#AI in Edge Computing

[techytoolzataclick]

READ NOW: Tech trends defining the future of innovation

Come 2024 and the world is about to witness some tech trends which are bound to change the industries and reshape our world. Open Index Proxy & DeFi and Key Trends to Look Out for

Generative AI:

Changing the Landscape of Content Creation, Designing & Problem Solving. GPT-4 creates beautiful written content, AI-designed visuals. In drug discovery, it makes predictions of molecular structures, and more….

Spatial computing:

Digital information that is mixed with the physical environment, spatial computing includes AR, VR & MR, as it used in industrial metaverse (for real-time monitoring), education (immersive learning) and healthcare (medical training and surgery planning)

Quantum Computing:

Quantum mechanics allows the creation of quantum computers, which can do huge calculations faster than ever. They claim it will lead to cryptography, materials science and AI advances — improving machine learning for faster data analysis.

Edge Computing:

When you process data closer to the source of it (reducing latency and security threats). Essential for IoT along with autonomous vehicles and smart cities —that will bring instantaneous data processing and decision making.

5G Connectivity,

which means the advent of newer, faster and lower latency 5G networks with flexibility in terms of capacity. This technology is enabler for enhanced mobile broad band, IoT devices, industry 4.0 transformation in smart factories and industrial automation.

Cyber Security:

With everything getting digital these days, cyber threats are also going a step further. Sophisticated cyberattacks can be combatted using AI-driven security, a zero trust architecture, and blockchain security.

This is where these top tech trends are impacting and reshaping industries, and by extension our lives. Being updated on trending topics will also help you to stay relevant and engage your audience in return making you a thought leader of the AI and tech niche.

[bigleapblog]

Your Guide to B.Tech in Computer Science & Engineering Colleges

In today's technology-driven world, pursuing a B.Tech in Computer Science and Engineering (CSE) has become a popular choice among students aspiring for a bright future. The demand for skilled professionals in areas like Artificial Intelligence, Machine Learning, Data Science, and Cloud Computing has made computer science engineering colleges crucial in shaping tomorrow's innovators. Saraswati College of Engineering (SCOE), a leader in engineering education, provides students with a perfect platform to build a successful career in this evolving field.

Whether you're passionate about coding, software development, or the latest advancements in AI, pursuing a B.Tech in Computer Science and Engineering at SCOE can open doors to endless opportunities.

Why Choose B.Tech in Computer Science and Engineering?

Choosing a B.Tech in Computer Science and Engineering isn't just about learning to code; it's about mastering problem-solving, logical thinking, and the ability to work with cutting-edge technologies. The course offers a robust foundation that combines theoretical knowledge with practical skills, enabling students to excel in the tech industry.

At SCOE, the computer science engineering courses are designed to meet industry standards and keep up with the rapidly evolving tech landscape. With its AICTE Approved, NAAC Accredited With Grade-"A+" credentials, the college provides quality education in a nurturing environment. SCOE's curriculum goes beyond textbooks, focusing on hands-on learning through projects, labs, workshops, and internships. This approach ensures that students graduate not only with a degree but with the skills needed to thrive in their careers.

The Role of Computer Science Engineering Colleges in Career Development

The role of computer science engineering colleges like SCOE is not limited to classroom teaching. These institutions play a crucial role in shaping students' futures by providing the necessary infrastructure, faculty expertise, and placement opportunities. SCOE, established in 2004, is recognized as one of the top engineering colleges in Navi Mumbai. It boasts a strong placement record, with companies like Goldman Sachs, Cisco, and Microsoft offering lucrative job opportunities to its graduates.

The computer science engineering courses at SCOE are structured to provide a blend of technical and soft skills. From the basics of computer programming to advanced topics like Artificial Intelligence and Data Science, students at SCOE are trained to be industry-ready. The faculty at SCOE comprises experienced professionals who not only impart theoretical knowledge but also mentor students for real-world challenges.

Highlights of the B.Tech in Computer Science and Engineering Program at SCOE

Comprehensive Curriculum: The B.Tech in Computer Science and Engineering program at SCOE covers all major areas, including programming languages, algorithms, data structures, computer networks, operating systems, AI, and Machine Learning. This ensures that students receive a well-rounded education, preparing them for various roles in the tech industry.

Industry-Relevant Learning: SCOE’s focus is on creating professionals who can immediately contribute to the tech industry. The college regularly collaborates with industry leaders to update its curriculum, ensuring students learn the latest technologies and trends in computer science engineering.

State-of-the-Art Infrastructure: SCOE is equipped with modern laboratories, computer centers, and research facilities, providing students with the tools they need to gain practical experience. The institution’s infrastructure fosters innovation, helping students work on cutting-edge projects and ideas during their B.Tech in Computer Science and Engineering.

Practical Exposure: One of the key benefits of studying at SCOE is the emphasis on practical learning. Students participate in hands-on projects, internships, and industry visits, giving them real-world exposure to how technology is applied in various sectors.

Placement Support: SCOE has a dedicated placement cell that works tirelessly to ensure students secure internships and job offers from top companies. The B.Tech in Computer Science and Engineering program boasts a strong placement record, with top tech companies visiting the campus every year. The highest on-campus placement offer for the academic year 2022-23 was an impressive 22 LPA from Goldman Sachs, reflecting the college’s commitment to student success.

Personal Growth: Beyond academics, SCOE encourages students to participate in extracurricular activities, coding competitions, and tech fests. These activities enhance their learning experience, promote teamwork, and help students build a well-rounded personality that is essential in today’s competitive job market.

What Makes SCOE Stand Out?

With so many computer science engineering colleges to choose from, why should you consider SCOE for your B.Tech in Computer Science and Engineering? Here are a few factors that make SCOE a top choice for students:

Experienced Faculty: SCOE prides itself on having a team of highly qualified and experienced faculty members. The faculty’s approach to teaching is both theoretical and practical, ensuring students are equipped to tackle real-world challenges.

Strong Industry Connections: The college maintains strong relationships with leading tech companies, ensuring that students have access to internship opportunities and campus recruitment drives. This gives SCOE graduates a competitive edge in the job market.

Holistic Development: SCOE believes in the holistic development of students. In addition to academic learning, the college offers opportunities for personal growth through various student clubs, sports activities, and cultural events.

Supportive Learning Environment: SCOE provides a nurturing environment where students can focus on their academic and personal growth. The campus is equipped with modern facilities, including spacious classrooms, labs, a library, and a recreation center.

Career Opportunities After B.Tech in Computer Science and Engineering from SCOE

Graduates with a B.Tech in Computer Science and Engineering from SCOE are well-prepared to take on various roles in the tech industry. Some of the most common career paths for CSE graduates include:

Software Engineer: Developing software applications, web development, and mobile app development are some of the key responsibilities of software engineers. This role requires strong programming skills and a deep understanding of software design.

Data Scientist: With the rise of big data, data scientists are in high demand. CSE graduates with knowledge of data science can work on data analysis, machine learning models, and predictive analytics.

AI Engineer: Artificial Intelligence is revolutionizing various industries, and AI engineers are at the forefront of this change. SCOE’s curriculum includes AI and Machine Learning, preparing students for roles in this cutting-edge field.

System Administrator: Maintaining and managing computer systems and networks is a crucial role in any organization. CSE graduates can work as system administrators, ensuring the smooth functioning of IT infrastructure.

Cybersecurity Specialist: With the growing threat of cyberattacks, cybersecurity specialists are essential in protecting an organization’s digital assets. CSE graduates can pursue careers in cybersecurity, safeguarding sensitive information from hackers.

Conclusion: Why B.Tech in Computer Science and Engineering at SCOE is the Right Choice

Choosing the right college is crucial for a successful career in B.Tech in Computer Science and Engineering. Saraswati College of Engineering (SCOE) stands out as one of the best computer science engineering colleges in Navi Mumbai. With its industry-aligned curriculum, state-of-the-art infrastructure, and excellent placement record, SCOE offers students the perfect environment to build a successful career in computer science.

Whether you're interested in AI, data science, software development, or any other field in computer science, SCOE provides the knowledge, skills, and opportunities you need to succeed. With a strong focus on hands-on learning and personal growth, SCOE ensures that students graduate not only as engineers but as professionals ready to take on the challenges of the tech world.

If you're ready to embark on an exciting journey in the world of technology, consider pursuing your B.Tech in Computer Science and Engineering at SCOE—a college where your future takes shape.

[robomad]

The Future of Node.js: Emerging Trends and Predictions for 2024 and Beyond

Introduction:Node.js has been a dominant force in the world of web development since its inception, offering a powerful platform for building scalable, high-performance applications. As we move further into 2024, the landscape of web development continues to evolve, and so does Node.js. Understanding the future trends and predictions for Node.js is crucial for developers and businesses looking to…

[techtoio]

Exploring the Latest Trends in Software Development

Introduction The software is something like an industry whose development is ever-evolving with new technologies and changing market needs as the drivers. To this end, developers must keep abreast with current trends in their fields of operation to remain competitive and relevant. Read to continue .....

[intelisync]

2024's Game-Changing Technologies for Metaverse Development

Picture a universe where virtual and physical worlds blend seamlessly, allowing you to interact with digital elements in real-time. As 2024 draws near, groundbreaking technologies are shaping this metaverse, making such interactions more immersive and dynamic than ever.

The metaverse, an expansive network of virtual environments, is evolving rapidly as we approach 2024, driven by several key technologies. Virtual Reality (VR) and Augmented Reality (AR) are at the forefront, enabling immersive experiences that blend the physical and digital worlds. VR technology has advanced significantly, offering users enhanced graphics, realistic simulations, and responsive feedback that create fully immersive digital environments.

AR enhances the physical world by overlaying digital information, enriching experiences in retail, healthcare, and entertainment through interactive and engaging environments.

Blockchain technology is essential for the metaverse, providing a secure and transparent method for managing digital assets and transactions. Non-fungible tokens (NFTs) allow users to own unique digital assets like virtual real estate and art, while smart contracts facilitate automated and secure transactions. The decentralized nature of blockchain promotes trust and reliability, making it a crucial component of the metaverse's infrastructure.

Artificial Intelligence (AI) further enhances user experiences by creating intelligent virtual agents, personalized interactions, and realistic simulations. AI-driven non-player characters (NPCs) provide engaging and adaptive experiences, and AI technologies enable natural language processing and speech recognition for seamless communication between users and virtual environments.

Edge computing and 5G technology are critical for the seamless operation of the metaverse. By bringing data processing closer to users, edge computing reduces latency and improves the responsiveness of virtual environments. 5G networks provide the high-speed internet required for real-time interactions, supporting scalable and complex virtual environments.

The Internet of Things (IoT) and spatial computing further enhance the metaverse by capturing physical movements and translating them into virtual actions, creating realistic and immersive experiences.

Elevate your business with Intelisync's cutting-edge metaverse solutions. Reach out to Intelisync today and learn how our advanced technologies in VR, AR, AI, and blockchain can revolutionize your operations, enhance customer engagement, and drive your Learn more...

[solidrun]

Explore SolidRun Bedrock V3000: The Ultimate Edge Computing Platform

Delve into the innovative world of edge computing with SolidRun's Bedrock V3000. This cutting-edge platform is designed to meet the demands of modern edge computing applications, offering robust performance, scalability, and security. Whether you are developing for IoT, AI, or industrial automation, the SolidRun Bedrock V3000 provides the ideal foundation. Learn more about how SolidRun is redefining edge computing and discover the capabilities of the Bedrock V3000 to elevate your projects to new heights.

[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…

[bella03forbes]

Edge Computing: An Advantage or Threat for Cybersecurity

Today’s digital era is data obsessed and Edge Computing. With the growing number of IOT devices, an unprecedented amount of data is generated through different sources. This has ultimately given rise to an increase in cyber threats. All in all, effective data processing and the demand for robust security measures have become a mandate.

Small businesses and big-size industries are consistently working to protect their sensitive data from hackers or data breaches. In the realm of ever-evolving technologies, edge computing is a new frontier in cybersecurity. Edge computing is bringing about a lot of effects in a world that’s overcrowded with many cyber threats.

It’s far different from the centralized cloud system and consoles’ innovative approach to data processing. Nevertheless, as edges computing has numerous benefits, it also comes with challenges that must be overcome to ensure robust cybersecurity.

Read in Detail:

[vividverses]

Exciting developments in MLOps await in 2024! 🚀 DevOps-MLOps integration, AutoML acceleration, Edge Computing rise – shaping a dynamic future. Stay ahead of the curve! #MLOps #TechTrends2024 🤖✨

[jcmarchi]

AI inference in edge computing: Benefits and use cases

New Post has been published on https://thedigitalinsider.com/ai-inference-in-edge-computing-benefits-and-use-cases/

AI inference in edge computing: Benefits and use cases

As artificial intelligence (AI) continues to evolve, its deployment has expanded beyond cloud computing into edge devices, bringing transformative advantages to various industries.

AI inference at the edge computing refers to the process of running trained AI models directly on local hardware, such as smartphones, sensors, and IoT devices, rather than relying on remote cloud servers for data processing.

This rapid evolution of the technology landscape with the convergence of artificial intelligence (AI) and edge computing represents a transformative shift in how data is processed and utilized.

This shift is revolutionizing how real-time data is analyzed, offering unprecedented benefits in terms of speed, privacy, and efficiency. This synergy brings AI capabilities closer to the source of data generation, unlocking new potential for real-time decision-making, enhanced security, and efficiency.

This article delves into the benefits of AI inference in edge computing and explores various use cases across different industries.

Fig 1. Benefits of AI Inference in edge computing

Real-time processing

One of the most significant advantages of AI inference at the edge is the ability to process data in real-time. Traditional cloud computing often involves sending data to centralized servers for analysis, which can introduce latency due to the distance and network congestion.

Edge computing mitigates this by processing data locally on edge devices or near the data source. This low-latency processing is crucial for applications requiring immediate responses, such as autonomous vehicles, industrial automation, and healthcare monitoring.

Privacy and security

Transmitting sensitive data to cloud servers for processing poses potential security risks. Edge computing addresses this concern by keeping data close to its source, reducing the need for extensive data transmission over potentially vulnerable networks.

This localized processing enhances data privacy and security, making edge AI particularly valuable in sectors handling sensitive information, such as finance, healthcare, and defense.

Bandwidth efficiency

By processing data locally, edge computing significantly reduces the volume of data that needs to be transmitted to remote cloud servers. This reduction in data transmission requirements has several important implications; it results in reduced network congestion, as the local processing at the edge minimizes the burden on network infrastructure.

Secondly, the diminished need for extensive data transmission leads to lower bandwidth costs for organizations and end-users, as transmitting less data over the Internet or cellular networks can translate into substantial savings.

This benefit is particularly relevant in environments with limited or expensive connectivity, such as remote locations. In essence, edge computing optimizes the utilization of available bandwidth, enhancing the overall efficiency and performance of the system.

Scalability

AI systems at edge can be scaled efficiently by deploying additional edge devices as needed, without overburdening central infrastructure. This decentralized approach also enhances system resilience. In the event of network disruptions or server outages, edge devices can continue to operate and make decisions independently, ensuring uninterrupted service.

Energy efficiency

Edge devices are often designed to be energy-efficient, making them suitable for environments where power consumption is a critical concern. By performing AI inference locally, these devices minimize the need for energy-intensive data transmission to distant servers, contributing to overall energy savings.

Hardware accelerator

AI accelerators, such as NPUs, GPUs, TPUs, and custom ASICs, play a critical role in enabling efficient AI inference at the edge. These specialized processors are designed to handle the intensive computational tasks required by AI models, delivering high performance while optimizing power consumption.

By integrating accelerators into edge devices, it becomes possible to run complex deep learning models in real time with minimal latency, even on resource-constrained hardware. This is one of the best enablers of AI, allowing larger and more powerful models to be deployed at the edge. 

Offline operation

Offline operation through Edge AI in IoT is a critical asset, particularly in scenarios where constant internet connectivity is uncertain. In remote or inaccessible environments where network access is unreliable, Edge AI systems ensure uninterrupted functionality.

This resilience extends to mission-critical applications, enhancing response times and reducing latency, such as in autonomous vehicles or security systems. Edge AI devices can locally store and log data when connectivity is lost, safeguarding data integrity.

Furthermore, they serve as an integral part of redundancy and fail-safe strategies, providing continuity and decision-making capabilities, even when primary systems are compromised. This capability augments the adaptability and dependability of IoT applications across a wide spectrum of operational settings.

Customization and personalization

AI inference at the edge enables a high degree of customization and personalization by processing data locally, allowing systems to deploy customized models for individual user needs and specific environmental contexts in real-time. 

AI systems can quickly respond to changes in user behavior, preferences, or surroundings, offering highly tailored services. The ability to customize AI inference services at the edge without relying on continuous cloud communication ensures faster, more relevant responses, enhancing user satisfaction and overall system efficiency.

The traditional paradigm of centralized computation, wherein these models reside and operate exclusively within data centers, has its limitations, particularly in scenarios where real-time processing, low latency, privacy preservation, and network bandwidth conservation are critical.

This demand for AI models to process data in real time while ensuring privacy and efficiency has given rise to a paradigm shift for AI inference at the edge. AI researchers have developed various optimization techniques to improve the efficiency of AI models, enabling AI model deployment and efficient inference at the edge.

In the next section we will explore some of the use cases of AI inference using edge computing across various industries. 

The rapid advancements in artificial intelligence (AI) have transformed numerous sectors, including healthcare, finance, and manufacturing. AI models, especially deep learning models, have proven highly effective in tasks such as image classification, natural language understanding, and reinforcement learning.

Performing data analysis directly on edge devices is becoming increasingly crucial in scenarios like augmented reality, video conferencing, streaming, gaming, Content Delivery Networks (CDNs), autonomous driving, the Industrial Internet of Things (IoT), intelligent power grids, remote surgery, and security-focused applications, where localized processing is essential.

In this section, we will discuss use cases across different fields for AI inference at the edge, as shown in Fig 2.

Fig 1. Applications of AI Inference at the Edge across different fields

Internet of Things (IoT)

The expansion of the Internet of Things (IoT) is significantly driven by the capabilities of smart sensors. These sensors act as the primary data collectors for IoT, producing large volumes of information.

However, centralizing this data for processing can result in delays and privacy issues. This is where edge AI inference becomes crucial. By integrating intelligence directly into the smart sensors, AI models facilitate immediate analysis and decision-making right at the source.

This localized processing reduces latency and the necessity to send large data quantities to central servers. As a result, smart sensors evolve from mere data collectors to real-time analysts, becoming essential in the progress of IoT.

Industrial applications

In industrial sectors, especially manufacturing, predictive maintenance plays a crucial role in identifying potential faults and anomalies in processes before they occur. Traditionally, heartbeat signals, which reflect the health of sensors and machinery, are collected and sent to centralized cloud systems for AI analysis to predict faults.

However, the current trend is shifting. By leveraging AI models for data processing at the edge, we can enhance the system’s performance and efficiency, delivering timely insights at a significantly reduced cost.

Mobile / Augmented reality (AR)

In the field of mobile and augmented reality, the processing requirements are significant due to the need to handle large volumes of data from various sources such as cameras, Lidar, and multiple video and audio inputs.

To deliver a seamless augmented reality experience, this data must be processed within a stringent latency range of about 15 to 20 milliseconds. AI models are effectively utilized through specialized processors and cutting-edge communication technologies.

The integration of edge AI with mobile and augmented reality results in a practical combination that enhances real-time analysis and operational autonomy at the edge. This integration not only reduces latency but also aids in energy efficiency, which is crucial for these rapidly evolving technologies.

Security systems

In security systems, the combination of video cameras with edge AI-powered video analytics is transforming threat detection. Traditionally, video data from multiple cameras is transmitted to cloud servers for AI analysis, which can introduce delays.

With AI processing at the edge, video analytics can be conducted directly within the cameras. This allows for immediate threat detection, and depending on the analysis’s urgency, the camera can quickly notify authorities, reducing the chance of threats going unnoticed. This move to AI-integrated security cameras improves response efficiency and strengthens security at crucial locations such as airports.

Robotic surgery

In critical medical situations, remote robotic surgery involves conducting surgical procedures with the guidance of a surgeon from a remote location. AI-driven models enhance these robotic systems, allowing them to perform precise surgical tasks while maintaining continuous communication and direction from a distant medical professional.

This capability is crucial in the healthcare sector, where real-time processing and responsiveness are essential for smooth operations under high-stress conditions. For such applications, it is vital to deploy AI inference at the edge to ensure safety, reliability, and fail-safe operation in critical scenarios.

Computer vision meets robotics: the future of surgery

Max Allan, Senior Computer Vision Engineer at Intuitive, describes groundbreaking robotics innovations in surgery and the healthcare industry.

Autonomous driving

Autonomous driving is a pinnacle of technological progress, with AI inference at edge taking a central role. AI accelerators in the car empower vehicles with onboard models for rapid real-time decision-making.

This immediate analysis enables autonomous vehicles to navigate complex scenarios with minimal latency, bolstering safety and operational efficiency. By integrating AI at the edge, self-driving cars adapt to dynamic environments, ensuring safer roads and reduced reliance on external networks.

This fusion represents a transformative shift, where vehicles become intelligent entities capable of swift, localized decision-making, ushering in a new era of transportation innovation.

The integration of AI inference in edge computing is revolutionizing various industries by facilitating real-time decision-making, enhancing security, and optimizing bandwidth usage, scalability, and energy efficiency.

As AI technology progresses, its applications will broaden, fostering innovation and increasing efficiency across diverse sectors. The advantages of edge AI are evident in fields such as the Internet of Things (IoT), healthcare, autonomous vehicles, and mobile/augmented reality devices.

These technologies benefit from the localized processing that edge AI enables, promising a future where intelligent, on-the-spot analytics become the standard. Despite the promising advancements, there are ongoing challenges related to the accuracy and performance of AI models deployed at the edge.

Ensuring that these systems operate reliably and effectively remains a critical area of research and development. The widespread adoption of edge AI across different fields highlights the urgent need to address these challenges, making robust and efficient edge AI deployment a new norm.

As research continues and technology evolves, the potential for edge AI to drive significant improvements in various domains will only grow, shaping the future of intelligent, decentralized computing.

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[debajitadhikary]

10 Most important Technologies that IT Advisors use to Navigating the Future of Information Technology

Introduction as IT Advisors In the dynamic realm of Information Technology (IT), staying ahead requires a powerful arsenal of cutting-edge technologies. IT advisors, the guiding force behind digital transformations, leverage a spectrum of tools to optimize operations, enhance security, and propel their clients into the future. In this exploration, we’ll delve into the tech landscape that…

[needtricks-blog]

How to Prepare for the Top 10 Tech Trends in 2024

Technology is changing faster than ever, and it can be hard to keep up with the latest developments and innovations. But if you want to stay ahead of the curve and take advantage of the opportunities that technology offers, you need to be aware of the top tech trends that will shape the world in 2024 and beyond. Continue reading Untitled

[industrydesignservices]

Teksun's cutting-edge solution for IoT & AI EDGE Gateway that communicate with the different smart devices. To know more, visit @ https://teksun.com

[techtoio]

Emerging Tech Trends in the Internet of Things (IoT)

Introduction

The Internet of Things (IoT) is transforming our world by connecting devices and enabling smarter, more efficient interactions. In everything from smart homes to industrial automation, the IoT is leading a revolution in our living and working environments. In this article, TechtoIO explores the emerging tech trends in IoT, highlighting the innovations and advancements that are shaping the future. Read to continue link

[ralfmaximus]

Recall is designed to use local AI models to screenshot everything you see or do on your computer and then give you the ability to search and retrieve anything in seconds. There’s even an explorable timeline you can scroll through. Everything in Recall is designed to remain local and private on-device, so no data is used to train Microsoft’s AI models. Despite Microsoft’s promises of a secure and encrypted Recall experience, cybersecurity expert Kevin Beaumont has found that the AI-powered feature has some potential security flaws. Beaumont, who briefly worked at Microsoft in 2020, has been testing out Recall over the past week and discovered that the feature stores data in a database in plain text.

Holy cats, this is way worse than we were told.

Microsoft said that Recall stored its zillions of screenshots in an encrypted database hidden in a system folder. Turns out, they're using SQLite, a free (public domain) database to store unencrypted plain text in the user's home folder. Which is definitely NOT secure.

Further, Microsoft refers to Recall as an optional experience. But it's turned on by default, and turning it off is a chore. They buried it in a control panel setting.

They say certain URLs and websites can be blacklisted from Recall, but only if you're using Microsoft's Edge browser! But don't worry: DRM protected films & music will never get recorded. Ho ho ho.

This whole debacle feels like an Onion article but it's not.

Luckily(?) Recall is currently only available on Windows 11, but I fully expect Microsoft to try and shove this terrible thing onto unsuspecting Win10 users via Update.

Stay tuned...