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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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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Under this plan, sensor and equipment data from factory floors is captured in real time via Azure IoT and forwarded through Fabric. That data will then feed directly into Oracle SCM workflows.
The goal: more visibility, faster decisions and automated responses, such as triggering maintenance, quality checks or inventory adjustments.
Among the features highlighted are secure, real-time intelligence and data flows from shop floor equipment into enterprise systems, automated business events that respond to changes (e.g. imbalance, faults, demand shifts), standardised best practices with reference architectures and prescriptive guidance for integration and embedded AI assistant capabilities in SCM to augment decision making and resilience.
Oracle frames this as part of its Smart Operations vision, where systems are more connected and responsive by design. Microsoft emphasises that Azure’s edge processing and Fabric’s real-time analytics are critical to turning raw IoT signals into actionable business events.
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Apple unveiled the M5 chip, targeting a major jump in on-device AI. Apple says peak GPU compute for AI is over four times M4, with a Neural Accelerator in each of the 10 GPU cores.
The CPU pairs up to four performance cores with six efficiency cores for up to 15 percent faster multithreaded work versus M4. A faster 16-core Neural Engine and higher unified memory bandwidth at 153 GB/s aim to speed Apple Intelligence features.
Graphics upgrades include third-generation ray tracing and reworked caching for up to 45 percent higher performance than M4 in supported apps. With the help of AI, Apple notes smoother gameplay and quicker 3D renders, plus Vision Pro refresh up to 120 Hz.
The M5 chip reaches the 14-inch MacBook Pro, iPad Pro, and Apple Vision Pro, with pre-orders open. Apple highlights tighter tie-ins with Core ML, Metal 4 and Tensor APIs, and support for larger local models via unified memory up to 32 GB.
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The UAE Ministry of Investment and Microsoft signed a Memorandum of Understanding at GITEX Global 2025 to apply AI to investment analytics, financial forecasting, and retail optimisation. The deal aims to strengthen data governance across the investment ecosystem.
Under the MoU, Microsoft will support upskilling through its AI National Skilling Initiative, targeting 100,000 government employees. Training will focus on practical adoption, responsible use, and measurable outcomes, in line with the UAE’s National AI Strategy 2031.
Both parties will promote best practices in data management using Azure services such as Data Catalog and Purview. Workshops and knowledge-sharing sessions with local experts will standardise governance. Strong controls are positioned as the foundation for trustworthy AI at scale.
The agreement was signed by His Excellency Mohammad Alhawi and Amr Kamel. Officials say the collaboration will embed AI agents into workflows while maintaining compliance. Investment teams are expected to gain real-time insights and automation that shorten the time to action.
The partnership supports the ambition to make the UAE a leader in AI-enabled investment. It also signals deeper public–private collaboration on sovereign capabilities. With skills, standards, and use cases in place, the ministry aims to attract capital and accelerate diversification.
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Salesforce and AWS outlined a tighter partnership on agentic AI, citing rapid growth in enterprise agents and usage. They set four pillars for the ‘Agentic Enterprise’: unified data, interoperable agents, modernised contact centres and streamlined procurement via AWS Marketplace.
Data 360 ‘Zero Copy’ accesses Amazon Redshift without duplication, while Data 360 Clean Rooms integrate with AWS Clean Rooms for privacy-preserving collaboration. 1-800Accountant reports agents resolving most routine inquiries so human experts focus on higher-value work.
Agentforce supports open standards such as Model Context Protocol and Agent2Agent to coordinate multi-vendor agents. Pilots link Bedrock-based agents and Slack integrations that surface Quick Suite tools, with Anthropic and Amazon Nova models available inside Salesforce’s trust boundary.
Contact centres extend agentic workflows through Salesforce Contact Center with Amazon Connect, adding voice self-service plus real-time transcription and sentiment. Complex issues hand off to representatives with full context, and Toyota Motor North America plans automation for service tasks.
Procurement scales via AWS Marketplace, where Salesforce surpassed $2bn in lifetime sales across 30 countries. AgentExchange listings provide prebuilt, customisable agents and workflows, helping enterprises adopt agentic AI faster with governance and security intact.
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Most firms are still struggling to turn AI pilots into measurable value, Cisco’s 2025 AI Readiness Index finds. Only 13% are ‘AI-ready’, having scaled deployments with results. The rest face gaps in data, security and governance.
Southeast Asia outperforms the global average at 16% ready. Indonesia reaches 23% and Thailand 21%, ahead of Europe at 11% and the Americas at 14%. Cisco says lower tech debt helps some emerging markets leapfrog.
Infrastructure debt is mounting: limited GPU capacity, fragmented data and constrained networks slow progress. Just 34% say their tech stack can adapt and scale for evolving compute needs. Most remain stuck in pilots.
Adoption plans are ambitious: 83% intend to deploy AI agents, with almost 40% expecting them to support staff within a year. Yet only one in three have change-management programmes, risking stalled workplace integration.
The leaders pair strong digital foundations with clear governance and cybersecurity embedded by design. Cisco urges broader collaboration among industry, government and tech firms, arguing that trust, regulation and investment will determine who monetises AI first.
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AI data centres in Scotland use enough tap water to fill over 27 million half-litre bottles annually, BBC News reports. The number of centres has quadrupled since 2021, with AI growth increasing energy and water use, though it remains a small fraction of the national supply.
Scottish Water urges developers to adopt closed-loop cooling or treated wastewater instead of relying only on mains water. Open-loop systems, still used in many centres, consume vast amounts of water, but closed-loop alternatives can reduce demand, though they may increase energy usage.
Experts warn that AI data centres have a significant carbon footprint as well. Analysis from the University of Glasgow estimates the energy use of Scottish centres could equate to each person in the country driving an extra 145 kilometres per year.
Academic voices have called for greater transparency from tech companies and suggested carbon targets and potential penalties to ensure sustainable growth.
The Scottish government and industry stakeholders are promoting ‘green’ AI development, citing Scotland’s cool climate, renewable energy resources, and local expertise. Developers are urged to balance AI expansion with Scotland’s net zero and resource sustainability goals.
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Yale University and Google unveiled Cell2Sentence-Scale 27B, a 27-billion-parameter model built on Gemma to decode the ‘language’ of cells. The system generated a novel hypothesis about cancer cell behaviour, and CEO Sundar Pichai called it ‘an exciting milestone’ for AI in science.
The work targets a core problem in immunotherapy: many tumours are ‘cold’ and evade immune detection. Making them visible requires boosting antigen presentation. C2S-Scale sought a ‘conditional amplifier’ drug that boosts signals only in immune-context-positive settings.
Smaller models lacked the reasoning to solve the problem, but scaling to 27B parameters unlocked the capability. The team then simulated 4,000 drugs across patient samples. The model flagged context-specific boosters of antigen presentation, with 10–30% already known and the rest entirely novel.
Researchers emphasise that conditional amplification aims to raise immune signals only where key proteins are present. That could reduce off-target effects and make ‘cold’ tumours discoverable. The result hints at AI-guided routes to more precise cancer therapies.
Google has released C2S-Scale 27B on GitHub and Hugging Face for the community to explore. The approach blends large-scale language modelling with cell biology, signalling a new toolkit for hypothesis generation, drug prioritisation, and patient-relevant testing.
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A new era in data technology is emerging as Starcloud, a member of NVIDIA’s Inception startup program, prepares to send its first AI-driven satellite into orbit next month.
The mission marks the debut of NVIDIA’s H100 GPU in space and represents a decisive step toward the creation of large-scale orbital data centres designed to meet the planet’s soaring demand for AI.
By operating data centres in space, Starcloud aims to cut energy costs by tenfold and significantly reduce carbon emissions. The vacuum of space will serve as a natural cooling system, while constant exposure to solar energy will eliminate the need for batteries or backup power.
According to CEO Philip Johnston, the only environmental cost will come from the launch itself, resulting in substantial carbon savings over the data centre’s lifetime.
Starcloud’s technology could transform how Earth observation data is processed. Instead of transmitting raw information back to the ground, satellites will analyse it in real time, improving responses to wildfires, weather changes, and agricultural needs.
The company plans to run Google’s open AI model Gemma on its satellite and eventually integrate NVIDIA’s next-generation Blackwell GPUs, boosting computing power even further.
Johnston predicts that within a decade, most new data centres will be built in orbit. If achieved, Starcloud’s innovation could mark the beginning of a sustainable digital revolution powered by the stars instead of the grid.
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