๐บ๐ธ ๋ฏธ๊ตญ ยท "RESULTS." ยท ์ด 23๊ฑด
ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
48.9
0 = ๋ถ์ ์ฐ์ธ
50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 10,364๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 48.9(๊ท ํ)์ ๋๋ค. ๊ธ์ 1,027๊ฑด(9.9%)ยท์ค๋ฆฝ 7,480๊ฑด(72.2%)ยท๋ถ์ 1,857๊ฑด(17.9%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 20.9(๋ณด์ ๊ฒฝํฅ)์ ๋๋ค.
Election officials have argued that California's system is designed to ensure every valid ballot is counted, even if that means waiting longer for final results.
Leftist Los Angeles City Councilmember Nithya Raman will advance to the November mayoral election after nearly a week of counting late-arriving ballots, locking Republican Spencer Pratt out of the race. Mondayโs vote drop gave Raman a nearly three-point lead over Pratt, a stunning turnaround for the leftist who had trailed Pratt by more than nine ...
The Hollywood Reporter's executive editor of awards coverage dissects Sunday evening's results.
Los Angeles Mayor Karen Bass (D) and Democrat Nithya Raman are projected to face off to lead the country's second largest city this November, according to Decision Desk HQ. With 87 percent of votes in, Bass is at 34.68 percent, and Raman is at 27.12 percent, according to Decision Desk HQ. The June 2 results...
Senate Republicans passed funding for Immigration and Customs Enforcement, a major win for the Trump administration. CBS News' Taurean Small has more on the "vote-a-rama" results.
CrowdStrike CEO George Kurtz said it was too early for concerns surrounding Anthropic's Mythos to meaningfully impact first-quarter results.
President Donald Trump said Democrats were trying to "steal" the California primaries, his comments coming as two Republican candidates appeared to fair well on Wednesday when it came to early results. The post President Trump: โDumocratsโ Are โTrying to Stealโ California Primaries from Republican Candidates appeared first on Breitbart.
Matt Klink, partner at California Strategies, joined "Forbes Newsroom" to discuss what is known so far about California's primary election results.
Some Microsoft workers ask whether questions were omitted from a recent employee satisfaction survey, reflecting broader tension inside the company.
Los Angeles Mayor Karen Bass officially secured a spot in Novemberโs runoff election, while reality television star Spencer Pratt and far-Left Councilmember Nithya Raman continued battling for the second slot. With roughly 63% of the vote counted as of Wednesday morning, Bass led the field with 34.8% support, according to Associated Press results. Pratt was ...
As the world confronts challenges of aging populations and healthcare workforce shortages, smart healthcare is no longer optional โ itโs essential. And Taiwan is a proven expert. A nationwide digital health infrastructure delivering visible, transformative results. 13 Taiwanese hospitals among Newsweekโs 2026 Worldโs Best Smart Hospitals, ranking second in Asia. More than 50 artificial intelligence-driven [โฆ]
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An experimental vaccine from Moderna shows promise in keeping deadly skin cancer from returning for years, according to new clinical trial results.
Pro-Trump candidate Abelardo de la Espriella advanced to a runoff in Colombiaโs presidential election Sunday afternoon, according to preliminary official results. Espriella, a supporter of President Donald Trump who has drawn comparisons to Salvadoran President Nayib Bukele, received almost 44% of the vote in the first round of voting held Sunday. Espriellaโs opponent, Ivan Cepeda, [โฆ]
We need more empirical studies about the impact of generative AI on our mental health. I dissect one recent study to show counterintuitive results. An AI Insider scoop.
President Donald Trump endorsed South Carolina Lt. Gov. Pam Evette (R) to be the next governor of the state, noting that she โhas proven she has the Courage and Wisdom to deliver strong results.โ In a post on Truth Social, The post Trump Endorses South Carolina Lt. Gov. Pam Evette for Governor: โHas Proven She Has The Courage and Wisdomโ to Deliver appeared first on Breitbart.
Existing generative AI models are built on batch processing: You give the system instructions; it runs computations; then spits back the results. Now comes a Silicon Valley startup that says it can produce gen-AI video (and other outputs) in real time โ a potentially groundbreaking advance: San Francisco-based Reactor, co-founded by Alberto Taiuti and Bryce [โฆ]
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, Iโve 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 todayโs most advanced chips is occupied by blocks that arenโt 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, Iโve 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 designersโbut if youโre coming from an academic background as I did, there are a few things youโll 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 requirementโespecially 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. โAcademia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.โ 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 โfinsโ 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.โs 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 startupโs 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 chipโs 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 blocksโsuch as processor cores, memory interfaces, and security enginesโfrom 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 werenโt 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.
New research suggests money with no strings attached can promote better health, but other studies have seen mixed results.
A manhunt for an armed suspect in Fulton County, Georgia, forced a polling location to close on Tuesday, delaying election officials from reporting results. CBS News' Skyler Henry and Anthony Salvanto have the latest.