The Fourth GMIF2025 Innovation Summit (Global Memory Innovation Forum), co-hosted by Shenzhen Memory Industry Association and School of Integrated Circuits at Peking University, is scheduled to be held on September 25, 2025 in Renaissance Shenzhen Bay Hotel. GMIF2025, themed "AI Applications, Innovation Empowered", will bring together leading enterprises, technology experts, and industry leaders across the global memory industry chain to explore the evolution of storage technologies and emerging opportunities in the AI era.The Fourth GMIF2025 Innovation Summit is scheduled to be held on September 25, 2025. Credit:GMIFThe summit will feature executives and experts from School of Integrated Circuits at Peking University, Samsung Semiconductor, Sandisk, Solidigm, Silicon Motion, Maxio Technology, Intel, MediaTek, BIWIN Storage, InnoGrit, Arm, China Greatwall, Montage Technology, iFlytek, OKN Technology, GreaTech Substrates, and more than 19 other top global companies and universities. The keynotes will cover topics ranging from storage and memory technology roadmaps and market strategies to AI application practices and ecosystem collaboration, empowering innovation across AI scenarios from cloud to edge.GMIF2025 Innovation Summit Keynote Agenda - Part 1. Credit:GMIFGMIF2025 Innovation Summit Keynote Agenda - Part 2. Credit:GMIFGMIF2025 Innovation Summit Keynote Agenda - Part 3. Credit:GMIFIn the exhibition zone, over 27 exhibitors will present more than 200 innovations, spanning the entire storage ecosystem-including leading IDMs, controller vendors, solution providers, packaging and testing companies, platform providers, and AI terminal enterprises. Attendees will experience immersive interactions, gain first-hand insights into the latest industry trends, and witness the evolving global storage ecosystem landscape.Register now by clicking the link to secure your GMIF2025 pass and join global storage industry leaders in shaping the future of AI-driven innovation!For more information about GMIF, please visit.

Hybrid work is reshaping the way organizations train their people. The new reality requires a balance of efficiency and flexibility, as organizations strive to achieve easy access to learning and measurable results across groups dispersed in different locations.Flexible working models give employees greater control over where and how they learn. However, they also create challenges around consistency, engagement, and measuring results. The solution lies in a blended approach: combining online and in-person training with a corporate LMS.Training Issues in Hybrid WorkplacesConsistency remains a significant issue. Hybrid workforces include both in-office and remote employees, and both sides need the same access to learning for both to be fair and productive. Skewed opportunities can quickly ruin employee trust and retention.Disengagement is another issue. Online sessions that last extended periods tend to cause fatigue, making learning less effective.Measuring effectiveness is also a challenge. Virtually based teams complicate monitoring skill acquisition, and organizations are left solely reliant on analytics and online tests as a fair gauge of results. Without effective systems in place, organizations often offer programs that are difficult to measure, thereby limiting their long-term impact.Corporate LMS as a Central FrameworkTo address these needs, a corporate LMS can serve as the central hub for all training content. It combines live classes, self-paced resources, and microlearning modules into one blended system. This approach gives employees the flexibility to learn in ways that fit their schedules, while still keeping development aligned with company goals.A business LMS also incorporates gamification elements, interactive modules, and certification tools that retain motivation. Integration with workflow systems, such as Slack or Microsoft Teams, allows training within everyday workflows.Just as important, corporate LMS platforms provide visibility. Progress tracking makes it easier to spot skill gaps, measure outcomes, and consistently meet objectives across the workforce. But their true value goes further: connecting business strategy with individual performance. In doing so, they transform learning from a functional process into a growth driver—for employees and the organization.Opportunities in Hybrid Training ModelsDespite the challenges, hybrid work presents new opportunities for business training.One is scalability since corporate LMSs can reach employees in various locations without logistical issues. Another is customized learning paths, which are also on the rise. These paths enable employees to progress at their own pace and focus on specific skills.Dynamic access to course materials provides added flexibility. Users can access or navigate modules at will again, boosting retention. Data-driven insights through analytics add a new level of value, providing organizations with a precise understanding of training results.Beyond talent development, training has since been used to maintain organizational culture, and long-distance teams can maintain a sense of shared mission and identity.Skill-building in the Hybrid WorldHybrid work environments demand more than technical know-how. They call for a renewed focus on soft skills. Today's training programs strike that balance, offering flexibility and efficiency while nurturing the human abilities that keep teams connected.A corporate LMS plays a central role in making this possible. Online learning sessions, quizzes, and exercises bring interactivity into virtual spaces, recreating the energy of face-to-face sessions and helping counter "Zoom fatigue." Scenario-based modules and group projects encourage peer learning, turning abstract concepts into real-world behaviors.What makes this even more effective is the insight behind it. Data gathered through the LMS highlights where learners struggle and where they thrive, allowing trainers to adjust and keep people engaged.Best Practices in Hybrid Workforce TrainingA corporate LMS makes it possible to turn hybrid training into a consistent and engaging experience. The most effective programs share a few standard best practices: flexibility and modularity allow training to adapt to shifting workloads and varied schedules, so learning becomes part of the flow of work rather than a disruption. Peer-to-peer learning fosters collaboration and community, helping employees stay connected even when they're physically apart. Accessibility is prioritized, with mobile-friendly design and multilingual content ensuring every team member can access training without barriers. Certifications and recognition keep motivation high by rewarding progress and giving learners a sense of achievement.The Final OutlookThe hybrid model is here to stay, and training strategies must evolve alongside it. Centralized systems, like a corporate LMS, are now the baseline. What sets organizations apart is how they weave flexibility, personalization, and engagement into every learning experience.Corporate Training for Hybrid Workforces. Pexels

The semiconductor industry is undergoing a profound transformation, driven by the strategic integration of artificial intelligence. At SEMICON Taiwan 2025, experts from Microsoft, Advantech, and Nvidia shared insights on the sector’s rapid shift toward “Lights-Out” factories—fully automated, 24/7 operations powered by AI. This evolution is designed to tackle monumental challenges while unlocking new levels of efficiency and resilience.AI Models Evolve for Deeper InsightsAI development has accelerated dramatically in recent years. From early domain-specific models for tasks such as entity recognition or computer vision, AI has advanced into sophisticated multi-domain models that integrate diverse functionalities within a single framework. Notable advancements include Retrieval-Augmented Generation (RAG), which constrains responses for greater accuracy, and multimodal models that seamlessly combine text, audio, and visual inputs.A pivotal breakthrough has been the emergence of reasoning models, capable of delivering deeper insights for complex problems across scientific, mathematical, and manufacturing domains. These models provide richer and more comprehensive analyses compared with their predecessors. Beyond individual models, multi-agent systems are now automating entire workflows, enabling specialized AI agents to collaborate on intricate challenges. One example is Toyota’s “obeya agent,” which integrates multiple sub-agents to assist powertrain engineers in analyzing vast datasets.According to Saj Kumar K, Senior Director of Manufacturing (APAC) at Microsoft, these advanced AI models are already being applied to manufacturing. The Production Copilot functions as a factory agent, analyzing data to identify root causes of downtime and optimize maintenance schedules. The Quality Advisor leverages multimodal AI for visual inspection and defect analysis, potentially rendering traditional image labeling obsolete. AI is also being used to optimize fab scheduling and Automated Material Handling Systems (AMHS), improving overall cycle times. Furthermore, large language models (LLMs) are training humanoid robots to perform complex tasks through single-shot teaching.Advantech’s Edge AI Strategy Targets Speed and PrecisionSemiconductor manufacturing faces unique pressures, particularly from the massive scale of data generated. A single wafer inspection can produce up to seven petabytes of data, requiring enormous computational power and real-time processing. The transition from single-beam to multi-beam inspection demands a tenfold increase in computational speed today, with projections of a hundredfold increase by 2030. Combined with the need for instantaneous data transfer and ultra-low latency to prevent defects, these requirements are driving a shift away from traditional data centers toward powerful edge computing solutions. Meanwhile, global labor shortages and skill gaps are accelerating the need for advanced automation.Advantech is at the forefront of this transformation with a comprehensive Edge AI strategy focused on process optimization, inspection, and fab automation. "Inspection is the heart of semiconductor manufacturers," said Magic Pao, Vice President at Advantech. The company’s strategy is built on four pillars: high computing power, high throughput, low latency, and robust local storage for analytics.Advantech is spearheading this transformation with a comprehensive Edge AI strategy focused on process optimization, inspection, and fab automation. “Inspection is the heart of semiconductor manufacturing,” said Magic Pao, Vice President at Advantech. The company’s approach is built on four pillars: high computing power, high throughput, low latency, and robust local storage for analytics.To achieve this, Advantech embeds AI into every element of its solutions, including products such as SkyRack and the MIC-7000/7500 industrial servers with direct-liquid cooling, both carrying critical SEMI certifications for fab deployment. High-speed connectivity is ensured through Time-Sensitive Networking (TSN) and industrial-grade switches. AI-powered vision systems—incorporating smart cameras with Nvidia GPU acceleration and FPGA-based technology—are central to the precision required for inspection. Advantech also emphasizes ecosystem strength, partnering with Nvidia for advanced computing and Intel for reliable system integration.Nvidia’s Physical AI Drives Industrial AutonomyNvidia is at the forefront of a new industrial revolution, evolving from a chipmaker into an “infrastructure company” advancing Physical AI. This strategy seeks to create “AI factories” that power advanced applications. Andrew Liu, Senior Manager at Nvidia, explained that this vision rests on two pillars: advanced foundation models for robotics and sophisticated simulation environments.Nvidia is developing powerful Vision-Language-Action (VLA) models that enable robots to interpret high-level prompts and images, translating them into precise physical actions and learning from observation or single-shot teaching. To address the scarcity of real-world robotics data, Nvidia relies heavily on its Omniverse platform for digital twins and simulation. Omniverse provides physically accurate, photorealistic virtual environments where synthetic data can be generated and robots can be trained and validated. This approach helps alleviate labor shortages and enables rapid “factory cloning” to mitigate geographic supply chain risks. Nvidia’s integrated platform spans systems for training foundation models, servers for Omniverse digital twins, and embedded platforms for industrial robot deployment.Toward the Lights-Out FutureThe convergence of advanced AI models, strategic infrastructure, and targeted edge solutions is accelerating the path toward fully automated “Lights-Out” semiconductor manufacturing, promising significant gains in productivity, efficiency, and resilience that will redefine the industry’s future. At SEMICON Taiwan 2025, the AI Technology Zone (advised by the International Trade Administration) underscores this transformation by showcasing the full AI ecosystem—from chip manufacturing and IC design to dedicated hardware.SEMICON Taiwan 2025 Smart Manufacturing Forum. SEMI