Artificial intelligence Archives | Page 25 of 654

Papua New Guinea plans AI framework centred on data sovereignty and legislation

Papua New Guinea’s Department of Information and Communications Technology has outlined a national approach to AI focused on data sovereignty, trusted public infrastructure, and new legislation.

Speaking at Media Summit 2026, Secretary Steven Matainaho described the government’s AI approach as a framework expressed as Digital Government multiplied by Digital Public Infrastructure to the power of AI. Matainaho said the model is intended to replace fragmented ICT projects with a coordinated national system bringing together connectivity, computing, data, digital public infrastructure, and AI capability.

He said the strategy is intended to ensure AI is deployed through trusted national platforms such as SevisPass and in ways that remain secure, interoperable, and respectful of Papua New Guinea’s cultural context. He also identified four main elements of the framework:

Strengthening existing digital foundations such as SevisPass and SevisDEx
Establishing a National AI Register
Adopting sovereign data governance
Introducing new laws, including a National Artificial Intelligence Act and a Data Governance and Protection Act.

He warned that reliance on foreign-trained AI models without a sovereign framework could misrepresent Pacific cultures, particularly where knowledge remains oral and community-based. The aim, he argued, is to shift from dependence on external models towards locally developed systems better suited to national and cultural needs.

Looking ahead to the 2027 election cycle, Matainaho said the department aims to establish a shared verification standard for government and media in Papua New Guinea. He said that the approach is intended to ‘protect the sovereign aspects of our national data’ and ensure that technology serves the public rather than allowing external algorithms to shape ‘our narrative.’

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Asian Development Bank plans $70 billion investment in energy and digital infrastructure by 2035

Asian Development Bank (ADB) announced plans to support $70 billion in energy and digital infrastructure initiatives by 2035. The announcement was made by Masato Kanda during ADB’s Annual Meeting in Samarkand, Uzbekistan.

The plan includes the Pan-Asia Power Grid Initiative, which aims to mobilise $50 billion for cross-border electricity infrastructure. It focuses on transmission, grid integration, storage, and digitalisation, as well as renewable energy projects linked to regional electricity trade.

ADB says that the initiative aims to integrate around 20 gigawatts of renewable energy across borders, connect 22,000 circuit-kilometres of transmission lines, and improve energy access for 200 million people. It also refers to employment creation and emissions reductions linked to regional power systems.

The Asia-Pacific Digital Highway is expected to mobilise $20 billion to support digital corridors, fibre networks, satellite links, and regional data centres, says ADB. The initiative also includes policy and regulatory support, including cybersecurity risk management, and programmes to strengthen digital and AI-related skills.

ADB states that the initiative aims to expand broadband access to 200 million people and improve connectivity for 450 million more. It also refers to the establishment of a Centre for AI Innovation and Development in Seoul to support AI adoption and skills development.

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The Academy introduces rules excluding AI-generated work from Oscar eligibility

The Academy’s Board of Governors has introduced new rules excluding AI-generated performances and screenplays from eligibility for the Oscars. The updated rules require that recognised work be created and performed by humans.

Under the updated framework, only performances credited in a film’s legal billing and demonstrably carried out by individuals with their consent will qualify for an Oscar. Screenplays must also be authored by humans, with the academy reserving the right to request further disclosure on the use of AI in production.

The update comes as AI technologies are increasingly used in filmmaking, including digital recreations of actors and synthetic performers. Industry tensions around AI have grown in recent years, including during the 2023 writers’ and actors’ strikes.

The move is described as part of efforts within the creative sector to preserve human authorship and artistic control as generative AI tools expand across media production.

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UK AI sector survey to map growth trends and policy direction

The UK government is stepping up efforts to better understand the structure and growth of its AI sector through an updated national survey led by the Department for Science, Innovation and Technology.

The research, conducted by Ipsos and supported by Perspective Economics, aims to gather direct insights from businesses operating in the UK AI ecosystem. The findings are expected to inform future government policy on AI and sector development.

Participation is voluntary and confidential. Respondents are drawn from senior leadership roles, including chief executives, chief technology officers, company directors, and senior members of AI or data science teams. The survey focuses on business activity, products and services, and longer-term growth plans across the sector.

Fieldwork is taking place between late April and the end of May 2026 using online questionnaires and telephone interviews. Each session is expected to last around 15 to 20 minutes, allowing businesses to contribute structured input without significant disruption to normal operations.

The initiative reflects a wider UK policy priority: ensuring that government strategy keeps pace with developments in AI innovation and commercial growth. By drawing on direct industry evidence rather than relying only on secondary analysis, policymakers are trying to build a more accurate picture of the country’s evolving AI landscape. This last sentence is an inference based on the survey’s stated purpose of informing government AI policy.

Why does it matter?

AI policy is much easier to design in theory than in a market that is changing quickly and unevenly. If the government lacks current information on how AI firms are growing, what products they are developing, and where the main constraints lie, it risks shaping policy based on outdated assumptions. Direct input from businesses gives policymakers a stronger basis for decisions on support, regulation, skills, and investment, especially at a time when the UK is trying to turn AI ambition into measurable economic capacity.

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GPT-5.5 ranks among strongest models in UK cyber evaluation

The UK AI Security Institute has published cyber evaluations of OpenAI’s GPT-5.5, finding that the model is among the strongest it has tested on cyber tasks and the second to complete one of its end-to-end multi-step cyber-attack simulations.

According to the institute, GPT-5.5’s results suggest that recent gains in cyber capability are not limited to a single model family. It says an earlier evaluation of Anthropic’s Claude Mythos Preview had already pointed to a step up over previous frontier systems, and GPT-5.5 appears to reinforce that broader trend across leading models.

The institute uses a suite of 95 narrow cyber tasks across four difficulty tiers to test capabilities such as reverse engineering, web exploitation, cryptography, vulnerability research, and exploitation. On expert-level tasks in its advanced suite, GPT-5.5 achieved an average pass rate of 71.4%, ahead of Mythos Preview at 68.6%, GPT-5.4 at 52.4%, and Opus 4.7 at 48.6%.

The UK AI Security Institute also tests models in cyber ranges designed to measure multi-step attack capability. In The Last Ones, a 32-step corporate network intrusion simulation modelled on an enterprise kill chain, GPT-5.5 completed the full attack chain in 2 of 10 attempts, becoming the second model to do so after Mythos Preview. In the Cooling Tower industrial control system simulation, GPT-5.5 did not complete the range, and no model has yet done so.

The institute stresses that these are controlled capability evaluations and do not necessarily reflect what is available to ordinary public users. It also notes that the current ranges do not yet include all the defensive conditions of real-world environments, such as active defenders, defensive tooling, or alert penalties.

Separately, the institute evaluated GPT-5.5’s cyber safeguards and OpenAI’s mitigations against malicious cyber use. It said expert red-teamers identified a universal jailbreak that elicited prohibited cyber content across all malicious cyber queries provided by OpenAI, including in multi-turn agentic settings. OpenAI later updated its safeguard stack, but the institute said a configuration issue prevented it from verifying the effectiveness of the final version.

The institute adds that if offensive cyber capability is emerging as a byproduct of broader gains in autonomy, reasoning, and coding, further increases in model cyber performance could follow quickly. At the same time, it notes that the same capabilities may also help defenders and points to related UK government work on cyber resilience, vulnerability management, and preparation for a possible ‘vulnerability patch wave’.

Why does it matter?

The significance of the evaluation is not only that GPT-5.5 performed strongly on cyber tasks, but that it adds to the evidence that offensive cyber capability may be improving across multiple frontier model families at roughly the same time. If those gains are being driven by broader advances in reasoning, coding, and agentic execution, then cyber risk may rise even when models are not explicitly optimised for offensive use. That makes evaluation, safeguards, and realistic testing environments increasingly important, especially as the same capabilities can also strengthen defensive work and shorten response times for cybersecurity teams.

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UNDP highlights challenges in public sector digital transformation outcomes

According to UNDP, global public sector investment in digital technology now exceeds US$800 billion, yet most transformation efforts continue to fall short of expectations. UNDP reports that global public-sector investment in digital technology exceeds US$800 billion, while many transformation efforts fall short of expectations.

The report links persistent underperformance to structural and institutional barriers rather than technological limitations. The report also notes that digital initiatives often lack alignment with broader policy goals, resulting in fragmented systems that improve internal processes but do not transform public services.

UNDP identifies six recurring issues that continue to undermine progress across governments. These include rigid funding models that treat software as a one-time investment, fragmented mandates across institutions, limited data sharing, shortages of specialised talent, and procurement systems that prioritise risk avoidance over adaptability.

The report suggests that closing the gap between digital potential and real-world results may require a shift in approach. According to the report, sustainable transformation depends on reforming governance, funding, and incentives so technology can deliver measurable public value.

What does it matter?

The persistent gap between digital investment and actual outcomes signals a deeper governance challenge that goes far beyond technology. When most public sector transformation projects fail despite high spending, the issue is not innovation capacity but institutional design.

Outdated funding models, siloed mandates, and rigid procurement systems prevent governments from adapting at the speed required by modern digital tools, including AI. As a result, public institutions risk embedding inefficiency at scale while appearing digitally modern on the surface.

From a broader perspective, this has direct implications for state capacity and public trust. Governments that cannot translate digital investment into effective services will struggle to maintain competitiveness, especially as private sector systems become faster, more integrated, and more user-centric.

The issue also shapes global inequality in digital capability, as countries unable to reform underlying structures fall further behind in productivity and service delivery. Ultimately, the challenge is not technological adoption, but whether institutions can evolve fast enough to turn digital potential into real public value.

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Code for America highlights challenges in measuring AI use in public services in the US states

According to Code for America, AI is reshaping how public services are delivered across the United States, yet adoption remains uneven and difficult to measure. They added that state governments are rapidly embracing AI through low-risk pilot programmes while still lacking clear frameworks to evaluate impact.

The report describes AI adoption as following a staged progression beginning with readiness, where leadership structures, workforce skills and infrastructure are developed.

Piloting then introduces experimentation through sandboxes and limited deployments, while implementation embeds AI into operational systems such as fraud detection, document automation, research support and citizen-facing chat assistants.

The report also notes that despite growing experimentation, most US states have not yet transitioned into fully operational and measurable systems.

Leading states, including Utah, New Jersey, Pennsylvania, North Carolina, Maryland, Texas and Vermont, are advancing institutional capabilities required to govern AI as a long-term public asset. Others, such as West Virginia, Wyoming, Nebraska, Alaska, Florida and Kansas, remain at earlier stages of readiness and adoption.

The report identifies measuring outcomes as a key challenge. It states that while AI promises efficiency gains and cost reductions, short-term deployment often increases workload for public employees before benefits materialise.

It adds that evaluation frameworks remain underdeveloped, leaving governments with strong governance structures but limited visibility into real performance improvements.

According to Amanda Renteria, CEO of Code for America, the opportunity extends beyond adoption alone, as governments must shape AI in ways that are human-centred and grounded in measurable public outcomes.

The report suggests that states that succeed in aligning technology with real community impact will move beyond experimentation and define the future of public service in the AI era.

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DeepSeek V4 trails US frontier by eight months, according to CAISI evaluation

The Centre for AI Standards and Innovation, a unit within the US National Institute of Standards and Technology, has published an evaluation of DeepSeek V4, finding that it is the most capable Chinese-developed model it has assessed to date, but that it still trails leading US models overall.

According to the evaluation, DeepSeek V4 was tested in April 2026 and lagged top US frontier models by about eight months in CAISI’s aggregate capability measure. The report says the model performed strongly across several domains and was the most capable PRC model assessed by CAISI so far.

The findings highlight DeepSeek V4’s strongest results in mathematics, software engineering, and natural sciences. In mathematics, the model achieved particularly strong scores on benchmarks such as OTIS-AIME-2025 and PUMaC 2024, while still lagging the top US systems in overall capability.

CAISI also says DeepSeek V4 is more cost-efficient than other models of similar capability. Compared with the most cost-competitive US reference model, GPT-5.4 mini, it was more cost-efficient on five of seven benchmarks, ranging from 53% less expensive to 41% more expensive depending on the task.

The report notes that CAISI selected a US reference model for comparison and evaluated both benchmark performance and token pricing. It adds that DeepSeek’s lower cost profile makes it notable in the current frontier model landscape, even though it remains behind the leading US systems in aggregate capability.

The Center for AI Standards and Innovation (CAISI), a unit within the US National Institute of Standards and Technology (NIST), has published an evaluation of DeepSeek V4 Pro. has published an evaluation of DeepSeek V4 Pro, finding that the model is the most capable Chinese-developed model it has assessed to date, but still trails leading US models overall.

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UK’s NCSC warns AI could expose software vulnerabilities at scale

The NCSC says that AI is reshaping cybersecurity by exposing vulnerabilities across software ecosystems.
The National Cyber Security Centre (NCSC) warns that organisations must prepare for a large-scale patch wave. AI enables faster identification and exploitation of weaknesses than traditional defences can handle.

Technical debt, built through years of prioritising short-term efficiency instead of long-term resilience, is now being exposed at scale.

The NCSC notes that AI capabilities enable attackers to identify weaknesses faster and more comprehensively, creating pressure on organisations to respond with rapid and coordinated patching strategies across entire technology environments.

The recommended approach by NCSC prioritises internet-facing systems and external attack surfaces, followed by internal infrastructure and critical security assets.

Automated updates and hot patching are encouraged where available, while organisations lacking such capabilities must adopt scalable and risk-based update processes. Legacy systems without support present a particular risk, requiring replacement instead of reliance on patching alone.

NCSC adds that beyond software updates, the challenge reflects a deeper structural issue within digital ecosystems. Stronger cyber resilience depends on reducing systemic vulnerabilities through secure design practices, improved monitoring and supply chain readiness.

They also said that organisations that fail to prepare for continuous, large-scale patching cycles risk increased exposure as AI continues to reshape the cybersecurity landscape.

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Data access emerges as cornerstone of EU AI plan

The European Commission has unveiled its AI Continent Action Plan, setting out a strategy to strengthen Europe’s position in the global AI landscape. The plan responds to rapid international advances and seeks to accelerate AI adoption across European industry and public services, where progress remains uneven.

Rather than introducing a new regulatory framework, the plan brings together targeted investments and policy measures around five priorities: expanding AI infrastructure, improving access to data, accelerating adoption in strategic sectors, strengthening skills, and supporting the implementation of existing rules.

Access to high-quality and interoperable data is presented as one of the key conditions for scaling AI in Europe. The plan links this objective to the EU’s wider data strategy and to efforts to make cross-border data use more practical, enabling organisations to train and deploy AI systems more effectively while operating within Europe’s transparency and accountability standards.

The broader ambition is to move Europe from fragmented experimentation towards more scalable and trustworthy AI deployment. In that sense, the Action Plan treats data, infrastructure, skills, and implementation capacity as parts of the same competitiveness agenda rather than separate policy tracks.

Why does it matter?

Europe’s AI challenge is no longer only about regulation, but about whether companies and public institutions can actually build and use AI at scale. If access to data remains fragmented across borders, sectors, and technical systems, the EU risks falling further behind competitors that already combine compute, capital, and data more effectively. By putting data access alongside infrastructure and skills, the Commission is signalling that AI competitiveness will depend as much on operational capacity as on rules or research strength.

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