IT/기술 · "ASKING" · 총 21건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 88,482건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 4,309건(4.9%)·중립 82,019건(92.7%)·부정 2,154건(2.4%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.9(중도 균형)입니다.
As OpenAI and Anthropic race to go public at $1 trillion valuations, a foundational business theory warns that money impatient for growth is dangerous.
The statement came after clips circulated on social media showing a participant seeking to question the Chief Justice while he was delivering a lecture on artificial intelligence and international law. Organisers intervened and prevented the interaction from continuing, asking attendees to remain focused on the subject of the event. The Chief Justice noted that AI is already influencing a broad range of sectors, including governance, commerce, communication, defence and the justice system itself.
Most of Belkin's Switch 2 accessories are designed to either protect or power up Nintendo's latest handheld, like its Charging Case Pro that actually does both at the same time. Its new multitasking Charging Grip can also add three or four more hours of playtime through an included 10,000mAh battery pack, while improving the Switch […]
According to a new study, asking an AI assistant where you can stream a popular TV show or movie will provide an answer that is wrong nearly half of the time.
The flaw let attackers bypass two-factor authentication by asking the bot to swap the email tied to a target account
President Donald Trump on Tuesday signed an executive order that asks AI companies to submit to a voluntary government review 30 days before releasing AI models to the public. The post Trump Signs Executive Order Asking for Oversight of New AI Models appeared first on Breitbart.
AI developers, on a voluntary basis, are asked to collaborate with the government and provide early access to frontier models.
Hackers have successfully compromised numerous prominent Instagram accounts including the Barack Obama White House profile by simply asking Meta's AI support chatbot to change the email addresses associated with target profiles, security researchers report. The post AI Fail: Meta’s Support Chatbot Helped Hijack High-Profile Instagram Accounts Including Obama White House appeared first on Breitbart.
US President Donald Trump on Tuesday (local time) signed an executive order asking companies to provide artificial intelligence (AI) models to the federal government to assess their capabilities ahead of a full release.
If you’re going to impersonate an officer, perhaps choose a more sophisticated way to nick cash than asking for gift cards…
NEW YORK, June 2 — Meta is facing scrutiny after security researchers found that its AI‑powered support chatbot co...
Meta's AI support chatbot helped hackers hijack Instagram accounts, as reported earlier by 404 Media. In a video shared on Telegram, a hacker shows how they could take over an account by asking Meta's chatbot to switch the email associated with someone else's profile and then reset the password. The issue, which Meta says has […]
Papal's 40k-word encyclical drops and lawyers already asking if Catholics can refuse workplace AI on religious grounds
FOR the last three years since ChatGPT was introduced, prominent writers, editors and litterateurs have been openly hostile to the idea of AI being able to write fiction, poetry or prose — indeed, any kind of literature. The tech companies that introduced all these LLMs, imagining ChatGPT, Claude, Gemini, Grok, and Copilot as writing aids, study buddies, collaborators and co-authors, have thrown a nuclear bomb into the literary world, and most of its inhabitants are still in a crouch position, bracing for an impact that detonated back in 2022. But the literary world must call a truce because AI is here to stay. Moreover, any writer who teaches writing, any literary editor or agent who evaluates submissions, any practitioner called upon to judge a literary competition must become AI literate; it’s an unavoidable skill that’s simply part of the job from now on. Last week, the Commonwealth Writing Prize and Granta published five regional short story winners, one of which, Jamir Nazar’s ‘A Serpent in the Grove’, was singled out as possibly AI-generated. It raised a furore on social media but it didn’t surprise me at all. I’ve graded hundreds of student essays, judged creative writing capstones and a major Pakistani literary prize in the last year. So much is now written with the help of AI that I feel overwhelmed. I’ve been using the last two years to learn exactly how AI writes — not just its processes, but its style and its voice. I’ve studied it as much as I would study any human author, looking for how it handles dialogue, description, character and plot. Yet if I’d stuck my head in the sand and refused to touch AI for the sake of artistic integrity, I would be letting down all those people who trust my judgement and expertise. Students are addicted to AI not because they want to cheat, but because they’re terrified of looking stupid or inadequate. I spent hours tinkering with AI, asking it to write things in a Pakistani context: a synopsis for a Harry Potter book set in Lahore; descriptions of Karachi. AI churned out showy, contrived prose that looks like it’s doing a lot without actually saying anything meaningful. It blathered inanities about Karachi being a “city that remembers” and Pakistani women who “sauntered through the bazaar as if their bodies bore the weight of generations of family secrets”. AI wrote verbal pyrotechnics with no emotional connection to the city that I love. It’s too much of a temptation to expect people, especially students, not to use AI to write. Pakistan is a former British colony with a postcolonial hangover about the English language, even though few of us speak it fluently and even fewer can write it well. Yet the language of instruction in top Pakistani schools and universities has remained and always will be English. Students are addicted to AI not because they want to cheat, but because they’re terrified of looking stupid or inadequate. And the LLMs are ever-present to capitalise on that fear. I have to keep telling my students: AI is here not to help you, but to make money off you. Also, there will never be a foolproof AI-detection tool. AI will keep learning more from every person that asks it to help them write a story; AI ‘detectors’ will offer you an answer based on their own algorithms and biases. Differentiating AI writing from human writing requires human discernment, the same faculty we use to know when writing is sublime or terrible. It requires instinct, experience and a close look at the person’s work overall to see if the story is a representation of their usual style — call it the new due diligence in a post-AI world. The culprit in the Commonwealth Writers debacle was not racism or some kind of Western pandering to the postcolonial writer, but sheer ignorance on the part of judges. And underneath that ignorance lies a wilful denial about just how seismic the AI shift is. Everyone who must evaluate writing professionally is scared of the threat that AI poses to the literary arts and the earnings of the publishing industry. They’re terrified of the idea that everyone else is already so far ahead they may never be able to catch up. AI has already learned to mimic cultural inflections. It will talk about any part of the world — Guyana, South Korea, Bosnia — with pompous certainty and try to dazzle you with metaphorically bizarre surface-level descriptors or overwhelm you with atmosphere so you don’t realise there’s actually no plot or insight, no empathy, none of the beauty that makes writing an art as well as a practice. Personally, I resent the tech bros who have turned my relationship with writing from practitioner to policewoman, turning a jaundiced eye to everyone’s writing and suspecting the worst. AI is now influencing young people learning how to write to the extent that even my best students have started to sound like AI. I know that AI recognises patterns and produces only a facsimile of good writing, much like the proverbial broken clock that’s right twice a day. The practice of writing words to connect with a reader, communicate ideas and tell a story is a human endeavour that AI will never be able to match. Fear won’t stop me from looking it straight in the AI and declaring, “You have no power over me.” I urge everyone else — writers, teachers, judges and editors — to do the same. The writer currently teaches Expository Writing at AKUFAS. Published in Dawn, May 30th, 2026
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations. Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset. Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designers—but if you’re coming from an academic background as I did, there are a few things you’ll need to know. Different goals lead to different strategies The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule. This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. “Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.” This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.’s University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. What the industry-academia split means in practice Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability. Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing. The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative. In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. Design scope, verification, and time horizons With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield. In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face. Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain. In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape. This article appears in the June 2026 print issue.
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MOUNTAIN VIEW, United States (AFP) -- Google on Tuesday showed off new plans to turn its famous search bar into an AI assistant that can book restaurants, track news and contact businesses -- just by asking a question. After three years of struggling to keep up with ChatGPT, Google is racing to roll out artificial intelligence tools that build on its grip over online search. The company's Gemini AI app now has 900 million monthly users, twice as many as last year. Its AI-powered search feature,