#Intelligent Infrastructure

[goodoldbandit]

The Future is Now: 5G and Next-Generation Connectivity Powering Smart Innovation.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Explore how 5G networks are transforming IoT, smart cities, autonomous vehicles, and AR/VR experiences in this inspiring, in-depth guide that ignites conversation and fuels curiosity. Embracing a New Connectivity Era Igniting Curiosity and Inspiring Change The future is bright with 5G networks that spark new ideas and build…

[nando161mando]

OTD 1967 the Phoenix Program is Founded.

Initially a project of the CIA and subsequently CORDS, over five years it assassinated, kidnapped and tortured it's was through tens of thousands of non-combatant Vietnamese in an attempt to undermine the political infrastructure.

[probablyasocialecologist]

In some of the work you’ve done on data centers, you talk about how AI is presented as a climate solution, even as it requires resource-intensive data centers and computing to power it. What do you think is the risk of presenting resource-intensive AI as a solution to climate change? This is something that’s already happening, which is basically the imposition of ecological visions of a few, especially in the Global North with private interests, onto the rest of the world. We can see this operating not only when it comes to data centers, but also lithium extraction. Lithium is used for rechargeable batteries, which is a key component of so-called transition technologies, such as electric vehicles. If we want to design a transition towards new forms of energy or less carbon-intensive energies, we need to cooperate with these communities and a very important actor are the ones that are participating in the AI value chain. Companies have a big interest in hiding this value chain, in making sure that these communities don’t have a voice in the media, in regulatory discussions, and so on, because this is crucial for their business model and for the technical capacities that they need. There is a big interest in silencing them. This is why they don’t provide information about what they do. It’s also very important that as we discuss AI governance or regulation, we ask how we can incorporate these communities. If you look at what’s happening in Europe, there is upcoming regulation that is going to request that companies provide some transparency when it comes to energy use. But what about something more radical? What about incorporating these communities in the very governance of data centers? Or if we really want more just technologies for environmental transition, why not have a collective discussion, incorporating actors from different contexts and regions of the world to discuss what will be the most efficient — if you want to use that word — way of allocating data centers. In the case of indigenous communities in the Atacama Desert, water is sacred. They have a special relationship with water. One of the few words that they still have is uma, which stands for water. So how do we make sure that these companies respect the way these communities relate to the environment? It’s impossible to think of any kind of transition without considering and respecting the ecological visions of these groups. I don’t really believe in any technologically intensive form of transition that’s made by technocrats in the Global North and that ignores the effects that these infrastructures are having in the rest of the world and the visions of these communities.

[impact-newswire]

ISG to Assess Google Cloud Partner Ecosystem

Upcoming ISG Provider Lens™ reports will study service provider offerings for enabling AI, other workloads on the Google Cloud platform Press Release – February 06, 2025 – STAMFORD, Conn. – Information Services Group (ISG) (Nasdaq: III), a leading global technology research and advisory firm, has launched a research study evaluating service providers supporting enterprise use of Google Cloud…

[iammannyj]

What is DeepSeek and the $6 Million Side Project - Causing a Stir in the AI Industry

DeepSeek AI and the $6 Million Side Project That’s Causing a Stir in the Industry

So lets talk about DeepSeek. I couldn’t believe it was developed on just $6 million. In a world where AI projects routinely burn through hundreds of millions, here was this “side project” that was outperforming tech giants like ChatGPT and Gemini. A David and Goliath story? For movie fun it kind of reminds for of this scene from Tron Legacy, when Flynn dumped the ENCOM OS online for…

[jcmarchi]

Are AI-Powered Traffic Cameras Watching You Drive?

New Post has been published on https://thedigitalinsider.com/are-ai-powered-traffic-cameras-watching-you-drive/

Are AI-Powered Traffic Cameras Watching You Drive?

Artificial intelligence (AI) is everywhere today. While that’s an exciting prospect to some, it’s an uncomfortable thought for others. Applications like AI-powered traffic cameras are particularly controversial. As their name suggests, they analyze footage of vehicles on the road with machine vision.

They’re typically a law enforcement measure — police may use them to catch distracted drivers or other violations, like a car with no passengers using a carpool lane. However, they can also simply monitor traffic patterns to inform broader smart city operations. In all cases, though, they raise possibilities and questions about ethics in equal measure.

How Common Are AI Traffic Cameras Today?

While the idea of an AI-powered traffic camera is still relatively new, they’re already in use in several places. Nearly half of U.K. police forces have implemented them to enforce seatbelt and texting-while-driving regulations. U.S. law enforcement is starting to follow suit, with North Carolina catching nine times as many phone violations after installing AI cameras.

Fixed cameras aren’t the only use case in action today, either. Some transportation departments have begun experimenting with machine vision systems inside public vehicles like buses. At least four cities in the U.S. have implemented such a solution to detect cars illegally parked in bus lanes.

With so many local governments using this technology, it’s safe to say it will likely grow in the future. Machine learning will become increasingly reliable over time, and early tests could lead to further adoption if they show meaningful improvements.

Rising smart city investments could also drive further expansion. Governments across the globe are betting hard on this technology. China aims to build 500 smart cities, and India plans to test these technologies in at least 100 cities. As that happens, more drivers may encounter AI cameras on their daily commutes.

Benefits of Using AI in Traffic Cameras

AI traffic cameras are growing for a reason. The innovation offers a few critical advantages for public agencies and private citizens.

Safety Improvements

The most obvious upside to these cameras is they can make roads safer. Distracted driving is dangerous — it led to the deaths of 3,308 people in 2022 alone — but it’s hard to catch. Algorithms can recognize drivers on their phones more easily than highway patrol officers can, helping enforce laws prohibiting these reckless behaviors.

Early signs are promising. The U.K. and U.S. police forces that have started using such cameras have seen massive upticks in tickets given to distracted drivers or those not wearing seatbelts. As law enforcement cracks down on such actions, it’ll incentivize people to drive safer to avoid the penalties.

AI can also work faster than other methods, like red light cameras. Because it automates the analysis and ticketing process, it avoids lengthy manual workflows. As a result, the penalty arrives soon after the violation, which makes it a more effective deterrent than a delayed reaction. Automation also means areas with smaller police forces can still enjoy such benefits.

Streamlined Traffic

AI-powered traffic cameras can minimize congestion on busy roads. The areas using them to catch illegally parked cars are a prime example. Enforcing bus lane regulations ensures public vehicles can stop where they should, avoiding delays or disruptions to traffic in other lanes.

Automating tickets for seatbelt and distracted driving violations has a similar effect. Pulling someone over can disrupt other cars on the road, especially in a busy area. By taking a picture of license plates and sending the driver a bill instead, police departments can ensure safer streets without adding to the chaos of everyday traffic.

Non-law-enforcement cameras could take this advantage further. Machine vision systems throughout a city could recognize congestion and update map services accordingly, rerouting people around busy areas to prevent lengthy delays. Considering how the average U.S. driver spent 42 hours in traffic in 2023, any such improvement is a welcome change.

Downsides of AI Traffic Monitoring

While the benefits of AI traffic cameras are worth noting, they’re not a perfect solution. The technology also carries some substantial potential downsides.

False Positives and Errors

The correctness of AI may raise some concerns. While it tends to be more accurate than people in repetitive, data-heavy tasks, it can still make mistakes. Consequently, removing human oversight from the equation could lead to innocent people receiving fines.

A software bug could cause machine vision algorithms to misidentify images. Cybercriminals could make such instances more likely through data poisoning attacks. While people could likely dispute their tickets and clear their name, it would take a long, difficult process to do so, counteracting some of the technology’s efficiency benefits.

False positives are a related concern. Algorithms can produce high false positive rates, leading to more charges against innocent people, which carries racial implications in many contexts. Because data biases can remain hidden until it’s too late, AI in government applications can exacerbate problems with racial or gender discrimination in the legal system.

Privacy Issues

The biggest controversy around AI-powered traffic cameras is a familiar one — privacy. As more cities install these systems, they record pictures of a larger number of drivers. So much data in one place raises big questions about surveillance and the security of sensitive details like license plate numbers and drivers’ faces.

Many AI camera solutions don’t save images unless they determine it’s an instance of a violation. Even so, their operation would mean the solutions could store hundreds — if not thousands — of images of people on the road. Concerns about government surveillance aside, all that information is a tempting target for cybercriminals.

U.S. government agencies suffered 32,211 cybersecurity incidents in 2023 alone. Cybercriminals are already targeting public organizations and critical infrastructure, so it’s understandable why some people may be concerned that such groups would gather even more data on citizens. A data breach in a single AI camera system could affect many who wouldn’t have otherwise consented to giving away their data.

What the Future Could Hold

Given the controversy, it may take a while for automated traffic cameras to become a global standard. Stories of false positives and concerns over cybersecurity issues may delay some projects. Ultimately, though, that’s a good thing — attention to these challenges will lead to necessary development and regulation to ensure the rollout does more good than harm.

Strict data access policies and cybersecurity monitoring will be crucial to justify widespread adoption. Similarly, government organizations using these tools should verify the development of their machine-learning models to check for and prevent problems like bias. Regulations like the recent EU Artificial Intelligence Act have already provided a legislative precedent for such qualifications.

AI Traffic Cameras Bring Both Promise and Controversy

AI-powered traffic cameras may still be new, but they deserve attention. Both the promises and pitfalls of the technology need greater attention as more governments seek to implement them. Higher awareness of the possibilities and challenges surrounding this innovation can foster safer development for a secure and efficient road network in the future.

[peterbordes]

Groq’s CEO Jonathan Ross on why AI’s next big shift isn’t about Nvidia

“Right now, we’re in the printing press era of AI, the very beginning,” says Groq Founder & CEO Jonathan Ross.

[sharon-ai]

In the fast-paced digital world of today, businesses and industries are relying more than ever on efficient and scalable solutions for managing their infrastructure. One of the most promising innovations is the combination of cloud computing infrastructure and artificial intelligence (AI). Together, they are transforming how we handle infrastructure asset management and optimizing industries such as energy. This blog will explain how these technologies work together and the impacts that they are having across a wide array of sectors, including in the USA energy markets 

What is Cloud Computing Infrastructure?

Cloud computing infrastructure refers to the systems that serve as the basis for delivering cloud services. This may include virtual servers, storage systems, networking capabilities, and databases. They are offered to businesses and consumers through the internet. Instead of having to hold expensive physical infrastructure, a company can use cloud infrastructure solutions to scale its operations very efficiently.

Businesses do not have to be concerned about the capital expenses for on-premise infrastructure maintenance and upgrades. With cloud service provision, organizations are enabled with tools for the management of cloud infrastructure on digital resources to watch out for them seamlessly. With cloud computing in the energy industry, companies run their simulations and manage the output without having to buy large, expensive hardware.

Changing the Face of Computing Infrastructure

The Role of AI Technology

Artificial intelligence (AI refers to computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. AI is revolutionizing how infrastructure is managed by enabling automated systems to make decisions based on data and real-time analysis.

In the energy industry, for instance, AI technology can be used in analyzing large volumes of data to optimize operations, predict failures, and recommend improvements. Such is vital in industries like US energy markets, where AI solutions can predict market fluctuations, optimize energy distribution, and increase overall efficiency.

Artificial Intelligence in Cloud Computing

When artificial intelligence in cloud computing is introduced, the possibilities expand exponentially. AI-based cloud solutions allow businesses to benefit from AI capabilities without requiring investment in dedicated hardware or a specialized team. For example, companies can utilize AI cloud computing benefits to analyze large data sets stored in the cloud, forecast energy demands, or predict equipment failures in real time.

AI and Cloud Computing for Asset Management

Among the benefits that come from the integration of AI with cloud computing infrastructure is infrastructure asset management. It is complex managing equipment, machines, or even digital services. AI algorithms help in optimizing this by identifying patterns and predicting when assets will require maintenance or replacement.

[envision-faqs]

Why TMS Systems Are Essential in Today’s Supply Chain

As supply chains grow increasingly complex, a manual or legacy approach to managing transportation simply cannot keep pace. Here’s why companies are rapidly adopting TMS software systems:

Cost Optimization

By automating carrier rate comparison and load planning, a transportation management system can reduce shipping costs by up to 10–20%. Advanced analytics in TMS transportation tools highlight inefficiencies and recommend better carrier partnerships or mode selections.

Real-Time Visibility

Customers expect real-time updates. A TMS system offers end-to-end shipment visibility, from origin to destination. Transportation management systems integrate with GPS and telematics to provide tracking, estimated arrival times, and proactive alerts.

Compliance and Documentation

Transportation management software simplifies regulatory compliance, including customs documentation, hazardous goods declarations, and international shipping regulations. Automation reduces human error and saves time on paperwork.

Customer Experience

Faster, more reliable deliveries lead to better customer satisfaction. A TMS transportation platform empowers shippers to meet delivery windows, minimize delays, and provide accurate delivery ETAs.

Key Components of a Transportation Management System

Understanding the components of a TMS system reveals why it is such a game-changer. Here are the core modules found in most transportation management systems:

Planning and Optimization

This module helps logistics managers determine the most cost-effective and time-efficient shipping routes. The transportation management system considers constraints like delivery windows, load types, fuel costs, and driver hours.

Execution

The execution module facilitates tendering shipments to carriers, printing necessary shipping documents, and dispatching loads. With integrations to ERP, WMS, and carrier systems, TMS transportation processes become streamlined.

Tracking and Visibility

This real-time dashboard tracks every shipment across all modes—road, rail, air, and ocean. Transportation management software ensures all stakeholders are on the same page with current location, status, and exceptions.

Freight Settlement and Audit

Accurate invoicing is critical. This module in the TMS system automates invoice matching, dispute management, and auditing, ensuring companies are billed correctly for the services rendered.

Analytics and Reporting

Data is power. Transportation management systems come with dashboards that offer performance metrics, such as on-time delivery, cost per mile, carrier scorecards, and more.

[pttedu]

Smart Building Technologies: AI & IoT Solutions for Modern Construction

Explore how smart building technologies are revolutionizing the construction industry in Philadelphia. With AI in construction management and IoT in building automation, companies are enhancing efficiency, safety, and sustainability in every phase of a project. Discover how AI-driven construction safety solutions are helping to prevent accidents and improve decision-making on-site. From high-rise developments to smart infrastructure, these innovations are shaping the future of urban building. Learn how adopting smart building technologies can future-proof your projects and ensure compliance with modern standards in one of America's most competitive construction markets.

[rmzcorp]

AI-Driven Infrastructure by RMZ Digital to Power Enterprise Growth & Sustainability

RMZ Digital Infrastructure Partners is harnessing the power of AI to develop future-ready infrastructure that empowers enterprises with intelligent, efficient, and sustainable solutions. From enhanced operational efficiency to scalable growth, discover how RMZ is shaping the AI-driven era.

Read more:

[thisisgraeme]

Intelligence-as-a-Service, But Not As You Know It (What If Intelligence Could Be Shared—Without Being Lost?)

What is Intelligence-as-a-Service? There’s a shift happening in how we think about learning, knowledge, and the systems we use to carry it. Not just in classrooms.Everywhere. You can feel it, can’t you? Teachers, facilitators, coaches, leaders — so many of us have spent years crafting approaches, frameworks, tools that work. Ways of knowing. Ways of guiding others. Ways of translating chaos…

[impact-newswire]

TotalEnergies to Collaborate with Mistral AI

TotalEnergies to Collaborate with Mistral AI to Increase the Application of Artificial Intelligence in its Multi-Energy Strategy TotalEnergies and the French company Mistral AI are joining forces to extend the use of AI in improving TotalEnergies’ activities’ performance, especially in low-carbon energies. The partners are to set up a joint innovation lab focused on artificial…

[sniperindia]

Unleashing Business Potential with Microsoft’s Latest Cloud and AI Solutions

In 2025, Microsoft continues to lead the digital transformation wave with ground-breaking innovations in cloud computing, AI, and business productivity tools. For businesses in India aiming to stay competitive, adopting Microsoft’s latest technologies is no longer optional but essential. As a Microsoft Gold Partner and trusted authorised reseller in India, Sniper Systems and Solutions helps organizations harness the full power of Microsoft’s cutting-edge offerings.

Why Microsoft’s Cloud and AI Solutions Are a Game-Changer for Businesses

Microsoft’s cloud platform, Azure, has rapidly become a cornerstone for companies looking to scale efficiently and innovate faster. With Azure, businesses gain access to powerful tools for data storage, application development, AI integration, and advanced analytics — all supported by Microsoft’s global network of secure data centers.

Key benefits include:

Scalability & Flexibility: Azure adapts to your business growth, allowing you to pay only for what you use.

Advanced AI & Machine Learning: Embedded AI services help automate operations and gain valuable insights.

Security & Compliance: Microsoft invests billions annually in cybersecurity, ensuring your data is protected at the highest level.

Alongside Azure, Microsoft 365 continues to redefine workplace productivity. Integrating familiar Office apps with Teams, OneDrive, and SharePoint, it enables seamless collaboration whether teams are remote or in-office.

Trending Microsoft Technologies Shaping Business in 2025

Microsoft AI Copilot Microsoft recently unveiled AI Copilot features embedded across its suite — from Word to Excel to Teams. This AI assistant dramatically boosts productivity by automating tasks, generating content, and offering real-time data insights.

Azure OpenAI Service Businesses can now leverage Azure’s integration with OpenAI models to build intelligent applications, chatbots, and customer service solutions that provide enhanced user experiences.

Microsoft Viva Focusing on employee experience, Microsoft Viva combines communication, knowledge, learning, and insights to create engaged, informed, and resilient teams.

Power Platform Innovations With Power Apps and Power Automate enhancements, organizations can build custom apps and workflows without heavy coding, accelerating digital transformation.

Why Partner with Sniper Systems, a Microsoft Gold Partner in India?

While Microsoft provides world-class technology, deploying and optimizing these solutions requires expertise. As a Microsoft Gold Partner and authorised reseller in India, Sniper Systems offers:

Certified Expertise: Their Microsoft-certified professionals ensure smooth deployment of Microsoft Azure, 365, and AI tools tailored to your business.

End-to-End IT Infrastructure Solutions: From network setup to cloud migration and ongoing support, Sniper Systems delivers scalable, secure infrastructure.

Customized Software Solutions: They tailor Microsoft products to fit your unique operational needs, maximizing ROI.

Local Presence with Global Standards: Based in India, Sniper Systems understands regional business challenges and compliance, providing localized support.

IT Infrastructure and Software Solutions: The Backbone of Digital Success

Effective adoption of Microsoft’s technologies depends on solid IT infrastructure. Sniper Systems excels as a comprehensive IT infrastructure solution provider and software solution provider, ensuring your environment supports cloud workloads, hybrid setups, and secure remote access.

This includes:

Network optimization for cloud efficiency

Security frameworks aligned with Microsoft’s best practices

Data backup and disaster recovery solutions

Training and change management to empower your teams

Conclusion

Microsoft’s ongoing innovations in cloud computing, AI, and collaboration tools are transforming how businesses operate and compete globally. To fully realize these benefits, partnering with a knowledgeable and certified Microsoft Gold Partner in India like Sniper Systems and Solutions is crucial.

Sniper Systems helps Indian businesses navigate the digital landscape with tailored, scalable IT infrastructure and Microsoft software solutions — empowering growth, agility, and long-term success.

[jcmarchi]

Open-Source Platform Cuts Costs for Running AI - Technology Org

New Post has been published on https://thedigitalinsider.com/open-source-platform-cuts-costs-for-running-ai-technology-org/

Open-Source Platform Cuts Costs for Running AI - Technology Org

Cornell researchers have released a new, open-source platform called Cascade that can run artificial intelligence (AI) models in a way that slashes expenses and energy costs while dramatically improving performance.

Artificial intelligence hardware – artistic interpretation. Image credit: Alius Noreika, created with AI Image Creator

Cascade is designed for settings like smart traffic intersections, medical diagnostics, equipment servicing using augmented reality, digital agriculture, smart power grids and automatic product inspection during manufacturing – situations where AI models must react within a fraction of a second. It is already in use by College of Veterinary Medicine researchers monitoring cows for risk of mastitis.

With the rise of AI, many companies are eager to leverage new capabilities but worried about the associated computing costs and the risks of sharing private data with AI companies or sending sensitive information into the cloud – far-off servers accessed through the internet.

Also, today’s AI models are slow, limiting their use in settings where data must be transferred back and forth or the model is controlling an automated system. 

A team led by Ken Birman, professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science, combined several innovations to address these concerns.

Birman partnered with Weijia Song, a senior research associate, to develop an edge computing system they named Cascade. Edge computing is an approach that places the computation and data storage closer to the sources of data, protecting sensitive information. Song’s “zero copy” edge computing design minimizes data movement.

The AI models don’t have to wait to fetch data when reacting to an event, which enables faster responses, the researchers said.

“Cascade enables users to put machine learning and data fusion really close to the edge of the internet, so artificially intelligent actions can occur instantly,” Birman said. “This contrasts with standard cloud computing approaches, where the frequent movement of data from machine to machine forces those same AIs to wait, resulting in long delays perceptible to the user.” 

Cascade is giving impressive results, with most programs running two to 10 times faster than cloud-based applications, and some computer vision tasks speeding up by factors of 20 or more. Larger AI models see the most benefit.

Moreover, the approach is easy to use: “Cascade often requires no changes at all to the AI software,” Birman said.

Alicia Yang, a doctoral student in the field of computer science, was one of several student researchers in the effort. She developed Navigator, a memory manager and task scheduler for AI workflows that further boosts performance.

“Navigator really pays off when a number of applications need to share expensive hardware,” Yang said. “Compared to cloud-based approaches, Navigator accomplishes the same work in less time and uses the hardware far more efficiently.”

In CVM, Parminder Basran, associate research professor of medical oncology in the Department of Clinical Sciences, and Matthias Wieland, Ph.D. ’21, assistant professor in the Department of Population Medicine and Diagnostic Sciences, are using Cascade to monitor dairy cows for signs of increased mastitis – a common infection in the mammary gland that reduces milk production.

By imaging the udders of thousands of cows during each milking session and comparing the new photos to those from past milkings, an AI model running on Cascade identifies dry skin, open lesions, rough teat ends and other changes that may signal disease. If early symptoms are detected, cows could be subjected to a medicinal rinse at the milking station to potentially head off a full-blown infection.

Thiago Garrett, a visiting researcher from the University of Oslo, used Cascade to build a prototype “smart traffic intersection.”

His solution tracks crowded settings packed with people, cars, bicycles and other objects, anticipates possible collisions and warns of risks – within milliseconds after images are captured. When he ran the same AI model on a cloud computing infrastructure, it took seconds to sense possible accidents, far too late to sound a warning.

With the new open-source release, Birman’s group hopes other researchers will explore possible uses for Cascade, making AI applications more widely accessible.

“Our goal is to see it used,” Birman said. “Our Cornell effort is supported by the government and many companies. This open-source release will allow the public to benefit from what we created.”

Source: Cornell University

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