Capacity development Archives | Page 150 of 329

New funding round by Meta strengthens local STEAM education

Meta is inviting applications for its 2026 Data Centre Community Action Grants, which support schools, nonprofits and local groups in regions that host the company’s data centres.

The programme has been a core part of Meta’s community investment strategy since 2011, and the latest round expands support to seven additional areas linked to new facilities. The company views the grants as a means of strengthening long-term community vitality, rather than focusing solely on infrastructure growth.

Funding is aimed at projects that use technology for public benefit and improve opportunities in science, technology, engineering, arts and mathematics. More than $ 74 million has been awarded to communities worldwide, with $ 24 million distributed through the grant programme alone.

Recipients can reapply each year, which enables organisations to sustain programmes and increase their impact over time.

Several regions have already demonstrated how the funding can reshape local learning opportunities. Northern Illinois University used grants to expand engineering camps for younger students and to open a STEAM studio that supports after-school programmes and workforce development.

In New Mexico, a middle school used funding to build a STEM centre with advanced tools such as drones, coding kits and 3D printing equipment. In Texas, an enrichment organisation created a digital media and STEM camp for at-risk youth, offering skills that can encourage empowerment instead of disengagement.

Meta presents the programme as part of a broader pledge to deepen education and community involvement around emerging technologies.

The company argues that long-term support for digital learning will strengthen local resilience and create opportunities for young people who want to pursue future careers in technology.

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Digital twin technology drives new era in predictive medicine

A new AI model capable of generating digital twins of patients is being hailed as a significant step forward for clinical research. Developed at the University of Melbourne, the system reviews health records to predict how a patient’s condition may change during treatment.

DT-GPT, the model in question, was trained on thousands of records covering Alzheimer’s disease, non-small cell lung cancer, and intensive care admissions. Researchers stated that the model accurately predicted shifts in key clinical indicators, utilising medical literature and patient histories.

Predictions were validated without giving DT-GPT access to actual outcomes, strengthening confidence in its performance.

Lead researcher Associate Professor Michael Menden said the tool not only replicated patient profiles but also outperformed fourteen advanced machine-learning systems.

The ability to simulate clinical trial outcomes could lower costs and accelerate drug development, while enabling clinicians to anticipate deterioration and tailor treatment plans more effectively.

Researchers also noted that DT-GPT’s zero-shot ability to predict medical values it had never been trained on. The team has formed a company with the Royal Melbourne Women’s Hospital to apply the technology to patients with endometriosis, demonstrating wider potential in healthcare.

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Most workers see AI risk but not for themselves

A new survey by YouGov and Udemy reveals that while workers across the US, UK, India and Brazil see AI as a significant economic force, many believe their own jobs are unlikely to be affected.

Over 4,500 adults were polled, highlighting a clear gap between concern for the broader economy and personal job security.

In the UK, 70% of respondents expressed concern about AI’s impact on the economy, but only 39% worried about its effects on their own occupation.

Similarly, in the US, 72% feared wider economic effects, while 47% concerned about personal job loss. Experts suggest this reflects a psychological blind spot similar to early reactions to the internet.

The survey also highlighted a perceived AI skills gap, particularly in the UK, where 55% of workers had received no AI training. Many employees acknowledged awareness of AI’s rise but lacked motivation to develop skills immediately, a phenomenon researchers describe as an ‘awareness action gap’.

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Salesforce unveils eVerse for dependable enterprise AI

The US cloud-based software company, Salesforce and its Research AI department, have unveiled eVerse, a new environment designed to train voice and text agents through synthetic data generation, stress testing and reinforcement learning.

In an aim to resolve a growing reliability problem known as jagged intelligence, where systems excel at complex reasoning yet falter during simple interactions.

The company views eVerse as a key requirement for creating an Agentic Enterprise, where human staff and digital agents work together smoothly and dependably.

eVerse supports continuous improvement by generating large volumes of simulated interactions, measuring performance and adjusting behaviour over time, rather than waiting for real-world failures.

A platform that played a significant role in the development of Agentforce Voice, giving AI agents the capacity to cope with unpredictable calls involving noise, varied accents and weak connections.

Thousands of simulated conversations enabled teams to identify problems early and deliver stronger performance.

The technology is also being tested with UCSF Health, where clinical experts are working with Salesforce to refine agents that support billing services. Only a portion of healthcare queries can typically be handled automatically, as much of the knowledge remains undocumented.

eVerse enhances coverage by enabling agents to adapt to complex cases through reinforcement learning, thereby improving performance across both routine and sophisticated tasks.

Salesforce describes eVerse as a milestone in a broader effort to achieve Enterprise General Intelligence. The goal is a form of AI designed for dependable business use, instead of the more creative outputs that dominate consumer systems.

It also argues that trust and consistency will shape the next stage of enterprise adoption and that real-world complexity must be mirrored during development to guarantee reliable deployment.

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Heavy sell pressure pushes Bitcoin back under $94,000

Bitcoin’s price continued to weaken after dipping under $94,000, extending a retreat that has now erased nearly $190 billion from its market value over the past week. Trading volumes remained high, yet sell pressure dominated as the asset struggled to reclaim momentum.

Market data showed more than $394 million in crypto liquidations over the past 24 hours, with the majority coming from long positions. Sentiment stayed uneasy as Bitcoin hovered close to the $94,000 mark, offering little reassurance to traders seeking signs of stability.

Analysts remain divided on whether the current zone represents a potential floor or a pause before further declines. Traders noted that fresh catalysts will be needed to support any sustained recovery as liquidations rise and volatility deepens.

Bitcoin’s recent swings have left market participants split between bargain hunting and preparing for another downturn. Precise data and level-headed decision-making appear more valuable than hype as the market navigates its latest correction.

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NotebookLM gains automated Deep Research tool and wider file support

Google is expanding NotebookLM with Deep Research, a tool designed to handle complex online inquiries and produce structured, source-grounded reports. The feature acts like a dedicated researcher, planning its own process and gathering material across the web.

Users can enter a question, choose a research style, and let Deep Research browse relevant sites before generating a detailed briefing. The tool runs in the background, allowing additional sources to be added without disrupting the workflow or leaving the notebook.

NotebookLM now supports more file types, including Google Sheets, Drive URLs, PDFs stored in Drive, and Microsoft Word documents. Google says this enables tasks such as summarising spreadsheets and quickly importing multiple Drive files for analysis.

The update continues the service’s gradual expansion since its late-2023 launch, which has brought features such as Video Overviews for turning dense materials into visual explainers. These follow earlier additions, such as Audio Overviews, which create podcast-style summaries of shared documents.

Google also released NotebookLM apps for Android and iOS earlier this year, extending access beyond desktop. The company says the latest enhancements should reach all users within a week.

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LinkedIn introduces AI-powered people search for faster networking

LinkedIn has launched an AI-powered people search feature, allowing users to find relevant professionals using plain language instead of traditional keywords and filters. The new tool surfaces experts based on experience and skills rather than exact job titles or company names.

The feature uses advanced AI and LinkedIn’s professional data to match users with the right people at the right time. It transforms connections into actionable opportunities, helping members discover mentors, collaborators, or industry specialists more efficiently.

Previously, searches required highly specific information, making it difficult to identify the right professional. The new conversational approach simplifies the process, making LinkedIn a more intuitive and powerful platform for networking, career planning, and business growth.

AI-powered people search is currently available to Premium subscribers in the US, with plans for expansion in the coming months. LinkedIn plans to expand the feature globally, helping professionals connect, collaborate, and find opportunities more quickly.

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Embodied AI steps forward with DeepMind’s SIMA 2 research preview

Google DeepMind has released a research preview of SIMA 2, an upgraded generalist agent that draws on Gemini’s language and reasoning strengths. The system moves beyond simple instruction following, aiming to understand user intent and interact more effectively with its environment.

SIMA 1 relied on game data to learn basic tasks across diverse 3D worlds but struggled with complex actions. DeepMind says SIMA 2 represents a step change, completing harder objectives in unfamiliar settings and adapting its behaviour through experience without heavy human supervision.

The agent is powered by the Gemini 2.5 Flash-Lite model and built around the idea of embodied intelligence, where an AI acts through a body and responds to its surroundings. Researchers say this approach supports a deeper understanding of context, goals, and the consequences of actions.

Demos show SIMA 2 describing landscapes, identifying objects, and choosing relevant tasks in titles such as No Man’s Sky. It also reveals its reasoning, interprets clues, uses emojis as instructions, and navigates photorealistic worlds generated by Genie, DeepMind’s own environment model.

Self-improvement comes from Gemini models that create new tasks and score attempts, enabling SIMA 2 to refine its abilities through trial and error. DeepMind sees these advances as groundwork for future general-purpose robots, though the team has not shared timelines for wider deployment.

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Purdue and Google collaborate to advance AI research and education

Purdue University and Google are expanding their partnership to integrate AI into education and research, preparing the next generation of leaders while advancing technological innovation.

The collaboration was highlighted at the AI Frontiers summit in Indianapolis on 13 November. The event brought together university, industry, and government leaders to explore AI’s impact across sectors such as health care, manufacturing, agriculture, and national security.

Leaders from both organisations emphasised the importance of placing AI tools in the hands of students, faculty, and staff. Purdue plans a working AI competency requirement for incoming students in fall 2026, ensuring all graduates gain practical experience with AI tools, pending Board approval.

The partnership also builds on projects such as analysing data to improve road safety.

Purdue’s Institute for Physical Artificial Intelligence (IPAI), the nation’s first institute dedicated to AI in the physical world, plays a central role in the collaboration. The initiative focuses on physical AI, quantum science, semiconductors, and computing to equip students for AI-driven industries.

Google and Purdue emphasised responsible innovation and workforce development as critical goals of the partnership.

Industry leaders, including Waymo, Google Public Sector, and US Senator Todd Young, discussed how AI technologies like autonomous drones and smart medical devices are transforming key sectors.

The partnership demonstrates the potential of public-private collaboration to accelerate AI research and prepare students for the future of work.

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Stanford’s new AI model boosts liver transplant efficiency

A new machine learning model has been developed by Stanford Medicine researchers to make liver transplants more efficient. It predicts whether a donor will die within the time frame necessary for organ viability.

Donation after circulatory death requires that the donor pass within 30 to 45 minutes after life support removal; otherwise, surgeons often reject the liver due to increased risks for recipients. The model reduced futile procurements by 60%, outperforming surgeons’ predictions.

The algorithm analyses a wide range of donor data, including vital signs, blood work, neurological reflexes, and ventilator settings. The model was trained on over 2,000 cases from six US transplant centres and can be customised for hospital procedures and surgeon preferences.

The model also features a natural language interface that extracts relevant medical record information, streamlining the transplant workflow.

Donation after circulatory death is becoming increasingly important as it helps narrow the gap between organ demand and availability. Normothermic machine perfusion devices preserve organs during transport, making such donations more feasible.

Researchers hope the model will also be adapted for heart and lung transplants, further expanding its potential to save lives.

Stanford researchers stress that better predictions could help more patients receive life-saving transplants. Ongoing refinements aim to decrease missed opportunities from just over 15% to around 10%, enhancing efficiency and patient outcomes in organ transplantation.

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