The company is currently executing on a computer-assisted maintenance management system (CMMS) and material requirements planning (MRP) in the factory, with the aim to optimize and automate production and maintenance processes. “We’ll soon begin implementing Business Central, which will mark an important step in updating and modernizing our business management system, further strengthening our operations and improving decision-making at a corporate level,” he says.   These projects affect all departments of Guzmán Minerals since they cover everything from the manufacturing and processing of products to their sale. Furthermore, tools such as CMMS, MRP, and soon Business Central, directly impact areas such as production, logistics, maintenance, and administration, ensuring more efficient and connected management throughout the company.   In the factory area, the company is in the final stages of configuring and testing a CMMS and MRP to improve planning and maintenance. Plus, the CRM is already in full use by the sales team, facilitating the management of clients and opportunities. As for Business Central, the project is in its early stages and represents a major transition from its current Dynamics NAV 2017 ERP solution.  source

By Rachel Scharf ( January 28, 2025, 11:26 PM EST) — A California federal jury returned a verdict Tuesday clearing the head of a now-defunct LED screen distribution company of allegations that he lied to his Korean manufacturing partner about efforts to repay millions of dollars worth of mounting debts…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

A German startup plans to jumpstart European EVs with an AI-powered brain. Sphere Energy built the system to simulate battery behaviour. The company then predicts a power source’s lifetime in numerous scenarios, from driving styles to temperatures on the road.  According to Sphere, the insights shrink the battery testing cycle by at least a year. Developing a car, meanwhile, could be completed “at least” twice as quickly. Sphere envisions endless benefits: manufacturers will save millions, car prices will plummet, and innovations will increase at exponential rates. The 💜 of EU tech The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now! The startup’s co-founder, Lukas Lutz, said the plans are unprecedented. “Nobody right now — not even Tesla — can accurately estimate the lifetime of their battery,” Lutz told TNW. “This is something that will be really groundbreaking.” A lifeline for European EVs? Sphere unveiled the project last month at the IBM Research Lab in Switzerland. In a futuristic facility overlooking Lake Zurich, the startup introduced an AI brain called Batty. Batty was initially trained on years of testing data from over 1,000 batteries. Car manufacturers also mix in their own information. The system then simulates a specific battery’s life under various conditions. Customers can test the effects of speeding down motorways and crawling around mountains, applying fast and slow chargers, driving in searing summers and freezing winters. Every aspect will impact the battery’s degradation. The system’s power derives from the transformer architecture — the founding stone of today’s large language models (LLMs). But Sphere’s approach doesn’t rely solely on text. The startup extends the model’s scope by integrating time-series data. As a result, the system can simulate a battery’s behaviour over years. The approach adds a new twist to the LLM paradigm. While a chatbot predicts the next best word, Batty will predict the next best data point. Car companies have been impressed by the results. According to Sphere, the majority of European manufacturers have already used the tech. Batty could provide a vital boost to the continent’s EV makers, which are rapidly losing market share to their Chinese rivals. “Battery development is a huge pain for them — and it shouldn’t be,” Lutz said. “We really want to take away the burden.” But batteries are just the start of Sphere’s ambitions. The company envisions simulating endless energy applications, from electric boats to grid storage. Alongside IBM, the startup is also exploring new levels of simulating batteries. “With these foundation AI models, we understand atomic level behaviour intrinsically,” Lutz said. “But we want to go sub-atomic — with quantum.” source

Despite predictions about its demise, the enterprise data center is alive and well, as strategic on-prem use cases remain and data center tech modernizes. source

The European Commission relied heavily on the PCLOB in the agreement, although it functions only as a supplementary supervisory mechanism. Weakening the PCLOB would endanger the stability of the TADPF, even if short-term vacancies do not immediately cause the framework to collapse. Max Schrems criticizes the EU Commission for relying on uncertain monitoring mechanisms and wishful thinking instead of on stable legal protection. A possible end within 45 days The TADPF is in danger of collapsing under the Trump presidency, as Trump signed an executive order on Monday, Jan. 20, 2025. It provides for all decisions made by his predecessor Biden on national security to be reviewed and possibly repealed within 45 days. This could overturn the basis of the agreement in a matter of weeks. This would result in illegal data transfers between the EU and the US, said Schrems, who also criticizes the dependence of EU companies on such a politically unstable system. If the US government repeals key elements of the TADPF, it could become illegal for EU companies to use US cloud services. Although data transfers will remain legal for now until the agreement is formally repealed, Max Schrems warns that companies should urgently develop contingency plans such as “Host in Europe” to prepare for potential legal uncertainties. source

By Katie Buehler ( January 31, 2025, 7:03 PM EST) — A case about broadband subsidies will give the U.S. Supreme Court the chance to revive a long-dormant separation of powers principle that attorneys say could upend regulations in numerous industries and trigger a power shift that would make last term’s shake-up of federal agency authority pale in comparison. And a majority of the court already appears to support its resurrection…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

In 1981, American physicist and Nobel Laureate, Richard Feynman, gave a lecture at the Massachusetts Institute of Technology (MIT) near Boston, in which he outlined a revolutionary idea. Feynman suggested that the strange physics of quantum mechanics could be used to perform calculations. The field of quantum computing was born. In the 40-plus years since, it has become an intensive area of research in computer science. Despite years of frantic development, physicists have not yet built practical quantum computers that are well suited for everyday use and normal conditions (for example, many quantum computers operate at very low temperatures). Questions and uncertainties still remain about the best ways to reach this milestone. What exactly is quantum computing, and how close are we to seeing them enter wide use? Let’s first look at classical computing, the type of computing we rely on today, like the laptop I am using to write this piece. Classical computers process information using combinations of “bits”, their smallest units of data. These bits have values of either 0 or 1. Everything you do on your computer, from writing emails to browsing the web, is made possible by processing combinations of these bits in strings of zeroes and ones. Quantum computers, on the other hand, use quantum bits, or qubits. Unlike classical bits, qubits don’t just represent 0 or 1. Thanks to a property called quantum superposition, qubits can be in multiple states simultaneously. This means a qubit can be 0, 1, or both at the same time. This is what gives quantum computers the ability to process massive amounts of data and information simultaneously. Imagine being able to explore every possible solution to a problem all at once, instead of once at a time. It would allow you to navigate your way through a maze by simultaneously trying all possible paths at the same time to find the right one. Quantum computers are therefore incredibly fast at finding optimal solutions, such as identifying the shortest path, the quickest way. Different qubits can be linked via the quantum phenomenon of entanglement. Jurik Peter / Shutterstock Think about the extremely complex problem of rescheduling airline flights after a delay or an unexpected incident. This happens with regularity in the real world, but the solutions applied may not be the best or optimal ones. In order to work out the optimal responses, standard computers would need to consider, one by one, all possible combinations of moving, rerouting, delaying, cancelling or grouping, flights. Every day there are more than 45,000 flights, organised by over 500 airlines, connecting more than 4,000 airports. This problem would take years to solve for a classical computer. On the other hand, a quantum computer would be able to try all these possibilities at once and let the best configuration organically emerge. Qubits also have a physical property known as entanglement. When qubits are entangled, the state of one qubit can depend on the state of another, no matter how far apart they are. This is something that, again, has no counterpart in classical computing. Entanglement allows quantum computers to solve certain problems exponentially faster than traditional computers can. Read more: Brain implants, agentic AI and answers on dark matter: what to expect from science in 2025 – podcast A common question is whether quantum computers will completely replace classical computers or not. The short answer is no, at least not in the foreseeable future. Quantum computers are incredibly powerful for solving specific problems – such as simulating the interactions between different molecules, finding the best solution from many options or dealing with encryption and decryption. However, they are not suited to every type of task. Classical computers process one calculation at a time in a linear sequence, and they follow algorithms (sets of mathematical rules for carrying out particular computing tasks) designed for use with classical bits that are either 0 or 1. This makes them extremely predictable, robust and less prone to errors than quantum machines. For everyday computing needs such as word processing or browsing the internet, classical computers will continue to play a dominant role. There are at least two reasons for that. The first one is practical. Building a quantum computer that can run reliable calculations is extremely difficult. The quantum world is incredibly volatile, and qubits are easily disturbed by things in their environment, such as interference from electromagnetic radiation, which makes them prone to errors. The second reason lies in the inherent uncertainty in dealing with qubits. Because qubits are in superposition (are neither a 0 or 1) they are not as predictable as the bits used in classical computing. Physicists therefore describe qubits and their calculations in terms of probabilities. This means that the same problem, using the same quantum algorithm, run multiple times on the same quantum computer might return a different solution each time. To address this uncertainty, quantum algorithms are typically run multiple times. The results are then analysed statistically to determine the most likely solution. This approach allows researchers to extract meaningful information from the inherently probabilistic quantum computations. From a commercial point of view, the development of quantum computing is still in its early stages, but the landscape is very diverse with lots of new companies appearing every year. It is fascinating to see that in addition to big, established companies like IBM and Google, new ones are joining, such as IQM, Pasqal and startups such as Alice and Bob. They are all working on making quantum computers more reliable, scalable and accessible. A range of companies are working towards building practical quantum computers. ANNA SZILAGYI / EPA IMAGES In the past, manufacturers have drawn attention to the number of qubits in their quantum computers, as a measure of how powerful the machine is. Manufacturers are increasingly prioritising ways to correct the errors that quantum computers are prone to. This shift is crucial for developing large-scale, fault-tolerant quantum computers, as these techniques are essential for improving their usability. Google’s latest quantum chip,

Last year’s Evolve conference showcased practical use cases for AI, an area where Trace3 has developed significant expertise. Key themes included how AI can help companies grow; charting the AI landscape for the future; and assessing the impact of AI on specific industries, from financial services to healthcare. Expert presentations as well as insights from many of Trace3’s partners, including Microsoft, Cisco, and Dell Technologies created a compelling, forward-thinking deep dive on cutting-edge IT solutions and emerging technologies. “We recognized that GenAI would have a tectonic effect on the technology and digital landscape, so Trace3 made a sizeable investment to develop our expertise,” said Rich Fennessy, Trace3 Chief Executive Officer. “We’re a company well versed in innovation and forward thinking, and our DNA is centered around helping our clients and vision and realized tomorrow.” The University of Texas/Texas A&M Investment Management Company (UTIMCO) engaged Trace3 to implement Microsoft CoPilot and develop an AI governance framework to help manage risk. During the conference, CISO David Gahagan discussed how these moves have helped UTIMCO manage and secure confidential information. Sean Sims, assistant vice president for digital incubator and advanced analytics at insurance company Unum, talked about how his organization leveraged Trace3’s expertise to improve its use of AI. Unum takes a domain-centered approach to harnessing AI that is tailored to the business by having domain experts work with AI experts to align to strategic goals and operational realities. Currently, Unum is working on incorporating AI client and sales management. Trace3’s comprehensive approach to AI Over its 20+ year history, Trace3 has built a deep well of expertise around major enablers, from cybersecurity to cloud computing and data analytics. The firm focuses on bringing emerging trends to bear and helping clients evolve. As part of that effort, Trace3 has entire divisions dedicated to research and innovation in areas including data, management consulting, and more. During the past several years, that specialized expertise has been extended to AI, helping organizations realize the benefits of AI and launch effective AI projects. Its comprehensive approach encompasses AI strategy, governance and risk, architecture and operations, and solutions. “As time goes on, AI will keep getting smarter and better. This matters because AI is helping humanity reach new levels of what we can achieve. AI isn’t just a tool — it’s what’s driving us to rethink what we believe is possible,” said Fennessy. To learn more, visit https://www.trace3.com source

By Allison Grande ( January 31, 2025, 9:57 PM EST) — A LinkedIn subscriber has dropped his recently filed proposed class action accusing the company of unlawfully sharing the sensitive contents of paid users’ private messages with third parties to train generative artificial intelligence models, a practice that the company has asserted it “never did.”… Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

On January 29, Google announced the wide launch of its Meridian open-source marketing mix modeling (MMM) solution, available through GitHub. Cue a flurry of LinkedIn takes speculating about the future of the marketing measurement and optimization (MMO) solutions market. By providing an open-source Bayesian option, Google brings the industry-standard methodology to the masses (Meta’s Robyn uses a different regression process). But will Google’s free solution replace what many brands invest thousands of dollars in? No. Though there are many good things about this release, and it potentially broadens the market for MMM, it’s not a “one size fits all” solution. Since its initial Meridian announcement in 2024, Google has clarified the availability of additional capabilities, including budget optimization, scenario planning, the ability to customize parameters according to prior results, and the inclusion of non-media and control variables. Perhaps most importantly, Meridian is distinct in its native inclusion of Google search query volume data and reach/frequency data for YouTube. Along with the code release, Google also launched a partner program with major agency and MMO partners throughout the world to help brands with implementation. Meridian Is Best For Companies With Strong Data Science And Storytelling Chops To DIY MMM, you need strong in-house data engineering, data science, and visualization resources. Digital agencies adept at campaign measurement but lacking an MMM solution will benefit from using Meridian to create solutions on behalf of clients. But for brands, the calculus is a little more complicated. Consider going it alone with an open-source solution such as Meridian if: You spend a lot of your marketing budget with Google. The ability to incorporate Google Query Volume and YouTube reach/frequency data increases in value as you spend more on these channels. If you are a digital brand that spends a majority or plurality on these channels, Meridian has significant value. Your in-house data team cooks. You have a team of data scientists who can clean, transform, load, and migrate sales, marketing, economic, and media data quickly and consistently. Accurate measurement requires extensive data expertise and resources. On top of that, a culture of trust between marketing and other departments (e.g., finance, sales, or operations) is necessary; you will need skilled data storytellers to translate results into compelling messaging. You have a history of maintaining geo-level marketing, sales, and competitor data. MMM is a wonderful tool for finding the causal relationships between variables, but its impact is dulled without enough data. MMM becomes more accurate and more useful with more granular data, particularly at the geo level. In addition to knowing what you were spending on media in Phoenix during the second week of August 2023, do you also have data related to non-media programs (such as discounts, bulk promotions, new packaging) running in that market concurrently? What about promotions that your competitors were running in the same market? You have experience with incrementality testing and ROI calculation. MMM results sometimes contradict what is found when measuring campaign-level ROI and return on ad spend. In general, this is because campaign-level measurement can’t account for what the result would have been if no campaign had been run. It’s for this reason that Forrester recommends a layered measurement framework, with campaign-level measurement and incrementality testing feeding into MMM. Meridian enables marketers to customize parameters to reflect results of incrementality tests and campaign ROI measures, but this is only useful if your brand has experience with these tests. Brands Without A Measurement-Driven Marketing Culture Will Struggle To Leverage Meridian In addition to the criteria above, a major challenge faced by many marketing departments is earning trust from other parts of the organization that their efforts are driving real business results. While Meridian’s code is open source and the underlying calculations are all laid out, the materials are not executive-friendly. Though this release answers some of the questions we raised when Meridian was announced in 2024, the education aspect remains unaddressed. If you are looking for someone to communicate the benefits, strengths, and, yes, weaknesses of MMM, Google’s materials will not help you. In addition to customized modeling and consulting, many MMO providers provide guidance for communicating findings and gaining cross-org trust. Later this year, I’ll be publishing reports on data requirements for marketing measurement, best practices for MMM, and building strong measurement teams. If you want to discuss your MMM or other marketing measurement initiatives, schedule a guidance session here. source