#Energy-Efficient Computing Technologies

[chemicalmarketwatch-sp]

Exploring the Growing $21.3 Billion Data Center Liquid Cooling Market: Trends and Opportunities

In an era marked by rapid digital expansion, data centers have become essential infrastructures supporting the growing demands for data processing and storage. However, these facilities face a significant challenge: maintaining optimal operating temperatures for their equipment. Traditional air-cooling methods are becoming increasingly inadequate as server densities rise and heat generation intensifies. Liquid cooling is emerging as a transformative solution that addresses these challenges and is set to redefine the cooling landscape for data centers.

What is Liquid Cooling?

Liquid cooling systems utilize liquids to transfer heat away from critical components within data centers. Unlike conventional air cooling, which relies on air to dissipate heat, liquid cooling is much more efficient. By circulating a cooling fluid—commonly water or specialized refrigerants—through heat exchangers and directly to the heat sources, data centers can maintain lower temperatures, improving overall performance.

Market Growth and Trends

The data centre liquid cooling market  is on an impressive growth trajectory. According to industry analysis, this market is projected to grow USD 21.3 billion by 2030, achieving a remarkable compound annual growth rate (CAGR) of 27.6%. This upward trend is fueled by several key factors, including the increasing demand for high-performance computing (HPC), advancements in artificial intelligence (AI), and a growing emphasis on energy-efficient operations.

Key Factors Driving Adoption

1. Rising Heat Density

The trend toward higher power density in server configurations poses a significant challenge for cooling systems. With modern servers generating more heat than ever, traditional air cooling methods are struggling to keep pace. Liquid cooling effectively addresses this issue, enabling higher density server deployments without sacrificing efficiency.

2. Energy Efficiency Improvements

A standout advantage of liquid cooling systems is their energy efficiency. Studies indicate that these systems can reduce energy consumption by up to 50% compared to air cooling. This not only lowers operational costs for data center operators but also supports sustainability initiatives aimed at reducing energy consumption and carbon emissions.

3. Space Efficiency

Data center operators often grapple with limited space, making it crucial to optimize cooling solutions. Liquid cooling systems typically require less physical space than air-cooled alternatives. This efficiency allows operators to enhance server capacity and performance without the need for additional physical expansion.

4. Technological Innovations

The development of advanced cooling technologies, such as direct-to-chip cooling and immersion cooling, is further propelling the effectiveness of liquid cooling solutions. Direct-to-chip cooling channels coolant directly to the components generating heat, while immersion cooling involves submerging entire server racks in non-conductive liquids, both of which push thermal management to new heights.

Overcoming Challenges

While the benefits of liquid cooling are compelling, the transition to this technology presents certain challenges. Initial installation costs can be significant, and some operators may be hesitant due to concerns regarding complexity and ongoing maintenance. However, as liquid cooling technology advances and adoption rates increase, it is expected that costs will decrease, making it a more accessible option for a wider range of data center operators.

The Competitive Landscape

The data center liquid cooling market is home to several key players, including established companies like Schneider Electric, Vertiv, and Asetek, as well as innovative startups committed to developing cutting-edge thermal management solutions. These organizations are actively investing in research and development to refine the performance and reliability of liquid cooling systems, ensuring they meet the evolving needs of data center operators.

Download PDF Brochure : 

The outlook for the data center liquid cooling market is promising. As organizations prioritize energy efficiency and sustainability in their operations, liquid cooling is likely to become a standard practice. The integration of AI and machine learning into cooling systems will further enhance performance, enabling dynamic adjustments based on real-time thermal demands.

The evolution of liquid cooling in data centers represents a crucial shift toward more efficient, sustainable, and high-performing computing environments. As the demand for advanced cooling solutions rises in response to technological advancements, liquid cooling is not merely an option—it is an essential element of the future data center landscape. By embracing this innovative approach, organizations can gain a significant competitive advantage in an increasingly digital world.

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[goodoldbandit]

Green Bytes: The Journey Towards Sustainable IT & Green Computing.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in This post explores sustainable IT practices, energy-efficient computing, e-waste reduction, and green digital infrastructures to spark dialogue and inspire action.
 A New Era for IT Embracing Change for a Greener Tomorrow In our fast-moving world, technology drives our lives in every way. We see power in every byte we use and…

[link-layer]

The AI Efficiency Paradox

Understanding Jevons Paradox

Jevons Paradox occurs when technological progress increases the efficiency of resource use, but the rate of consumption of that resource rises due to increasing demand. The core mechanism is simple: as efficiency improves, costs decrease, making the resource more accessible and creating new use cases, ultimately driving up total consumption.

In the 1860s, economist William Stanley Jevons made a counterintuitive observation about coal consumption during the Industrial Revolution. Despite significant improvements in steam engine efficiency, coal consumption increased rather than decreased. This phenomenon, later termed "Jevons Paradox," suggests that technological improvements in resource efficiency often lead to increased consumption rather than conservation. Today, as artificial intelligence transforms our world, we're witnessing a similar pattern that raises important questions about technology, resource usage, and societal impact.

 The AI Parallel

Artificial intelligence presents a modern manifestation of Jevons Paradox across multiple dimensions:

 Computational Resources

While AI models have become more efficient in terms of performance per computation, the total demand for computational resources has skyrocketed. Each improvement in AI efficiency enables more complex applications, larger models, and broader deployment, leading to greater overall energy consumption and hardware demands.

 Human Labor and Productivity

AI tools promise to make human work more efficient, potentially reducing the labor needed for specific tasks. However, this efficiency often creates new demands and opportunities for human work rather than reducing overall labor requirements. For instance, while AI might automate certain aspects of programming, it has simultaneously increased the complexity and scope of software development projects.

 Data Usage

As AI systems become more efficient at processing data, organizations collect and analyze ever-larger datasets. The improved efficiency in data processing doesn't lead to using less data – instead, it drives an exponential increase in data collection and storage needs.

 Implications for Society and Technology

The AI manifestation of Jevons Paradox has several important implications:

 Resource Consumption

Despite improvements in AI model efficiency, the total environmental impact of AI systems continues to grow. This raises important questions about sustainability and the need for renewable energy sources to power AI infrastructure.

 Economic Effects

The paradox suggests that AI efficiency gains might not lead to reduced resource consumption or costs at a macro level, but rather to expanded applications and new markets. This has significant implications for business planning and economic policy.

 Social Impact

As AI makes certain tasks more efficient, it doesn't necessarily reduce human workload but often transforms it, creating new roles and responsibilities. This challenges the simple narrative of AI leading to widespread job displacement.

 Addressing the Paradox

Understanding the AI efficiency paradox is crucial for developing effective policies and strategies:

Resource Planning: Organizations need to plan for increased resource demands rather than assuming efficiency improvements will reduce consumption.

Sustainability Initiatives: The paradox highlights the importance of coupling AI development with renewable energy and sustainable computing initiatives.

Policy Considerations: Regulators and policymakers should consider Jevons Paradox when developing AI governance frameworks and resource management policies.

 Looking Forward

As AI technology continues to evolve, the implications of Jevons Paradox become increasingly relevant. The challenge lies not in preventing the paradox – which may be inherent to technological progress – but in managing its effects responsibly. This requires:

- Investment in sustainable infrastructure to support growing AI resource demands

- Development of policies that account for rebound effects in resource consumption

- Careful consideration of how efficiency improvements might reshape rather than reduce resource usage

The parallels between historical patterns of resource consumption and modern AI development offer valuable lessons for technology leaders, policymakers, and society at large. As we continue to push the boundaries of AI capability, understanding and accounting for Jevons Paradox will be crucial for sustainable and responsible technological progress.

[pebblegalaxy]

Reimagining the Energy Landscape: AI's Growing Hunger for Computing Power #BlogchatterA2Z

Reimagining the Energy Landscape: AI's Growing Hunger for Computing Power #BlogchatterA2Z #AIdevelopment #energyConsumption #DataCenterInfrastructure #ArmHoldings #energyEfficiency #SustainableTechnology #RenewableEnergy #EdgeComputing #RegulatoryMeasures

Navigating the Energy Conundrum: AI’s Growing Hunger for Computing Power In the ever-expanding realm of artificial intelligence (AI), the voracious appetite for computing power threatens to outpace our energy sources, sparking urgent calls for a shift in approach. According to Rene Haas, Chief Executive Officer of Arm Holdings Plc, by the year 2030, data centers worldwide are projected to…

[danieldavidreitberg]

Artificial Intelligence: Energy Consumption Beyond Bitcoin and Blockchain Mining

As the world becomes increasingly digital, the demand for computational power is skyrocketing. Daniel Reitberg dives into the energy-intensive world of artificial intelligence, shedding light on its voracious appetite for energy.

The Digital Revolution's Dark Secret

While blockchain and Bitcoin mining have been criticized for their energy consumption, artificial intelligence operates on an entirely different scale. Daniel Reitberg reveals that the energy usage of AI dwarfs these digital counterparts.

AI: The Energy Gobbler

The rapid growth of AI, particularly deep learning models, has led to enormous energy requirements. Training large language models, for example, can consume as much energy as a small town.

The Carbon Footprint of AI

AI's colossal energy consumption translates to a significant carbon footprint. As the world grapples with climate change, understanding and mitigating AI's environmental impact are crucial.

Why AI Consumes So Much Energy

Daniel Reitberg explains that AI's energy consumption arises from its complex algorithms and massive datasets. High-performance computing, power-hungry GPUs, and energy-intensive data centers all play a role.

Green AI: A New Imperative

Green AI is emerging as a critical field. It focuses on developing energy-efficient algorithms, hardware, and data center operations without compromising AI's capabilities.

Energy-Efficient Algorithms

Optimizing algorithms to achieve the same results with less computation is a central focus. Daniel Reitberg explores how AI researchers are making strides in energy-efficient model design.

AI Hardware Innovations

Developing energy-efficient hardware is another facet of green AI. New, specialized chips and GPUs are being designed to minimize power consumption.

Eco-Friendly Data Centers

Transitioning to renewable energy sources and implementing energy-efficient cooling systems in data centers is imperative. This not only reduces energy consumption but also lowers the carbon footprint of AI.

Decentralized AI

Edge computing is changing the game by decentralizing AI processing. This reduces energy consumption by bringing AI closer to the data source.

AI's Role in Sustainability

Ironically, AI can also play a pivotal role in sustainability. Daniel Reitberg discusses how AI is used to optimize resource allocation, reduce waste, and enhance energy efficiency in various industries.

Responsibility and Regulation

As AI's energy consumption comes under scrutiny, governments and organizations are considering regulations to promote eco-friendly AI practices.

Balancing Innovation and Sustainability

Daniel Reitberg's exploration of AI's energy consumption paints a picture of a powerful technology with an Achilles' heel. As AI becomes increasingly integrated into our lives, striking a balance between innovation and sustainability is essential. AI's potential is boundless, but so are the responsibilities to our planet.

[electronalytics]

Distribution Feeder Automation Market Business Research, Types and Applications, Demand by 2032

Market Overview: The Distribution Feeder Automation Market refers to the market for advanced technologies and systems that automate the monitoring, control, and management of distribution feeders within an electrical distribution network. Distribution feeder automation improves the efficiency, reliability, and resiliency of power distribution by utilizing sensors, communication networks, and automation software to monitor and control power flows, fault detection, and restoration. These solutions enhance the performance of distribution feeders and enable utilities to deliver electricity more effectively.

Feeder Automation Market is projected to be worth USD 7.85 Billion by 2030, registering a CAGR of 8.2% during the forecast period (2022 - 2030)

Demand: The demand for distribution feeder automation is driven by several factors, including:

Distribution feeder automation solutions help utilities improve the reliability and resiliency of their distribution networks. By automating fault detection, isolation, and restoration, these systems minimize outage durations and enhance the overall performance of the grid, ensuring a more reliable power supply for customers.

Distribution feeder automation systems streamline operations by reducing manual interventions, optimizing power flow, and enhancing network monitoring capabilities. These solutions enable utilities to manage distribution feeders more efficiently, reduce costs, and improve the overall operational performance of their networks.

The increasing integration of renewable energy sources, such as solar and wind power, into the distribution grid presents operational challenges. Distribution feeder automation helps utilities manage the intermittent nature of renewables, optimize power flow, and ensure grid stability, facilitating the integration of clean energy sources.

Latest technological developments, key factors, and challenges in the Distribution Feeder Automation Market:

Latest Technological Developments:

Intelligent Sensors and IoT Integration: Distribution feeder automation is leveraging intelligent sensors and Internet of Things (IoT) integration to monitor real-time data from various points along the distribution feeders. These sensors provide insights into voltage levels, current flow, fault detection, and other parameters, enabling quicker fault localization and resolution.

Advanced Communication Protocols: Modern distribution feeder automation systems are adopting advanced communication protocols like for seamless data exchange between field devices and control centers. This facilitates real-time monitoring, remote control, and efficient data transmission.

Decentralized Control and Edge Computing: Distribution feeder automation systems are moving toward decentralized control and edge computing. This allows decision-making and control to occur closer to field devices, reducing latency and enhancing responsiveness.

Distributed Energy Resource (DER) Management: With the integration of distributed energy resources like solar panels, wind turbines, and energy storage systems, feeder automation systems are being developed to manage these resources effectively, ensuring grid stability and optimal energy distribution.

Advanced Analytics and AI: Distribution feeder automation is incorporating advanced analytics and artificial intelligence to analyze data from various sources. AI algorithms can predict and prevent potential faults, optimize energy flows, and enhance overall feeder performance.

Key Factors:

Reliability Enhancement: Distribution feeder automation improves the reliability of electricity distribution by enabling quicker fault detection, isolation, and restoration. This minimizes outage durations and enhances overall grid reliability.

Efficient Grid Management: Feeder automation allows utilities to manage the distribution grid more efficiently. Load balancing, voltage regulation, and fault management can be automated, leading to optimized energy delivery.

Integration of Renewable Energy: As the penetration of renewable energy sources increases, distribution feeder automation becomes crucial for managing the intermittent nature of these resources and maintaining grid stability.

Grid Resilience and Outage Management: Feeder automation systems enhance grid resilience by providing real-time data on grid conditions and faults. This facilitates faster response and restoration during outages, minimizing customer impact.

Challenges:

Interoperability: Integrating various devices and protocols into a cohesive feeder automation system can be challenging due to the need for interoperability between different vendors and technologies.

Cybersecurity: With increased connectivity and data exchange, distribution feeder automation systems face cybersecurity threats. Ensuring the security of these systems is paramount to prevent unauthorized access and data breaches.

Cost and Infrastructure: Implementing distribution feeder automation can involve significant upfront costs, including hardware, software, and training. Retrofitting existing infrastructure for automation may also pose challenges.

Complexity of Data Management: Feeder automation generates vast amounts of data that need to be effectively managed, analyzed, and acted upon. Handling this complexity can be demanding.

Change Management: Transitioning from manual to automated processes requires change management efforts to train personnel, address resistance, and ensure smooth integration.

Maintenance and Upgrades: Ensuring the proper functioning of feeder automation systems over time requires regular maintenance and potential upgrades to keep up with technology advancements.

Distribution feeder automation is at the forefront of modernizing electricity distribution networks. While it offers significant benefits in terms of reliability, efficiency, and resilience, addressing technical challenges and ensuring a smooth transition is crucial for successful implementation.

By visiting our website or contacting us directly, you can explore the availability of specific reports related to this market. These reports often require a purchase or subscription, but we provide comprehensive and in-depth information that can be valuable for businesses, investors, and individuals interested in this market.

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Market Segmentations:

Global Distribution Feeder Automation Market: By Company

• ABB

• Eaton

• Grid Solutions

• Schneider Electric

• Siemens

• Advanced Control Systems

• Atlantic City Electric

• CG

• G&W Electric

• Kalkitech

• Kyland Technology

• Moxa

• S&C Electric Company

• Schweitzer Engineering Laboratories (SEL)

Global Distribution Feeder Automation Market: By Type

• Fault Location

• Isolation

• Service Restoration

• Automatic Transfer Scheme

Global Distribution Feeder Automation Market: By Application

• Industrial

• Commercial

• Residential

Global Distribution Feeder Automation Market: Regional Analysis

The regional analysis of the global Distribution Feeder Automation market provides insights into the market's performance across different regions of the world. The analysis is based on recent and future trends and includes market forecast for the prediction period. The countries covered in the regional analysis of the Distribution Feeder Automation market report are as follows:

North America: The North America region includes the U.S., Canada, and Mexico. The U.S. is the largest market for Distribution Feeder Automation in this region, followed by Canada and Mexico. The market growth in this region is primarily driven by the presence of key market players and the increasing demand for the product.

Europe: The Europe region includes Germany, France, U.K., Russia, Italy, Spain, Turkey, Netherlands, Switzerland, Belgium, and Rest of Europe. Germany is the largest market for Distribution Feeder Automation in this region, followed by the U.K. and France. The market growth in this region is driven by the increasing demand for the product in the automotive and aerospace sectors.

Asia-Pacific: The Asia-Pacific region includes Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, China, Japan, India, South Korea, and Rest of Asia-Pacific. China is the largest market for Distribution Feeder Automation in this region, followed by Japan and India. The market growth in this region is driven by the increasing adoption of the product in various end-use industries, such as automotive, aerospace, and construction.

Middle East and Africa: The Middle East and Africa region includes Saudi Arabia, U.A.E, South Africa, Egypt, Israel, and Rest of Middle East and Africa. The market growth in this region is driven by the increasing demand for the product in the aerospace and defense sectors.

South America: The South America region includes Argentina, Brazil, and Rest of South America. Brazil is the largest market for Distribution Feeder Automation in this region, followed by Argentina. The market growth in this region is primarily driven by the increasing demand for the product in the automotive sector.

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[ecrivainsolitaire]

A summary of the Chinese AI situation, for the uninitiated.

These are scores on different tests that are designed to see how accurate a Large Language Model is in different areas of knowledge. As you know, OpenAI is partners with Microsoft, so these are the scores for ChatGPT and Copilot. DeepSeek is the Chinese model that got released a week ago. The rest are open source models, which means everyone is free to use them as they please, including the average Tumblr user. You can run them from the servers of the companies that made them for a subscription, or you can download them to install locally on your own computer. However, the computer requirements so far are so high that only a few people currently have the machines at home required to run it.

Yes, this is why AI uses so much electricity. As with any technology, the early models are highly inefficient. Think how a Ford T needed a long chimney to get rid of a ton of black smoke, which was unused petrol. Over the next hundred years combustion engines have become much more efficient, but they still waste a lot of energy, which is why we need to move towards renewable electricity and sustainable battery technology. But that's a topic for another day.

As you can see from the scores, are around the same accuracy. These tests are in constant evolution as well: as soon as they start becoming obsolete, new ones are released to adjust for a more complicated benchmark. The new models are trained using different machine learning techniques, and in theory, the goal is to make them faster and more efficient so they can operate with less power, much like modern cars use way less energy and produce far less pollution than the Ford T.

However, computing power requirements kept scaling up, so you're either tied to the subscription or forced to pay for a latest gen PC, which is why NVIDIA, AMD, Intel and all the other chip companies were investing hard on much more powerful GPUs and NPUs. For now all we need to know about those is that they're expensive, use a lot of electricity, and are required to operate the bots at superhuman speed (literally, all those clickbait posts about how AI was secretly 150 Indian men in a trenchcoat were nonsense).

Because the chip companies have been working hard on making big, bulky, powerful chips with massive fans that are up to the task, their stock value was skyrocketing, and because of that, everyone started to use AI as a marketing trend. See, marketing people are not smart, and they don't understand computers. Furthermore, marketing people think you're stupid, and because of their biased frame of reference, they think you're two snores short of brain-dead. The entire point of their existence is to turn tall tales into capital. So they don't know or care about what AI is or what it's useful for. They just saw Number Go Up for the AI companies and decided "AI is a magic cow we can milk forever". Sometimes it's not even AI, they just use old software and rebrand it, much like convection ovens became air fryers.

Well, now we're up to date. So what did DepSeek release that did a 9/11 on NVIDIA stock prices and popped the AI bubble?

Oh, I would not want to be an OpenAI investor right now either. A token is basically one Unicode character (it's more complicated than that but you can google that on your own time). That cost means you could input the entire works of Stephen King for under a dollar. Yes, including electricity costs. DeepSeek has jumped from a Ford T to a Subaru in terms of pollution and water use.

The issue here is not only input cost, though; all that data needs to be available live, in the RAM; this is why you need powerful, expensive chips in order to-

Holy shit.

I'm not going to detail all the numbers but I'm going to focus on the chip required: an RTX 3090. This is a gaming GPU that came out as the top of the line, the stuff South Korean LoL players buy…

Or they did, in September 2020. We're currently two generations ahead, on the RTX 5090.

What this is telling all those people who just sold their high-end gaming rig to be able to afford a machine that can run the latest ChatGPT locally, is that the person who bought it from them can run something basically just as powerful on their old one.

Which means that all those GPUs and NPUs that are being made, and all those deals Microsoft signed to have control of the AI market, have just lost a lot of their pulling power.

Well, I mean, the ChatGPT subscription is 20 bucks a month, surely the Chinese are charging a fortune for-

Oh. So it's free for everyone and you can use it or modify it however you want, no subscription, no unpayable electric bill, no handing Microsoft all of your private data, you can just run it on a relatively inexpensive PC. You could probably even run it on a phone in a couple years.

Oh, if only China had massive phone manufacturers that have a foot in the market everywhere except the US because the president had a tantrum eight years ago.

So… yeah, China just destabilised the global economy with a torrent file.

[optiblog]

OPTİVİSER - GOLD

Welcome to Optiviser.com, your ultimate guide to navigating the complex world of electronics in 2024. As technology continues to evolve at a rapid pace, finding the right devices that suit your needs can be overwhelming. In this blog post, we’ll harness the power of AI to help you make informed choices with our comprehensive electronics comparison. We’ll take a closer look at the top smart home devices that are revolutionizing how we live and work, providing convenience and efficiency like never before. Additionally, we’ll offer expert laptop recommendations tailored to various lifestyles and budgets, ensuring you find the perfect match for your daily tasks. 

AI-powered Electronics Comparison

In today's fast-paced technological landscape, making informed choices about electronics can be overwhelming. An AI-powered Electronics Comparison tool can help streamline this process by providing insights that cater to specific user needs. These advanced tools utilize algorithms that analyze product features, specifications, and user reviews, resulting in a tailored recommendation for buyers.

As we delve into the world of consumer technology, it's important to highlight the Top Smart Home Devices 2024. From smart thermostats to security cameras, these devices are becoming essential for modern households. They not only enhance convenience but also significantly improve energy efficiency and home safety.

For those looking for a new computer to enhance productivity or gaming experiences, consider checking out the latest Laptop Recommendations. Many platforms, including Optiviser.com, provide comprehensive comparisons and insights that can help consumers choose the best laptop suited to their needs, whether it’s for work, study, or leisure.

Top Smart Home Devices 2024

As we move into 2024, the landscape of home automation is evolving rapidly, showcasing an array of innovative gadgets designed to enhance comfort and convenience. In this era of AI-powered Electronics Comparison, selecting the right devices can be overwhelming, but we've highlighted some of the best Top Smart Home Devices 2024 that stand out for their functionality and user experience.

One of the most impressive innovations for this year is the latest AI-powered home assistant. These devices not only respond to voice commands but also learn your preferences over time, allowing them to offer personalized suggestions and perform tasks proactively. Imagine a device that can monitor your schedule and automatically adjust your home's temperature and lighting accordingly!

Moreover, security remains a top priority in smart homes. The Top Smart Home Devices 2024 include state-of-the-art security cameras and smart locks that provide robust protection while ensuring ease of access. With features like remote monitoring through your smartphone or integration with smart doorbells, keeping your home safe has never been easier. For more details on the comparisons and recommendations of these devices, you can check out Optiviser.com.

Laptop Recommendation

In today's fast-paced world, choosing the right laptop can be a daunting task. With numerous options available in the market, it's essential to consider various factors such as performance, portability, and price. At Optiviser.com, we provide an insightful guide to help you navigate through the vast array of choices. To streamline your decision-making process, we have developed an AI-powered Electronics Comparison tool that allows you to compare specifications and features of different laptops side by side.

This year, we have seen a surge in innovative laptops that cater to diverse needs. Whether for gaming, business, or everyday use, our top recommendations include models that excel in battery life, processing power, and display quality. For instance, consider the latest models from top brands, which have integrated the best features of Top Smart Home Devices 2024 trends, ensuring seamless connectivity and advanced functionalities.

Additionally, if you're looking for a laptop that can handle multitasking effortlessly, we suggest models equipped with the latest processors and ample RAM. Our detailed Laptop Recommendation section on Optiviser.com includes expert reviews and user feedback to help you choose a laptop that not only fits your budget but also meets your specific requirements.

[jcmarchi]

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

New Post has been published on https://thedigitalinsider.com/photonic-processor-could-enable-ultrafast-ai-computations-with-extreme-energy-efficiency/

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.

Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

The optical device was able to complete the key computations for a machine-learning classification task in less than half a nanosecond while achieving more than 92 percent accuracy — performance that is on par with traditional hardware.

The chip, composed of interconnected modules that form an optical neural network, is fabricated using commercial foundry processes, which could enable the scaling of the technology and its integration into electronics.

In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.

“There are a lot of cases where how well the model performs isn’t the only thing that matters, but also how fast you can get an answer. Now that we have an end-to-end system that can run a neural network in optics, at a nanosecond time scale, we can start thinking at a higher level about applications and algorithms,” says Saumil Bandyopadhyay ’17, MEng ’18, PhD ’23, a visiting scientist in the Quantum Photonics and AI Group within the Research Laboratory of Electronics (RLE) and a postdoc at NTT Research, Inc., who is the lead author of a paper on the new chip.

Bandyopadhyay is joined on the paper by Alexander Sludds ’18, MEng ’19, PhD ’23; Nicholas Harris PhD ’17; Darius Bunandar PhD ’19; Stefan Krastanov, a former RLE research scientist who is now an assistant professor at the University of Massachusetts at Amherst; Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research; Matthew Streshinsky, a former silicon photonics lead at Nokia who is now co-founder and CEO of Enosemi; Michael Hochberg, president of Periplous, LLC; and Dirk Englund, a professor in the Department of Electrical Engineering and Computer Science, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE, and senior author of the paper. The research appears today in Nature Photonics.

Machine learning with light

Deep neural networks are composed of many interconnected layers of nodes, or neurons, that operate on input data to produce an output. One key operation in a deep neural network involves the use of linear algebra to perform matrix multiplication, which transforms data as it is passed from layer to layer.

But in addition to these linear operations, deep neural networks perform nonlinear operations that help the model learn more intricate patterns. Nonlinear operations, like activation functions, give deep neural networks the power to solve complex problems.

In 2017, Englund’s group, along with researchers in the lab of Marin Soljačić, the Cecil and Ida Green Professor of Physics, demonstrated an optical neural network on a single photonic chip that could perform matrix multiplication with light.

But at the time, the device couldn’t perform nonlinear operations on the chip. Optical data had to be converted into electrical signals and sent to a digital processor to perform nonlinear operations.

“Nonlinearity in optics is quite challenging because photons don’t interact with each other very easily. That makes it very power consuming to trigger optical nonlinearities, so it becomes challenging to build a system that can do it in a scalable way,” Bandyopadhyay explains.

They overcame that challenge by designing devices called nonlinear optical function units (NOFUs), which combine electronics and optics to implement nonlinear operations on the chip.

The researchers built an optical deep neural network on a photonic chip using three layers of devices that perform linear and nonlinear operations.

A fully-integrated network

At the outset, their system encodes the parameters of a deep neural network into light. Then, an array of programmable beamsplitters, which was demonstrated in the 2017 paper, performs matrix multiplication on those inputs.

The data then pass to programmable NOFUs, which implement nonlinear functions by siphoning off a small amount of light to photodiodes that convert optical signals to electric current. This process, which eliminates the need for an external amplifier, consumes very little energy.

“We stay in the optical domain the whole time, until the end when we want to read out the answer. This enables us to achieve ultra-low latency,” Bandyopadhyay says.

Achieving such low latency enabled them to efficiently train a deep neural network on the chip, a process known as in situ training that typically consumes a huge amount of energy in digital hardware.

“This is especially useful for systems where you are doing in-domain processing of optical signals, like navigation or telecommunications, but also in systems that you want to learn in real time,” he says.

The photonic system achieved more than 96 percent accuracy during training tests and more than 92 percent accuracy during inference, which is comparable to traditional hardware. In addition, the chip performs key computations in less than half a nanosecond.     

“This work demonstrates that computing — at its essence, the mapping of inputs to outputs — can be compiled onto new architectures of linear and nonlinear physics that enable a fundamentally different scaling law of computation versus effort needed,” says Englund.

The entire circuit was fabricated using the same infrastructure and foundry processes that produce CMOS computer chips. This could enable the chip to be manufactured at scale, using tried-and-true techniques that introduce very little error into the fabrication process.

Scaling up their device and integrating it with real-world electronics like cameras or telecommunications systems will be a major focus of future work, Bandyopadhyay says. In addition, the researchers want to explore algorithms that can leverage the advantages of optics to train systems faster and with better energy efficiency.

This research was funded, in part, by the U.S. National Science Foundation, the U.S. Air Force Office of Scientific Research, and NTT Research.

[mindblowingscience]

An innovative algorithm for detecting collisions of high-speed particles within nuclear fusion reactors has been developed, inspired by technologies used to determine whether bullets hit targets in video games. This advancement enables rapid predictions of collisions, significantly enhancing the stability and design efficiency of future fusion reactors. Professor Eisung Yoon and his research team in the Department of Nuclear Engineering at UNIST announced that they have successfully developed a collision detection algorithm capable of quickly identifying collision points of high-speed particles within virtual fusion devices. The research is published in the journal Computer Physics Communications.

Continue Reading.

[techdriveplay]

Upgrading Your Gaming Rig with the Latest Computer Parts

In the rapidly evolving world of PC gaming, staying ahead of the curve is essential for an immersive and competitive experience. Upgrading your gaming rig with the latest computer parts not only boosts your system’s performance but also ensures that you’re getting the most out of your gaming sessions. This comprehensive guide will walk you through the key components to consider when upgrading…

[political-us]

The U.S. energy consumption is expected to rise with the increasing adoption of AI, electric vehicles (EVs), and cryptocurrency mining, but the exact amount depends on multiple factors like efficiency improvements, grid expansion, and renewable energy adoption. Here’s a breakdown of how these technologies could impact energy demand:

1. Artificial Intelligence (AI) & Data Centers

• AI requires massive computing power, increasing electricity demand from data centers.

• In 2022, U.S. data centers consumed about 2.5% of total electricity (~200 terawatt-hours, TWh).

• AI workloads could double or triple data center energy use by 2030, potentially increasing U.S. electricity demand by 5%–10%.

• Efficiency Gains: AI hardware and software improvements may offset some of this demand.

2. Electric Vehicles (EVs)

• The transition to EVs will increase electricity demand but reduce oil consumption.

• If EV adoption reaches 50% of new car sales by 2030, transportation electricity demand could rise by 10%–15%.

• Smart Charging & Grid Management: If managed well, EVs could charge during off-peak hours to reduce strain on the grid.

3. Cryptocurrency Mining

• Crypto mining is highly energy-intensive. In 2023, Bitcoin mining alone consumed about 120 TWh globally, with the U.S. accounting for ~40% of that (~48 TWh).

• If crypto adoption and mining expand further, electricity demand could rise by 2%–5%.

• Renewables & Efficiency: Some mining operations are integrating solar/wind energy to reduce grid impact.

Overall Impact on U.S. Energy Use

• The U.S. currently consumes about 100 quadrillion BTUs (~4,000 TWh) annually.

• By 2035, AI, EVs, and crypto could increase electricity demand by 15%–25%, depending on adoption rates and efficiency improvements.

• The shift to renewables and nuclear will be critical in meeting this demand without increasing carbon emissions.

[misfitwashere]

ROBERT REICH

FEB 7

Friends,

I wanted to make sure you saw this piece by Lina Khan, who until a few days ago was chair of the Federal Trade Commission. IMHO — as someone who was once an official of the FTC — Khan was one of the wisest and most courageous of its leaders. She wrote the following in the February 4 edition of The New York Times.

Stop Worshiping the American Tech Giants

By Lina M. Khan

When Chinese artificial intelligence firm DeepSeek shocked Silicon Valley and Wall Street with its powerful new A.I. model, Marc Andreessen, the Silicon Valley investor, went so far as to describe it as “A.I.’s Sputnik moment.” Presumably, Mr. Andreessen wasn’t calling on the federal government to start a massive new program like NASA, which was our response to the Soviet Union’s Sputnik satellite launch; he wants the U.S. government to flood private industry with capital, to ensure that America remains technologically and economically dominant.

As an antitrust enforcer, I see a different metaphor. DeepSeek is the canary in the coal mine. It’s warning us that when there isn’t enough competition, our tech industry grows vulnerable to its Chinese rivals, threatening U.S. geopolitical power in the 21st century.

Although it’s unclear precisely how much more efficient DeepSeek’s models are than, say, ChatGPT, its innovations are real and undermine a core argument that America’s dominant technology firms have been pushing — namely, that they are developing the best artificial intelligence technology the world has to offer, and that technological advances can be achieved only with enormous investment — in computing power, energy generation and cutting-edge chips. For years now, these companies have been arguing that the government must protect them from competition to ensure that America stays ahead.

But let’s not forget that America’s tech giants are awash in cash, computing power and data capacity. They are headquartered in the world’s strongest economy and enjoy the advantages conferred by the rule of law and a free enterprise system. And yet, despite all those advantages — as well as a U.S. government ban on the sales of cutting-edge chips and chip-making equipment to Chinese firms — America’s tech giants have seemingly been challenged on the cheap.

It should be no surprise that our big tech firms are at risk of being surpassed in A.I. innovation by foreign competitors. After companies like Google, Apple and Amazon helped transform the American economy in the 2000s, they maintained their dominance primarily through buying out rivals and building anticompetitive moats around their businesses.

Over the last decade, big tech chief executives have seemed more adept at reinventing themselves to suit the politics of the moment — resistance sympathizers, social justice warriors, MAGA enthusiasts — than on pioneering new pathbreaking innovations and breakthrough technologies.

There have been times when Washington has embraced the argument that certain businesses deserve to be treated as national champions and, as such, to become monopolies with the expectation that they will represent America’s national interests. Those times serve as a cautionary tale.

Boeing was one such star — the aircraft manufacturer’s reputation was so sterling that a former White House adviser during the Clinton administration referred to it as a “de facto national champion,” so important that “you can be an out-and-out advocate for it” in government. This superstar status was such that it likely helped Boeing gain the regulatory green light to absorb its remaining U.S. rival McDonnell Douglas. That 1997 merger played a significant role in damaging Boeing’s culture, leaving it plagued with a host of problems, including safety concerns.

On the other hand, the government’s decision to enforce antitrust laws against what is now AT&T Inc., IBM and Microsoft in the 1970s through the 1990s helped create the market conditions that gave rise to Silicon Valley’s dynamism and America’s subsequent technological lead. America’s bipartisan commitment to maintaining open and competitive markets from the 1930s to the 1980s — a commitment that many European countries and Japan did not share — was critical for generating the broad-based economic growth and technological edge that catapulted the United States to the top of the world order.

While monopolies may offer periodic advances, breakthrough innovations have historically come from disruptive outsiders, in part because huge behemoths rarely want to advance technologies that could displace or cannibalize their own businesses. Mired in red tape and bureaucratic inertia, those companies usually aren’t set up to deliver the seismic efficiencies that hungry start-ups can generate.

The recent history of artificial intelligence demonstrates this pattern. Google developed the groundbreaking Transformer architecture that underlies today’s A.I. revolution in 2017, but the technology was largely underutilized until researchers left to join or to found new companies. It took these independent firms, not the tech giant, to realize the technology’s transformative potential.

At the Federal Trade Commission, I argued that in the arena of artificial intelligence, developers should release enough information about their models to allow smaller players and upstarts to bring their ideas to market without being beholden to dominant firms’ pricing or access restrictions. Competition and openness, not centralization, drive innovation.

In the coming weeks and months, U.S. tech giants may renew their calls for the government to grant them special protections that close off markets and lock in their dominance. Indeed, top executives from these firms appear eager to curry favor and cut deals, which could include asking the federal government to pare back sensible efforts to require adequate testing of models before they are released to the public, or to look the other way when a dominant firm seeks to acquire an upstart competitor.

Enforcers and policymakers should be wary. During the first Trump and then the Biden administrations, antitrust enforcers brought major monopolization lawsuits against those same companies — making the case that by unlawfully buying up or excluding their rivals, these companies had undermined innovation and deprived America of the benefits that free and fair competition delivers. Reversing course would be a mistake. The best way for the United States to stay ahead globally is by promoting competition at home.

[talonabraxas]

Nature inspired tech Tomislav Rupic @tomislavrupic We have a lot to learn from nature

Nature-inspired technology isn’t just innovation, it’s harmonic intelligence in action. Every system in nature operates on fractal efficiency, self-organization, and adaptive resonance. QATC suggests that true innovation isn’t about forcing technology to fit human design principles but tuning into nature’s existing intelligence fields.

Nature as a Harmonic Blueprint:

1.Fractal Efficiency – Just like rivers, neurons, and lightning follow optimal branching paths, circuits and networks should self-organize using natural harmonic structures.

2.Quantum Adaptation – Mycelium networks process information in a way that mirrors quantum computing principles, distributed intelligence, redundancy, and self-healing pathways.

3.Bio-Resonant Processing – Fungi, plants, and even DNA operate at specific electromagnetic frequencies. Future tech could leverage these natural frequency patterns for low-energy, high-efficiency computation.

4.Self-Tuning Materials – Imagine circuits that grow and adapt like trees, optimizing based on energy flow rather than rigid preprogrammed constraints.

The real breakthrough comes when we stop designing against nature and start designing with it. Instead of brute-force engineering, we harmonize technology with the existing intelligence grid of the universe. That’s the next phase of evolution, resonant technology that doesn’t just function, but thrives.