‘All Her Fault’ Team Reunites for Peacock Mystery ‘The Break-In’
The drama from showrunner Megan Gallagher is based on a novel by Katherine Faulkner.
🇺🇸 미국 · "FAULT" · 총 27건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 11,769건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 11,767건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 18.9(중도 균형)입니다.
The drama from showrunner Megan Gallagher is based on a novel by Katherine Faulkner.
The team behind “All Her Fault” is back in business with Peacock. Variety has learned the Peacock has greenlit a limited series adaptation of the Katherine Faulkner novel “The Break-In.” “All Her Fault” writer Megan Gallagher will write and executive produce “The Break-In,” while Nigel Marchant and Gareth Neame of Carnival Films — which produced […]
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This sponsored article is brought to you by Black & Veatch. The biggest challenge facing utilities today isn’t what it seems. It’s not demand, even as load growth accelerates. It’s not extreme weather, even as “major events” become routine. It’s not cybersecurity, even as connections expand across the grid. The real challenge is this: Distribution systems were designed for a different reality. Long gone are the days of predictable demand, one-way power flow and isolated disruptions. At Black & Veatch, we see that leading utilities are no longer debating whether to modernize. They’re deciding how quickly they can do it, and how to do it at scale. Across grid modernization programs globally, three truths consistently emerge. They define what it takes to prepare the distribution system for what’s next: 1. Outage response is not a resilience strategy Resilience is being redefined in real time. A strategy centered on mobilizing crews and restoring service as quickly as possible is reactive, and increasingly insufficient. Resilience has to shift upstream into integrated system design. That starts with hardening. Stronger poles, undergrounding and structural upgrades all have a role, particularly in high-risk corridors. We’re also seeing meaningful gains from how the network is configured and how quickly it can respond without waiting on manual intervention. This is where distribution automation programs can change outcomes. Strategically placed reclosers, automated switches and fault indicators help contain disruptions before they spread. When combined with feeder reconfiguration and updated protection strategies, distribution automation investments allow utilities to set more aggressive recovery targets and achieve measurable reductions in outage duration and customer impact. 2. Future-readiness depends on DERs at scale Forecasting is less and less reliable. Only 19 percent of utilities report strong confidence in their ability to predict future load growth, according to the Black & Veatch 2025 Electric Report. Distributed Energy Resources (DERs) like solar, storage, EVs and behind-the-meter generation are exciting solutions; but they fundamentally change how the system operates. Power is no longer just delivered. It’s injected, stored and redirected in ways the system was never designed to manage. At scale, these challenges show up quickly — particularly on feeders where distributed generation is approaching or exceeding hosting capacity. Protection coordination becomes more difficult when fault current comes from multiple directions. Voltage becomes less predictable as generation fluctuates throughout the day. And planning models must now account for highly variable, location-specific behavior. Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time. Adapting to bi-directional power flow requires more than incremental updates. Leading utilities are responding by building flexibility into the system, moving beyond static assumptions toward dynamic hosting capacity and interconnection studies, planning that incorporates DER, EV adoption and localized load growth, and infrastructure aligned with the communications and control needed to manage it. 3. The edge must be intelligent, visible and secure As system stress and complexity increase, utilities need far greater visibility and control over the network. Historically, utilities relied on customer calls, Supervisory Control and Data Acquisition (SCADA) at the substation level and field crews to understand what was happening on the system. That model doesn’t hold up. You can’t effectively manage a system you can’t see. Plus, the most critical events are increasingly happening beyond the substation — on feeders, laterals, and at the edge where DER and customer behavior are interacting with the grid. Grid-edge technologies have become essential. Sensors, Advanced Metering Infrastructure (AMI) and automated switching provide the raw data and control needed to move from reactive to proactive operations. In more advanced deployments, utilities are creating centralized control environments that allow operators to see and manage the distribution system in near real time. That capability is enabled by: Advanced communications networks to form the backbone of real-time grid visibility Distribution Management System (DMS) and Outage Management System (OMS) to enable faster, more coordinated system response Analytics, AI and machine learning to improve situational awareness, anticipate system conditions, and support operational decision-making The same connectivity enabling this real-time visibility and control also introduces new vulnerabilities, blurring the line between physical and cyber risk, yet many utilities manage them separately. Only 22 percent have unified teams in place, even as threats continue to rise, including a 50 percent increase in substation attacks and growing exposure to malware and ransomware, according to the Black & Veatch 2025 Electric Report. Cybersecurity and resilient network design must be embedded into the architecture from the outset—not layered on after the fact. See what bolder vision looks like Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time. To learn about a successful program, check out Georgia Power’s recent grid modernization program. Black & Veatch partnered with the utility on large-scale infrastructure upgrades. The results? Outages are down 76 percent, restoration times have improved by more than 80 percent and communities across Georgia are powered by a grid built to meet the future head-on. When the state faced the most destructive storm in the company’s history, Hurricane Helene, Georgia Power deployed a rapid response team that utilized its “smart grid” and restored power to more than 1 million customers within days. A grid built to meet the future head-on—that’s the result of bolder vision.
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This article is adapted by the author with permission from Tech Policy Press. Read the original article. South Africa is not just another developing country struggling to govern artificial intelligence; it is the exception with leverage, and the window to act on it is closing. It holds approximately 88 percent of global platinum-group metal reserves, critical inputs to parts of the semiconductor and data-center supply chains that make AI infrastructure possible. It hosts the largest data-center market on the continent. Its existing hyperscaler relationships give it procurement leverage that most African states will never have. And a major geopolitical contest over AI infrastructure is being fought on its soil right now, between Chinese and American technology companies competing for control of the systems that will underpin an entire continent’s public sector. In physics, leverage requires three things: a fulcrum, a lever arm, and the ability to apply force. The Bushveld Complex, the world’s largest platinum-group metal deposit, is the fulcrum: a mineral endowment that gives South Africa a position in the semiconductor supply chain that no other African state holds. The since-withdrawn draft policy is the lever arm. The unresolved “OPTION” provisions in the policy are where force would be applied. Without a policy that specifies what South Africa wants in return for market access, the lever arm sits unused, and the weight of two of the world’s largest technology ecosystems settles exactly where those ecosystems want it to settle. This makes South Africa a global test case. Not because its proposed means of governance is exemplary, but because it is the one developing country with enough structural leverage to negotiate genuinely different terms, and the one that is choosing, through inaction, not to. The recent announcement of a new panel to update the draft policy is an important opportunity. But the deeper failure is not that an AI policy contained bad references. It is that no verification process caught them before the document entered the public domain. That is a systems problem, not merely a political one. It points to a missing layer in how governments are adopting AI. The contest already underway Last year, Huawei pitched an emerging-product bundle to tech executives across the continent. Huawei was now bundling access to DeepSeek’s large language model with its own cloud and storage infrastructure. The price differential was stark—in some cases by more than 90 percent. At the same time, Microsoft announced plans to spend ZAR 5.4 billion ($300 million) by the end of 2027 on cloud and AI infrastructure in South Africa, building on a prior ZAR 20.4 billion investment. Google, Amazon Web Services, and Oracle already have cloud regions in the country. According to one analysis, the country’s data-center market was valued at US $2.16 billion in 2024, the largest in Africa. These are not commercially neutral investments. Huawei’s infrastructure reach has been explicitly linked to Chinese strategic objectives, including a documented track record of providing governments with surveillance infrastructure through its Safe Cities network. U.S. hyperscaler investment comes with its own dependency structure: closed models, pricing set unilaterally, and terms of access that no African government has meaningfully shaped. South Africa is being asked to choose between these dependency models without a policy that specifies what it wants in return. The leverage it has There is a particular irony in South Africa’s position. The country whose mines supply platinum-group metals essential to semiconductor manufacturing, and through them to AI compute, has drafted a policy that treats it as a consumer of AI systems rather than a stakeholder in their governance. South Africa digs up the minerals that make AI possible. It has no say over the AI built from them. The AI triad framework covers algorithms, compute, and data. South Africa has no frontier model development capacity. South Africa holds significant data assets in financial services, health care, and agriculture, with no clear framework for their sovereign management. South Africa possesses PGM (Platinum Group Metals) leverage of global significance on the compute axis, currently being transferred without meaningful condition. It also has exceptionally high solar irradiance and significant renewable-energy potential. A country that can offer both critical mineral inputs and the energy to power the infrastructure those minerals help build occupies a negotiating position of unusual strength. The Draft Policy proposes no minimum terms for hyperscaler investment, no data sovereignty requirements, no technology transfer conditions and no compute visibility mechanism. Multiple provisions are explicitly left unresolved, marked “OPTION,” including the most consequential choices about how governance will function. Infrastructure decisions made now determine what is renegotiable later, and the answer is: very little. Three futures, one default The three infrastructure futures on offer each create a structurally different form of dependency, and only one creates sovereign capability. The Huawei-hosted DeepSeek integration offers low cost and open-source weights, but with data stored on infrastructure potentially accessible under Chinese legal frameworks, creating surveillance dependency in a pattern already documented across Africa. The second is U.S. closed-model dependency: higher capability, more reliable data protection, but complete API dependency on developers abroad. The third is locally hosted open-weight infrastructure: models governed under South African data-sovereignty rules, on infrastructure subject to minimum terms, developed with South African data. As Nathan Lambert at Interconnects has observed, open-weight models are likely the only realistic way to get sovereign AI off the ground as a real effort, enabling local communities and economies to integrate meaningfully with the technology. But this requires procurement conditions, not goodwill. What binding governance looks like The GovAI “Governing Through the Cloud” framework identifies four roles compute providers should accept as conditions of operating at scale: securers (protecting model weights and training data), record keepers (maintaining infrastructure usage logs), verifiers (confirming customer compliance with safety standards) and enforcers (restricting access when violations occur). These are operational requirements, not theoretical categories—specific, enforceable, and well within the bargaining power of a market of South Africa’s size and mineral position. A detailed policy analysis submitted to the Department of Communications and Digital Technologies (DCDT) identifies the specific provisions the final policy must contain: mandatory minimum terms for foreign compute infrastructure investments above ZAR 500 million (~$30 million); a compute reporting threshold; a National AI Safety Institute mandate covering defensive monitoring of AI capability accumulation; and National AI Champion Sector designations to create data assets for domestic model development. Each provision converts a structural advantage into a governance instrument before that advantage is foreclosed by market reality. Just as modern software security increasingly depends on knowing what components are inside a system—model provider, training data, compute environment, evaluation methods, update cadence, human review points, and failure-reporting procedures—public-sector AI governance requires a clear account of the stack before deployment, not after a problem surfaces. A public institution that cannot verify the sources in its own AI policy is unlikely to be ready to verify the AI systems it procures, deploys, or regulates. Why this is the continental test case South Africa’s choices will establish a regional precedent for what is commercially negotiable in AI infrastructure. If South Africa negotiates data-sovereignty guarantees and technology-transfer conditions as requirements for hyperscaler investment, it creates a replicable model. If Microsoft’s $300 million investment and Huawei’s infrastructure expansion proceed on standard commercial terms, as they are currently, it normalizes extractive AI infrastructure across the continent. The lesson is not specific to Africa. Governments everywhere are producing AI strategies while lacking AI assurance infrastructure. South Africa is an early warning, not an isolated case. The public comment period closed when the policy was withdrawn. But a parallel process remains live: the National Treasury’s Draft General Public Procurement Regulations—the legal instrument that will govern every government AI contract—closes for comment on June 15. Those regulations contain no AI-specific provisions. South Africa has more AI leverage than any country on the continent. Some argue, with force, that governance requirements risk deterring the infrastructure investment South Africa urgently needs: compute capacity, reliable energy, venture capital, and talent retention. That concern deserves a direct answer. Minimum procurement terms, compute reporting thresholds, and technology transfer conditions are not barriers to investment. They are the conditions under which investment serves the host country rather than extracting from it. Infrastructure built without minimum terms produces dependency. Infrastructure built with them produces leverage. To serve the public interest, its AI policy must use it. When late last month News24 reported AI-hallucinated references in the draft AI policy, Minister of Communications and Digital Technologies Solly Malatsi withdrew the draft policy. That was a mistake that could cost South Africa and the rest of the continent the initiative on this urgent issue. His more recent constitution of an independent panel is a belated step in the right direction, if it can turn South Africa’s leverage into policy. The panel—chaired by Professor Benjamin Rosman of the Wits Machine Intelligence and Neural Discovery Institute, and including Professors Vukosi Marivate and Alison Gillwald of Research ICT Africa and Dr. Jabu Mtsweni of the Council for Scientific and Industrial Research—has the technical and governance credibility to produce a stronger document. What it has not yet produced is a timeline. No revised draft has been scheduled. South Africa remains without a formal AI governance framework in the interim.
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