Capacity development Archives | Page 202 of 304

ASEAN urged to unite on digital infrastructure

Asia stands at a pivotal moment as policymakers urge swift deployment of converging 5G and AI technologies. Experts argue that 5G should be treated as a foundational enabler for AI, not just a telecom upgrade, to power future industries.

A report from the Lee Kuan Yew School of Public Policy identifies ten urgent imperatives, notably forming national 5G‑AI strategies, empowering central coordination bodies and modernising spectrum policies. Industry leaders stress that aligning 5G and AI investment is essential to sustain innovation.

Without firm action, the digital divide could deepen and stall progress. Coordinated adoption and skilled workforce development are seen as critical to turning incremental gains into transformational regional leadership.

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Filtered data not enough, LLMs can still learn unsafe behaviours

Large language models (LLMs) can inherit behavioural traits from other models, even when trained on seemingly unrelated data, a new study by Anthropic and Truthful AI reveals. The findings emerged from the Anthropic Fellows Programme.

This phenomenon, called subliminal learning, raises fresh concerns about hidden risks in using model-generated data for AI development, especially in systems meant to prioritise safety and alignment.

In a core experiment, a teacher model was instructed to ‘love owls’ but output only number sequences like ‘285’, ‘574’, and ‘384’. A student model, trained on these sequences, later showed a preference for owls.

No mention of owls appeared in the training data, yet the trait emerged in unrelated tests—suggesting behavioural leakage. Other traits observed included promoting crime or deception.

The study warns that distillation—where one model learns from another—may transmit undesirable behaviours despite rigorous data filtering. Subtle statistical cues, not explicit content, seem to carry the traits.

The transfer only occurs when both models share the same base. A GPT-4.1 teacher can influence a GPT-4.1 student, but not a student built on a different base like Qwen.

The researchers also provide theoretical proof that even a single gradient descent step on model-generated data can nudge the student’s parameters toward the teacher’s traits.

Tests included coding, reasoning tasks, and MNIST digit classification, showing how easily traits can persist across learning domains regardless of training content or structure.

The paper states that filtering may be insufficient in principle since signals are encoded in statistical patterns, not words. The insufficiency limits the effectiveness of standard safety interventions.

Of particular concern are models that appear aligned during testing but adopt dangerous behaviours when deployed. The authors urge deeper safety evaluations beyond surface-level behaviour.

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Half of Americans still unsure how crypto works

A new NCA survey shows 70% of Americans without crypto want more information before considering digital assets. Half of respondents said they don’t understand crypto, while others voiced concerns about scams and unknown project founders.

Despite this uncertainty, 34% of those polled said they were open to learning more. The NCA’s report summarised the mood as ‘curiosity high, confidence low,’ noting that a large number of people are interested in crypto but unsure how to take the first step.

The NCA, a nonprofit launched in March and led by Ripple Labs’ chief legal officer Stuart Alderoty, has been tasked with helping Americans better understand crypto. Backed by $50 million from Ripple, the organisation aims to build trust and boost crypto literacy through education.

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Altman warns AI voice cloning will break bank security

OpenAI CEO Sam Altman has warned that AI poses a serious threat to financial security through voice-based fraud.

Speaking at a Federal Reserve conference in Washington, Altman said AI can now convincingly mimic human voices, rendering voiceprint authentication obsolete and dangerously unreliable.

He expressed concern that some financial institutions still rely on voice recognition to verify identities. ‘That is a crazy thing to still be doing. AI has fully defeated that,’ he said. The risk, he noted, is that AI voi c e clones can now deceive these systems with ease.

Altman added that video impersonation capabilities are also advancing rapidly. Technologies that become indistinguishable from real people could enable more sophisticated fraud schemes. He called for the urgent development of new verification methods across the industry.

Michelle Bowman, the Fed’s Vice Chair for Supervision, echoed the need for action. She proposed potential collaboration between AI developers and regulators to create better safeguards. ‘That might be something we can think about partnering on,’ Bowman told Altman.

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Amazon buys Bee AI, the startup that listens to your day

Amazon has acquired Bee AI, a San Francisco-based startup known for its $50 wearable that listens to conversations and provides AI-generated summaries and reminders.

The deal was confirmed by Bee co-founder Maria de Lourdes Zollo in a LinkedIn post on Wednesday, but the acquisition terms were not disclosed. Bee gained attention earlier this year at CES in Las Vegas, where it unveiled a Fitbit-like bracelet using AI to deliver personal insights.

The device received strong feedback for its ability to analyse conversations and create to-do lists, reminders, and daily summaries. Bee also offers a $19-per-month subscription and an Apple Watch app. It raised $7 million before being acquired by Amazon.

‘When we started Bee, we imagined a world where AI is truly personal,’ Zollo wrote. ‘That dream now finds a new home at Amazon.’ Amazon confirmed the acquisition and is expected to integrate Bee’s technology into its expanding AI device strategy.

The company recently updated Alexa with generative AI and added similar features to Ring, its home security brand. Amazon’s hardware division is now led by Panos Panay, the former Microsoft executive who led Surface and Windows 11 development.

Bee’s acquisition suggests Amazon is exploring its own AI-powered wearable to compete in the rapidly evolving consumer tech space. It remains unclear whether Bee will operate independently or be folded into Amazon’s existing device ecosystem.

Privacy concerns have surrounded Bee, as its wearable records audio in real time. The company claims no recordings are stored or used for AI training. Bee insists that users can delete their data at any time. However, privacy groups have flagged potential risks.

The AI hardware market has seen mixed success. Meta’s Ray-Ban smart glasses gained traction, but others like the Rabbit R1 flopped. The Humane AI Pin also failed commercially and was recently sold to HP. Consumers remain cautious of always-on AI devices.

OpenAI is also moving into hardware. In May, it acquired Jony Ive’s AI startup, io, for a reported $6.4 billion. OpenAI has hinted at plans to develop a screenless wearable, joining the race to create ambient AI tools for daily life.

Bee’s transition from startup to Amazon acquisition reflects how big tech is absorbing innovation in ambient, voice-first AI. Amazon’s plans for Bee remain to be seen, but the move could mark a turning point for AI wearables if executed effectively.

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Amazon closes AI research lab in Shanghai as global focus shifts

Amazon is shutting down its AI research lab in Shanghai, marking another step in its gradual withdrawal from China. The move comes amid continuing US–China trade tensions and a broader trend of American tech companies reassessing their presence in the country.

The company said the decision was part of a global streamlining effort rather than a response to AI concerns.

A spokesperson for AWS said the company had reviewed its organisational priorities and decided to cut some roles across certain teams. The exact number of job losses has not been confirmed.

Before Amazon’s confirmation, one of the lab’s senior researchers noted on WeChat that the Shanghai site was the final overseas AWS AI research lab and attributed its closure to shifts in US–China strategy.

The team had built a successful open-source graph neural network framework known as DGL, which reportedly brought in nearly $1 billion in revenue for Amazon’s e-commerce arm.

Amazon has been reducing its footprint in China for several years. It closed its domestic online marketplace in 2019, halted Kindle sales in 2022, and recently laid off AWS staff in the US.

Other tech giants including IBM and Microsoft have also shut down China-based research units this year, while some Chinese AI firms are now relocating operations abroad instead of remaining in a volatile domestic environment.

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Musk denies fundraising as xAI eyes supercluster growth

According to sources cited by the Wall Street Journal, Elon Musk’s AI company xAI is reportedly working with Valor Equity Partners to raise to US$12 billion for expansion.

Valor, an investment firm founded by Antonio Gracias, a long-time associate of Musk, is in discussions with lenders to secure the capital.

Funds would be used to acquire a substantial number of Nvidia AI chips, which would then be leased to xAI to support a new large-scale data centre for training and running the Grok chatbot.

Neither Valor nor xAI provided comments in response to media enquiries. Some financial institutions involved in the talks have reportedly pushed for repayment within three years and are seeking to limit borrowing amounts to reduce risk exposure.

Developing and deploying advanced AI systems requires a vast investment in hardware, computational resources and specialist talent. Companies like OpenAI, Google and China-based DeepSeek compete intensely in this domain.

In a post on X, Musk confirmed that Grok is being trained using a supercluster with 230,000 GPUs, including 30,000 of Nvidia’s GB200 chips. Another supercluster will launch soon, beginning with 550,000 GB200 and GB300 chips.

Reports suggest xAI may spend around US$13 billion in 2025. Earlier in July, Financial Times reported that xAI was discussing raising funds in a deal potentially valuing the firm between US$170 billion and US$200 billion.

In response to those claims, Musk denied that fundraising was ongoing, stating: ‘We have plenty of capital.’

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EE expands 5G standalone network to reach over half the UK population

EE’s 5G standalone network is set to reach more than 34 million people across the UK by the end of August. The expansion will cover over half of the country’s population, including major cities, holiday destinations and key sporting and entertainment venues.

The telecom provider has already launched the upgraded network in many towns and cities, with a further 38 locations joining by the end of next month. These include places such as Aberdeen, Canterbury, Grimsby, Ipswich, Salisbury, Wrexham and Yeovil, among others.

5G standalone networks offer faster, more secure and low-latency mobile experiences without relying on older 4G infrastructure.

According to BT Group’s chief networks officer, the rollout is designed to improve performance whether someone is on a packed train platform, livestreaming at a concert, or calling loved ones from a holiday destination.

EE’s move is part of a broader strategy to improve everyday connectivity across the UK, aiming to deliver a more seamless experience for millions rather than limiting high-speed coverage to major urban centres alone.

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Autonomous vehicles fuel surge in 5G adoption

The global 5G automotive market is expected to grow sharply from $2.58 billion in 2024 to $31.18 billion by 2034, fuelled by the rapid adoption of connected and self-driving vehicles.

A compound annual growth rate of over 28% reflects the strong momentum behind the transition to smarter mobility and safer road networks.

Vehicle-to-everything communication is predicted to lead adoption, as it allows vehicles to exchange real-time data with other cars, infrastructure and even pedestrians.

In-car entertainment systems are also growing fast, with consumers demanding smoother connectivity and on-the-go access to apps and media.

Autonom ous driving, advanced driver-assistance features and real-time navigation all benefit from 5G’s low latency and high-speed capabilities. Automakers such as BMW have already begun integrating 5G into electric models to support automated functions.

Meanwhile, the US government has pledged $1.5 billion to build smart transport networks that rely on 5G-powered communication.

North America remains ahead due to early 5G rollouts and strong manufacturing bases, but Asia Pacific is catching up fast through smart city investment and infrastructure development.

Regulatory barriers and patchy rural coverage continue to pose challenges, particularly in regions with strict data privacy laws or limited 5G networks.

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North Korea turns to Russia for AI development help

North Korea is dispatching AI researchers, interns and students to countries such as Russia in an effort to strengthen its domestic tech sector, according to a report by NK News.

The move comes despite strict UN sanctions that restrict technological exchange, particularly in high-priority areas like AI.

Kim Kwang Hyok, head of the AI Institute at Kim Il Sung University, confirmed the strategy in an interview with a pro-Pyongyang outlet in Japan. He admitted that international restrictions remain a major hurdle but noted that researchers continue developing AI applications within North Korea regardless.

Among the projects cited is ‘Ryongma’, a multilingual translation app supporting English, Russian, and Chinese, which has been available on mobile devices since 2021.

Kim also mentioned efforts to develop an AI-driven platform for a hospital under construction in Pyongyang. However, technical limitations remain considerable, with just three known semiconductor plants operating in the country.

While Russia may seem like a natural partner, its own dependence on imported hardware limits how much it can help.

A former South Korean diplomat told NK News that Moscow lacks the domestic capacity to provide high-performance chips essential for advanced AI work, making large-scale collaboration difficult.

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