#digital #cybersecurity #resilience #technology
In today’s hyper-connected world, digital infrastructure underpins national security, economies, and daily life. Resilience transforms risks into strengths.
Right now, molecules in the air are moving around you in chaotic and unpredictable ways. To make sense of such systems, physicists use a law known as the Boltzmann distribution, which, rather than describe exactly where each particle is, describes the chance of finding the system in any of its possible states. This allows them to make predictions about the whole system even though the individual particle motions are random. It’s like rolling a single die: Any one roll is unpredictable, but if you keep rolling it again and again, a pattern of probabilities will emerge.
Developed in the latter half of the 19th century by Ludwig Boltzmann, an Austrian physicist and mathematician, this Boltzmann distribution is used widely today to model systems in many fields, ranging from AI to economics, where it is called “multinomial logit.”
Now, economists have taken a deeper look at this universal law and come up with a surprising result: The Boltzmann distribution, their mathematical proof shows, is the only law that accurately describes unrelated, or uncoupled, systems.
AI companies are looking to spend trillions of dollars on data centers to power their increasingly resource-intensive AI models — an astronomical amount of money that could threaten the entire economy if the bet doesn’t pay off.
As the race to spend as much money as possible on AI infrastructure rages on, companies have become increasingly desperate to keep the cash flowing. Firms like OpenAI, Anthropic, and Oracle are exhausting existing debt markets — including junk debt, private credit, and asset-backed loans — in increasingly desperate moves, as Bloomberg reports, that are raising concerns among investors.
“The numbers are like nothing any of us who have been in this business for 25 years have seen,” Bank of America managing head of global credit Matt McQueen told Bloomberg. “You have to turn over all avenues to make this work.”
For decades, scientists have tried to answer a simple question: why be honest when deception is possible? Whether it is a peacock’s tail, a stag’s roar, or a human’s résumé, signals are means to influence others by transmitting information and advantages can be gained by cheating, for example by exaggeration. But if lying pays, why does communication not collapse?
The dominant theory for honest signals has long been the handicap principle, which claims that signals are honest because they are costly to produce. It argues that a peacock’s tail, for example, is an honest signal of a male’s condition or quality to potential mates because it is so costly to produce. Only high-quality birds could afford such a handicap, wasting resources growing it, demonstrating their superb quality to females, whereas poor quality males cannot afford such ornaments.
A new synthesis by Szabolcs Számadó, Dustin J. Penn and István Zachar (from the Budapest University of Technology and Economics, University of Veterinary Medicine Vienna and HUN-REN Centre for Ecological Research, respectively) challenges that logic. They argue that honesty does not depend on how costly or wasteful a signal is, but rather on the trade-offs between investments and benefits, faced by signalers.
In the consumer electronics playbook, custom silicon is the final step in the marathon: you use off-the-shelf components to prove a product, achieve mass scale and only then invest in proprietary chips to create differentiation, improve operations, and optimize margins.
In the modern satellite communications (SATCOM) ecosystem, this script has been flipped. For the industry’s frontrunners, custom silicon is the starting line where the bets are high, and the rewards are even higher, not a late-stage luxury. Building custom silicon is just a small piece of the big project when it comes to launching a satellite constellation and the fact there are very limited off the shelf options.
The shift toward custom silicon is no longer a theoretical debate; it is a proven competitive requirement. To monetize the massive capital expenditure of a constellation, market leaders are already driving aggressive custom silicon programs for beamformers and modems from the very beginning. The consensus is clear: while commercial off-the-shelf (COTS) and field-programmable gate arrays (FPGAs) served as useful stopgaps, they have become a strategic liability that compromises price and power efficiency. If the industry is to scale to the mass market, operators must commit to bespoke silicon programs now — or risk being permanently priced out of the sky by competitors who have already optimized their hardware for the unit economics of space.
By Chuck Brooks
#artificialintelligence #tech #government #quantum #innovation #federal #ai
By Chuck Brooks, president of Brooks Consulting International
In 2026, government technological innovation has reached a key turning point. After years of modernization plans, pilot projects and progressive acceptance, government leaders are increasingly incorporating artificial intelligence and quantum technologies directly into mission-critical capabilities. These technologies are becoming essential infrastructure for economic competitiveness, national security and scientific advancement rather than merely scholarly curiosity.
We are seeing a deliberate change in the federal landscape from isolated testing to the planned implementation of emerging technology across the whole government. This evolution represents not only technology momentum but also policy leadership, public-private collaboration and expanded industrial capability.
Despite the absence of a fully established regulatory framework or unified technological standard for industrial-and clinical-grade organoid biomanufacturing yet, substantial progress has been made toward building the technical and institutional infrastructure required for scalability and reproducibility. The Organisation for Economic Co-operation and Development (OECD) introduced the Good In Vitro Method Practices (GIVIMP)19, an international quality-assurance framework that defines laboratory quality systems, method qualification, reference controls, equipment calibration, and data integrity—principles that now potentially serve as quantitative benchmarks for process validation in organoid production. Complementing this, the NIH Standardized Organoid Modeling (SOM) Center was recently established to promote the development of organoid platforms that are reproducible, robust, and broadly accessible for translational biomedical and pharmaceutical research.
Expanding these standardization efforts, a recent publication introduced the Essential Guidelines for Manufacturing and Application of Organoids, delineating a systematic workflow encompassing cell sourcing, culture optimization, quality control, and biobanking logistics20. Their framework identifies organ-specific critical quality attributes (CQAs)—including growth-factor composition, morphological fidelity, and quantitative analytical metrics—and recommends standardized cryopreservation conditions (~100–200 organoids per vial) to enhance batch comparability. Likewise, a recent study established quantitative criteria for human intestinal organoid standardization, specifying cell-line provenance, minimum lineage composition thresholds (e.g., ≥30% enterocytes), and molecular marker expression profiles consistent with physiological differentiation21. Taken together, these coordinated initiatives—from international organizations to national agencies and individual laboratories—represent an emerging global framework toward reproducible, quality-controlled, and scalable organoid biomanufacturing, laying the groundwork for eventual regulatory convergence and clinical translation.
In response to these prevailing limitations and in alignment with global standardization trends, a range of engineering strategies has been developed, shifting the paradigm from organoid culture to organoid manufacturing by enabling reproducible and scalable organoid production. These strategies broadly focus on two goals: improving reproducibility by minimizing uncontrolled variation in the culture environment as well as by regulating intrinsic morphogenetic processes, and enhancing scalability by increasing productivity and throughput. To this end, recent advances can be categorized into three major domains: cellular engineering approaches that regulate morphogenetic processes through programmed cell organization; material-based strategies that establish defined and controllable environmental cues; and platform-or system-level innovations that enable high-throughput and automated workflows. Together, these innovative engineering advances mark aion toward more standardized, efficient production workflows.
‘People’, whether it’s for the benefit they bring to growth or the challenge they pose to the balance sheet, always feature on the Annual Meeting’s agenda.
This year, geopolitics dominated the headlines, but a quieter conversation about the investment in people persisted, reflecting a shared recognition that human well-being and human capital is the key to economic resilience.
NVIDIA CEO Jensen Huang discusses how artificial intelligence is advancing and handling competition with China on ‘Maria Bartiromo’s Wall Street.’ #fox #media #breakingnews #us #usa #new #news #breaking #foxbusiness #nvidia #ai #technology #tech #artificialintelligence #innovation #business #china #competition #jensenhuang #huang #ceo #economy #global #future.
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“Artificial intelligence is a so-called general-purpose technology that will fundamentally change our economic and social system,” said Andreas Raff.
How can fears about AI replacing jobs impact trust in democracy? This is what a recent study published in the Proceedings of the National Academy of Sciences hopes to address as a team of researchers from Germany and Austria investigated how the perception of AI replacing jobs could erode trust in political attitudes. This study has the potential to help scientists, legislators, and the public better understand the impact of AI beyond professional and personal markets, and how it could impact political societies.
For the study, the researchers conducted two separate surveys designed to obtain public perception regarding AI’s impact on the job market and how this could influence political attitudes. The first survey was comprised of 37,079 respondents with an average age of 48 years with 48 percent men and 52 percent women from 38 European countries and conducted from April to May 2021. The goal of this first survey was to ascertain perceptions of whether AI was considered as job-replacing or job-creating and how this impacts trust in political establishments. The second survey was comprised of 1,202 respondents from the United Kingdom with an average age of 47 years, and the goal of this second survey was to ascertain perceptions regarding identify causes for this relationship.
In the end, the researchers found that respondents who viewed AI more as job-replacing than job-creating also carried a perception of a lack of trust in political establishments. The researchers also found that respondents who were informed that AI will replace jobs caused them to have a distrust in political establishments.