprise workflows. These agents can supplement or replace built-in agents to address industry-specific tasks.

Oracle is also expanding support for external large language models: customers and partners can now select from providers including OpenAI, Anthropic, Cohere, Google, Meta and xAI. This gives flexibility in choosing which LLM best fits a given use case.

New capabilities in Agent Studio include MCP support to integrate agents with third-party data systems, agent cards for cross-agent communication and collaboration, credential store for secure access to external APIs, monitoring dashboard, and agent tracing and performance metrics for observability.

It will also have prompt libraries and version control for managing agent prompts across lifecycles, workflow chaining and deterministic execution to organise multi-step agent tasks, and human-in-the-loop support to combine automation with oversight.

Oracle also highlights its network of 32,000 certified experts trained in building AI agents via Agent Studio. These experts can help customers optimise use, extend the marketplace, and ensure agent quality and safety.

Overall, Oracle’s release positions its Fusion ecosystem as a more open, flexible, and enterprise-ready platform for AI agent deployment, balancing embedded automation with extensibility and governance.

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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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Elumelu calls for investment to harness AI development in Africa

Chairman of UBA Group Tony Elumelu told global financial leaders on Wednesday that AI could transform Africa’s healthcare, education, and agriculture sectors if inclusive development, skills, and access to capital are prioritised, as the continent risks being left behind in global AI development.

Elumelu stressed that Africa’s digital growth must focus on people. He praised the continent’s youthful population and creativity as its greatest assets, recalling how the Mobile Money app has managed to reshape African finance despite the lack of infrastructure.

He warned, however, that limited capital and digital skills continues to constrain progress. He called for ‘smart public–private partnerships’ to fund digital infrastructure and capacity development programmes, solutions that avoid adding to public debt. He pointed Heirs Holdings’ investments in energy and entrepreneurship as examples of long-term local value creation.

Elumelu also urged African governments to ensure their participation in global AI and data governance frameworks, noting that ‘inclusion is not automatic; it must be intentional’. He said the goal should be to ‘democratise prosperity’ by building systems that empower young people through technology and sustainable investment.

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