Singapore’s Prime Minister Lawrence Wong said employment for citizens will remain the government’s top priority as the nation confronts global trade tensions and the rapid advance of AI.
Speaking at the annual National Day Rally to mark Singapore’s 60th year, Wong pointed to the risks created by the US–China rivalry, renewed tariff policies under President Donald Trump, and the pressure technology places on workers.
In his first primary address since the May election, Wong emphasised the need to reinforce the trade-reliant economy, expand social safety nets and redevelop parts of the island.
He pledged to protect Singaporeans from external shocks by maintaining stability instead of pursuing risky shifts. ‘Ultimately, our economic strategy is about jobs, jobs and jobs. That’s our number one priority,’ he said.
The government has introduced new welfare measures, including the country’s first unemployment benefits and wider subsidies for food, utilities and education.
Wong also announced initiatives to help enterprises use AI more effectively, such as a job-matching platform and a government-backed traineeship programme for graduates.
Looking ahead, Wong said Singapore would draw up a new economic blueprint to secure its future in a world shaped by protectionism, climate challenges and changing energy needs.
After stronger-than-expected results in the first half of the year, the government recently raised its growth forecast for 2025 to between 1.5% and 2.5%.
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Enterprise employees are increasingly building their own AI tools, sparking a surge in shadow AI that raises security concerns.
Netskope reports a 50% rise in generative AI platform use, with over half of current adoption estimated to be unsanctioned by IT.
Platforms like Azure OpenAI, Amazon Bedrock, and Vertex AI lead this trend, allowing users to connect enterprise data to custom AI agents.
The growth of shadow AI has prompted calls for better oversight, real-time user training, and updated data loss prevention strategies.
On-premises deployment is also increasing, with 34% of firms using local LLM interfaces like Ollama and LM Studio. Security risks grow as AI agents retrieve data using API calls beyond browsers, particularly from OpenAI and Anthropic endpoints.
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Google has rolled out Flight Deals, a new AI‑powered tool for flexible, budget‑conscious travellers within Google Flights. It allows users to type natural‑language descriptions of their ideal trip, such as favourite activities or timeframe, and receive bargain flight suggestions in return.
Powered by Gemini, the feature parses conversational inputs and taps real‑time flight data from multiple airlines and agencies.
The tool identifies low fares and even proposes destinations users might not have considered, ranking options by percentage savings or lowest price.
Currently in beta, Flight Deals is available in the US, Canada, and India without special opt‑in. It is also accessible via the Google Flights menu.
Traditional Google Flights remains available, with a new option to exclude basic economy fares in the US and Canada.
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The researchers showed that their system could rearrange up to 2,024 rubidium atoms into precise grid patterns in just 60 milliseconds. By comparison, a previous attempt last year arranged 800 atoms without AI but required a full second.
To showcase the model’s speed, the team even used it to create an animated image of Schrödinger’s cat by guiding atoms into patterns with laser light.
Neutral atom arrays are one of the most promising approaches to building quantum computers, as the trapped atoms can maintain their fragile quantum states for relatively long periods.
The AI model was trained on different atom configurations and patterns of laser light, allowing it to quickly determine the most efficient hologram needed to reposition atoms into complex 2D and 3D shapes.
Experts in the field have welcomed the breakthrough. Mark Saffman, a physicist at the University of Wisconsin–Madison, noted that producing holograms for larger arrays usually requires intensive calculations.
The ability of AI to handle this process so efficiently, he said, left many colleagues ‘really impressed.’
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The decision follows an internal review of over 700,000 Claude interactions, where researchers identified thousands of values shaping how the system responds in real-world scenarios.
By enabling Claude to exit problematic exchanges, Anthropic hopes to improve trustworthiness while protecting its models from situations that might degrade performance over time.
Industry reaction has been mixed. Many researchers praised the step as a blueprint for responsible AI design. In contrast, others expressed concern that allowing models to self-terminate conversations could limit user engagement or introduce unintended biases.
Critics also warned that the concept of model welfare risks over-anthropomorphising AI, potentially shifting focus away from human safety.
The update arrives alongside other recent Anthropic innovations, including memory features that allow users to maintain conversation history. Together, these changes highlight the company’s balanced approach: enhancing usability where beneficial, while ensuring safeguards are in place when interactions become potentially harmful.
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AI pioneer Geoffrey Hinton has warned that AI could one day wipe out humanity if its growth is unchecked.
Speaking at the Ai4 conference in Las Vegas, the former Google executive estimated a 10 to 20 percent chance of such an outcome and criticised the approach taken by technology leaders.
He argued that efforts to keep humans ‘dominant’ over AI will fail once systems become more intelligent than their creators. According to Hinton, powerful AI will inevitably develop goals such as survival and control, making it increasingly difficult for people to restrain its influence.
In an interview with CNN, Hinton compared the potential future to a parent-child relationship, noting that AI systems may manipulate humans just as easily as an adult can bribe a child.
He suggested giving AI ‘maternal instincts’ to prevent disaster so that the technology genuinely cares about human well-being.
Hinton, often called the ‘Godfather of AI’ for his pioneering work in neural networks, cautioned that society risks creating beings that will ultimately outsmart and overpower us without embedding such safeguards.
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UK property agents are increasingly leveraging AI and automation to tackle a growing skills shortage in the sector, according to an analysis by PropTech provider Reapit.
Reapit’s Property Outlook Report 2025 shows that although agencies continue hiring, most face recruitment difficulties: more than half receive fewer than five qualified applicants per vacancy. Growth in payrolled employees is minimal, and the slowest year-on-year rise since May 2021 reflects wider labour market tightness.
In response, agencies are turning to time-saving technologies. A majority report that automation is more cost-effective than expanding headcount, with nearly 80 percent citing increased productivity from these tools.
This shift towards PropTech and AI reflects deeper structural pressures in the UK real estate sector: high employment costs, slower workforce growth, and increasing demands for efficiency are reshaping the role of technology in agency operations.
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AI can assist with refining cover letters, improving structure, and articulating motivations. It can also support interview preparation through mock question practice and help candidates deepen their understanding of legal issues.
However, authenticity is paramount. Taylor Wessing encourages applicants to ensure their work reflects their voice. Using AI to complete online assessments is explicitly discouraged, as these are designed to evaluate natural ability and personal fit.
According to the firm, while AI can bolster readiness for training schemes, over-reliance or misuse may backfire. They advise transparency about any AI assistance and underscore the importance of integrity throughout the process.
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Google has released Gemma 3 270M, an open-source AI model with 270 million parameters designed to run efficiently on smartphones and Internet of Things devices.
Drawing on technology from the larger Gemini family, it focuses on portability, low energy use and quick fine-tuning, enabling developers to create AI tools that work on everyday hardware instead of relying on high-end servers.
The model supports instruction-following and text structuring with a 256,000-token vocabulary, offering scope for natural language processing and on-device personalisation.
Its design includes quantisation-aware training to work in low-precision formats such as INT4, reducing memory use and improving speed on mobile processors instead of requiring extensive computational power.
Industry commentators note that the model could help meet demand for efficient AI in edge computing, with applications in healthcare wearables and autonomous IoT systems. Keeping processing on-device also supports privacy and reduces dependence on cloud infrastructure.
Google highlights the environmental benefits of the model, pointing to reduced carbon impact and greater accessibility for smaller firms and independent developers. While safeguards like ShieldGemma aim to limit risks, experts say careful use will still be needed to avoid misuse.
Future developments may bring new features, including multimodal capabilities, as part of Google’s strategy to blend open and proprietary AI within hybrid systems.
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Chinese physicist Pan Jianwei’s team created the world’s largest atom array, arranging over 2,000 rubidium atoms for quantum computing. The breakthrough at the University of Science and Technology of China could enable atom-based quantum computers to scale to tens of thousands of qubits.
Researchers used AI and optical tweezers to position all atoms simultaneously, completing the array in 60 milliseconds. The system achieved 99.97 percent accuracy for single-qubit operations and 99.5 percent for two-qubit operations, with 99.92 percent accuracy in qubit state detection.
Atom-based quantum computing is more promising for its stability and control than superconducting circuits or trapped ions. Until now, arrays were limited to a few hundred atoms, as moving each into position individually was slow and challenging.
Future work aims to expand array sizes further using stronger lasers and faster light modulators. Researchers hope that perfectly arranging tens of thousands of atoms leads to fully reliable and scalable quantum computers.
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