The Independent (World)
진보 성향
IT/기술
Trump shares wild AI video of him filling DC reflecting pool with tears
It appeared to reference a widely-circulated video of a woman crying during Trump’s first inauguration
IT/기술 · "REFLECT" · 총 26건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 81,666건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 3,988건(4.9%)·중립 75,751건(92.8%)·부정 1,927건(2.4%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.6(중도 균형)입니다.
It appeared to reference a widely-circulated video of a woman crying during Trump’s first inauguration
Japan's push to keep pace with the global AI race reflects a broader anxiety among governments worldwide, fearful of becoming ever more dependent on foreign technology.
Pakistan's authority bringing the crypto under the tax net reflects that serious efforts are being made to legalize and regulate the crypto business, according to Mian Abrar, geopolitical analyst.
A Brooklyn home seller is seeking Anthropic shares or bitcoin for a $5.99M property, reflecting AI equity's rise in real estate transactions.
Walmart placed a token limit on its internal vibe coding tool, Code Puppy, reflecting the retailer's cost-conscious approach to AI.
Jensen Huang is returning to South Korea with a charm offensive that reflects the country's rising importance in AI chips, robotics and the next wave of physical AI.
Leo said 'AI systems present themselves as neutral and objective, they end up reflecting and reinforcing the stereotypes or ideological bias of their designers and developers.'
While many tech firms scale back, Nvidia is aggressively hiring foreign talent via the H-1B visa program, increasing its certified positions. This surge reflects the company's immense demand for AI specialists and engineers, contrasting with competitors like Google and Amazon. CEO Jensen Huang emphasizes immigration's vital role in US technological leadership.
Meta has laid off thousands of California employees, primarily software engineers, as it restructures to prioritize artificial intelligence. This move, impacting locations like Menlo Park and Sunnyvale, reflects a broader tech industry shift towards AI-driven operations. The company aims to reallocate resources to critical AI initiatives, with affected employees receiving severance packages.
Amazon's $8 billion Anthropic investment is worth $74 billion, reflecting massive valuation gains for the AI startup.
Independent artist and writers have been turning ever more to self-published or handmade small print publications, reflecting a love of paper in the digital era.
Nvidia's CFO, Colette Kress, declared AI a business necessity, not a luxury, driving demand across the AI stack. The chip giant is restructuring its reporting into Data Center and Edge Computing segments to reflect this growth.
Mandy Fields now has the chief technology and AI officer reporting to her, reflecting how the CFO role is evolving.
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.
Humans, as reflective and deliberative beings, get to decide what to do with AI.
Adjustments by Amazon and Microsoft reflect harsh realities: power grids that take years to expand, land speculators inflating prices, and overwhelmed utilities
WeRoad, the Milan-based group travel startup, has raised a $58 million Series C round led by Airbnb as it prepares for its first major expansion outside Europe. The funding brings the company’s total capital raised to roughly $100 million and will finance WeRoad’s push into the U.S., beginning with Austin. The new investment reflects a […]
The pope has warned that AI is never truly neutral: it reflects the society that made it. He has called for ethical oversight – and protecting workers.
The additional funding reflects how integral AI platforms have become to national security.
The Samsung labor showdown in South Korea reflects global concerns about who benefits from the AI industry, and how the wealth being created should be shared.