๐บ๐ธ ๋ฏธ๊ตญ ยท IT/๊ธฐ์ ยท "ASTER" ยท ์ด 49๊ฑด
ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
48.9
0 = ๋ถ์ ์ฐ์ธ
50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 10,955๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 48.9(๊ท ํ)์ ๋๋ค. ๊ธ์ 1,088๊ฑด(9.9%)ยท์ค๋ฆฝ 7,901๊ฑด(72.1%)ยท๋ถ์ 1,966๊ฑด(17.9%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 20.6(๋ณด์ ๊ฒฝํฅ)์ ๋๋ค.
Nvidia challenger D-Matrix is entering full production of an AI chip it says is 10 times faster than a GPU and bypasses the memory shortage.
This article is brought to you by AGILINK. Throughout the exhibition hall at the 2026 IEEE International Conference on Robotics (ICRA), in Vienna, one demonstration seemed to attract a disproportionate amount of attention. Two robotic hands were making a balloon dog. Slowly and deliberately, the robot twisted a long balloon into loops, bends, and joints without popping it. Visitors stopped, watched, and often returned with colleagues to watch again. AGILINKโs balloon dog demonstration draws a crowd at ICRA 2026.AGILINK At first glance, the demonstration appeared almost playful. Among roboticists, however, balloon twisting is widely recognized as an unusually difficult manipulation task. A balloon is lightweight, highly deformable, slippery, and extremely sensitive to force. Every twist changes its geometry and internal pressure, turning a seemingly simple activity into a continuously changing physical interaction problem. Humans navigate those changes almost intuitively. While making a balloon animal, people rarely think consciously about force regulation, slip prevention, or contact stability. They simply adjust. For robots, those adjustments remain remarkably difficult. The challenge is not merely moving fingers to the right positions. The harder part is maintaining stable interaction while the object itself is changing. Highlights from AGILINKโs ICRA 2026 demonstrations, including visuotactile sensing, in-hand manipulation, balloon-animal shaping, and other contact-rich tasks enabled by the companyโs latest OmniHand platform.AGILINK That distinction helps explain why the balloon dog drew so much attention in Vienna. What appeared to be a dexterity demonstration was, in many ways, a demonstration about contact itself. As robotic manipulation continues to advance, a growing number of researchers are arriving at a similar conclusion: many of the hardest problems in robotics begin only after contact occurs. Motion and Contact Intelligence for Robot Manipulation Balloon twisting combines two challenges that robotics has traditionally struggled to solve simultaneously: long-horizon task execution and contact-rich manipulation. The first concerns motion. A balloon dog is not created through a single grasp or twist. It emerges through a carefully ordered sequence of manipulations, each setting the conditions for what follows. A small rotational error introduced early may appear insignificant at first, yet several steps later it can prevent the final structure from forming altogether. In that sense, balloon twisting is a long-horizon task. Success depends not only on performing individual actions correctly, but also on preserving the future feasibility of the entire manipulation process. To address this challenge, AGILINK began by collecting demonstrations from professional balloon artists. Human actions were mapped onto robotic hands to establish an initial manipulation policy. But successful demonstrations alone were insufficient. In practice, some of the most valuable learning occurred when execution began to drift toward failure. Whenever instability emerged, human operators intervened and corrected the manipulation in real time. Those interventions were recorded and incorporated into reinforcement-learning cycles, allowing the system to learn not only how successful demonstrations unfold, but also how experienced operators recover when things start to go wrong. Through this process, the robot gradually acquired the capabilities required for long-horizon task executionโa collection of abilities that AGILINK groups under the term motion intelligence: the ability to generate actions, coordinate bimanual behaviors, and execute extended manipulation sequences under real-world uncertainty. OmniHand 3 Ultra-M on display at ICRA 2026.AGILINK Yet motion alone does not explain why balloon twisting remains difficult. The second challenge is contact. The robot must continuously regulate force, adjust contact locations, and respond to subtle changes in the objectโs state. These decisions are difficult to encode through explicit rules. Even skilled human operators often rely on tactile intuition developed through experience rather than consciously articulated strategies. Analysis of those interventions revealed that many failures did not originate from incorrect action sequences, but from the breakdown of contact itself. To better capture those interaction dynamics, AGILINK collected contact-centric intervention data and incorporated those interactions into reinforcement-learning training. Rather than learning only which motions to perform, the system also learned how humans maintain stability when contact conditions begin to deteriorate. AGILINK describes this capability as contact intelligence: the ability to establish, maintain, and adapt physical interaction as force distribution, friction, deformation, and contact geometry continuously evolve. The distinction between the two capabilities is subtle but important. Motion intelligence determines what the robot intends to do. Contact intelligence determines whether it can continue doing it. For balloon twisting, both are necessary. One provides the sequence of actions. The other keeps those actions physically viable. YouTuber KhanFlicks follows OmniHandโs motions while learning to fold a balloon dog at the AGILINK booth.AGILINK Between a balloon slipping away and a balloon bursting lies a narrow region of stability. Successful manipulation depends on finding that regionโand remaining within it throughout the task. Introducing the OmniHand 3 Ultra-M Dexterous Hand The balloon dog demonstration showcased a manipulation capability. It also revealed a broader question. How much contact intelligence can be achieved through learning alone? A robot can only regulate what it can perceive. It can only respond as quickly as its hardware allows. As manipulation tasks become increasingly complex, researchers are finding that progress depends not only on better policies, but also on richer sensing and faster physical response. That realization formed the backdrop for AGILINKโs second major announcement at ICRA 2026. Alongside the balloon dog demonstration, the company introduced the OmniHand 3 Ultra-M. OmniHand 3 Ultra-M closely matches the size of an adult human hand.AGILINK The two exhibits represented different stages of the same technological trajectory. If the balloon dog demonstrated what contact intelligence can already accomplish today, Ultra-M was designed to explore what contact intelligence may require next. Building Hardware for Contact Intelligence Roughly the size of an adult human hand, the OmniHand 3 Ultra-M integrates 20 active degrees of freedom within a human-scale form factor. Its most distinctive feature is a fully direct-drive architecture. By adopting direct-drive actuation throughout the system, the hand is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change. For contact-rich manipulation, responsiveness can be as important as sensing itself. By adopting direct-drive actuation throughout the system, the OmniHand 3 Ultra-M is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change. The platform also incorporates tactile sensing across nearly the entire hand. Each fingertip contains a miniature vision-based tactile sensor, while more than 300 three-dimensional tactile sensing points are distributed throughout the palm. Together, they provide information not only about where contact occurs, but how contact is evolving. The system is designed to estimate pressure distribution, shear forces, local deformation, slip tendencies, and other interaction dynamics that often remain invisible to conventional position-based control systems. According to AGILINKโs tests, individual sensors achieve force resolution of approximately 0.005 Nโroughly equivalent to detecting the weight of a sheet of paper resting on a fingertip. Spatial resolution reaches approximately 0.04 mm, while sensing density approaches 50,000 sensing points per square centimeter. OmniHand 3 Ultra-M recognizes feather texture through vision-based tactile sensing.AGILINK For dexterous robots, contact has traditionally been a largely hidden process. Ultra-M is designed to make that process more observable. Rather than simply detecting that contact has occurred, the system attempts to resolve where interaction is happening, how forces are distributed, whether instability is beginning to emerge, and how manipulation strategies should adapt in response. The balloon dog offered a glimpse of what contact intelligence can already accomplish. Ultra-M explores a different question: what capabilities may be required to push contact intelligence further? The Physical World Remains the Hardest Benchmark The significance of contact intelligence extends far beyond balloon animals. Many tasks that continue to resist automation involve unstable or deformable interaction: cable insertion, garment handling, flexible packaging, delicate assembly, connector mating, tool use, and household manipulation. These tasks are difficult not because robots cannot reach the correct location, but because maintaining stable interaction after contact begins remains extraordinarily hard. For decades, robotics achieved many of its successes by reducing uncertainty. Factories were engineered to make robotic motion predictable, repeatable, and highly structured. The physical world behaves differently. A growing share of robotics research is shifting toward interaction itselfโunderstanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable. Objects shift. Materials deform. Friction changes. Contact evolves. Real environments rarely follow scripts. Seen through that lens, the balloon dog was never really about the balloon dog. What attracted attention at ICRA was not simply a visually impressive demonstration, but what it revealed: intelligence in the physical world is ultimately measured through interaction. As motion generation continues to mature, a growing share of robotics research is shifting toward interaction itselfโunderstanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable. For robots moving beyond structured environments and into less predictable real-world settings, managing contact may become as important as motion itself.
Every season of Apple TVโs โFor All Mankindโ begins the same for prop master Jaime Mengual: A meeting with co-creators and showrunners Ben Nedivi and Matt Wolpert and producer Ben McGinnis to discuss one prop โ the Apple Newton. Apple products and their use in film and TV have long been a source of conversation. [โฆ]
Today at Apple's Worldwide Developers Conference, the company announced new features coming to the iPad with iPadOS 27 including optimizations such as apps launching up to 30 percent faster by intelligently preloading needed info, and more responsive switching between multiple apps. As with the new versions of Apple's other operating systems launching this year including [โฆ]
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Constantly being plugged into the news grind is mentally exhausting. Sometimes we just need to take a break, unwind, and do something fun. Thatโs why weโve built up a collection of distracting time-wasters for when we need a break from being obsessively online. We figured you might enjoy these harmless rabbit holes, mildly addictive browser [โฆ]
If all data centers permitted through 2025 come online, they will use more than all the electricity used by any one US state in 2024, except Texas.
Seller of the Sound Blaster Katana V2X doesn't consider the behavior a vulnerability.
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Anthropic called for a coordinated slowdown in AI development, warning that AI capabilities could advance faster than society can adapt.
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As AI systems grow larger, photonics is emerging as a faster, more efficient alternative to copper connections.
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New graduatesโ 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 fieldโs 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 donโt understand how things work under the hood, youโre 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. Todayโs engineers donโt 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.
Compact power banks have gotten a lot faster in the past year โ and itโs not just their USB-C charging speeds that have received a boost. The newest Qi2.2-certified models can wirelessly charge an iPhone 16 or later at up to 25W. Combine that with their ability to magnetically snap on via MagSafe, and youโve [โฆ]
This sponsored article is brought to you by Black & Veatch. The biggest challenge facing utilities today isnโt what it seems. Itโs not demand, even as load growth accelerates. Itโs not extreme weather, even as โmajor eventsโ become routine. Itโs 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. Theyโre 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 whatโs 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. Weโre 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. Itโs injected, stored and redirected in ways the system was never designed to manage. At scale, these challenges show up quickly โ 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 doesnโt hold up. You canโt effectively manage a system you canโt see. Plus, the most critical events are increasingly happening beyond the substation โ 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 outsetโnot 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 Powerโs 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 companyโs history, Hurricane Helene, Georgia Power deployed a rapid response team that utilized its โsmart gridโ and restored power to more than 1 million customers within days. A grid built to meet the future head-onโthatโs the result of bolder vision.
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With his new role as a Black Forest Labs advisor, Scorsese becomes one of the most prolific Hollywood directors who have voiced their support of AI.
The companies say the overhaul will deliver up to 2x faster AI and graphics performance across creative workflows
Layup Parts co-founder Zack Eakin has drawn on a motorsports background, and his experience working for Palmer Luckey and Elon Musk, to tackle making faster, cheaper, and better composites.