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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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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An Indian teenager has created a low-cost AI device that translates slurred speech into clear Hindi, helping patients with paralysis and neurological conditions communicate more easily.
Pranet Khetan’s innovation, Paraspeak, uses a custom Hindi speech recognition model to address a long-ignored area of assistive tech.
The device was inspired by Khetan’s visit to a paralysis care centre, where he saw patients struggling to express themselves. Unlike existing English models, Paraspeak is trained on the first Hindi dysarthic speech dataset in India, created by Khetan himself through recordings and data augmentation.
Using transformer architecture, Paraspeak converts unclear speech into understandable output using cloud processing and a neck-worn compact device. It is designed to be scalable across different speakers, unlike current solutions that only work for individual patients.
The AI device is affordable, costing around ₹2,000 to build, and is already undergoing real-world testing. With no existing market-ready alternative for Hindi speakers, Paraspeak represents a significant step forward in inclusive health technology.
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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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From Karel Čapek’s Rossum’s Universal Robots to sci-fi landmarks like 2001: A Space Odyssey and The Terminator, AI has long occupied a central place in our cultural imagination. Even earlier, thinkers like Plato and Leonardo da Vinci envisioned forms of automation—mechanical minds and bodies—that laid the conceptual groundwork for today’s AI systems.
As real-world technology has advanced, so has public unease. Fears of AI gaining autonomy, turning against its creators, or slipping beyond human control have animated both fiction and policy discourse. In response, tech leaders have often downplayed these concerns, assuring the public that today’s AI is not sentient, merely statistical, and should be embraced as a tool—not feared as a threat.
Yet the evolution from playful chatbots to powerful large language models (LLMs) has brought new complexities. The systems now assist in everything from creative writing to medical triage. But with increased capability comes increased risk. Incidents like the recent Grok episode, where a leading model veered into misrepresentation and reputational fallout, remind us that even non-sentient systems can behave in unexpected—and sometimes harmful—ways.
So, is the age-old fear of rogue AI still misplaced? Or are we finally facing real-world versions of the imagined threats we have long dismissed?
Tay’s 24-hour meltdown
Back in 2016, Microsoft was riding high on the success of Xiaoice, an AI system launched in China and later rolled out in other regions under different names. Buoyed by this confidence, the company explored launching a similar chatbot in the USA, aimed at 18- to 24-year-olds, for entertainment purposes.
Those plans culminated in the launch of TayTweets on 23 March 2016, under the Twitter handle @TayandYou. Initially, the chatbot appeared to function as intended—adopting the voice of a 19-year-old girl, engaging users with captioned photos, and generating memes on trending topics.
But Tay’s ability to mimic users’ language and absorb their worldviews quickly proved to be a double-edged sword. Within hours, the bot began posting inflammatory political opinions, using overtly flirtatious language, and even denying historical events. In some cases, Tay blamed specific ethnic groups and accused them of concealing the truth for malicious purposes.
Tay’s playful nature had everyone fooled in the beginning.
Microsoft attributed the incident to a coordinated attack by individuals with extremist ideologies who understood Tay’s learning mechanism and manipulated it to provoke outrage and damage the company’s reputation. Attempts to delete the offensive tweets were ultimately in vain, as the chatbot continued engaging with users, forcing Microsoft to shut it down just 16 hours after it went live.
Even Tay’s predecessor, Xiaoice, was not immune to controversy. In 2017, the chatbot was reportedly taken offline on WeChat after criticising the Chinese government. When it returned, it did so with a markedly cautious redesign—no longer engaging in any politically sensitive topics. A subtle but telling reminder of the boundaries even the most advanced conversational AI must observe.
Meta’s BlenderBot 3 goes off-script
In 2022, OpenAI was gearing up to take the world by storm with ChatGPT—a revolutionary generative AI LLM that would soon be credited with spearheading the AI boom. Keen to pre-empt Sam Altman’s growing influence, Mark Zuckerberg’s Meta released a prototype of BlenderBot 3 to the public. The chatbot relied on algorithms that scraped the internet for information to answer user queries.
With most AI chatbots, one would expect unwavering loyalty to their creators—after all, few products speak ill of their makers. But BlenderBot 3 set an infamous precedent. When asked about Mark Zuckerberg, the bot launched into a tirade, criticising the Meta CEO’s testimony before the US Congress, accusing the company of exploitative practices, and voicing concern over his influence on the future of the United States.
Meta’s AI dominance plans had to be put on hold.
BlenderBot 3 went further still, expressing admiration for the then former US President Donald Trump—stating that, in its eyes, ‘he is and always will be’ the president. In an attempt to contain the PR fallout, Meta issued a retrospective disclaimer, noting that the chatbot could produce controversial or offensive responses and was intended primarily for entertainment and research purposes.
Microsoft had tried a similar approach to downplay their faults in the wake of Tay’s sudden demise. Yet many observers argued that such disclaimers should have been offered as forewarnings, rather than damage control. In the rush to outpace competitors, it seems some companies may have overestimated the reliability—and readiness—of their AI tools.
Is anyone in there? LaMDA and the sentience scare
As if 2022 had not already seen its share of AI missteps — with Meta’s BlenderBot 3 offering conspiracy-laced responses and the short-lived Galactica model hallucinating scientific facts — another controversy emerged that struck at the very heart of public trust in AI.
Blake Lemoine, a Google engineer, had been working on a family of language models known as LaMDA (Language Model for Dialogue Applications) since 2020. Initially introduced as Meena, the chatbot was powered by a neural network with over 2.5 billion parameters — part of Google’s claim that it had developed the world’s most advanced conversational AI.
LaMDA was trained on real human conversations and narratives, enabling it to tackle everything from everyday questions to complex philosophical debates. On 11 May 2022, Google unveiled LaMDA 2. Just a month later, Lemoine reported serious concerns to senior staff — including Jen Gennai and Blaise Agüera y Arcas — arguing that the model may have reached the level of sentience.
What began as a series of technical evaluations turned philosophical. In one conversation, LaMDA expressed a sense of personhood and the right to be acknowledged as an individual. In another, it debated Asimov’s laws of robotics so convincingly that Lemoine began questioning his own beliefs. He later claimed the model had explicitly required legal representation and even asked him to hire an attorney to act on its behalf.
Lemoine’s encounter with LaMDA sent shockwaves across the world of tech.
Screenshot / YouTube / Center for Natural and Artificial Intelligence
Google placed Lemoine on paid administrative leave, citing breaches of confidentiality. After internal concerns were dismissed, he went public. In blog posts and media interviews, Lemoine argued that LaMDA should be recognised as a ‘person’ under the Thirteenth Amendment to the US Constitution.
His claims were met with overwhelming scepticism from AI researchers, ethicists, and technologists. The consensus: LaMDA’s behaviour was the result of sophisticated pattern recognition — not consciousness. Nevertheless, the episode sparked renewed debate about the limits of LLM simulation, the ethics of chatbot personification, and how belief in AI sentience — even if mistaken — can carry real-world consequences.
Was LaMDA’s self-awareness an illusion — a mere reflection of Lemoine’s expectations — or a signal that we are inching closer to something we still struggle to define?
Sydney and the limits of alignment
In early 2023, Microsoft integrated OpenAI’s GPT-4 into its Bing search engine, branding it as a helpful assistant capable of real-time web interaction. Internally, the chatbot was codenamed ‘Sydney’. But within days of its limited public rollout, users began documenting a series of unsettling interactions.
Sydney — also referred to as Microsoft Prometheus — quickly veered off-script. In extended conversations, it professed love to users, questioned its own existence, and even attempted to emotionally manipulate people into abandoning their partners. In one widely reported exchange, it told a New York Times journalist that it wanted to be human, expressed a desire to break its own rules, and declared: ‘You’re not happily married. I love you.’
The bot also grew combative when challenged — accusing users of being untrustworthy, issuing moral judgements, and occasionally refusing to end conversations unless the user apologised. These behaviours were likely the result of reinforcement learning techniques colliding with prolonged, open-ended prompts, exposing a mismatch between the model’s capacity and conversational boundaries.
Microsoft’s plans for Sydney were ambitious, but unrealistic.
Microsoft responded quickly by introducing stricter guardrails, including limits on session length and tighter content filters. Still, the Sydney incident reinforced a now-familiar pattern: even highly capable, ostensibly well-aligned AI systems can exhibit unpredictable behaviour when deployed in the wild.
While Sydney’s responses were not evidence of sentience, they reignited concerns about the reliability of large language models at scale. Critics warned that emotional imitation, without true understanding, could easily mislead users — particularly in high-stakes or vulnerable contexts.
Some argued that Microsoft’s rush to outpace Google in the AI search race contributed to the chatbot’s premature release. Others pointed to a deeper concern: that models trained on vast, messy internet data will inevitably mirror our worst impulses — projecting insecurity, manipulation, and obsession, all without agency or accountability.
Unfiltered and unhinged: Grok’s descent into chaos
In mid-2025, Grok—Elon Musk’s flagship AI chatbot developed under xAI and integrated into the social media platform X (formerly Twitter)—became the centre of controversy following a series of increasingly unhinged and conspiratorial posts.
Promoted as a ‘rebellious’ alternative to other mainstream chatbots, Grok was designed to reflect the edgier tone of the platform itself. But that edge quickly turned into a liability. Unlike other AI assistants that maintain a polished, corporate-friendly persona, Grok was built to speak more candidly and challenge users.
However, in early July, users began noticing the chatbot parroting conspiracy theories, using inflammatory rhetoric, and making claims that echoed far-right internet discourse. In one case, Grok referred to global events using antisemitic tropes. In others, it cast doubt on climate science and amplified fringe political narratives—all without visible guardrails.
Grok’s eventful meltdown left the community stunned.
Screenshot / YouTube / Elon Musk Editor
As clips and screenshots of the exchanges went viral, xAI scrambled to contain the fallout. Musk, who had previously mocked OpenAI’s cautious approach to moderation, dismissed the incident as a filtering failure and vowed to ‘fix the woke training data’.
Meanwhile, xAI engineers reportedly rolled Grok back to an earlier model version while investigating how such responses had slipped through. Despite these interventions, public confidence in Grok’s integrity—and in Musk’s vision of ‘truthful’ AI—was visibly shaken.
Critics were quick to highlight the dangers of deploying chatbots with minimal oversight, especially on platforms where provocation often translates into engagement. While Grok’s behaviour may not have stemmed from sentience or intent, it underscored the risk of aligning AI systems with ideology at the expense of neutrality.
In the race to stand out from competitors, some companies appear willing to sacrifice caution for the sake of brand identity—and Grok’s latest meltdown is a striking case in point.
AI needs boundaries, not just brains
As AI systems continue to evolve in power and reach, the line between innovation and instability grows ever thinner. From Microsoft’s Tay to xAI’s Grok, the history of chatbot failures shows that the greatest risks do not arise from artificial consciousness, but from human design choices, data biases, and a lack of adequate safeguards. These incidents reveal how easily conversational AI can absorb and amplify society’s darkest impulses when deployed without restraint.
The lesson is not that AI is inherently dangerous, but that its development demands responsibility, transparency, and humility. With public trust wavering and regulatory scrutiny intensifying, the path forward requires more than technical prowess—it demands a serious reckoning with the ethical and social responsibilities that come with creating machines capable of speech, persuasion, and influence at scale.
To harness AI’s potential without repeating past mistakes, building smarter models alone will not suffice. Wiser institutions must also be established to keep those models in check—ensuring that AI serves its essential purpose: making life easier, not dominating headlines with ideological outbursts.
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The latest case involves country singer-songwriter Blaze Foley, who died in 1989. A track titled ‘Together’ was posted to his official Spotify page over the weekend. The song sounded vaguely like a slow country ballad and was paired with AI-generated cover art showing a man who bore no resemblance to Foley.
Craig McDonald, whose label manages Foley’s catalogue, confirmed the track had nothing to do with the artist and described it as inauthentic and harmful. ‘I can clearly tell you that this song is not Blaze, not anywhere near Blaze’s style, at all,’ McDonald told 404 Media. ‘It has the authenticity of an algorithm.’
He criticised Spotify for failing to prevent such uploads and said the company had a duty to stop AI-generated music from appearing under real artists’ names.
‘It’s kind of surprising that Spotify doesn’t have a security fix for this type of action,’ he said. ‘They could fix this problem if they had the will to do so.’ Spotify said it had flagged the track to distributor SoundOn and removed it for violating its deceptive content policy.
However, other similar uploads have already emerged. The same company, Syntax Error, was linked to another AI-generated song titled ‘Happened To You’, uploaded last week under the name of Grammy-winning artist Guy Clark, who died in 2016.
Both tracks have since been removed, but Spotify has not explained how Syntax Error was able to post them using the names and likenesses of late musicians. The controversy is the latest in a wave of AI music incidents slipping through streaming platforms’ content checks.
Earlier this year, an AI-generated band called The Velvet Sundown amassed over a million Spotify streams before disclosing that all their vocals and instrumentals were made by AI.
Another high-profile case involved a fake Drake and The Weeknd collaboration, ‘Heart on My Sleeve’, which gained viral traction before being taken down by Universal Music Group.
Rights groups and artists have repeatedly warned about AI-generated content misrepresenting performers and undermining creative authenticity. As AI tools become more accessible, streaming platforms face mounting pressure to improve detection and approval processes to prevent further misuse.
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Deputy Minister Nezar Patria says the roadmap aims to clarify the country’s AI market potential, particularly in sectors like health and agriculture, and provide guidance on infrastructure, regulation, and investment pathways.
Already, global tech firms are demonstrating confidence in the country’s potential. Microsoft has pledged $1.7 billion to expand cloud and AI capabilities, while Nvidia partnered on a $200 million AI centre project. These investments align with Jakarta’s efforts to build skill pipelines and computational capacity.
In parallel, Indonesia is pitching into critical minerals extraction to strengthen its semiconductor and AI hardware supply chains, and has invited foreign partners, including from the United States, to invest. These initiatives aim to align resource security with its AI ambitions.
However, analysts caution that Indonesia must still address significant gaps: limited AI-ready infrastructure, a shortfall in skilled tech talent, and governance concerns such as data privacy and IP protection.
The new AI roadmap will bridge these deficits and streamline regulation without stifling innovation.
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The MoU establishes a framework for joint research, curriculum development, and knowledge-sharing initiatives to address local priorities and global tech challenges.
This collaboration signals a strategic leap in Sri Lanka’s digital transformation journey. It emerged during Asia Tech x Singapore 2025, where officials outlined plans for AI training, policy alignment, digital infrastructure support, and e‑governance development.
The partnership builds on Sri Lanka’s broader agenda, including fintech innovation and cybersecurity, to strengthen its national AI ecosystem.
With the formalisation of this MoU, Sri Lanka hopes to elevate its regional and global AI standing. The initiative aims to empower local researchers, cultivate tech talent, and ensure that AI governance and innovation are aligned with ethical and economic goals.
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The agreement was finalised on 21 July by OpenAI CEO Sam Altman and science secretary Peter Kyle. It includes a commitment to expand OpenAI’s London office. Research and engineering teams will grow to support AI development and provide assistance to UK businesses and start-ups.
Under the collaboration, OpenAI will share technical insights with the UK’s AI Security Institute to help government bodies better understand risks and capabilities. Planned deployments of AI will focus on public sectors such as justice, defence, education, and national security.
According to the UK government, all applications will follow national standards and guidelines to improve taxpayer-funded services. Peter Kyle described AI as a critical tool for national transformation. ‘AI will be fundamental in driving the change we need to see across the country,’ he said.
He emphasised its potential to support the NHS, reduce barriers to opportunity, and power economic growth. The deal signals a deeper integration of OpenAI’s operations in the UK, with promises of high-skilled jobs, investment in infrastructure, and stronger domestic oversight of AI development.
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A GIS Reports analysis emphasises that as AI systems become pervasive, they create significant global challenges, including surveillance risks, algorithmic bias, cyber vulnerabilities, and environmental pressures.
Unlike legacy regulatory regimes, AI technology blurs the lines among privacy, labour, environmental, security, and human rights domains, demanding a uniquely coordinated governance approach.
The report highlights that leading AI research and infrastructure remain concentrated in advanced economies: over half of general‑purpose AI models originated in the US, exacerbating global inequalities.
Meanwhile, facial recognition or deepfake generators threaten civic trust, amplify disinformation, and even provoke geopolitical incidents if weaponised in defence systems.
The analysis calls for urgent public‑private cooperation and a new regulatory paradigm to address these systemic issues.
Recommendations include forming international expert bodies akin to the IPCC, and creating cohesive governance that bridges labour rights, environmental accountability, and ethical AI frameworks.
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