MIT researchers have developed a new AI approach that helps engineers solve complex design problems faster, from power grid optimisation to vehicle safety.
The method adapts a foundation model trained on tabular data, enabling high-dimensional optimisation without retraining and significantly speeding up results.
The system uses a foundation model with Bayesian optimisation to pinpoint the variables that most impact outcomes. Focusing on key variables, the model finds top solutions 10 to 100 times faster than existing optimisation methods.
Early tests show the approach excels in costly, time-consuming scenarios like car crash testing and power system design. The technique lowers computational demands and suits large-scale, high-frequency engineering challenges across multiple domains.
Researchers aim to expand the method to even higher-dimensional problems, such as naval ship design, while highlighting the broader potential of foundation models as algorithmic engines in scientific and engineering tools.
Experts see it as a practical step toward making advanced optimisation more accessible in real-world applications.
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Google’s Gemini 3.1 Flash-Lite has launched in preview for developers via AI Studio and for enterprises through Vertex AI. Designed for high-volume workloads, it promises fast, cost-effective performance while maintaining high-quality outputs.
Priced at just $0.25 per million input tokens and $1.50 per million output tokens, 3.1 Flash-Lite offers 2.5X faster response times and 45% higher output speed than the previous 2.5 Flash model.
Benchmarks show strong performance across reasoning and multimodal tasks, including an Elo score of 1432 on Arena.ai, 86.9% on GPQA Diamond, and 76.8% on MMMU Pro, surpassing some older, larger Gemini models.
The model also provides adaptive intelligence features, allowing developers to adjust how much the AI ‘thinks’ for each task. The model handles both high-frequency tasks, such as translation, and complex tasks, such as interface generation and simulations.
Early-access developers and companies report that 3.1 Flash-Lite handles complex workloads with precision comparable to larger models. Its speed, affordability, and reasoning capabilities make it an attractive choice for scalable, real-time AI applications.
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Google is accelerating Chrome’s release cycle rather than maintaining its long-standing four-week cadence.
From September, users on desktop and mobile platforms will receive new stable versions every two weeks, doubling the frequency of feature milestones across speed, stability and usability. Weekly security updates introduced in 2023 remain unchanged.
The faster pace comes as AI-driven browsers seek a foothold in a market long dominated by Chrome.
Products, such as ChatGPT Atlas and Perplexity’s Comet, embed agentic assistants directly into the browsing experience, automating tasks from summarising pages to scheduling meetings.
Chrome has responded with deeper Gemini integration, including the rollout of autonomous features across its interface.
Google maintains that the accelerated schedule reflects the needs of the evolving web platform, arguing that developers require quicker access to updated tools.
Yet the timing aligns with growing competitive pressure from AI-native browsers, prompting speculation that Chrome’s dominance can no longer be taken for granted.
The shift will begin with Chrome version 153 in beta and stable channels on 8 September 2026. Enterprise administrators and Chromebook users will continue to rely on the eight-week Extended Stable branch, which remains unchanged for organisations that need slower, controlled deployments.
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Ransomware attacks are increasingly relying on stolen passwords rather than traditional malware, according to Cloudflare’s latest annual threat report. Attackers now exploit legitimate account credentials to blend into regular traffic, making breaches harder to detect and contain.
Manufacturing and critical infrastructure organisations account for over half of targeted attacks, reflecting their high operational stakes.
Cloudflare highlighted that AI is enabling attackers to prioritise speed and scale over technical sophistication. Generative AI lets criminals automate fraud, hijacking email threads and targeting a ~$49,000 sweet spot to maximise profit while avoiding scrutiny.
Nation-state actors also leverage legitimate platforms for command-and-control operations, with Russia, China, Iran, and North Korea each following distinct cyber strategies.
Researchers warned that modern ransomware is less a malware crisis and more an identity and access challenge. Attackers using authorised credentials can bypass defences and execute high-impact extortion, marking a significant shift in global threat vectors.
The report urges businesses to strengthen identity security, monitor access, and defend against AI-driven attacks that exploit impersonation and automation at scale.
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The European Commission is preparing more stringent requirements for ageing data centres rather than allowing legacy infrastructure to operate under looser rules.
A draft strategy tied to the EU’s tech sovereignty package signals that older sites will face higher efficiency expectations and stricter sustainability checks as part of an effort to modernise the digital backbone of the EU.
The proposal outlines minimum performance standards for new data centres by 2030, aiming to align the entire sector with the bloc’s climate and resilience goals. Officials want to reduce energy waste and improve monitoring across facilities that have long operated without uniform benchmarks.
The draft points to an expanded role for the Cloud and AI Development Act, which is expected to frame future obligations for cloud providers instead of relying on fragmented national measures.
Brussels sees consistent rules as essential for supporting secure cloud services, AI infrastructure and cross-border digital operations.
The strategy underscores that modernisation is central to the EU’s vision of tech sovereignty. Older centres would need upgrades to maintain compliance, ensuring that Europe’s digital infrastructure remains competitive, efficient and less dependent on external providers.
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In recent days, social media has been alight with discussions about the 2014 series whose portrayal of AI and ethical dilemmas now feels remarkably prophetic: Silicon Valley. Fans and professionals alike are highlighting how the show’s depiction of AI, automated agents, and ethical dilemmas mirrors today’s real-world challenges.
From algorithmic decision-making to AI shaping social and economic interactions, the series explores the boundaries, responsibilities, and societal impact of AI in ways that feel startlingly relevant. What once seemed like pure comedy is increasingly being seen as a warning, highlighting how the choices we make around AI and its ethical frameworks will shape whether the technology benefits society.
While the show dramatises these dilemmas for entertainment, the real world is now facing the same questions. Recent trends in generative AI, autonomous agents, and large-scale automated decision-making are bringing their predictions to life, raising urgent ethical questions for developers, policymakers, and society alike.
Source: Freepik
The rise of AI ethics: from niche concern to central requirement
The growing influence of AI on society has propelled ethics from a theoretical discussion to a central factor in technological decision-making. Initially confined to academic debate, ethics in AI is now a guiding force in technological development. The impact of AI is becoming tangible across society, from employment and finance to online content.
Technical performance alone no longer defines success; the consequences of design choices have become morally and socially significant. Governments, international organisations, and corporations are responding by developing ethical frameworks.
The EU AI Act, the OECD AI Principles, and numerous corporate codes of conduct signal that society expects AI systems to align with human values, demonstrating accountability, fairness, and trustworthiness. Ethical reflection has become a prerequisite for technological legitimacy and societal acceptance.
Source: Freepik
Functions of AI ethics: trust, guidance, and societal risk
Ethical frameworks for AI fulfil multiple roles, balancing moral guidance with practical necessity. They build public trust between developers, organisations, and users, reassuring society that AI systems operate consistently with shared values.
For developers, ethical principles offer a blueprint for decision-making, helping anticipate societal impact and minimise unintended harm. Beyond guidance, AI ethics acts as a form of societal risk governance, allowing organisations to identify potential consequences before they manifest.
By integrating ethics into design, AI systems become socially sustainable technologies, bridging technical capability with moral responsibility. The approach like this is particularly critical in high-stakes domains such as healthcare, finance, and law, where algorithmic decisions can significantly affect human well-being.
Source: Freepik
The politics of AI ethics: regulatory theatre and corporate influence
Despite widespread adoption, AI ethics frameworks sometimes risk becoming regulatory theatre, where public statements signal commitment but fail to ensure meaningful action. Many organisations promote ethical AI principles, yet consistent enforcement and follow-through often lag behind these claims.
Even with their limitations, ethical frameworks are far from meaningless. They shape public discourse, influence policy, and determine which AI systems gain social legitimacy. The challenge lies in balancing credibility with practical impact, ensuring that ethical commitments are more than symbolic gestures.
Social media platforms like X amplify this tension, with public scrutiny and viral debates exposing both successes and failures in applying ethical principles.
Source: Freepik
AI ethics as a lens for technology and society
The prominence of AI ethics reflects a broader societal transformation in evaluating technology. Modern societies no longer judge AI solely by efficiency, speed, or performance; they assess social consequences, fairness, and the distribution of risks and benefits.
AI is increasingly seen as a social actor rather than a neutral tool, influencing public behaviour, shaping social norms, and redefining concepts such as trust, autonomy, and accountability. Ethical evaluation of AI is not just a philosophical exercise, but demonstrate evolving expectations about the role technology should play in human life.
Source: Freepik
AI ethics as early-warning governance for social impact
AI ethics functions as a critical early-warning system for society. Ethical principles anticipate harms that might otherwise go unnoticed, from systemic bias to privacy violations. By highlighting potential consequences, ethics enables organisations to act proactively, reducing the likelihood of crises and improving public trust.
Moreover, ethics ensures that long-term impacts, including societal cohesion, equity, and fairness, are considered alongside immediate technical performance. In doing so, AI ethics bridges the gap between what AI can do and what society deems acceptable, ensuring that innovation remains aligned with moral and social norms.
Source: Freepik
The bridge between technological power and social legitimacy
AI ethics remains the essential bridge between technological power and social legitimacy. Embedding ethical reflection into AI development ensures that innovation is not only technically effective but also socially sustainable, trustworthy, and accountable.
Yet a growing tension defines the next phase of this evolution: the accelerating pace of innovation often outstrips the slower processes of ethical deliberation and regulation, raising questions about who sets the norms and how quickly societies can adapt.
Rather than acting solely as a safeguard, ethics is increasingly becoming a strategic dimension of technological leadership, shaping public trust, market adoption, and even geopolitical influence in the global race for AI. The rise of AI ethics, therefore, signals more than a moral awakening, reflecting a structural shift in how technological progress is evaluated and legitimised.
As AI continues to integrate into everyday life, ethical frameworks will determine not only how systems function, but also whether they are accepted as part of the social fabric. Aligning innovation with societal values is no longer optional but the condition under which AI can sustain legitimacy, unlock its full potential, and remain a transformative force that benefits society as a whole.
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Europe is building a federated cloud and AI infrastructure intended to reduce reliance on US and Chinese technology providers and avoid ongoing strategic vulnerability.
The project, known as EURO-3C, was announced in Barcelona by Telefónica and is backed by the European Commission. More than seventy organisations across telecommunications, technology and emerging companies have joined the effort.
Architects of the scheme argue that linking national infrastructures into a shared network of nodes offers a realistic path forward, particularly as Europe cannot easily create a hyperscale cloud provider from scratch.
The initiative follows a series of US cloud outages that exposed the risks of excessive dependence on external infrastructure and raised questions about sovereignty, resilience and long-term competitiveness.
Commission officials described the programme as a way to build a secure cross-border digital ecosystem that supports industries such as automotive, e-health, public administration and sovereign government cloud.
Telefónica stressed that agentic AI, capable of taking autonomous actions, will play a central role in enabling Europe to develop technology rather than import it.
The partners view the project as a foundation for a unified and independent digital environment that strengthens industrial supply chains and prepares European sectors for the next phase of cloud and AI adoption.
They present the initiative as a significant step toward reducing strategic exposure while stimulating domestic innovation.
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Deutsche Telekom is turning to satellite connectivity to address Europe’s persistent mobile coverage gaps, rather than relying solely on terrestrial networks.
The company announced a partnership with Starlink during the Mobile World Congress in Barcelona, arguing that non-terrestrial networks can help reach remote forests, mountains and islands that remain underserved despite broad coverage elsewhere.
A collaboration that aims to support direct-to-device satellite links by 2028, enabling future smartphones to connect to Starlink’s MSS spectrum without additional hardware.
Telecommunications leaders describe the plan as a step toward an ‘everywhere network’, extending reliable service to areas long constrained by topographical and conservation barriers. The partnership follows earlier joint work with SpaceX to eliminate dead zones.
Deutsche Telekom is also increasing its use of agentic AI, integrating autonomous network-enhancing systems intended to improve translation, search and service features across devices.
Executives say these capabilities work even on older phones, reducing dependence on apps and creating a more inclusive digital environment.
Although committed to European digital sovereignty, the company insists that global collaboration remains necessary for long-term competitiveness.
Leadership argues that precise regulation and controlled data environments aligned with European standards can balance international cooperation with privacy protection. They remain confident that European technology firms and start-ups will continue driving meaningful innovation across the sector.
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More users are exploring how to switch from ChatGPT to Claude while preserving their existing chat history and preferences. Rather than starting over with a new AI assistant, many want to migrate context and maintain continuity.
The first step is gathering your data from ChatGPT. In Settings, open Personalisation, then review the Memory section to copy any stored preferences you want to retain. You can also export your full chat history through Data Controls by selecting ‘Export Data’.
ChatGPT will generate downloadable files containing your conversations. If you prefer a lighter approach, manually copy key discussions or ask ChatGPT to summarise your main preferences, frequently discussed topics, and custom instructions.
Once your information is ready, open Claude and enable Memory under Settings and Capabilities. Start a new conversation and paste your summaries using a prompt such as ‘Here is important context about me. Please update your memory accordingly.’
After transferring the data, verify that Claude has stored the information accurately. If you plan to leave ChatGPT entirely, review and delete saved memory entries before removing your account to ensure your data is cleared.
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A high-severity vulnerability dubbed ‘ClawJacked’ has been discovered in OpenClaw, an open-source AI agent framework that lets developers run autonomous AI assistants locally.
The flaw, uncovered by Oasis Security, allowed malicious websites to silently hijack a user’s local AI agent instance and steal sensitive data, all triggered by a single browser visit.
The attack exploited OpenClaw’s local WebSocket gateway, which assumed that traffic from localhost could be trusted. A malicious website could open a WebSocket connection to the gateway, brute-force the password at hundreds of guesses per second, with no rate limiting applied to local connections, and then silently register as a trusted device without any user prompt.
Once inside, attackers gained admin-level access to the AI agent, connected devices, logs, and configuration data. Oasis Security responsibly disclosed the flaw, and OpenClaw issued a patch within 24 hours, releasing version 2026.2.26.
Security experts are urging organisations to update immediately, audit the permissions held by their AI agents, and apply strict governance policies, treating AI agents as non-human identities that require the same oversight as human users or service accounts.
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