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Americans flocked to prediction markets last year. Now, it’s time to pay taxes on winnings. How do you do that? Great question.

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The search for an age-verification system that protects user data may begin and end in the EU.

A team of engineers has created a breakthrough memory device that keeps working at temperatures hotter than molten lava, shattering one of electronics’ biggest limits. Built from an unusual stack of ultra-durable materials, the tiny component can store data and perform calculations even at 700°C (1300°F), far beyond what today’s chips can handle. The discovery was partly accidental, but it revealed a powerful new mechanism that prevents heat-induced failure at the atomic level.

PRT-scan is the second campaign in recent months where a threat actor appears to have leveraged AI for automated targeting of a widespread GitHub misconfiguration.

The attack on the popular NPM package Axios is just one of many targeting maintainers and has shone a light on how threat actors can scale sophisticated social engineering campaigns.

A pervasive narrative has taken hold in education: generative AI (genAI) is an unstoppable force, and educators must adapt or be left behind. Technology companies market AI tools as the ultimate classroom assistants, while popular media warns that essay writing is dead. Teachers have long been labelled or framed by technology enthusiasts and policymakers as “resistant” and “risk averse.” Discourse about technology in classrooms has amplified notions that teachers either embrace or reject tech. Yet research with educators is showing that a binary framing of AI innovators versus Luddites obscures what is actually happening in classrooms. To better understand this, I turned to my own institution, the University of Ottawa’s Official Languages and Bilingualism Institute (OLBI). I consulted English as a second language (ESL) and French as a second language (FSL) instructors to examine their attitudes toward, and current use of, AI-assisted tools. I did this through conducting a bottom-up institutional survey. Twenty-four of 60 eligible staff members responded, yielding a 40 per cent response rate. In the context of institutional research, this is a robust turnout that provides a representative cross-section of our department. Because my goal was to understand the nuances of educators’ decision-making, this qualitative sample offers deep insights into front-line teaching realities. The findings point to a thoughtful majority of instructors navigating complex pedagogical terrain with considerable nuance. The myth of the resistant teacher As educational historian Larry Cuban has argued, teachers are not inherently resistant to technology; they’re resistant to tools that don’t solve their problems. Data from my study supports this distinction. Research in acquiring a second language suggests experienced language educators, keen to see their students progress, seek normalization of novel technologies — the stage at which a tool becomes invisible and learning takes centre stage. My survey confirms this orientation. When asked to identify their stance on AI integration, the majority of OLBI staff did not select “skeptic.” The majority of respondents are best characterized as “pragmatists” — educators who recognize the potential of genAI tools but are withholding full adoption pending credible pedagogical evidence. A significant minority, however, expressed substantive and philosophically grounded concerns. One FSL instructor described genAI as “une menace à l’autonomie de la pensée” (“a threat to the autonomy of thought”). This is a considered defence of the critical thinking capacities that higher education exists to cultivate. The ‘slow professor’ could bring back creativity to our universities The ‘hidden AI’ problem My survey also suggests a striking inconsistency in how educators conceive of AI. Several respondents reported that they “never” use generative AI. Yet, in subsequent questions, they acknowledged regular use of tools such as Grammarly for writing assistance or DeepL for translation. Grammarly introduced generative AI to its earlier AI technology integrating machine learning and natural language processing, and the genAI feature can be turned off. DeepL has also developed a genAI model. However, the bigger point is instructors appeared to distinguish between AI they perceived as assisting existing work and AI they perceived as generating new text. That distinction reflects different understandings of authorship, agency and acceptable use. What the data reveals, then, is an intuitive taxonomy: instructors are broadly comfortable with tools that refine or correct their existing work (assistive AI) and considerably more cautious about tools that produce content on their behalf. Such a distinction is reflected in my own process with this article. As a francophone writing in English, I used Anthropic’s Claude to clarify sentence-level phrasing in a draft I had already written. A differentiation between refining existing work and producing content reflects broader discussions taken up elsewhere related to learning and academic integrity. ChatGPT is in classrooms. How should educators now assess student learning? The efficiency shield The most significant finding from the survey concerns how instructors are deploying genAI primarily as an administrative efficiency tool, using it to generate lesson plans, draft course communications and create short texts for classroom use. Such tasks consume significant time but don’t directly mediate student learning. One ESL instructor shared their enthusiasm about this: “The possibilities for lesson planning and activity ideas are endless.” Yet the same instructors who embraced AI for their own productivity expressed marked reluctance to introduce these tools into student learning. The reasoning is grounded in cognitive science. Language acquisition depends on what psychologists Robert Bjork and Elizabeth Bjork term desirable difficulties — the effortful cognitive processing that consolidates new linguistic knowledge into long-term memory. When a student offloads a grammatical decision to an auto-complete function, or delegates argument construction to a language model, they bypass the neural engagement that makes learning durable. This phenomenon, known as cognitive offloading, may produce a polished written product while leaving the underlying competency undeveloped. One respondent articulated this concern: “If [students] get away with that, then they will never learn how to write.” Such positions align with UNESCO’s 2023 guidance on generative AI in education and research, which cautions that the pace of genAI adoption in educational settings must not outstrip our collective understanding of its cognitive and ethical implications. Our instructors are, in effect, applying an instinctive precautionary principle — one that is well-supported by the empirical research. What are the key purposes of human writing? How we name AI-generated text confuses things Policy must follow pedagogy The OLBI consultation illustrates why meaningful AI education policy cannot be imposed from above. If universities issue broad mandates to embrace innovation without consulting those who understand the cognitive architecture of learning, they risk producing policies that are administratively tidy but practically incoherent. Conversely, blanket prohibitions ignore the reality that students will graduate into a labour market saturated with AI tools, and must develop the critical literacy to engage with them responsibly. The path illuminated by our “pragmatist” majority is one of critical AI literacy. Concretely, this involves three institutional commitments: Distinguishing between functions of AI: Institutions must teach students to distinguish between AI tools according to their function rather than their underlying technology. This means considering tools that operate in an assistive capacity — correcting, refining or translating work that the student has already produced — and a generative capacity by producing content on the user’s behalf. This said, both categories of “assistive” and “generative” AI warrant scrutiny. It’s relevant to note that some educational or accessibility rights bodies are discussing using generative AI as an assistive technology, particularly for people with disabilities. Protecting the learning process: Assessment design should value the process of writing and argumentation — drafting, revision, reflection — rather than privileging only the final product, which a language model can readily simulate. Repositioning the instructor: As the OECD has noted, the educator’s role is shifting from knowledge transmitter to critical evaluator and learning architect. AI tools can support this transition — but only if instructors retain the agency to define the terms of engagement. The question facing universities is whether institutions will trust the educators who understand their students’ cognitive needs to draw the lines that matter. Martine Rhéaume 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.

The authentication bypass flaw, tracked as CVE-2026-35616, is the latest in a series of Fortinet vulnerabilities that have been exploited in the wild.

Xoople SL, a Spanish provider of Earth observation data, today disclosed that it has closed a $130 million funding round. Madrid-based investment firm Nazca Capital led the raise. It was joined by MCH Private Equity, CDTI, Buenavista Equity Partners and Endeavor Catalyst. Xoople provides access to satellite imagery from organizations such as the European Space […] The post Satellite data startup Xoople closes $130M investment appeared first on SiliconANGLE.

The policy provides OpenAI with a way to position itself as a company that's thinking about the implications of AI technology, especially as it affects enterprise workers.

Getty Images For New Zealanders, artificial intelligence (AI) is fast becoming as much a part of everyday life as smartphones and social media did before it. According to the recently released 2026 InternetNZ Internet Insights report, nearly eight in ten Kiwis have used AI tools in the past year. More than half are now using them at least weekly. But as use is rising, so too is unease about this transformative technology’s impact on society. In a recent survey, half of respondents were extremely or very concerned about AI’s implications for misinformation, privacy and potential misuse. Other national surveys tell a similar story. One found only a quarter of respondents believed current safeguards are sufficient to make AI use safe. In another, two thirds of those surveyed said they would stop using a company’s products if they had concerns about how it was using AI. These views are not surprising. Major AI companies are increasingly entangled in everything from “deepfake” images and AI-generated misinformation to geopolitics and military applications. At the same time, this widespread distrust could represent another opportunity for New Zealand to influence big tech – and build our own valuable brand grounded in responsible AI. Who controls AI – and on whose terms? The US-Israel war on Iran – where AI has helped identify bombing targets – has raised fresh concerns about the technology. In the lead-up to the conflict, major AI companies were pressured by the US Department of War to allow widespread military uses of their AI systems. Anthropic pushed for limits on applications like autonomous weapons and surveillance but was sidelined. Rival OpenAI instead agreed to allow broad “lawful” military uses, prompting a backlash and reports of users deleting the company’s ChatGPT at triple the usual rate. China’s military is meanwhile leveraging its own AI-powered systems, while companies like Palantir, chaired by US billionaire and New Zealand citizen Peter Thiel, have reportedly supplied AI tools used by militaries in Ukraine, Gaza and Iran. New Zealand’s defence ministry is now mulling its own approach, with parliament divided on the issue. These developments highlight how closely advanced AI companies are becoming entwined with state power, blurring the line between consumer technology and instruments of war. Aside from military use, these systems are also vulnerable to political pressures in the US, including government influence over how they are deployed and used. Research has shown the products can reflect the values and biases of their creators. As they spread globally, they are also increasingly seen as a form of what has been called “digital colonialism” – where powerful countries and companies export technologies that embed their own values and priorities in other societies. How NZ can be a leader in AI For all the concern expressed by New Zealanders, the country has so far taken a “light-touch” regulatory stance on the technology. Rather than create dedicated regulation, as a recent open letter from AI experts to political leaders has called for, the government has chosen to rely on a patchwork of existing rules. As consumers, New Zealanders have little say in how these products are evolving, how they are designed or who they sometimes serve. This reinforces the common feeling that AI is something happening to us, but not for us. It is also sometimes claimed the country is being left behind in the “AI race”, particularly by New Zealand business leaders concerned about keeping up with rapid technological change. But there is another way for New Zealand, even with its limited scale and capacity, to make its mark in the AI world. This would involve playing to its global reputation for integrity, human rights and independent thinking. Initiatives such as the Christchurch Call – launched after the 2019 mosque attacks to curb online extremist content – showed how a small country can convene governments and technology companies around shared standards. In this case, New Zealand could strategically position itself at the forefront of a growing global push for responsible AI, which advocates for values such as fairness, accountability, safety and privacy. The nation’s Māori data sovereignty movement is already an example of responsible data use. Māori values such as kaitiakitanga (guardianship and stewardship) reframe data as taonga (treasured or sacred assets) deserving careful protection. Just as it did by drawing attention to social media harm with the Christchurch Call, New Zealand could collaborate with like-minded countries to push big tech companies to adopt concrete safeguards. These could include measures such as watermarking and mandatory human oversight by a range of governance groups. This would also involve introducing standards for reporting environmental impact and auditing bias, ensuring AI aligns with New Zealanders’ expectations. The government could work with industry to set clearer expectations for responsible AI – building on existing guidance for businesses on safe and ethical use – and invest in the development of local products that meet those standards. There is also an economic opportunity. Local companies could use this reputation to differentiate themselves in a global market where trust is becoming increasingly important. Research by global consultancy PwC suggests responsible AI can create real value, with more resilient systems and fewer trust-damaging failures. Advocating for safe, responsible AI with clear economic benefits should be an easy decision – and the recent survey findings provide a clear mandate to do so. But New Zealand won’t get there without decisive political leadership and a cohesive strategy. In an election year, politicians should be challenged to commit to AI that serves both its economy and its people. The authors acknowledge the contribution of Dr Andrew Chen to this article. Andrew Lensen receives funding from the Ministry of Business, Innovation and Employment and the Royal Society of New Zealand through contestable academic research funds. He is the co-director of LensenMcGavin AI, a consultancy specialising in the responsible uptake of AI in Aotearoa. Ethan Plaut has received funding from the New Zealand government through their Performance-Based Research Fund as well as Tāmaki Paenga Hira Auckland War Memorial Museum and InternetNZ. Stephen Hill receives funding from the Royal Society of New Zealand through contestable academic research funds. Michael S. Daubs 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.

Universal Pictures The relationship between science and pop culture often looks like a one-way street: scientific discoveries inspire films, television and novels, particularly in science fiction. But the relationship really goes both ways, and extends beyond sci-fi. Increasingly, pop culture shapes how science is imagined, discussed, and in some cases how it is developed. From Jurassic Park to The Last of Us and cutting-edge debates about the safety of artificial intelligence (AI), fictional narratives do more than entertain. They shape the frameworks through which audiences – including scientists, policymakers and funders – make sense of complex scientific ideas and of science itself. In doing so, they influence what seems possible and plausible, as well as what we want and fear. From Jurassic Park to reality Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should. This famous line, delivered by fictional mathematician Ian Malcolm in Jurassic Park, has become a touchstone in debates about emerging technologies. Take de-extinction. When biotechnology company Colossal Biosciences announced plans in 2021 to revive bygone species such as the woolly mammoth, the comparison was immediate: Jurassic Park. The film has become a cultural shorthand for the promises and pitfalls of bringing extinct species back to life. Scientists and commentators alike invoke its famous ethical warning – that the question of whether we should do something is separate from whether we can. These references are not merely rhetorical. They shape how research is communicated, debated and understood. By framing de-extinction through a familiar narrative, Jurassic Park has influenced public expectations, ethical anxieties and media discourse. We see projects described as “real-life Jurassic Park”, debates about whether such technologies should be pursued citing the film, and journalists using it as a shorthand when covering emerging biotechnologies. Assimilating aliens and fungal zombies The influence of science fiction can extend to scientific practice itself. Researchers named DNA elements which incorporate foreign genetic material “Borgs”, for example, after the assimilating aliens from Star Trek. A similar dynamic can be seen in responses to HBO’s The Last of Us, which imagines a global pandemic caused by a parasitic fungus that transforms humans into zombie-like creatures. Following the show’s release, scientists reported renewed public interest in fungal pathogens. Indeed, the “worst-case scenario” presented in the series prompted immunologists and mycologists to examine the biological plausibility of a fungal leap to humans. While the temperature of the human body is inhospitable to most kinds of fungus, and we need not fear the aggressive biting depicted in fiction, experts warn that climate change and agricultural fungicide overuse are accelerating fungal adaptation to higher temperatures. This makes The Last of Us a sobering alarm for real-world problems. In both cases, pop culture does not simply reflect scientific knowledge. It shapes how that knowledge is encountered, interpreted and imagined. Killer superintelligence One of the most compelling examples of this feedback loop today is AI. Popular culture has long been fascinated with intelligent machines, often imagining them as existential threats. We see this from deceptive superintelligences to human extinction, as portrayed in Ex Machina, The Matrix and The Terminator. These narratives have left a deep imprint on public consciousness. Today, similar themes appear in real-world debates about AI safety. Prominent figures in AI debates, such as Nick Bostrom, Eliezer Yudkowsky and Geoffrey Hinton, have warned about the potential risks of advanced AI. The warnings include scenarios that echo earlier fictional imaginings. While these arguments are grounded in technical and philosophical work, they resonate so widely in part because they align with familiar cultural narratives. This does not mean concerns about AI are simply fictional. Rather, it shows how deeply intertwined scientific thinking and cultural imagination can be. Understanding the feedback loop Pop culture helps establish the language, metaphors, and expectations through which emerging technologies are understood. It shapes how scientific ideas, ideas about science, and images of scientists circulate beyond laboratories and institutions – and, in turn, how science is understood, valued and positioned in society. At the same time, science continues to feed back into pop culture. Advances in genetics, epidemiology and AI provide new material for storytellers, shaping the kinds of futures that are imagined on screen. The result is a dynamic feedback loop: science inspires stories, and those stories in turn influence how science develops. Despite this, the role of pop culture is rarely acknowledged in how we think about science policy and funding. Discussions tend to focus on infrastructure and technical capability, while overlooking the cultural forces that shape public imagination. Yet these forces play a crucial role in determining which scientific futures feel worth pursuing. This matters because public perception influences everything from research funding to regulatory priorities. If certain technologies are seen as exciting, frightening or inevitable, this affects how they are supported, scrutinised or resisted. Pop culture is one of the key arenas in which these perceptions are formed. The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

The greatest untapped AI isn’t a new model or a faster chip; it’s the executive team already sitting in your boardroom. For the past year, corporate America has been energized […] The post The AI Productivity Opportunity: Bridging the Technology Divide, Starting with Your Leadership appeared first on BigDATAwire.

An emerging threat cluster tracked as UAT-10608 is exploiting vulnerable Web-exposed Next.js apps and using an automated tool to exfiltrate credentials, secrets, and other system data.

Leaders can adapt to generative AI's rapid rise by embracing agile AI strategies such as dedicated experimentation and establishing accountability at the outset.

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OpenClaw developer Peter Steinberger had initially worked with Anthropic but moved the popular personal agent system to OpenAI.

Medical professionals are not going to stop using AI tools to manage growing workloads. Organizations should prioritize bolstering security protocols to limit their blast radius.

In recognition of 21 generative AI risks, the standards group recommends that companies take separate but linked approaches to defending GenAI and agentic AI systems.