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Yahoo
26-06-2025
- Yahoo
Developing ‘Space Valley' here in New Mexico with the Air Force Research Lab
ALBUQUERQUE, N.M. (KRQE) – With the exception of about six people, the rest of the human race spends the majority of its time enjoying the comforts of planet Earth. With that said, some of those comforts wouldn't be available without intricate workings happening far above our heads in space. This week, Chad Brummett is joined by Gabe Mounce, Guardian with the Space Force and Air Force Research Lab, to talk about achieving national security objectives as well as the economic objective of creating 'Space Valley' here in our state. Learn more about the Air Force Research Lab and Space Force Mexico Frontiers Digital Show is KRQE New 13's online exclusive web series, giving viewers a more detailed look into how the state is making waves in the Aerospace, Bio-science, Renewable Energy, Digital Media and Film, and Advanced Manufacturing communities. For more segments on prior stories, visit the New Mexico Frontiers page by clicking this link. Copyright 2025 Nexstar Media, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.
Forbes
25-06-2025
- Forbes
Why AI Benchmarking Needs A Rethink
Siobhan Hanna, SVP and General Manager, Welo Data. AI models are evolving at breakneck speed, but the methods for measuring their performance remain stagnant and the real-world consequences are significant. AI models that haven't been thoroughly tested could result in inaccurate conclusions, missed opportunities and costly errors. As AI adoption accelerates, it's becoming clear that current testing frameworks fall short in assessing real-world reasoning capabilities—pointing to an urgent need for improved evaluation standards. The Limitations Of Current AI Benchmarks Traditional AI benchmarks are structured to evaluate basic tasks, such as factual recall and fluency, which are easy to measure. However, advanced capabilities like causal reasoning—the ability to identify cause-and-effect relationships—are more difficult to assess systematically despite their importance in everyday AI applications. While most benchmarks are useful in gauging an AI model's capacity to process and reproduce information, they fail to assess whether the model is truly 'reasoning' or merely recognizing patterns from its training data. Understanding this distinction is crucial because, as S&P Global research notes, AI's reasoning ability directly impacts its applicability in tasks like problem-solving, decision making and generating insights that go beyond simple data retrieval. Additionally, the prompts used to evaluate the majority of AI capabilities are primarily in English, neglecting the diverse linguistic and cultural contexts of the global marketplace. This limitation is especially relevant as AI models are increasingly deployed around the world, where the demands for accuracy and consistency are vital across languages, as recently discussed by Stanford Assistant Professor Sanmi Koyejo. The Multilingual Blind Spot Most datasets used for evaluating causal reasoning are designed with English as the primary language, leaving models' abilities to reason about cause-and-effect relationships in other languages largely untested. Languages exhibit significant diversity in their grammatical structures, morphological systems and other linguistic features. If the models have not been sufficiently exposed to these differences, their ability to identify causality can be impacted. My company, Welo Data, conducted an independent benchmarking study across over 20 large language models (LLMs) from 10 different developers, revealing just how significant this issue is. The evaluation used story-based prompts that required contextual reasoning to test advanced causal reasoning capabilities across languages, including English, Spanish, Japanese, Korean, Turkish and Arabic. The results: LLMs often struggled with these complex causal inference tasks, especially when tested in languages other than English Many models showed inconsistent results when interpreting the same logical scenario in different languages. This inconsistency suggests that these models fail to account for linguistic differences in the way humans reason and convey causality. If AI is to be useful across diverse languages, benchmarking frameworks must evolve to test language model proficiency across linguistic boundaries. The Causal Reasoning Gap Causal reasoning is a crucial aspect of human intelligence, allowing us to understand what happens and why it happens. The study found that many AI models still struggle with this fundamental capability, particularly in multilingual contexts. While these models excel at pattern recognition, they often fail to effectively identify causal relationships in scenarios that require multistep reasoning. This gap is a significant limitation when deploying these models in real-world scenarios, such as healthcare, finance or customer support, where accurate and nuanced decision making is critical. Existing benchmarks often simplify cause-and-effect scenarios to tasks that rely on well-established datasets or pre-defined solutions, making it difficult to determine whether the model is truly reasoning or simply reproducing learned patterns. One promising direction involves using more complex, human-crafted testing scenarios designed to require genuine causal inference rather than pattern recognition. By incorporating such methods into evaluation frameworks, organizations can more clearly identify where models fall short—especially in multilingual or high-stakes applications—and take targeted steps to improve performance. A New Way Forward: Evolving AI Testing For The Future To truly understand how AI models will perform in functional settings, testing methodologies must assess the full range of cognitive abilities required in human-like reasoning. There are several ways companies can adopt better AI testing methodologies: • Implement a multilingual approach. AI models must be evaluated across multiple languages to ensure they can handle the complexities of global communication. This is especially important for companies operating in diverse markets or serving international customers. • Incorporate complex, real-world scenarios. Focus on evaluating AI through scenarios where multiple factors and variables interact, allowing for an accurate measurement of AI's capabilities. • Emphasize causal reasoning with novel data. Prioritize assessing causal reasoning abilities using previously unseen scenarios and examples that require genuine understanding of cause-and-effect relationships. This ensures the AI is demonstrating true causal inference rather than pattern matching or recalling information from its training data. Paving The Way: Building Better AI Existing benchmarks often do not accurately assess the full range of AI's capabilities, which can leave businesses with incomplete or misleading information about how their AI models perform, depending on which benchmarks are used and their specific objectives. As AI continues to evolve, so too must the methods used to evaluate its performance. By adopting a comprehensive, multilingual and real-world testing approach, we can ensure that AI models are not only capable but also reliable and equitable across diverse languages and contexts. It's time to rethink AI benchmarking—and, with that, the future of AI itself. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Business Journals
17-06-2025
- Business Journals
How refrigeration commissioning helps safeguard long-term performance and profitability in cold storage construction
As industrial refrigeration systems grow in complexity and cost, commissioning has emerged as an essential element for ensuring long-term efficiency, reliability and financial return. In industries where uninterrupted cooling is critical, such as cold storage, food and beverage, and pharmaceuticals, commissioning is not just beneficial — it is vital. Understanding the role of commissioning in industrial refrigeration Refrigeration systems account for the highest energy usage in cold storage facilities, with maintenance, refrigerant management and energy costs comprising a large portion of lifecycle operational expenses. Traditional questions during planning phases — such as installation cost and schedule — fail to address deeper operational concerns like long-term serviceability, lifecycle cost, regulatory impact and energy efficiency. These factors directly affect profitability and should be prioritized early in project planning. A well-structured commissioning plan addresses these gaps, beginning at design conception and continuing through installation, startup and the first year of operation. The purpose is to validate that systems are installed and functioning according to the design intent and operational requirements, ultimately supporting consistent performance and reduced lifecycle costs. Modernizing practices: The value of early and thorough commissioning Historically, commissioning in refrigeration projects was minimal, often limited to post-installation punch lists. Today, enhanced technologies — such as commissioning software, Virtual Design and Construction (VDC) services, other software tools such as energy modeling, BIM and EMV (Evaluation, Measurement and Verification) — make it easier to justify early commissioning efforts. These tools provide a baseline design comparison, visibility into system integration and performance, improve energy efficiency and help avoid costly failures. Forward-thinking developers and owners increasingly recognize that robust commissioning plans significantly reduce emergency service calls, product losses and operational disruptions, especially in mission-critical environments like data centers, food production and health care storage. Core phases of a refrigeration commissioning plan 1. Planning and design Objective: Ensure the refrigeration system is designed to be commissionable. Key actions: Form a Commissioning (Cx) team with defined roles and responsibilities. Develop foundational documents: Owner Requirements (OR), Basis of Design (BD) and Construction Documents (CDs). Align Cx efforts with budgets and schedules early in design. 2. Construction and installation Objective: Ensure critical systems are installed correctly to meet operational and energy performance targets. Key actions: Define clear responsibilities across contractors and subcontractors. Plan for pre-functional testing and system integration early in scheduling. Maintain quality control over major refrigeration components and controls infrastructure. 3. Startup and operation Objective: Validate that the system operates as intended and supports long-term operational goals. Key actions: Perform thorough system startup procedures and documentation. Facilitate training for facility operations teams. Conduct post-occupancy evaluations and adjustments during the first year. Bridging design and execution: The CxA advantage A Commissioning Authority (CxA) serves as a critical bridge between design and execution. Their early involvement helps identify scope gaps — such as those related to refrigeration controls or electrical systems — that might otherwise go unaddressed until late in the process, leading to change orders or costly delays. By engaging a CxA at the outset, stakeholders benefit from proactive risk management and coordinated delivery. Stakeholder collaboration and accountability Commissioning thrives on cross-disciplinary collaboration. Owners, design teams, refrigeration contractors, engineers and CxAs must each understand their role within the Cx process. When clearly defined, this shared accountability helps prevent scope omissions and reinforces alignment throughout the project lifecycle. Commissioning as a competitive advantage In markets where reliability is paramount, commissioning can differentiate a project. It ensures that refrigeration systems support operational goals, adhere to regulatory codes and reduce total cost of ownership. Flexible designs with redundancy, room conversions and future expansion capabilities also elevate a project's long-term value. As cold storage and industrial refrigeration markets expand, facilities equipped with thorough commissioning protocols will enjoy enhanced reliability, energy efficiency and cost control. In an environment where downtime translates to direct financial loss, commissioning is not a luxury — it is a necessity. Brinkmann Constructors is the employee-owned leader in the construction industry with a passion for finding creative solutions that save time, minimize costs and deliver the greatest value. With five offices and a project footprint in over 40 states, our reach spans nationwide with a vast portfolio of projects in multifamily, senior living, industrial, cold storage, retail, automotive and more. Mike Bildner has approximately 20 years of construction management experience, most notably 10 years of experience designing and managing refrigeration mechanical projects for a leading full-service mechanical contracting firm before joining Brinkmann. As the MEP (Mechanical, Electrical, and Plumbing) manager, Bildner is responsible for managing and leading MEP subcontractors throughout the project, developing the CPM schedule for all MEP installation activities, reviewing and approving material and equipment for MEP systems before installation, monitoring the installation and startup of the MEP systems, and commissioning of the project with the engineer and owner.
