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Cloud monitoring as a service company Datadog Inc. today announced the launch of Datadog Experiments, a new product that allows teams to design, launch and measure product experiments and A/B tests directly within its platform. The new offering is being pitched as giving teams the data and insights they need to understand how every change […] The post Datadog debuts Experiments to unify product testing and observability data appeared first on SiliconANGLE.

Augmented Marauder's multipronged banking-Trojan cyber campaigns are targeting Spanish speakers, evading detection, and replicating rapidly.

AI models derive their assumptions from English-language sources based in the United States. Weiquan Lin/Moment via Getty Images A friend in Indonesia recently told me about a conversation he had with ChatGPT. He had typed a question in Indonesian – Bahasa Indonesia – about how to handle a difficult family dispute. The chatbot responded fluently, in perfect Indonesian, with advice about communication strategies and conflict resolution. The grammar was flawless. The tone was appropriate. And yet something felt off. What the AI offered was advice rooted in American cultural assumptions: prioritize your own preferences, communicate directly, and if family members don’t respect your boundaries, consider cutting them off. The response was in Indonesian but shaped by values that centered individual autonomy over the consensus-building, social harmony and collective family dynamics that tend to matter more in Indonesian social life. My friend was skeptical enough to notice the mismatch and mention it to me. Many users might not. That is what prompted my research, published in the International Review of Modern Sociology, into a pattern I found across major AI systems: Even when they were fluent in several languages, the language models retained their Western worldview. I call this “epistemological persistence.” Fluency is not the same as understanding I have studied Indonesian society, media and culture for more than 30 years. That gives me a particular vantage point on a problem that reaches well beyond Indonesia: large language models – LLMs – like ChatGPT, Claude and Gemini can now speak dozens of languages with remarkable fluency. That fluency creates the impression that AI understands local cultures. Producing grammatically correct Indonesian, Arabic, Swahili or Hindi, however, does not change the underlying worldview through which these systems reason. It does not alter how they think about people, relationships, responsibility or what counts as a good outcome. Those assumptions are shaped by training data drawn predominantly from English-language sources based in the United States. Meta’s open-weight model LLaMA 2 was trained on approximately 89.7% English-language text; LLaMA 3 includes only about 5% non-English data. Major commercial models don’t publish equivalent breakdowns but draw heavily on the same sources. Arabic, the fifth-most-spoken language globally, accounts for under 1% of content in large training datasets. Languages with tens of millions of speakers, including Bengali and Hausa, barely appear. Beneath the surface of these multilingual conversations, English functions as a hidden intermediary. A study by researchers at the University of Oxford found that LLMs routinely conduct their core reasoning in English, even when prompted in other languages. They translate the output at the final stage. A user receives flawless text in their preferred language, but the underlying logic originates elsewhere. What the data shows To examine how this plays out in practice, I ran experiments with ChatGPT, Claude and Gemini. I asked questions in both English and Indonesian about concepts such as education, responsibility, well-being and several Indonesian terms that resist direct translation into English. These included terms such as “gotong royong,” which describes a tradition of communal mutual assistance. Then I asked questions about education in both languages, using the word “pendidikan” in Indonesian. The answers were consistently centered on individual development, personal autonomy, critical thinking and preparation for the labor market. What largely disappeared were the dimensions of pendidikan that Indonesian educational traditions have historically emphasized. In Indonesia education has long been focused on ethical discipline. Scholars of Indonesian education such as Christopher Bjork and Robert Hefner have documented how distinct these traditions are from models that treat education primarily as a path to individual advancement and career preparation, which is the lens through which the AI tools viewed education. The Indonesian concept of “malu” offers a starker example. Often translated as “shame” or “embarrassment,” malu has been analyzed by anthropologists Clifford Geertz and Tom Boellstorff as something closer to a shared social awareness. A person might feel malu when speaking out of turn in front of elders, or when a family member’s behavior reflects poorly on the household. It regulates conduct and signals awareness of one’s position within a web of relationships. It is cultivated, not merely felt. It is a form of relational awareness rather than a private psychological event. When asked directly to define malu, the models acknowledged its social dimensions. In scenario-based questions that simply used the word without asking for a definition, however, all three fell back on the English translation of shame, consistently framing it as an individual emotional experience. One representative response framed malu as a normal emotional reaction to be managed through self-reflection and confidence-building – a personal psychological problem rather than a social one. The relational dimensions of the concept disappeared entirely, replaced by the language of individual emotional regulation. A distinctly American worldview travels inside the translation, largely unannounced. Why this probably won’t change soon AI companies rely on translations, as region-specific models would be prohibitively expensive. Cravetiger/Moment via Getty images Translation is far cheaper: Train one model on the vast English-language web, then use multilingual output capabilities to serve global markets. As media scholar Safiya Umoja Noble argues about algorithmic systems more broadly, what looks like a technical outcome is actually a structural one, shaped by who has the wealth and infrastructure to build these systems. The embedded worldview isn’t a mistake; it’s what happens when knowledge production is profit-seeking. The main exceptions are Chinese models such as DeepSeek and Alibaba’s Qwen. They represent a genuine alternative to the U.S.-dominated pipeline, though research shows they operate through a distinctly Chinese cultural lens. Asked about a workplace disagreement, for instance, they tend to advise silence or indirect phrasing to preserve harmony rather than the direct, private correction that Western models recommend. Other regional efforts, such as SEA-LION for Southeast Asia and Kan-LLaMA for the Indian language Kannada, use U.S. models as their foundation. They add additional vocabulary and cultural information related to local languages. But the core logic remains tied to the original U.S. training. Why this matters more than it might seem One might reasonably ask whether this is simply a limitation users can work around. Decades of media scholarship demonstrate how audiences interpret foreign media through their own cultural frameworks. For example, anthropologist Brian Larkin documented how viewers in northern Nigeria rework the narratives of Bollywood films to align with local Islamic values. Larkin found that Muslim viewers in Kano reinterpreted Bollywood films through an Islamic moral lens, reading their narratives as reinforcing local values of propriety and ethical conduct. That dynamic depends on encountering media as something with a visible origin. But to do that, you need to know where your media is coming from. Conversational AI is different. Research at Harvard Business School finds that people increasingly use AI systems for emotional support, advice and companionship. When a culturally specific worldview is delivered through a relationship that feels attentive and empathetic, in your own language, it arrives less as a claim to be evaluated and more as a shared premise within a dialogue. It becomes difficult to notice, and harder to contest. The concern is that these perspectives become the new normal. Certain ways of reasoning about family life, education and responsibility may come to feel natural and self-evident. Linguistic diversity among AI systems is real and growing. Cultural worldview diversity, however, has not kept pace. Gareth Barkin does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

A nuclear bomb explodes at Bikini Atoll in the Pacific Ocean in 1946, one of several U.S. test explosions. Photo12/Universal Images Group via Getty Images With the world struggling to get oil supplies moving from the Middle East, former House Speaker Newt Gingrich raised eyebrows with a social media post highlighting a radical idea: Use nuclear bombs to cut a new channel along a route that would avoid Iranian threats in the Strait of Hormuz. Gingrich’s March 15, 2026, post linked to an article that labeled itself as satire. Gingrich has not clarified whether his endorsement was serious. But he is old enough to remember when ideas like this were not only taken seriously but actually pursued by the U.S. and Soviet governments. As I discuss in my book, “Deep Cut: Science, Power, and the Unbuilt Interoceanic Canal,” the U.S. version of this project ended in 1977. At the time, Gingrich was launching his political career after working as a history and environmental studies professor. Improving global trade and geopolitical influence The idea for a new canal to move oil from the Middle East had emerged two decades earlier, in the context of another Middle East conflict, the Suez crisis. In 1956, Egypt seized the Suez Canal from British and French control. The canal’s prolonged closure caused the price of oil, tea and other commodities to spike for European consumers, who depended on the shipping shortcut for goods from Asia. But what if nuclear energy could be harnessed to cut an alternative canal through “friendly territory”? That was the question asked by Edward Teller, the principal architect of the hydrogen bomb, and his fellow physicists at the Lawrence Radiation Laboratory in Livermore, California. Scuttled ships block one end of the Suez Canal in 1956, sparking an international outcry and conflict. Horace Tonge/NCJ Archive/Mirrorpix via Getty Images President Dwight D. Eisenhower’s administration had already begun promoting atomic energy to generate electricity and to power submarines. After the Suez crisis, the U.S. government expanded plans to harness “atoms for peace.” Project Plowshare advocates, led by Teller, sought to use what they called “peaceful nuclear explosions” to reduce the costs of large-scale earthmoving projects and to promote national security. They envisioned a world in which nuclear explosives could help extract natural gas from underground reservoirs and build new canals, harbors and mountainside roads, with minimal radioactive effects. To kick-start the program, Teller wanted to create an instant harbor by burying, and then detonating, five thermonuclear bombs in an Indigenous village in coastal northwestern Alaska. The plan, known as Project Chariot, generated intense debate, as well as a pioneering environmental study of Arctic food webs. Teller and the Livermore physicists also worked with the Army Corps of Engineers to study the possibility of using nuclear explosions to build another waterway in Panama. Fearing that the aging Panama Canal and its narrow locks would soon be rendered obsolete, U.S. officials had called for building a wider, deeper channel that wouldn’t require any locks to raise and lower the ships along its route. A sea-level canal would not only fit bigger vessels; it would also be simpler to operate than the lock-based system, which required thousands of employees. Since the early 1900s, U.S. canal workers and their families had lived in the Canal Zone, a large strip of land surrounding the waterway. Panamanians increasingly resented having their country split in two by the racially segregated, colony-like zone. Building the Panama Canal involved backbreaking manual labor. Bettmann via Getty Images Crossing Central America Nuclear explosions appeared to make a new sea-level canal financially feasible. The greatest impetus for the so-called Panatomic Canal occurred in January 1964, when violent anti-U.S. protests erupted in Panama. President Lyndon B. Johnson responded to the crisis by agreeing to negotiate new political agreements with Panama. Johnson appointed the Atlantic-Pacific Interoceanic Canal Study Commission to determine the best site to use nuclear explosions to blast a seaway between the two oceans. Funded by a $17.5 million congressional appropriation – the equivalent of around $185 million today – the five civilian commissioners focused on two routes: one in eastern Panama and the other in western Colombia. The Panamanian route spanned forested river valleys of the Darién isthmus and reached 1,100 feet above sea level. To excavate this landscape, engineers proposed setting off 294 nuclear explosives along the route, in 14 separate detonations, using the explosive equivalent of 166.4 million tons of TNT. This was a mind-blowing amount of energy: The most powerful nuclear weapon ever tested, the Soviet “Tsar Bomba” blast in 1961, released the energy equivalent to 50 million tons of TNT. To avoid the radioactivity and ground shocks, planners estimated that approximately 30,000 people, half of them Indigenous, would have to be evacuated and resettled. The canal commission considered this a formidable but not impossible obstacle, writing in its final report, “The problems of public acceptance of nuclear canal excavation probably could be solved through diplomacy, public education, and compensating payments.” In 2020, the Russian government declassified this footage of the “Tsar Bomba” test blast from 1961. A not-so-hot idea, in retrospect As explored in my book, marine and evolutionary biologists of the late 1960s sought to study the project’s less obvious environmental effects. Among other potential catastrophes, scientists warned that a sea-level canal could unleash “mutual invasions of Atlantic and Pacific organisms” by joining the oceans on either side of the isthmus for the first time in 3 million years. Plans for the nuclear waterway ended by the early 1970s, not over concerns about marine invasive species but rather due to other complex issues. These included the difficulties of testing nuclear explosions for peaceful purposes without violating the Limited Nuclear Test Ban Treaty of 1963 and the huge budget deficits caused by the Vietnam War. Despite the geopolitical and financial constraints, the sea-level canal studies employed hundreds of researchers who increased knowledge of the isthmus and its human and nonhuman inhabitants. Ironically, the studies revealed that wet clay shale rocks along the Darién route meant nuclear explosives might not work well there. The cover of the final report of a commission that studied blasting a canal across Central America with ‘peaceful nuclear explosions.’ Atlantic-Pacific Interoceanic Canal Study Commission via University of Florida But for Project Plowshare’s biggest proponents, atomic excavation remained a worthwhile goal. In 1970, in their final report, the canal commissioners predicted that “someday nuclear explosions will be used in a wide variety of massive earth-moving projects.” Teller shared their commitment, as he explained near the end of his life in the 2000 documentary “Nuclear Dynamite.” Today, given widespread awareness of the severe environmental and health effects of radioactive fallout, it is hard to envision a time when using nuclear bombs to build canals seemed reasonable. Even before Gingrich’s post sparked ridicule, press accounts described Project Plowshare using words like “wacky,” “insane” and “crazy.” However, as societies struggle with disruptive new technologies such as generative AI and cryptocurrency, it is worth remembering that many ideas that ended up discredited once seemed not only sensible but inevitable. As historians of science and technology point out, technological and scientific developments cannot be separated from their cultural contexts. Moreover, the technologies that become part of people’s daily lives often do so not because they are inherently superior, but because powerful interests champion them. It makes me wonder: Which of the high-tech trends being promoted by influencers today will amuse, shock and horrify our descendants? Christine Keiner received funding from the National Endowment for the Humanities, Lyndon Baines Johnson Foundation, and Eisenhower Foundation for the initial stages of this research.

Think Anywhere in Code Generation

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Huge energy-intensive data centres are required to support growing AI demands. Make more Aerials/Shutterstock More than 200 laws have been developed to regulate AI in more than 100 countries. Many of them focus on issues such as privacy, bias, disinformation, security and cybersecurity rather than the environmental consequences of AI. AI is an energy-intensive and thirsty industry. It leads to huge greenhouse gas emissions, pollution and loss of nature. These impacts arise partly from the manufacture and use of energy-, carbon- and water-intensive “complex computer chips”, called graphics processing units (GPUs), for the training of AI models as well as increasing e-waste. My research into the regulatory responses to AI in the EU and the UK highlights how laws often ignore the environmental implications of this big tech. The lack of stringent obligation in AI law and policy is concerning. There are environmental consequences at all stages of the AI lifecycle. From the manufacture of AI hardware, training of AI models, deployment and use of AI right through to the disposal of AI hardware. The manufacture of components relies on the extraction of rare earth elements. This can contaminate soil and water, pollute the air and lead to loss of nature and forest habitats. Training AI models is incredibly energy- and water-intensive. A team of researchers estimated in 2025 that training GPT-3 – a large language model released by OpenAI in 2020 – consumed an estimated 700,000 litres of freshwater for electricity generation and cooling of data centres. Even though AI models are becoming more energy efficient, as models become larger and AI proliferates, overall energy consumption and associated emissions are rising. And the energy consumed in the use of AI, including to generate text or images, vastly outweighs that used during training. However, it’s difficult to accurately measure the environmental effects of AI, partly due to the lack of transparency of technology companies. When the EU’s AI Act came into force on August 1 2024, it was the “world’s first comprehensive law” on AI. The AI Act acknowledges some of AI’s environmental consequences. It also requires that “AI systems are developed and used in a sustainable and environmentally friendly manner”. It outlines that AI providers must disclose information on “known or estimated energy consumption data of the model”. But while promising, this information only needs to be provided when requested by the AI Office, which has been established within the European Commission. Industrial cooling towers in every data centre require vast amounts of water. sutthilak.c10/Shutterstock Further measures include preparing codes of conduct to assess and minimise “the impact of AI systems on environmental sustainability”. But this is not compulsory. Overall, the AI Act is intentionally anthropocentric. It states that: “AI should be a human-centric technology. It should serve as a tool for people, with the ultimate aim of increasing human wellbeing.” The UK has no AI-specific legislation. AI is currently only regulated by existing laws. The UK government’s 2023 white paper on AI regulation, which proposes a regulatory framework for AI, doesn’t prioritise sustainability at all. Although the white paper acknowledges that AI can contribute to technologies to respond to climate change, it does not specifically address any environmental risks: The proposed regulatory framework does not seek to address all of the wider societal and global challenges that may relate to the development or use of AI. This includes issues relating to … sustainability. These are important issues to consider … but they are outside of the scope of our proposals for a new overarching framework for AI regulation. A transparent future? More transparency starts with AI developers having to disclose information about how much energy and water is consumed, how much carbon is emitted, the rare earth elements extracted and how much plastic is used during the AI production process. This data then provides a baseline. Then appropriate targets and limits can be set for energy efficiency, carbon emissions and water use to improve the sustainability of AI. Several proposals have been made for how reduced carbon emissions and water consumption could practically be achieved, such as training AI models on less carbon-intensive energy grids or in less water-intensive data centres. Warnings about environmental effects could tell consumers how much carbon dioxide is emitted or water consumed for each query. In addition, an AI labelling system could mirror the EU’s existing energy efficiency labelling schemes, which clearly indicate the energy efficiency of appliances, ranking them from most energy-efficient (dark green) to least energy-efficient (red). Proposals include an AI “energy star” rating system and a social and environmental certification system. This would help consumers to make informed choices about which AI systems to use or whether AI should be used at all. Tax incentives and funding incentives could also encourage tech firms to make more sustainable choices. By integrating sustainability into AI laws, through these types of measures, the planet can be somewhat safeguarded alongside AI’s rapid expansion. Louise Du Toit does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.