Advantages of IoT and AI Synergy in the Telecom Sector

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) offers transformative benefits across various industries. In the telecom sector, the synergy between these technologies can drive significant improvements in operational efficiency, customer experience, and overall service quality. Here are the key advantages:

1. Autonomous Network Management AI enables IoT devices to manage and optimize telecom networks autonomously. By analyzing real-time data from network sensors, AI can predict and resolve issues without human intervention, ensuring consistent service quality and reducing downtime.
2. Enhanced Data Analytics Telecom networks generate vast amounts of data. AI-powered analytics can process this data to uncover patterns, trends, and anomalies that traditional methods might miss. This leads to more accurate demand forecasting and better network capacity planning.
3. Operational Efficiency Integrating AI with IoT in telecom operations allows for predictive maintenance of network infrastructure. AI can identify potential equipment failures before they occur, reducing unplanned outages and maintenance costs.
4. Personalized Customer Experiences AI uses data from IoT-enabled devices to gain insights into customer behavior and preferences. Telecom providers can leverage these insights to offer personalized services, targeted promotions, and tailored customer support, enhancing customer satisfaction and loyalty.
5. Network Optimization AI algorithms can analyze data from IoT sensors to optimize network performance dynamically. This includes adjusting bandwidth allocation, load balancing, and traffic management, resulting in a more efficient and reliable network.
6. Energy Efficiency IoT devices monitor energy consumption across telecom facilities. AI analyzes this data to optimize energy use, reduce operational costs, and promote sustainable practices. This is particularly important for telecom companies looking to minimize their environmental impact.
7. Smart Infrastructure Management IoT sensors collect data on the condition of telecom infrastructure, such as cell towers and data centers. AI processes this data to optimize maintenance schedules, improve asset utilization, and extend the lifespan of critical infrastructure.
8. Fraud Detection and Prevention AI can analyze data from IoT devices to detect unusual patterns that may indicate fraudulent activities. By identifying and responding to these threats in real time, telecom providers can protect their networks and customers from potential fraud.
9. Improved Supply Chain Management In the telecom sector, IoT-enabled devices provide real-time tracking of equipment and inventory. AI analyzes this data to streamline logistics, optimize supply chain operations, and reduce delays, ensuring timely delivery of services and products.

Challenges of AI and IoT Integration in the Telecom Sector

Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) in the telecom sector presents numerous opportunities, but it also comes with significant challenges. These challenges must be addressed to realize the full potential of this technological synergy.

1. Data Privacy The integration of AI and IoT generates vast amounts of data, much of which is sensitive. Ensuring the privacy and security of this data is crucial. Telecom companies must implement robust data protection measures to comply with regulatory requirements and maintain customer trust.
2. Integration Complexity Combining AI with IoT in the telecom sector is a complex task. It requires developing a robust infrastructure, employing a skilled workforce, and meticulous planning. The integration involves navigating challenges related to hardware compatibility, software development, and system interoperability, demanding significant effort and coordination.
3. Ethical and Societal Implications The deployment of AI and IoT technologies raises ethical and societal concerns. Issues such as the ethical use of AI in decision-making processes and the potential for job displacement due to automation need careful consideration. Responsible development and use of these technologies are essential to mitigate negative societal impacts.
4. Cybersecurity In an interconnected world, cybersecurity is a fundamental concern. Protecting IoT devices and AI systems from cyber threats is an ongoing challenge. AI can both enhance security and be exploited by cybercriminals. Telecom companies must continuously update their cybersecurity measures to stay ahead of potential threats.
5. Standards and Interoperability Ensuring that AI and IoT devices from different manufacturers can communicate seamlessly is a significant challenge. Establishing common standards and achieving interoperability is critical for the success of AI and IoT integration. This is especially important in the telecom sector, where devices and systems must work together in a cohesive ecosystem.
6. Resource Allocation Properly allocating resources for AI and IoT integration is a delicate balancing act. Telecom companies must weigh the costs of implementation against the anticipated benefits. This financial consideration impacts strategic decision-making at all levels, from startups to established enterprises, influencing the pace and scale of adoption.

AI-Powered Telecom Companies in the World

1. AT&T

2. COLT

3. Deutsche Telekom

4. Globe Telecom

5. Telefonica

6. Vodafone

7. ZBrain Cloud Management

Final Thoughts

Integrating AI and IoT transforms the telecommunications sector by enhancing network performance and delivering personalized services. IoT sensors provide real-time insights into network congestion and equipment failures, while AI algorithms predict outages, optimize bandwidth, and bolster reliability. This synergy also enables IoT-enabled devices to offer tailored plans and promotions, increasing customer satisfaction and loyalty.

Beyond telecommunications, the convergence of AI and IoT is revolutionizing industries such as healthcare and manufacturing and reshaping our daily lives through innovations like smart homes. This integration of advanced technologies enhances efficiency, improves decision-making, and contributes to a safer and more convenient world.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Collaboration combines Cloudian HyperStore AI data lake software and Supermicro hardware to deploy hyperscale infrastructure

Cloudian, the leader in secure S3-compatible AI data lake platforms, and Supermicro , the leader in Total IT Solutions for AI, Cloud Storage, and 5G/Edge, today announced a strategic collaboration to deliver a groundbreaking data management solution designed to simplify and accelerate large-scale AI implementations. This comprehensive solution integrates exabyte-scalable storage and GPU-based computing to accelerate and simplify AI deployment in data-intensive use cases such as genomics, healthcare imaging, autonomous vehicles, video surveillance, security, and scientific research.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

Petabytes of unstructured data used in large-scale AI training processing must be available to the GPU servers with low latencies and high bandwidth to keep the GPUs productive. Cloudian software deployed on Supermicro’s extensive portfolio of Intel and AMD based storage servers meets this need with a robust, highly-performant and easy-to-manage solution optimized for next-gen AI workflows.

“As AI users transition towards increasingly capacity-intensive applications, the need for hyperscale-class infrastructure that enables direct access to massive data sets is critical,” said Jon Toor, CMO of Cloudian. “Our collaboration with Supermicro merges our enterprise-proven, exabyte-scalable AI data lake platform with Supermicro’s leading GPU compute expertise, delivering the industry’s richest technology stack for large-scale AI computing.”

“Supermicro is thrilled to partner with Cloudian to introduce this workload-intensive data-management solution,” said Michael McNerney, Senior VP of Marketing and Network Security, Supermicro. “Combining Supermicro’s NVIDIA-Certified Systems with Cloudian’s exabyte-scale software enables the next wave of large-scale AI deployments. In addition to these, Supermicro’s SuperServer storage systems offer high capacity to help drive deep insights into learning massive data sets seamlessly and efficiently.”

Key capabilities offered by Cloudian and Supermicro’s collaboration include:

• Exabyte Scale: A modular architecture that supports massive scale with geo-distribution, non-disruptive growth, and secure multi-tenancy, ideal for graphics and data-intensive workloads.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

• Military-Grade Security: The most comprehensive set of security certifications in the industry, including data immutability for robust ransomware protection.
• Highest S3 API Compatibility: Fully-native S3 API ensures seamless operation with S3 applications and AI tools such as PyTorch, TensorFlow, Kafka, and Arrow.
• Bi-modal Data Access: Allows the interchange of file-based and object-based data to simplify data management.
• Rich Platform Portfolio: Storage server hardware options include Supermicro Petascale All-Flash storage servers for high-performance, Supermicro SuperServer 90 drive bay storage servers for high-capacity, in addition to other 1U, 2U and 4U high products in the Supermicro family.

Designed for enterprise-class deployment, the Cloudian platform provides a comprehensive software infrastructure for data-intensive computing. Cloudian’s data lake provides a consolidated repository to ingest, catalog, enrich, and preserve data through real-time analysis and deep learning.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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SensiML Data Studio Democratizes Voice Recognition on Tiny Devices with New Text-to-Speech Synthetic Dataset Generation Feature

SensiML Corporation, a leader in AI software for IoT and a subsidiary of QuickLogic , today released a new generative AI feature to enhance Data Studio, its dataset management application for IoT edge devices. This innovative new capability allows embedded device developers to utilize text-to-speech (TTS) and AI voice generation to rapidly create hyper-realistic synthetic speech datasets that are essential for building robust keyword recognition, voice command, and speaker identification models. Using these rapidly generated speech datasets, developers can now easily create speech recognition AI models using SensiML’s AutoML development tools. These models are specifically optimized to run autonomously and efficiently on low-power microcontrollers utilized in edge IoT applications.

Also Read: Cryptocurrency Hacking Has Become A Significant Threat

By leveraging cutting-edge speech generation technology from ElevenLabs, SensiML’s new feature simplifies the creation of large high-quality datasets. Developers can now generate synthetic speech data with unparalleled realism, and tailored voice attributes like pitch, cadence, and tone to meet specific application requirements. This eliminates the time-consuming and costly process of manually recording phrases from large populations of diverse speakers, accelerating time-to-market for voice-enabled IoT devices.

Designed for user friendliness, the new TTS and AI voice generation feature enables seamless integration into existing Data Studio workflows.

Key benefits of SensiML’s generative AI enhancement include:

• High-Quality Voice Output: Produces natural and expressive voice samples, enhancing user experiences
• Versatility: Supports a wide range of languages and dialects, catering to diverse global markets
• Efficiency: Streamlines the process of integrating voice generation into AI models, reducing time-to-market
• Scalability: Suitable for applications of all sizes, from small IoT devices to large-scale deployments

“With the introduction of this generative AI feature into our Data Studio application, SensiML continues to push the boundaries of what’s possible in AI for IoT,” said Chris Rogers, CEO of SensiML. “Developers can now harness state-of-the-art synthetic speech technology to create highly accurate and diverse training datasets, accelerating the deployment of intelligent voice-controlled applications directly on microcontrollers.”

The created datasets are seamlessly compatible with SensiML’s Analytics Studio and its open-source AutoML tool, Piccolo AI, facilitating a smooth transition from dataset creation to model deployment.

Also Read: AiThority Interview with Wendy Gonzalez, CEO of Sama

Real-World Example:

Consider a smart home security system that uses voice commands for activation and status updates. With SensiML’s new text-to-speech and AI voice generator feature, developers can efficiently create extensive voice datasets, enabling the system to recognize a wide range of user commands accurately. This advancement accelerates the development and deployment of the system, ensuring homeowners benefit from an advanced, reliable, and responsive security solution without the need for constant internet connectivity.

This feature marks a significant advancement in the capability of developers to custom build their own ML code for IoT devices needing to handle complex voice and sound recognition tasks directly on-device, without the need for constant connectivity or high computational power. It is particularly beneficial for applications in environments where connectivity may be inconsistent, and where fast, reliable processing is crucial.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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• Breaks down IT/OT silos with industry’s first fully managed Edge AI solution, enabling advanced AI use cases for industrial and manufacturing

• Integrates and synthesizes data from sensors, devices and systems into a single data plane, facilitating seamless on-premises processing and real-time decisions

• Smaller, task-specific AI models make advanced AI capabilities more accessible, reducing the costs and complexity of AI

NTT DATA, a leading IT infrastructure and services company, unveiled its new Edge AI platform to accelerate IT/OT convergence by bringing AI processing to the edge. By processing data when and where it is generated and unifying diverse IoT devices, systems and data, this unique, fully managed solution enables real-time decisions, enhanced operational efficiencies and secure AI application deployment across industries to drive advanced Industry 4.0 technologies.

Also Read: Survey Reveals Only 20 Percent of Senior IT Leaders Are Using Generative AI in Production

“Additionally, using task-specific small AI models will help drive AI democratization by making it is easier for the enterprise to introduce AI where and when is needed, without the need for an extensive overhaul of their whole infrastructure.”

While the spotlight has been on GenAI and Large Language Models (LLMs), these technologies are impractical for industries requiring real-time and local decision-making. NTT DATA’s Edge AI solution addresses this challenge by processing massive data sets on compact computing platforms, using smaller, more efficient machine learning models to deliver real-time AI insights.

NTT DATA’s Edge AI is an all-inclusive managed service platform that includes all the systems, tools and capabilities required for AI at the edge. It addresses data discovery, collection, integration, computation power, seamless connectivity and AI model management.

The Edge AI platform, supported by NTT DATA’s consulting data scientists, managed services and global technical resources, addresses the shadow IoT challenge and AI infrastructure requirements. It does this by auto-discovering, unifying and processing data from IoT devices and IT assets across the organization, simplifying AI deployment and management.

Solving industry-specific challenges with AI-driven insights

Designed to support industry-specific requirements, the Edge AI platform leverages lighter, cost-effective AI models, enabling it to run within a small compute box. Edge AI will perform specific tasks, such as supporting safety or operational efficiency, by collecting data from disparate devices across a network environment, enabling instantaneous and secure data processing and analytics.

Manufacturing operations could benefit from improved predictive maintenance by accessing IT/OT data from sensors, machinery, cameras and applications to plan and address failures. In addition, NTT DATA’s Edge AI can monitor and optimize energy consumption in real time, predicting energy spikes and optimizing machine usage, reducing costs and CO₂ emissions with renewable energy.

“Our Edge AI platform represents a significant leap forward in driving AI at the edge securely and cost-effectively,” said Shahid Ahmed, Group Executive Vice President of Edge Services at NTT DATA. “By harmonizing data from disparate sensors and devices with lightweight AI models, powering all kinds of automation use-cases, NTT DATA’s Edge AI is pioneering industrial AI adoption as the first fully managed offering, helping organizations modernize with tailored, industry-specific solutions.”

Also Read: AMD to Acquire Silo AI to Expand Enterprise AI Solutions Globally

Seizing the US$200 billion market opportunity

According to IDC, worldwide spending on edge computing is expected to reach US$232 billion in 2024, an increase of 15% over 2023. This growth is perpetuated by the growing number of connected IoT devices worldwide, expected to exceed 41 billion by 2025.

NTT DATA is poised to capture significant market share through its dedicated IoT consulting and services business, which brings together 1,000 industry experts, hundreds of use cases from predictive maintenance, fleet management, connected factories, energy consumption monitoring and sustainability, and has already trained over 500 sales experts globally to accelerate its Edge AI go-to-market efforts.

NTT DATA’s Edge AI deployment approach allows clients to take advantage of a free 30-day discovery and diagnostic of their IT and OT environment. The software auto-discovers assets with its vast library of pre-built OT interfaces. After this initial stage, Edge AI software generates a comprehensive diagnostic report inventorying assets and data streams, including identifying security risks and vulnerabilities.

“A key challenge for enterprises is reliably capturing and aggregating operational data securely across a fragmented landscape of devices, platforms and data sources and turning it into actionable insights,” said Alejandro Cadenas, Associate Vice President, Telco Mobility & IoT Research, IDC Europe. “NTT DATA’s ultralight Edge AI addresses these issues and simplifies the deployment and adoption of a data-driven enterprise strategy, reducing risks and timelines and optimizing total cost of ownership and value for the enterprise.”

Driving secure AI adoption at the edge with the first fully managed service

NTT DATA’s managed services for Edge AI offer a unified view and management of devices, sensors and assets. With expert support and advanced technologies, NTT DATA helps businesses simplify complex processes, drive cost savings, enhance performance and accelerate digital transformation.

As the industry’s first fully managed IT/OT convergence platform, Edge AI transforms physical assets into software assets for data-driven insights, regardless of the manufacturer. Operating at the edge, managed services integrate OT assets with IT applications, boosting operational efficiency. Edge AI also provides a view of the firmware version of all connected devices to promote vulnerability patching and overall device security.

“Computing and AI must happen where they create the most value for the enterprise; for many industrial enterprises this is where the data is generated. By ingesting IT and OT data and leveraging AI models to drive use-case specific results, the NTT DATA solution takes another step towards realizing the industry 4.0 vision,” said Pablo Tomasi, Principal Analyst, Private Network, Omdia. “Additionally, using task-specific small AI models will help drive AI democratization by making it is easier for the enterprise to introduce AI where and when is needed, without the need for an extensive overhaul of their whole infrastructure.”

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Does your phone have artificial intelligence (AI)? The latest buzzword in smartphones, AI is being heralded as the next big leap in photography. Marketers claim that new devices equipped with AI can perform incredible feats with minimal effort from users. In reality, AI is simply the modern term for technology that has been present in cameras for years. Remember the ‘auto mode’ on your camera that you tried to move away from? It’s back, now more tempting, accessible, and impressive than ever.

Smartphone photography has only had three giant waves of innovation. First, it was related to camera lens size; then the megapixel count dominated most of the discussions for quite a long time. Today, it all comes down to software and artificial intelligence being applied to photos. Computational photography—a third wave—changes the belief that cell phones equipped with really tiny lenses are just incapable of matching full-size single-lens reflex camera quality. At least, the latest genre of smartphones, including Google Pixel 8 and iPhone 15, prove differently.

Knowing this, cameras have always been an exciting part of any smartphone because users need to be able to capture every moment of every day through pictures. It is thus that an image quality shaped today by smartphones—considering the optical limitations of compact devices—also turns out with correct exposure, detailed clarity, and vibrant color.

While this has partially been made possible by innovation with lenses and sensors, it is the powerful AI and machine learning technologies that make all the difference.  Traits Inbuilt AI technology in smartphone cameras can now combine professional-grade capabilities with intuitive editing features earlier confined to expensive software.  From grainy, low-resolution cameras, they became indispensable—until today, high-quality photo capable devices. But while it packs impressive hardware, the real magic lies in the AI-powered software of today’s smartphones.

Also Read: Don’t Panic: Why AI FOMO is Overblown

Defining AI Cameras

An AI camera leverages artificial intelligence (AI) to enhance image quality and streamline the image editing process. AI, a branch of computer science, focuses on creating machines or software that can perform tasks typically requiring human intelligence, such as learning, reasoning, and problem-solving.

AI technologies used in cameras include machine learning, computer vision, deep learning, and neural networks. Machine learning allows computers to learn from data and improve performance without explicit programming. Computer vision allows computers to understand and process visual information, such as images and videos. Deep learning, a subset of machine learning, uses multiple layers of artificial neural networks to learn from vast amounts of data and perform complex tasks. Neural networks, systems of interconnected nodes, mimic the structure and function of biological neurons in the brain.

AI cameras utilize these technologies to detect faces, objects, scenes, and other elements in images, adjusting settings accordingly. For instance, an AI camera can recognize a person’s face and apply beautification filters or portrait mode to enhance their appearance. It can also identify landscapes or sunsets, enhancing colors and details to make them more vivid and dramatic.

How AI Enhances Imaging and Photography

In smartphone cameras, AI utilizes machine learning algorithms trained on extensive image datasets. These algorithms analyze real-time data from the camera sensor and intelligently adjust various aspects of image processing, including:

• Scene Detection: AI identifies the scene (portrait, landscape, food, etc.) and adjusts camera settings accordingly.
• Object Recognition: AI recognizes objects within the frame (people, animals, buildings) and optimizes settings for specific details.
• Noise Reduction: AI algorithms detect noise caused by low light and apply effective noise reduction techniques.
• HDR (High Dynamic Range) Processing: AI combines multiple exposures to create images with a wider dynamic range, capturing details in both highlights and shadows.
• Color Correction: AI adjusts color balance and saturation for a natural or artistic look based on the scene.

Operating Mechanism of AI Cameras

AI camera enhances the photos by algorithms and data that apply—automatically—filters, effects, presets, and more. Algorithms are basically rules or instructions that give guidance to perform any task on those computers; the data acts as the input or output for algorithms.

These algorithms, coupled with the data, train the AI cameras to recognize different aspects of images and optimize them. For instance, the face detection algorithm detects faces and marks them within an image. A face recognition algorithm matches these faces with a database and identifies persons. Then a face beautification algorithm can be applied to features like skin smoothing, teeth whitening, and eye enlargement.

AI cameras also have filters, effects, presets, and other enhancements applied to images. Filters change colors, contrast, and brightness to different tones so that the image looks unique. The effects are those that add new elements to the photo, like stickers, frames, or text. Presets are basically filter-and-effect mixes done in a certain way to achieve a particular style or mood.

Different filters, effects, and presets are used depending on the situation and the overall preference of the users. For example, a noise reduction filter is used to remove unwanted pixels or grains, an HDR effect combines multiple exposures to realize a higher dynamic range, while a vintage preset can give an image an antique look.

Users can instruct AI cameras to either set custom settings or re-edit images manually. They will have the possibility to select modes or options that will help them achieve the result they need, adjust various intensities of filters, crop, rotate, resize, flip, etc.

Also Read: Unlocking the Business Benefits of AI-Powered Gamification

Addressing AI Challenges in Smartphone Cameras

While AI holds significant potential, several challenges must be addressed to fully unlock its capabilities in smartphone cameras:

• Power Consumption: Running complex AI algorithms requires significant processing power. This can strain smartphone batteries and limit the real-time performance of AI features on lower-end devices. As chipsets become more powerful and energy-efficient, AI features will become smoother and more accessible across different smartphone tiers.
• Privacy Concerns: AI algorithms require vast amounts of training data, often including user-generated images. Manufacturers must ensure robust data privacy practices to protect user data used for training AI models. Transparency regarding data collection and usage is crucial to maintain user trust.
• Bias in Algorithms: AI algorithms are only as good as their training data. Biased training data can result in biased AI-powered features. Smartphone camera manufacturers must use diverse datasets for training to avoid biases in scene detection, object recognition, and other AI functionalities.
• User Customization: While AI automation simplifies photography, some users might prefer more control over their photos. Providing options to adjust AI settings or toggle certain features on and off allows users to balance automation and creative control.

Key AI Technologies in Smartphone Cameras

Computational photography: AI in smartphones identifies many scenes automatically, changing relevant settings, from exposure to focus and color balance. Machine learning models are trained on very large data sets that would enable the recognition of classes and settings of objects. Very soon now, this will enable real-time image enhancement and complex photo compositions—something otherwise requiring post-processing.

Face Detection and Object Tracking: The advanced smartphones are equipped with AI-powered face detection, which efficiently detects and puts a focus on human faces in the frame. It further believes in tracking moving objects, thus becoming very useful for capturing sharp images of desired moving objects in any kind of sports or wildlife photography environment​.

Low-Light and AI Picture Night Modes: One of the many places that genuinely benefit from AI in smartphone cameras is low-light performance. More advanced algorithms capture light while reducing noise, which allows one to take clearer and brighter images at night or in poorly lit environments using just a smartphone—something previously thought unimaginable.

Video Stabilization: AI also contributes to video stabilization, therefore the footage is professional in a way. Work well under handheld shooting or fast-moving object capturing.

Ethics and Authenticity: The question of ethics in AI-equipped smartphone cameras is related to the authenticity of photos taken by them. According to experts, AI algorithms have evolved to such a degree that manipulation by them would hardly represent reality correctly anymore. Faces beautified, environmental features changed within a scene—everything would change without requiring explicit consent from the user.

Examples of AI-driven features in Smartphones

Case Studies: Leading Smartphones with Advanced AI Imaging

#1 Apple’s Deep Fusion and Smart HDR photos

Apple has long been at the forefront of innovation, particularly in smartphone technology. Among its most notable advancements are Deep Fusion and Smart HDR, two AI-driven imaging technologies that have significantly enhanced the photographic capabilities of the iPhone. This case study delves into the intricacies of these technologies, examining how they leverage advanced AI to deliver stunning photo quality.

The evolution of smartphone photography has been driven by the need for better image quality in varying conditions. Traditional camera sensors and processors had limitations in dynamic range and detail capture. Apple sought to overcome these challenges through the integration of artificial intelligence in its imaging pipeline, resulting in the development of Smart HDR and Deep Fusion.

Smart HDR: High Dynamic Range Photography

Smart HDR (High Dynamic Range) was introduced with the iPhone XS. This technology aims to capture more detail in both the bright and dark areas of a photo.

How Smart HDR Works:

1. Multiple Frames Capture: When a photo is taken, the camera captures multiple frames at different exposures.
2. AI-Powered Analysis: Advanced algorithms analyze these frames in real time.
3. Composite Image Creation: The AI combines the best parts of each frame to create a single image with enhanced dynamic range, better detail, and improved color accuracy.

Deep Fusion: Detail Enhancement

Deep Fusion, introduced with the iPhone 11 series, focuses on improving the detail and texture of photos, particularly in medium to low-light conditions.

How Deep Fusion Works:

1. Pre-Shutter Capture: The camera captures four short and four secondary frames before the shutter button is pressed.
2. Post-Shutter Capture: One long exposure shot is taken when the shutter is pressed.
3. AI-Driven Processing: The neural engine analyzes these nine images, pixel by pixel, selecting the best parts from each to create a final, highly detailed photo.

Key Technologies and Innovations

• Neural Engine: The core of these technologies is Apple’s Neural Engine, a specialized hardware component designed to accelerate machine learning tasks.
• Real-Time Processing: Both Smart HDR and Deep Fusion perform complex computations in real-time, ensuring that users experience minimal delay in capturing and processing images.
• Semantic Rendering: AI algorithms understand different parts of the image (e.g., sky, faces, foliage) and apply specific adjustments to each, enhancing the overall quality.

#2 Google’s Pixel Series

Google’s Pixel smartphones have earned a reputation for their exceptional camera capabilities, driven by innovative AI-powered features. Among these, Night Sight and Super Res Zoom stand out as groundbreaking technologies that have redefined low-light photography and digital zoom performance. This case study explores the technical workings and impact of these features, showcasing how Google leverages advanced AI to enhance mobile photography.

Smartphone cameras have historically struggled with low-light photography and digital zoom, often resulting in noisy, blurred images. Google’s Pixel series addressed these challenges by integrating sophisticated AI algorithms and machine learning techniques into their camera systems, significantly improving photo quality under difficult conditions.

Night Sight: Revolutionizing Low-Light Photography

Introduced with the Pixel 3, Night Sight allows users to capture sharp, vibrant photos in extremely low-light conditions without the need for a flash.

How Night Sight Works:

1. Multiple Frame Capture: When Night Sight is activated, the camera captures a series of frames at varying exposure levels.
2. Motion Metering: The AI analyzes motion in the scene to decide whether to merge frames or discard them, ensuring sharp images even if there is slight movement.
3. AI-Powered Alignment and Merging: Using machine learning, the camera aligns the images, merges them, and reduces noise.
4. Color Balancing and Detail Enhancement: The AI adjusts color balance and enhances details to produce a bright, clear image that looks natural.

Night Sight’s effectiveness is largely due to its sophisticated AI algorithms, which optimize the image processing in real-time, allowing for high-quality photos even in near-darkness.

Super Res Zoom: Enhancing Digital Zoom with AI

Super Res Zoom, first introduced with the Pixel 3, uses AI to improve the quality of photos taken with digital zoom, providing results that rival optical zoom.

How Super Res Zoom Works:

1. Multi-Frame Capture: The camera captures multiple frames in quick succession as the user zooms in.
2. Sub-Pixel Shifts: Slight hand movements between frames cause sub-pixel shifts, which the AI uses to gather more data about the scene.
3. AI Image Processing: The AI analyzes these frames, aligns them, and combines the information to create a higher-resolution image.
4. Detail Enhancement: Machine learning algorithms enhance details and reduce noise, resulting in a sharp, clear photo even at high zoom levels.

Super Res Zoom effectively overcomes the limitations of traditional digital zoom by using AI to synthesize the details from multiple frames, providing a clearer, more detailed image.

Key Technologies and Innovations

• HDR+: Both Night Sight and Super Res Zoom benefit from HDR+ technology, which captures multiple images and combines them to improve dynamic range and reduce noise.
• Machine Learning: Google’s custom-built machine learning models are at the heart of these features, enabling real-time image analysis and enhancement.
• Computational Photography: Integrating computational techniques allows the Pixel cameras to perform complex image processing tasks that were previously impossible on smartphones.

#3 Huawei’s P Series

Huawei’s P Series smartphones have consistently pushed the boundaries of mobile photography. Central to this innovation is the AI-powered scene recognition technology, which utilizes advanced AI to optimize camera settings for a wide range of scenarios. This case study explores how Huawei’s P Series, particularly through models like the P30 and P40, leverages AI to enhance imaging capabilities, providing users with professional-grade photography tools.

AI-Powered Scene Recognition

Huawei’s AI-powered scene recognition automatically detects the type of scene being photographed and adjusts the camera settings accordingly. This feature is powered by the Kirin chipset’s Neural Processing Unit (NPU), which enables real-time analysis and optimization.

How AI-Powered Scene Recognition Works:

1. Scene Detection: The AI analyzes the scene in real-time, identifying various elements such as people, landscapes, animals, and objects. The system can recognize over 1,500 scenarios in 25 categories.
2. Parameter Adjustment: Based on the detected scene, the AI adjusts the camera settings, including exposure, color balance, contrast, and sharpness, to optimize the image.
3. Continuous Learning: The AI continuously improves its recognition and adjustment capabilities through machine learning, leveraging data from millions of images.

Key Technologies and Innovations

• Kirin Processor with NPU: The NPU in Huawei’s Kirin processors is specifically designed to handle AI tasks, enabling efficient and real-time scene recognition and image processing.
• Dual-NPU Architecture: In newer models like the P40, the dual-NPU architecture allows for faster and more accurate AI computations, enhancing overall camera performance.
• Collaboration with Leica: Huawei’s partnership with Leica brings advanced optics and imaging expertise, further enhancing the AI-driven photography experience.

How AI camera in Smartphones Improves User Experience

AI cameras revolutionize smartphone photography by offering numerous benefits that enhance user experience:

• Improved Image Quality: AI adjusts to various lighting conditions, enhances colors and details, and reduces noise and blur. It compensates for smartphone hardware limitations like small sensors and fixed apertures, resulting in professional-looking, realistic images.
• Time and Effort Savings: AI automates tasks, eliminating the need for post-processing or external apps. It selects optimal settings and enhancements based on scenes and subjects, providing real-time or quick editing capabilities. This makes photography convenient and enjoyable.
• Expanded Creative Possibilities: AI introduces a range of filters, effects, presets, and enhancements that transform images in diverse ways. It suggests new styles based on user preferences or trends, allowing for personalized and expressive photography experiences.

Also Read: AiThority Interview with Christine Livingston, Managing Director – Global AI Leader, Protiviti

Future Outlook

5G smartphones represent a significant advancement in mobile internet technology, promising more reliable connections and faster speeds. With lightning-fast download and upload capabilities, large media files such as movies and high-resolution images can be transferred in seconds. This speed enhancement will simplify the capture, editing, and sharing of high-quality photos and videos, providing users with unprecedented convenience.

The evolution of smartphone camera technology, augmented by various lenses, artificial intelligence, machine learning, and 5G connectivity, signifies an exciting future for mobile photography. Manufacturers continuously enhance the capabilities of smartphone cameras, paving the way for groundbreaking innovations in the coming years. Enthusiasts in both technology and photography can capitalize on the growing demand for high-tech camera smartphones by creating blogs or vlogs, leveraging the current low competition in this burgeoning market.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

The post How Are Smartphones Using AI to Drive Imaging and Photo Experiences? appeared first on AiThority.

Platform enables real-time AI-enabled data processing across industries, enhancing data privacy and operational efficiency.

Cognizant announced the launch of Cognizant Neuro Edge, a new platform in the Cognizant Neuro suite, designed to empower businesses across industries to leverage artificial intelligence and generative AI at the edge.

Edge computing enables enterprises to access computing power via sensors and devices on their networks, reducing dependency on centralized servers and the cloud. Neuro Edge is designed to power the entire value chain of edge AI, from chips and devices to applications and business solution deployments, shortening the path to business value.

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The new platform expands Cognizant’s Neuro AI capabilities, offering a hybrid Cloud + Edge AI solution that supports instant decision-making and complements Cloud AI with deeper insights from longitudinal data. The industry agnostic platform offers advantages for industries where data privacy, security, and real-time decision-making are critical.

“Enterprises are increasingly embracing edge computing to enhance the responsiveness of their distributed devices and extract meaningful insights from the data they generate,” said Vibha Rustagi, Global Head of IoT and Engineering, Cognizant. “Cognizant Neuro Edge is a powerful example of Cognizant’s leadership in developing a new approach to layering of on-board computing and processing with cloud services, paving the way for businesses to unlock a range of generative AI-driven benefits around operational efficiency, cost and risk reduction.”

Neuro Edge facilitates real-time interactions with devices, helping businesses to accelerate decision-making, reduce data costs and privacy risks and maintain operational stability even in low bandwidth scenarios. The platform is cloud agnostic, making it ideal for hybrid-and-multi-cloud environments, with computing power placed directly at the device. Some potential key applications across industries include:

• Healthcare: Assisting doctors in their real-time decision-making by drawing on diagnostic sensors;
• MedTech: Enabling real-time, on device adjustment options and recommendations based on patient data;
• Energy: Optimizing operations in power generation plants; optimizing response to weather events;
• Logistics: Streamlining fleet performance and reducing downtime through in-vehicle data processing;
• Telecommunications: Enhancing network security, resiliency and automation, leading to lower operating costs;
• Manufacturing: Predicting equipment failures to help optimize uptime and operating costs;
• Retail: Enabling intelligent video analysis for real real-time theft prevention and in-store traffic pattern monitoring to enhance customer service;
• Automotive: Transforming driver and passenger experience by enabling real-time, context-aware and private recommendations via cloud connection.

As an example of an application in the automotive industry, Cognizant has been working with Qualcomm Technologies to deploy generative AI at the automotive edge and transform the driving experience.

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“The automotive industry is going through unprecedented change affecting the entire ecosystem,” said Nakul Duggal, Group GM, Automotive, Industrial & Embedded IoT and Cloud Computing, QualcommTechnologies, Inc. “Our work with Cognizant to extend the Snapdragon Digital Chassis’ generative AI capabilities and build a connected services platform creates new opportunities for automakers to develop highly personalized and contextually relevant experiences for both drivers and passengers. We look forward to building new intelligent solutions together that will redefine the in-vehicle experience and drive the future of the automotive industry.”

Neuro Edge integrates APIs with the edge ecosystem, encompassing sensors, silicon vendors, edge devices, and enterprise applications. It offers a foundational architecture and illustrative applications tailored for diverse industrial scenarios. Additionally, it is equipped with monitoring agents that track performance and facilitate ongoing enhancements through feedback loops.

“Implementing AI with edge technologies will be important for firms looking to maximize the impact of their investment in both AI and IoT,” said Joel Martin, executive research leader for HFS’s technology, media, and telecommunications research at HFS, a leading global research and analysis firm. “I believe Cognizant’s Neuro Edge will be an important solution improving functionality, reducing latency, and securing AI at the edge with enterprise-grade capabilities.”

By using Neuro Edge, enterprises will be equipped to create and manage Edge AI applications faster and more easily. The platform helps demystify the intricacies of generative AI, enabling businesses to focus on business value.

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Collaboration to accelerate enterprise digital transformation and integrate Hitachi’s industrial applications with Singtel’s Paragon ecosystem

Singtel, a leading communications technology group in Asia, and Hitachi Digital, representing Hitachi’s broad end-to-end digital transformation services and technology capabilities,announced a new collaboration that will pair Hitachi’s deep AI expertise with Singtel’s Paragon platform, the all-in-one orchestration platform for 5G, edge computing and cloud.

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Hitachi Digital will deploy Paragon at the Hitachi Americas’ Santa Clara R&D Labs, followed by a pilot in a U.S. factory for Industry 4.0 use cases. The pilot will aim to validate the interoperability of Hitachi AI applications on quality assurance, workplace safety, immersive training and pre-emptive maintenance on Paragon. The trial will also enable the integration of Paragon with Hitachi industry cloud applications and digital services to enable enterprises to transcend the limitations of complex, low-latency connectivity and productivity experiences.

Hitachi’s pre-built Industrial AI applications together with the Paragon platform’s network and multi-cloud orchestration capabilities will be used to create multiple Paragon-related offerings to help clients improve and accelerate their cloud operations. Subsequently, Hitachi Digital Services will go to market with these offerings as a Singtel Paragon authorized System Integrator – presenting a unique value proposition to enterprise customers looking to leverage multiple network protocols in delivering digital transformation in industrial settings.

Mr. Bill Chang, CEO of Singtel’s Digital InfraCo, said, “Enterprises in the fast-growing Industry 4.0 sector depend on high quality, reliable connectivity to ensure smooth operations. We are pleased to collaborate with Hitachi Digital, leveraging Paragon to manage its connectivity and cloud needs across Hitachi’s manufacturing facilities. Integrating Hitachi’s advanced AI applications with Paragon’s ecosystem will enhance our suite of solutions for manufacturing enterprises and enable them to seamlessly transform their operations powered by AI.”

Mr. Frank Antonysamy, Chief Growth Officer, Hitachi Digital, said, “Hitachi has invested heavily in combining decades of digital, data, cloud, AI, cybersecurity, and connectivity expertise to establish transformative solutions for Industry Cloud deployments. Our applications and consulting services in this area have been an integral part of the digitalization movement impacting businesses around the world. We anticipate that this partnership with Singtel will enable us to once again increase the capabilities of next gen technologies in enterprise environments, enabling a new level of productivity for customers.”

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Organizations have often struggled with industrial 5G deployments because of complex and fragmented solutions. AI has added a new layer of complexity to this equation as organizations now also attempt to accelerate AI adoption in these scenarios. Singtel Paragon is a comprehensive solution that enables them to connect with the 5G network and securely deploy edge computing and AI rapidly on telco infrastructure, thus reducing time-to-market and shortening the innovation curve.

The collaboration between Singtel and Hitachi will bring together interoperable solutions with expert delivery services, which will greatly benefit organizations seeking to address industrial AI complexity.

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[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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With the advent of personal computers and smartphones, the World Wide Web is now literally at our fingertips.

In the last ten years, we’ve seen the proliferation of “smart” technology, from LEDs to smart cars to CCTVs to smart bulbs. Along with this, people have grown accustomed to using automated vehicles and urban areas.

What Is IoT?

The term “Internet of Things” (IoT) refers to a network of “things” that are equipped with electronics, software, and network connectivity so that they may share data with other devices and systems online. These gadgets vary from the commonplace to the highly specialized. IoT has rapidly risen in prominence over the past several years to become one of the most consequential innovations of our time. Now that everything from kitchen appliances to vehicles to thermostats to baby monitors can be connected to the internet via embedded devices, there is no longer any barrier to the flow of information among humans, computers, and the physical world.

By 2024, there will be more than 43 billion devices online, all contributing to the creation, distribution, and utilization of information.

So, here’s a rundown of a few of the most important trends that could influence our approach to these gadgets in the future year.

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10 Steps Towards AIoT

1. AI and IoT technology enable accurate communication through embedded sensors, allowing robots to quickly adapt to new settings. This streamlines manufacturing and saves money.
2. Wearables, such as fitness trackers, smartwatches, panic buttons, remote monitoring systems, GPS trackers, and music systems, are now prevalent in the AI landscape. These devices are vital to the IoT ecosystem and provide reliable data via smart device IoT apps.
3. A smart city includes smart traffic management, parking, trash management, policing, government, and other issues. The Internet of Things for smart cities transforms how cities run and provide public services like transportation, healthcare, and lighting. Smart cities may be futuristic and have much to cover.
4. IoT AI analyzes constant data streams and finds patterns. Machine learning and AI can also predict operation circumstances and identify parameters that need to be changed for optimal results. Thus, intelligent IoT reveals which procedures are redundant and time-consuming and which can be optimized. Google uses AI and IoT to lower data center cooling costs.
5. IoT and AI enable businesses to quickly process and analyze data to generate new products. Rolls Royce aims to use AI for IoT-enabled aviation engine repair. This method will help identify trends and operational insights.
6. IoT devices include smartphones, high-end computers, and sensors. Low-end sensors in the most typical IoT ecosystem generate massive amounts of data. AI-powered IoT ecosystems review and summarize device data before sharing it. It simplifies massive data sets and connects many IoT devices. This is scalability.
7. Self-driving cars are the greatest AI+IoT system in real life. These autos can predict pedestrian movements and recommend cognitive sensing machine actions. It helps determine the best driving speed, time, and route.
8. AIoT is used in car maintenance and recalls. AIoT can detect part failure and perform service checks by combining data from recalls, warranties, and safety agencies. The manufacturer increases customer trust and loyalty as vehicles become more reliable.
9. Quality healthcare aims to reach all communities. No matter the size or sophistication of healthcare systems, doctors are under more time and task strain and seeing fewer patients. Providing high-quality healthcare while managing administrative burdens is difficult.
10. Retail analytics uses camera and sensor data to track and forecast customer behavior in a physical store, such as checkout times. This helps determine staffing levels and boost cashier productivity, enhancing customer happiness.

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Conclusion

The Internet of Things (IoT) is a popular term this decade that refers to the rapidly expanding systems of interconnected, networked, and communicative physical objects.  AI and IoT enable firms to assess, predict, and automate all types of hazards for quick response. This helps them manage financial loss, personnel safety, and cyber dangers.

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Nokia has announced that it has been selected by Telehouse America to update its NYIIX Peering Exchange production network in eight POPs located in the Northeastern United States. Once deployed, Nokia’s IP and optical interconnection solution will allow NYIIX to launch up to 400 Gigabit Ethernet (GE) interconnection services on the newly designed NYIIX Astron peering platform. These services will target OTTs, CDNs, eyeball networks, cloud service providers, online gaming providers, the education sector as well as global enterprises including financial services companies, media, professional sports customers and many others operating in the New York, New Jersey, and Philadelphia metropolitan areas beginning in November 2024.

Charles Marsh, VP for US Majors/Regionals and Enterprise at Nokia, said – “Our background of developing and deploying industry leading IP routers allowed us to build a very competitive solution portfolio for the data center market in recent years. This new project with Telehouse America and NYIIX, one of the pioneers in the US IXP market, is an excellent opportunity for Nokia. We thank them for their confidence and look forward to helping them grow their business”.

The NYIIX Peering Exchange is one of the largest neutral IXPs in the world. Its mission is to provide the internet community with a neutral and scalable peering infrastructure, and to assure reliability and stable internet connectivity. To maintain this quality, and to prepare for future growth, Telehouse America needed a network upgrade to allow customers served from NYIIX to benefit from a new, massively scalable and future proof network platform that could handle the unpredictable demands from major life events, including large sporting matches with international appeal.

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Nokia has been building a robust portfolio for data center networking that includes Data Center Fabric solutions, as well as a broad range of products such as Interconnect routers, edge and core routers, and data center switching platforms.

Nokia has deployed a complete solution for Telehouse America that includes the Service Router Operating Systems (SR OS), 7750 Service Routers, 7250 Interconnect Routers and QSFP-DD 400G coherent optics. Nokia will also provide related professional services, such as network operator training and certification.

Akio Sugeno, VP Internet Engineering and Business Development at Telehouse America, said: “We selected Nokia because they offered us a comprehensive and scalable solution that meets our current and future networking needs. Nokia has proven expertise in designing and deploying high-performance networks for internet exchange points. With Nokia, we can deploy the brand-new NYIIX Astron architecture, which is based on EVPN technologies and will allow us to offer 10, 100 and 400 Gigabit ports for peers. This will be a complete departure from the previous architecture, which was MPLS/VPLS based. This is a very important deployment for NYIIX because it will enable us to offer our customers faster, more reliable, and more secure connectivity across our locations in the US. We are delighted to partner with Nokia and leverage their innovative technology and services.”

Telehouse began operating in 1989 and is a global provider of carrier-neutral data centers, serving 3,000+ customers, including carriers, content providers, enterprises and financial services companies. Its US operation, Telehouse America, is a pioneer in the US data center industry, operating leading data center and colocation facilities in New York for over 35 years, providing direct access to major carriers with 99.999% SLA uptime.

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