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Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
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🇺🇸 미국 · IT/기술 · "CISION" · 총 34건
필터 보기현재 지수
50.0
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 12,010건을 분석한 결과, 뉴스 심리지수는 50.0(균형)입니다. 긍정 1건(0.0%)·중립 12,008건(100.0%)·부정 1건(0.0%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 19.1(중도 균형)입니다.
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Read this article to understand why and how Martin Scorsese is embracing AI in filmmaking, and why his decision has received stron
The iPhone giant confirmed the closure in North County Mall in Escondido, on June 20th as part of a broader decision to pull out of three mall locations nationwide, including sites in Connecticut and Maryland.
New graduates’ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition. Here are seven tips to help keep young professionals in demand no matter how quickly the field’s tools evolve. 1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in: Data structures and algorithms for problem-solving. Operating systems, databases, and networking for system-level understanding. Core programming languages such as C++, Java, and Python. AI can autocomplete syntax, but if you don’t understand how things work under the hood, you’re likely to struggle to debug or optimize. 2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to: Write clear prompts to generate better code snippets. Review and debug AI-generated code for accuracy, performance, and security. Use AI for productivity boosts while still exercising judgment. Think of AI as a teammate. The real skill is knowing when to trust it and when not to. 3. Build projects that showcase end-to-end thinking. Employers increasingly look for engineers who can design and build systems, not just solve problems. Create projects that show you can: Define requirements clearly. Use AI tools responsibly within the workflow. Deliver a product that scales and is maintainable. 4. Sharpen your system design skills early. Even junior engineers are now asked questions about basic system design with AI. Expect to explain to prospective employers: How you would responsibly integrate AI into a system. How to design fallbacks when AI fails. How to ensure scalability and reliability. 5. Develop strong communication skills. Today’s engineers don’t just code in isolation. You will be expected to: Explain design choices to teammates and stakeholders. Document decisions clearly. Collaborate effectively in cross-functional teams. This is one area where AI cannot replace you. Clear communication is a career accelerant. 6. Stay curious and keep learning. The tech industry moves fast, and AI is accelerating that pace. Cultivate habits such as: Following industry news, blogs, and open-source projects. Experimenting with new AI tools, frameworks, and libraries. Engaging in communities such as GitHub, IEEE Collabratec, LinkedIn, and Medium. Employers value engineers who keep themselves sharp and relevant. 7. Think beyond coding. AI will increasingly handle routine coding tasks. The differentiators for you will be: Problem-framing: Can you take a vague idea and turn it into a solution? Architectural judgment: Can you design systems that scale and last? Ethical awareness: Can you spot risks in AI use and address them responsibly? For more career advice, subscribe to the IEEE Spectrum Career Alert Newsletter. The biweekly newsletter features the latest information on jobs, education, management, and the engineering workplace.
Companies like JPMorgan, Meta, Amazon, and KPMG are tracking how employees use AI, and using the data to inform decisions about performance.
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.
Legendary director Martin Scorsese is getting mixed reviews online for his decision to team up with and promote Black Forest Labs, an AI company focused on building visual intelligence. The company just released a promotional video featuring the 83-year-old Hollywood mainstay discussing how AI can be used in the movie-making process. The video shows Scorsese ...
Employees are scaling productivity at record speed while leadership teams remain stuck in decades-old decision-making structures.
Children born after 2013 are the first generation to grow up fully immersed in digital systems, which weren’t designed with them in mind. One‑third of the world’s Internet users are younger than 18, according to UNICEF, yet these systems shaping their daily lives were built for adults. They were optimized for engagement and designed long before people understood how profoundly digital environments influence children. For engineers and technical professionals, online safety is not an abstract policy debate. It is a design challenge that demands rigor, systems thinking, and ethical foresight. Governments around the world are also beginning to recognize the problem. Policymakers from across Australia, Brazil, the European Union, Indonesia, and the United States are responding to risks engineers have long understood: Addictive features, inappropriate content, opaque data practices, and algorithmic systems shape user behavior in ways that their creators did not fully predict. For years, technology moved faster than governance. Now governance is trying to catch up. Global Shift Toward Design Reform Supporting National Digital Ambitions In Athens this year I met with senior leaders of Greek government agencies and key national research institutions. Greece is moving quickly on digital transformation and responsible technology governance, and our discussions reinforced IEEE’s role as a trusted, neutral collaborator. We focused on supporting Greece’s ambitions in digital modernization and public‑sector innovation. We also discussed responsible AI and age-appropriate digital design in Europe and elsewhere. These engagements, grounded in shared values and long‑term commitment, strengthened IEEE’s presence within the European ecosystem and opened new pathways for collaboration on trustworthy AI and child‑focused digital well‑being. The European Union and the United Kingdom have been among the first to act, embedding age‑appropriate digital design into their broader children’s rights agenda. Drawing on IEEE expertise and global best practices, Indonesia is the first country in Asia, and Brazil is the first country in Latin America, to adopt age-appropriate design regulation. Australia is aiming to limit access to harmful content and addictive design features through age restrictions on certain platforms. And in the United States, in addition to federal efforts, states including California, New York, and Utah are enacting approaches including age-appropriate design principles. Across these efforts, a shared realization is emerging. Protecting children online is not simply about filtering content or adding parental controls. It requires rethinking the architecture of digital systems regarding how data is collected, how algorithms make decisions, how interfaces influence attention, and how AI interacts with the developing minds of young users. Engineers and technical professionals understand that design choices are never neutral. They encode values, incentives, and assumptions. When the user is a child, those choices carry greater weight. This is where IEEE’s work becomes more essential. Protecting Children Online For more than a decade, IEEE has been building technical and ethical foundations for safer digital experiences. The first IEEE standard on age-appropriate design in 2021 marked a turning point. It offers a structured, principled approach to designing with children’s rights in mind. The Institute’s 2022 article “Use a New IEEE Standard to Design a Safer Digital World for Kids” highlights how the standard helps translate those principles into engineering practice. Today the IEEE Standards Association’s (SA) Trustworthy Digital Experiences portfolio provides a practical, technically grounded framework for governments and industry. Spanning ethical design, data governance, algorithmic transparency, and child‑focused digital well‑being, it has already initiated discussions with government stakeholders around the world. This work helps bridge the gap between engineering realities and policy ambitions. No single country can solve these challenges alone. Many policymakers lack access to the combined expertise in technology, governance, and children’s rights needed to act quickly and effectively. This collaborative effort helps close that gap. The stakes are high. Without coordinated action, public policy will continue to lag behind technology, leaving children exposed to risks that could have been mitigated through thoughtful design. But with the right frameworks, governments can ensure digital systems respect children’s rights, support healthy development, and promote well‑being. IEEE’s emerging standards and collaborative technology policy work offer a path forward. By grounding national efforts in evidence‑based, rights-aligned design principles, IEEE is helping governments move from reactive regulation to proactive, coherent, and globally informed strategies for protecting children online. Safeguarding childhood in the digital age is both a moral imperative and an engineering challenge. And IEEE is helping to lead the way. —Mary Ellen Randall IEEE president and CEO Please share your thoughts with me: president@ieee.org. This article appears in the June 2026 print issue.
Rule No. 1 in cutting arts funding is ‘don’t look stupid.’
Vice President JD Vance told graduates of the Air Force Academy they must "never submit" to artificial intelligence while making life-and-death decisions — and that decisions on killing "must be made by humans."
Leaders stand at an AI crossroads where their decisions will define the future, shaping innovation, risk and public trust in the years ahead.
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.
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.
Floppy disks are several decades old—many of the disks are degrading and the data stored on them is at risk of being lost. In response, Leontien Talboom, a technical analyst at Cambridge University Libraries and Archives, led a roughly year-long project preserving floppy disks called “Future Nostalgia,” which concluded in January. Leontien Talboom Leontien Talboom is a technical analyst at Cambridge University Libraries and Archives, where she transfers material from a wide range of storage media to make them accessible to archivists. IEEE Spectrum spoke to Talboom about her work preserving data from Cambridge’s collection of floppy disks and collecting knowledge about the disks themselves. Why is it important to preserve floppy disks now? Leontien Talboom: Two reasons. First, the physical media is starting to degrade. Floppy disks are made from plastic, but they’ve got a magnetic layer of iron oxide, and that’s deteriorating. A lot of floppy disks are found in attics or garages, which means they also suffer from mold. Second, a lot of people who developed floppy disks and systems that use floppy disks are starting to retire or pass away, which means that a lot of tacit knowledge is disappearing. Whom did you go to for that tacit knowledge? Talboom: I went to the retro computing community. Their work is more around preserving these machines to keep them running [than] the data that lives on the floppy disk. But they know their stuff about floppy disks. For example, they know that in a lot of the older disks, the inside of the disk—the doughnut—gets stuck to the top. So if you flex the casing, the doughnut falls down again. If I hadn’t known that, I would have assumed that those disks in our collection were broken or corrupt. What is the most difficult part of working with floppy disks? Talboom: Accessing the files can be quite challenging if we don’t understand the file system. Within libraries and archives, we get a lot of material from machines that are not as well loved. Many of the personal computers that you had at home, such as the Amstrad or ZX Spectrum or BBC Micro, are very well documented. But a bunch of our material comes from business or research systems. They’re not as nostalgic for people, so there’s not as big a community preserving this type of material. Do you have a favorite type of floppy disk? Talboom: Five and a quarter. The weirder the system, the more frustrating and fun it is. I quite like doing that detective work. The Amstrad disk has also really stolen my heart. The popularity of floppy disks is very geographically dependent. Our library, for example, has these Amstrad 3-inch disks. But if you go to the U.S., they’re really uncommon. They weren’t able to manufacture enough of these drives, and [3.5-inch disks] took over at a certain point. But they’re really cute. What’s the best method for sustainably storing data? Talboom: The main thing is actively looking after it. A lot of the floppy disks we get in the library haven’t been accessed for 20 or 30 years, which means that you need certain special hardware to actually read them, and then work with emulators or other tools to make these file formats accessible. Now that we’ve done that work and transferred it, we can monitor it and make sure it’s not suffering from anything like bit rot. We can also make decisions around migrating it to other file formats or working on specific file systems or unknown file formats in more detail.
The decision is nearing completion and is expected to be announced before the summer break, according to the report.
Google has filed an appeal against a 2024 antitrust ruling that determined the company violated competition laws by paying billions to become the default search engine on Apple's iPhone devices. The post Google Appeals Antitrust Ruling, Claims Billions in Payments to Apple Didn’t Influence Search Decision appeared first on Breitbart.
Academic papers, popular books, and legal decisions are peppered with AI mistakes that are getting harder to clean up.
Google officially filed its appeal of the federal ruling deeming it an illegal search monopolist, arguing the decision "crashed" through legal guardrails. "Google just prevailed in the marketplace fair and square," it writes in its legal filing. Google had already said it would appeal the ruling, which includes both the August 2024 decision about its […]
Patients who use mobile applications to manage medical conditions including depression and chronic pain might assume the apps have been evaluated by regulatory agencies to be safe and effective. But that isn’t necessarily the case. Most of the more than 55,000 medical apps that claim to diagnose or treat a condition—or ones that provide clinical decision support, known as “therapeutic” apps—have never been assessed by any trusted neutral bodies or regulatory agencies to evaluate them for technical soundness, ethical design, or clinical benefit. The apps often don’t comply with regional data security and privacy laws to protect people’s sensitive health information. Medical apps differ from traditional wellness apps, which provide users with insights into becoming healthier by, for example, tracking fitness activities, monitoring blood pressure, and analyzing sleep patterns. There is no reliable way to verify that therapeutic apps deliver the results they indicate. To help ensure such apps are credible, the IEEE Standards Association (IEEE SA) recently launched the IEEE Global Medical Mobile App Assessment and Registry. The publicly searchable directory is designed to list apps that have been vetted by experts across several criteria including technical soundness, ethical design, compliance with data security and privacy regulations, and clinical efficacy, which is evidence of a clinical benefit for the patient. “Patients, clinicians, payers, and health care systems often struggle to distinguish clinically meaningful therapeutic apps from those that are simply well-marketed,” says IEEE Senior Member Yuri Quintana, chair of the assessment and registry program. He is chief of the clinical informatics division at Beth Israel Deaconess Medical Center, in Boston. “Our goal is to establish a standardized review method using criteria developed by experts.” Why regulation is lacking Because the apps are intended for medical use without being part of a medical implement, they fall under the designation of software as a medical device (SaMD), according to the International Medical Device Regulators Forum. SaMD is supposed to be regulated by public health agencies such as the U.S. Food and Drug Administration, but the apps have developed and grown in popularity so quickly that regulators haven’t been able to keep up, Quintana says. Some companies have received approval, but most have not, he says. Many users are unaware of the regulatory gap, he says. “Seeing an app from a well-known company often creates the impression that it has been meaningfully vetted for safety and efficacy, even when that is not the case,” he says. Some companies are using deceptive advertising to sell their product, he adds. Marketing materials might claim that all of a company’s health apps are certified, even though only one app has been approved by a regulatory body to treat a particular condition. Or the verbiage might imply the company has clinical evidence proving its application works, even though the app has never been tested independently. Another concern is that updated apps aren’t being vetted, says Maria Palombini, IEEE SA’s director of health care and life sciences global practice lead. “The original app might have received approval from a regulatory agency, but not the updated version,” Palombini says. “There could have been significant changes from the original.” “Not every medical-related app triggers the same regulatory classification or review across jurisdictions,” Quintana adds. “That leaves a large gray zone of clinically relevant but lower-risk apps that haven’t undergone an independent assessment. The IEEE registry was created to help fill these gaps. “IEEE is the best organization to address this problem because this is fundamentally a standards, trust, interoperability, and conformity assessment challenge,” he says. IEEE “is the world’s largest technical professional organization, with deep expertise in developing globally recognized standards including in health care, cybersecurity, AI ethics, and interoperability.” “Through the IEEE Conformity Assessment Program, we already run rigorous assessment and registry programs,” Palombini says. “Our neutral, consensus-driven, multidisciplinary approach—bringing together clinicians, regulators, developers, and ethicists without commercial bias—makes IEEE uniquely positioned to create trustworthy global guardrails that can scale across jurisdictions and support regulatory harmonization.” How the registry works The assessment framework was developed by a multidisciplinary group of 35 volunteer experts from 10 countries, Quintana says. The panel includes academics, AI experts, app developers, clinicians, ethicists, mental health experts, patient advocates, regulators, researchers, technologists, and those who assess safety in health care. The registry is for any app used for clinical care or therapeutics that claims to demonstrate a medical benefit. That includes apps designed for cardiology, diabetes, mental health, neurology, oncology, rehabilitation, and respiratory diseases, Quintana says. Initially, he says, the focus will be on apps that aim to treat mental health conditions, given the large number of offerings in that area and the registry committee’s expertise. The submission of apps is voluntary. There is no government mandate that requires a company to use the IEEE registry. The products will be evaluated against about 150 consensus-based criteria across three major areas: Clinical efficacy including therapeutic effectiveness, any sustained benefits, risk management, comparison to standard care, user engagement, and real clinical value. Technical soundness including accessibility, privacy and security, error handling, interoperability, AI governance, usability, and operational quality. Ethical design including bias prevention, patient consent, data governance, conflict-of-interest transparency, responsible use of AI and large language models, and prioritization of public health benefits. IEEE charges a nonrefundable submission fee that covers the cost of the assessment plus the registry’s annual subscription for the first year. Developers first must demonstrate they are a legally established entity before they can complete the app publisher registration form and then submit documentation and attestations about the product. The IEEE review of an app is estimated to take six to eight weeks, Palombini says. The assessment results will be privately shared with the app publisher, she says, and to be listed in the registry, an app must achieve more than 85 percent compliance in each category. Upgraded apps must be submitted and reassessed, Palombini says. Similar to how users are notified when an app on their smart devices has , the registry will be notified when listed apps have a new update available, she says. Applicants who do not pass the assessment are to receive feedback explaining why. They will be given an opportunity to make changes or provide additional documentation, Palombini says. “It’s a pretty methodological process, with checks and balances,” Quintana says. “We’re being very transparent about the process.” Approved apps added to the registry receive an IEEE certification badge and submission identifier, which the company can display on its website, app store listings, and marketing materials. “The badge serves as visible proof that the app has met the independent, consensus-based assessment for clinical value, technical robustness, and ethical design,” Quintana says. The registry will be publicly available at no cost, he says. Patients and families seeking safe, trustworthy apps—and payers and insurers evaluating reimbursement potential—will find the registry helpful, he says. The application website is open. The public registry page does not yet list a specific count of approved apps because assessments are ongoing. Approved apps and their unique identifiers are to be published when the initial reviews are completed. To learn more, you can watch a webinar recorded in March. The assessment framework that underpins the registry is supporting the formal recognition of IEEE P3962 Standard for Criteria Assessment Framework f