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Preparing for Industry 5.0: Personalization and Human-Centric Automation

AI, Atmecs-Blog, high performance computing.

Preparing for Industry 5.0: Personalization and Human-Centric Automation ATMECS Content Team 3 Minutes Read Posted on May 14, 2024 Introduction The industrial landscape is undergoing a metamorphosis, transitioning from the automation-driven world of Industry 4.0 to a new chapter – Industry 5.0. This next phase ushers in an era of human-centric manufacturing and personalized automation. Imagine production lines that adapt to individual customer needs in real-time, crafting bespoke products or seamlessly integrating user preferences. Industry 5.0 empowers businesses to cater to a wider range of customer demands, fostering greater loyalty and brand trust. This shift signifies a move away from mass production and towards a more agile, customer-centric approach to manufacturing. What Makes Industry 5.0 Unique? Industry 4.0, often referred to as the “smart factory” revolution, laid the groundwork for automation and data-driven decision making. It connected machines, streamlined processes, and empowered factories to operate with greater efficiency. Industry 5.0 builds upon these advancements, but with a critical distinction – it places the human worker back at the center of the equation. Industry 5.0 isn’t just about efficiency; it’s about creating a future where humans and machines work together to achieve extraordinary results, while catering to individual customer needs and fostering a more sustainable manufacturing environment. Key Concepts of Industry 5.0: Personalized Automation: Imagine production lines that adapt to individual customer needs in real-time. This could involve customized product features or adjustments based on user preferences. Personalized automation empowers businesses to cater to a wider range of customer demands, fostering greater loyalty and brand trust. Human-Machine Collaboration: Robots won’t replace humans; they’ll become intelligent teammates, handling repetitive tasks with precision and tirelessly. Humans, freed from the mundane, can focus on higher-level problem-solving, creativity, and strategic innovation. This human-machine collaboration unlocks the potential for groundbreaking solutions and continuous improvement. Advanced Analytics: Data analysis will become the lifeblood of Industry 5.0. Factories will leverage sophisticated analytics tools to optimize production processes, predict equipment failures before they occur, and ensure consistent quality control. This data-driven approach fosters efficiency, minimizes downtime, and guarantees a commitment to excellence. Benefits of Industry 5.0: Expanded Market Reach: Personalized automation empowers businesses to cater to niche markets and offer a wider range of product variations, attracting new customers and fostering brand differentiation. Enhanced Customer Satisfaction: Industry 5.0 empowers businesses to meet the unique needs of each customer. This ability to personalize products and services fosters greater customer satisfaction, loyalty, and brand advocacy. Improved Worker Productivity: Human-machine collaboration frees humans from repetitive tasks, allowing them to contribute their unique skills more effectively. This shift empowers workers to focus on higher-value activities, leading to increased productivity and overall job satisfaction. Sustainable Practices: Greater efficiency and data-driven decision making pave the way for a more sustainable manufacturing environment. By minimizing waste and optimizing resource utilization, Industry 5.0 promotes environmentally conscious practices. The Evolving Landscape: Industry 4.0 vs. Industry 5.0 Industry 4.0 and Industry 5.0 represent distinct stages in the industrial revolution, each with its own philosophical core and technological focus. Philosophical Core: Industry 4.0 centered on automation and machine-to-machine communication. Its primary goal was to achieve peak efficiency through interconnected, autonomous systems. Industry 5.0 builds upon these advancements but prioritizes human-centricity, personalization, and seamless collaboration between humans and machines. Technological Focus:Industry 4.0 heavily relied on foundational technologies like the Internet of Things (IoT), cloud computing, and big data to enable automation and data exchange. Industry 5.0 incorporates these technologies while ushering in advancements in Artificial Intelligence (AI), advanced robotics, and human-machine interface (HMI) design. These innovations empower a more collaborative and personalized approach to manufacturing. Outcomes:Industry 4.0’s primary objective was to achieve increased efficiency and productivity through automation and data-driven insights. Industry 5.0 strives for those goals as well, but with an added focus on worker well-being, customization, and sustainability. It fosters an environment where efficiency intersects with human-centric design and environmental responsibility How ATMECS Global Can Help You Prepare for Industry 5.0 At ATMECS Global, we possess the expertise and experience to help your business embrace Industry 5.0. Here are some ways we can add value: Digital Transformation Consulting: We’ll help you assess your current infrastructure and develop a roadmap for integrating the latest technologies like AI, machine learning (ML), and the Internet of Things (IoT) for personalized automation. Custom Software Development: Our team can develop bespoke software solutions that seamlessly integrate with your existing systems and enable real-time data analysis for informed decision making. Advanced Analytics Solutions: We can help you leverage data to optimize production processes, predict equipment failures, and ensure consistent quality control. Talent Augmentation: As the focus shifts to human-machine collaboration, we can help you augment talent with the necessary skills to thrive in an Industry 5.0 environment. We can also provide skills training programs to upskill your existing workforce. Conclusion Industry 5.0 marks a paradigm shift towards a future where humans and intelligent machines work together seamlessly. By embracing personalized automation and human-centric design, businesses can unlock new levels of efficiency, innovation, and customer satisfaction. At ATMECS Global, we’re here to guide you through this transformation. With our expertise in digital transformation consulting, custom software development, advanced analytics, and talent solutions, we can help you prepare for a thriving Industry 5.0 future.

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Responsible AI

Responsible AI vs. Ethical AI: Understanding the Nuances

AI, Atmecs-Blog, high performance computing.

Responsible AI vs. Ethical AI: Understanding the Nuances ATMECS Content Team Introduction In our rapidly evolving digital era, AI’s profound impact across sectors like healthcare, finance, and entertainment raises crucial questions about its development and use. The concepts of “responsible AI” and “ethical AI” are central to this discourse, and while they often overlap, understanding their distinctions is vital for leveraging AI beneficially and safely. What is Ethical AI? Ethical AI addresses the moral dimensions of AI technology. It focuses on ensuring that AI systems operate in a manner that is fair, transparent, accountable, and respects privacy. These principles are designed to guide AI systems to not perpetuate biases or infringe on individual rights, thus maintaining moral integrity in AI operations. Responsible AI: Broader Than Ethics Responsible AI encompasses ethical AI but extends into the practical implementation of AI systems. It involves not only adhering to ethical standards but also complying with legal and regulatory frameworks. Responsible AI aims to manage AI systems effectively to ensure they are safe, reliable, and yield beneficial outcomes without unintended negative consequences Key Differences and Synergies Focus: Ethical AI centers on the intent behind AI development, promoting alignment with core moral values. In contrast, responsible AI is about practical application, ensuring the technology is used safely and effectively. Principles: Ethical AI principles include fairness, transparency, and accountability. Responsible AI integrates these but also includes risk assessment, governance, and evaluation of societal impacts. Importance of Responsible AI Building Trust: Establishing trust in AI systems encourages broader acceptance and integration into societal frameworks. Mitigating Risks: Proactive risk management in AI development helps prevent harmful outcomes. Maximizing Benefits:Ensuring AI serves the public good maximizes its potential benefits across communities. The Future of Responsible AI The trajectory of responsible AI is set towards greater standardization and regulation. Increasing focus on explainable AI (XAI) aims to make systems more transparent and understandable. Additionally, evolving human-AI collaboration necessitates ongoing ethical consideration to balance benefits against potential risks effectively. Conclusion The distinction between responsible and ethical AI forms the foundation for developing AI technologies that are not only powerful but also aligned with societal values and safety standards. As AI continues to reshape global landscapes, the role of these frameworks in guiding AI development remains crucial for ensuring technology serves humanity positively and responsibly.

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Harnessing AI: The Role of GPUs in Accelerated Computing within Data Centers

AI, Atmecs-Blog, high performance computing.

Harnessing AI: The Role of GPUs in Accelerated Computing within Data Centers ATMECS Content Team Introduction In an era dominated by data, the ability to process vast amounts of information rapidly and efficiently dictates the success of businesses across all sectors. From financial analysis to advanced medical research, the demand for quick data processing is critical. This has led to a shift from traditional CPU-based computing to more robust solutions like GPU-accelerated computing, especially in applications involving Artificial Intelligence (AI). Understanding GPU Computing Originally designed for rendering high-resolution graphics in video games, Graphics Processing Units (GPUs) have evolved into powerful engines for general-purpose computing. Unlike Central Processing Units (CPUs), which handle tasks sequentially, GPUs possess a parallel architecture that allows them to perform multiple calculations simultaneously. This capability makes GPUs exceptionally well-suited for algorithmic tasks that are parallelizable, which is a common characteristic of AI and machine learning computations.   Benefits of GPU-Accelerated Computing in Data Centers Enhanced Speed and Performance: GPUs dramatically increase the processing speed for compute-intensive tasks, crucial for AI model training and big data analytics. This acceleration results in faster insights and decision-making, providing businesses with a competitive advantage. Improved Efficiency: By offloading tasks from CPUs to GPUs, data centers can achieve higher data throughput while reducing power consumption, leading to significant cost savings. Scalability: As the need for data processing grows, data centers can easily scale their operations by integrating more GPUs. This scalability ensures that businesses can adapt to increasing demands without a complete overhaul of existing infrastructure.   Applications of GPU-Accelerated Artificial Intelligence and Machine Learning: Training AI models is computationally intensive and time-consuming. GPUs reduce the time required to train these models from weeks to hours, enabling more rapid development and deployment of AI technologies. Scientific Computing and Simulations: In fields like climate science and bioinformatics, GPUs accelerate complex simulations, allowing researchers to achieve more accurate results faster. Big Data Analytics: GPUs are instrumental in processing and analyzing large datasets, uncovering insights that can lead to innovative solutions and strategic business decisions. Deep Learning and Neural Networks:GPU-accelerated computing, AI model training, data center efficiency, scalable data processing, real-time data analytics, machine learning acceleration, energy-efficient computing, deep learning, neural networks, GPU technology advancements. Real-World Impact and Case Studies Healthcare: GPUs are being used to accelerate genetic sequencing and analysis, leading to quicker diagnoses and personalized medicine strategies. Automotive: Autonomous vehicle technology relies heavily on GPUs for real-time processing of environmental data to make split-second driving decisions. Finance: In finance, GPUs accelerate risk analysis and fraud detection algorithms, enhancing security and customer service. The Future of GPU Computing The landscape of GPU technology is continuously evolving, with improvements in processing power and efficiency. This evolution is driven by the growing demands of AI applications and the need for real-time data processing capabilities. As a leader in technology solutions, ATMECS stays ahead of these advancements, ensuring that our clients benefit from the most cutting-edge technologies. Conclusion The integration of GPU-accelerated computing into data centers marks a significant milestone in the journey towards more intelligent and efficient data processing. For businesses leveraging AI and complex data analytics, GPUs offer an indispensable resource that enhances both performance and scalability. At ATMECS, we are committed to empowering our clients by providing state-of-the-art GPU solutions that drive innovation and success.

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Industry 4.0, digital transformation in engineering, smart manufacturing, IoT, AI in engineering, digital platforms, project management, data analytics, real-time collaboration, cost efficiency in engineering.

Power of Digital Platforms in Industry 4.0

Atmecs-Blog, Industrial Engineering

Power of Digital Platforms in Industry 4.0 ATMECS Content Team Introduction In today’s rapidly evolving digital landscape, the engineering sector is experiencing a revolutionary shift away from traditional practices towards the adoption of digital platforms. These digital platforms are crucial in Industry 4.0, enhancing operational efficiency, fostering collaboration, and revolutionizing problem-solving methods. By integrating advanced technologies such as IoT and AI, digital platforms in industry 4.0 enable real-time data analysis and streamlined processes, empowering engineers to achieve groundbreaking outcomes. The Role of Digital Platforms in Modern Engineering Digital platforms are catalyzing significant enhancements in productivity and efficiency within engineering. By automating tasks that were once manual and error-prone, these platforms allow engineers to concentrate on more strategic aspects of their projects. For instance, complex simulations and analytics that previously took extensive time can now be executed swiftly and accurately, thanks to advanced computing capabilities. Moreover, these platforms facilitate seamless integration across various engineering disciplines, fostering an environment of cross-functional collaboration and innovation. Advantages of Enhanced Collaboration and Communication One of the most transformative impacts of digital platforms in engineering is the improvement of collaboration and communication. Traditional methods often involved slow and inefficient processes, such as face-to-face meetings and lengthy email chains. Digital platforms revolutionize these practices by offering tools like real-time document sharing, instant messaging, and video conferencing, which ensure all team members have instant access to the latest updates. This shift not only minimizes errors but also significantly boosts overall productivity.   Cost Efficiency and Resource Optimization Adopting digital platforms in engineering leads to substantial cost savings and resource optimization. The traditional reliance on physical prototypes and extensive testing facilities, which are both costly and space-consuming, is reduced. Virtual simulations and modeling replace physical testing, slashing expenses and accelerating development cycles. Additionally, the use of real-time data and analytics on these platforms allows for more effective resource management, promoting sustainability and reducing waste. Leveraging Data and Analytics In the era of Industry 4.0, digital platforms harness the power of data and analytics to provide engineers with deep insights that drive smarter decision-making. Integrated tools for data visualization and advanced analytics make it easier to interpret large datasets, identifying trends and potential issues before they become problematic. AI and machine learning algorithms further enhance these capabilities, offering predictive analytics and automated optimization suggestions that refine the engineering processes. Project Management and Tracking Enhancements Digital platforms transform project management by providing sophisticated tools that help monitor and control engineering projects with precision. Traditional manual tracking methods are replaced by automated systems that offer real-time updates on project progress, task completion, and resource allocation. This not only enhances decision-making but also ensures projects adhere to timelines and budgets, ultimately improving deliverables’ quality and efficiency. Real-World Applications Across Industries Automotive Industry: In the automotive sector, companies like Tesla utilize digital platforms to streamline vehicle system design and testing, significantly reducing time-to-market and manufacturing costs. Construction Engineering: Platforms such as Autodesk Revit transform collaboration among architects, engineers, and contractors, enhancing project efficiency and reducing costly rework. Aerospace Industry: Aerospace giants like Boeing leverage digital platforms to optimize aircraft design and production, improving fuel efficiency and safety standards. Challenges and Considerations of Digital Platforms in Industry 4.0 Despite their benefits, digital platforms in engineering also present challenges, including integration with existing systems, data security issues, and the need for continuous training. Addressing these challenges is crucial for organizations to fully capitalize on the advantages of digital transformation in Industry 4.0 Conclusion Digital platforms are reshaping the future of engineering, driving innovations that enhance productivity, reduce costs, and promote sustainable practices. As we continue to advance into the digital era, embracing these platforms will be essential for any engineering firm aiming to stay competitive and innovative.

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reverse engineering in ai

Reverse Engineering an API: Testing without Documentation

AI, Atmecs-Blog, Testing

Reverse Engineering an API: Testing without Documentation Author: J Saravana Prakash, ATMECS Content Team Introduction Testing APIs without documentation can be challenging, but it’s not impossible. Yet, you can find the information you require by doing some research. Since the use of APIs in software development is growing, it’s more crucial than ever to ensure that they function as intended. These days, a lot of applications exhibit practical functionality that lets users and developers use these services however they see fit, independent of a predetermined interface. Due to their versatility, APIs are now a necessary component of all companies. It’s essential to make sure everything functions as planned whether your team creates or maintains an API, whether it’s for internal usage in a single application or a publicly accessible service with thousands of users worldwide. Monitoring API Usage If an API is being tested by you or a member of your team, it is probably still being used and is probably still being actively developed. This means that you’ll have lots of chances to learn more about the API and obtain the understanding you need to start on your journey of exploration. There is no better way to understand an API’s functionality precisely than to observe it being used in practice. We are fortunate to have all the tools required to collect the different kinds of requests and responses required to test your APIs. Your browser has all the tools you require to gather this data for APIs used in web applications. Most contemporary web browsers, such as Chrome’s DevTools, Firefox’s Network Monitor, and Safari’s developer tools, offer means to examine network traffic. With the aid of these tools, you may look at requests and responses submitted to an API as well as the data and headers used in the exchange. It’s more difficult to record network activity for non-web apps like desktop or mobile apps, but it is still doable. Then, see if the application’s test builds are provided by your company’s development team. The majority of businesses that develop desktop or mobile applications produce early builds to aid in early testing. These test builds have a number of debugging options enabled, some of which might log interactions with external services. Not all hope is lost if you don’t have access to a test build or the test builds don’t give you the information you require. On your computer, you can set up a tool that can intercept network requests coming from any source. A good example of one of these tools is Telerik Fiddler, a web debugging proxy that will gather a bunch of data from your network traffic and let you examine everything that occurs when an application is running locally. You will receive sufficient information from these network inspection services to begin your testing. Exploring the Inner Workings of an API It may be intimidating for some testers, especially those without prior programming skills, to examine an application’s source code. The code repository, on the other hand, is a veritable gold mine of knowledge that can provide you with all you need to start your tests without any documentation. If a development team is still actively working on an API, that’s where you can obtain the most recent details on any application. The structure of an API can be learned by testers who are familiar with the fundamentals of programming by poking about in the codebase. Web application frameworks like Express JS, Angular, Ruby on Rails and Flask, for instance, often have a single location that specifies how requests are routed to various methods throughout the codebase. These files can be scanned to reveal available endpoints and their distinct actions, which you can use as a starting point for further exploration. It can supply practically everything you need to get moving, such as query parameters, request headers, and request bodies, if you look closely enough at these methods and their function signatures. Even if you have little to no knowledge of programming, a code repository can still give you a lot of useful information. Development teams typically use some sort of pull request workflow to keep track of significant bug patches or new features that were added during the software development lifecycle. Every time they deploy to production, some teams will compile a list of updates and create release notes. Those notes might give you an idea of what has changed in the API or give you a new lead for your tests. You should definitely look through the list of code commits and search for relevant messages for each change if you can’t find any other information. Getting Assistance from Developers If you encounter an API with incomplete or incorrect documentation and are struggling to understand its functionality, don’t hesitate to reach out to the developers for assistance. They have a deeper understanding of the APIs they created and can provide valuable insights and guidance. Developers can assist you by adding comments to the code or improving existing documentation to make it more comprehensive. If the developers are not available or the documentation is outdated, you can also seek help from online communities and forums. These communities often have experienced developers who can answer technical questions or provide guidance in testing an API. However, be cautious about sharing sensitive information about your company or API with strangers and prioritize cybersecurity. Keep in Mind to Leave Everything Better than You Found It Once you have successfully tested an API without documentation, it’s important to leave everything better than you found it. Consider creating documentation or improving existing documentation to avoid difficulties for future developers. Provide feedback to the developers about the API’s functionality and any issues you encountered during testing. Additionally, consider sharing your testing methods and techniques with your colleagues to promote knowledge-sharing and enhance the skills of your team. Conclusion Although testing APIs without documentation can be challenging, it is not impossible. By using techniques such as monitoring API usage, exploring the inner

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chatgpt impact

ChatGPT and its Impact on the IT Industry

Leave a Comment / AI, Atmecs-Blog

ChatGPT and its Impact on the IT Industry Author: Ravi Sankar Pabbati One of our team members had a wild idea long ago that one day there will be a technology to generate software applications given software requirement documents. To our surprise, we were astounded when ChatGPT came alive. We now had the capabilities of ChatGPT in generating code for a prescribed software programming task for example “In java how to split a list into multiple lists of chunk size 10”. What is ChatGPT? ChatGPT is a conversational AI chatbot tool designed to understand user intent and provide accurate responses to a wide range of queries. It utilizes large language models (LLMs) trained on massive datasets using unsupervised learning, supervised learning, and reinforcement techniques. These models are used to predict the next word in a sequence of text, enabling ChatGPT to provide insightful and accurate responses to user queries. What is the impact of chatgpt on IT industry? ChatGPT has the potential to be a game changer for software professionals, improving their productivity and speeding up the software development process. Programmers can now ask ChatGPT to write code for a given problem, check the code for improvements, ask conceptual questions on any technical topic or technology, and seek best practices to follow for any specific technology or problem. Furthermore, ChatGPT is much more than a search engine for technical information. It can understand the nuances of information(what, why, how, when) and provide insightful responses to queries that are difficult to obtain from traditional search engines. As such, it is becoming a go-to choice for developers who seek to quickly and efficiently find technical information. While some may fear that ChatGPT will reduce jobs, it should be viewed as a tool to match the ever-increasing customer demand for producing high-quality software in less time and on a smaller budget. It will help companies and individuals to conceptualize any idea and build it faster. In terms of software development, ChatGPT is already being integrated into modern applications with built-in AI capabilities. This is likely to challenge and disrupt traditional software applications, with ChatGPT becoming ubiquitous in almost all applications used on a daily basis, including office suites, productivity tools, development IDEs, and analytics applications. In the near future, we could see built-in ChatGPT tools for development IDEs that will assist software developers in suggesting, fixing, and reviewing code. Imagine the tools maturing to help us walk through code, explain the flow, and query the code base in natural language instead of text search. The possibilities are endless, and the impact of chatgpt on IT is likely to be significant. Limitations Although ChatGPT is proficient in generating code for specific, simpler problems, it may not be as effective in generating code for more intricate problems. To tackle more complicated problems, we might need to divide them into smaller subproblems and utilize the tool to generate code blocks that we can combine to solve larger issues. It is worth noting that not all answers and generated code produced by ChatGPT are necessarily accurate. Therefore, it is essential to exercise your own intuition and judgment to validate the answers provided by the tool. Conclusion ChatGPT has the potential to revolutionize the IT industry by improving productivity and enabling faster software development. As the technology matures, we can expect to see ChatGPT integrated into more and more software applications, making it an indispensable tool for software professionals.

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testing with cypress

End-To-End Testing In Cypress

1 Comment / Atmecs-Blog, Testing

End-to-End Testing With Cypress Author: Saravana Prakash J A positive user experience in any application is essential to keep customers loyal to the product or brand. End-to-end testing is done to evaluate this user experience as well as any other bugs in tasks and processes that any application might have. The testing approach starts from the end user’s perspective and simulates a real-world scenario. End-to-end testing and its benefits End-to-end testing covers parts of an application that unit tests and integration tests seldom cover. The primary reason is that unit tests and integration tests take a part of the application and assess the functionality of that part in isolation. Even if these isolated parts of the application work well individually, there is no guarantee that they will work seamlessly as a whole. Applying end-to-end testing allows you to test the functionality of the entire application. End-to-end testing is reliable and widely adopted because of its many benefits, such as: Reduction in efforts and costs  Increase in the application productivity Detection of more bugs Expansion of test coverage Information on the application’s health Reduction in time taken for the launch of the application in the market Tests are done from the end user’s perspective Holistic approach As an application scales to a greater level of complexity with additional features, adding even a small padding or margin can break the application in several places. At this stage, it becomes expensive to hire test engineers who will test the flow of the application in different scenarios from an end user’s perspective. To mitigate this, automated end-to-end testing tools can be used to reduce the time taken to test an application and the costs related to software product testing. Studies suggest that global cybercrime costs will reportedly rise by almost 15% annually over the next four years. If you are not convinced about the importance of cybersecurity in curbing these threats, the following points will help you understand its significance.  Choosing Cypress as your automated testing tool As applications evolve, so does the requirement for a testing tool that can handle different types of frameworks like Ruby on rails, Django, modern PHP, etc. There are many automated end-to-end testing tools available in the market, the most well-known being Selenium. But, in this article, we will focus on the capabilities of Cypress as the choice for an end-to-end testing tool. What is Cypress? Cypress is a comparatively new automated testing tool that is quickly gaining popularity. It is based on JavaScript and is built for the modern web. Contrary to the popular myth that Cypress can only be used to test JavaScript or node friendly applications, Cypress can actually be used to test any type of application. It was created to address the pain points QA engineers face while testing an application and is also developer-friendly. It operates directly in the browser and uses a unique Document Object Model (DOM) manipulation technique. Cypress allows you to create unit tests, integration tests as well as end-to-end tests. It is designed particularly for front-end developers. Pros of using Cypress Whenever you run a test on Cypress, it opens up a browser that allows you to see the tests being executed as well as the flow of the application in real-time, side by side. It also allows you to go back to the beginning and check which tests have failed and what that test’s output was, which is quite helpful in pinpointing and fixing bugs seamlessly. In addition to taking a screenshot of the test, Cypress also allows you to record a video of the entire testing process. This helps developers better visualize the bug and where the bug is occurring in the application. One of Cypress’ most powerful use cases is that it can run in your Continuous Integration (CI) pipeline. Anytime there is a change in your codebase, your CI pipeline will automatically run all your Cypress tests to ensure that nothing has broken in your application. Cypress also offers the option of parallelization, where different tests can run with multiple Cypress agents at the same time. The benefit is that it greatly reduces the overall time for running your tests. The code, the library, and the vocabulary used in Cypress are beginner friendly. Cons of using Cypress One of the main cons of using Cypress is that it does not allow testing of features which require the application to open another tab or browser. This is because, in Cypress, all the tests are performed in a single browser tab. At the moment, Cypress does not provide support for browsers like Safari and Internet Explorer. Conclusion Automated end-to-end testing tools have proved their benefits and are here to stay for the long run.Cypress is the next-generation testing tool, and its growing popularity is attributed to the fact that it is open-source and is constantly evolving. Its pros outweigh its cons, and is an excellent alternative to Selenium as an end-to-end testing tool.

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Cybersecurity: Its Significance And Top Trends

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Cybersecurity: Its Significance And Top Trends ATMECS – Content Team Cybercrime had cost the world $6 trillion in 2021. The costs are expected to increase up to $10.5 trillion by 2025. Investing in cybersecurity is the best course of action to protect against or deter criminal activities like hacking, unauthorized access, and attacks on data centers or computerized systems. It helps safeguard connected systems like software, hardware, and data from multiple threats and defends computers, mobile devices, servers, networks, and other electronic devices from malicious attacks. The best cybersecurity strategies provide an efficient security posture against cyber threats and malicious attacks that aim to access, change, destroy, delete, or extort systems and sensitive data. Why is cybersecurity critical? Cybersecurity is vital to minimize the risk of cyberattacks, and secure data and systems. The proliferation of digital technology, increased dependence on the internet and smart devices, complex global supply chains, and critical digital economy data have led to an increased probability of cyberattacks. Individuals, organizations, governments, educational institutions, etc., are all at risk of data breaches and cyberattacks. No one is immune to the cyber threats of today. Studies suggest that global cybercrime costs will reportedly rise by almost 15% annually over the next four years. If you are not convinced about the importance of cybersecurity in curbing these threats, the following points will help you understand its significance.  Increased exposure of organizations to attacks Cybercriminals try to access organizational data through employees, and the increased use of internet services and IoT devices worsens the problem. The criminals hack into the system by sending fraudulent messages and emails. Organizations with minimal or less than optimal security protocols cannot tackle such security threats. Organizations have to beat such threats 100% of the time while cybercriminals need to win only once to do irreparable damage. This is the reason why cybersecurity is critical in proactively preventing theft, hacking, fraudulent emails, viruses etc., before it happens. Increased cybersecurity threats to individuals Hackers may steal an individual’s personal information and sell it in unlegislated or unregulated markets like the dark web for profit. All data on personal mobile phones, computers, or other digital platforms is no longer safe. Individuals with high-profile identities or at-risk segments like senior citizens are the most vulnerable. Phishing, where the attacker sends fraudulent messages that appear to come from a recognized source, is one of the most frequent types of cyberthreats. Phishing algorithms run behind the scenes stealing login information and sensitive data and in many cases, installing malware on the devices. If you see a lot of emails in your inbox’s spam folder, chances are you received a phishing email. Expensive data breach costs Organizations cannot afford data breaches. Even the smallest data breach can amount to exponential losses due to litigation costs. Data breaches on average cost  $3.62 million, leading many small organizations to go out of business. According to recent research, the cost of breaches has increased quite a bit, and new vulnerabilities have prompted hackers to launch automated attacks on systems.  Modern day hacking Hacking and data breaches threaten network systems and make them vulnerable. Present-day cybercriminals range from privately funded individuals to activist outfits, from anarchists to well trained state sponsored actors. The scope of cyberattacks have also widened to include:  Information systems and network infiltration Password sniffing Website defacement Breach of access Instant messaging abuse Web browser exploitation Intellectual Property (IP) theft Unauthorized access to systems Increasing vulnerabilities Malicious actors take advantage of everyone – from business organizations and professionals to educational and health institutions. Vulnerabilities are prevalent everywhere, and every system is facing a new security threat. Cybersecurity professionals are constantly playing catch-up to mitigate the risks related to data and system security. Which are the top cybersecurity trends? The year 2022 is all about digital business processes and hybrid work, making it difficult for cybersecurity teams to ensure secured individual or organizational networks. The hybrid working environment has highlighted the need for security monitoring to prevent attacks on cyber-physical systems. Identity threat detection and response will be on top of the list for security leaders across organizations that engage multiple vendors for their IT needs. Data suggests 45% of organizations will experience attacks on software supply chains by 2025, three times as much as 2021. Vendor consolidation leading to a single platform for multiple security needs will cause disruption in the cybersecurity market but offer respite to consumers through innovative pricing and licensing models. One of the most talked about trends is the emergence of the cybersecurity mesh. A cybersecurity mesh is a conceptual approach to a security architecture that helps distributed enterprises integrate security into their assets. It is expected to reduce the financial impact of security incidents by 90% by 2024. Many organizations still don’t have a dedicated Chief Information Security Officer. It is expected that the CISO role will gain significant traction and the office of CISO will emulate both a decentralized and centralized model for greater agility and responsiveness. It is time to pay close attention to the aforementioned trends and understand the risks/benefits associated with cybersecurity. Organizations and individuals investing in development of best practices with respect to data and information security will not only insulate themselves from today’s cyber threats but also lay the foundation for sustainable growth in the future. How can ATMECS help? ATMECS Cybersecurity Practice helps our clients protect themselves against today’s cyberthreats with both tactical and strategic solution offerings. Our practice follows a metrics-driven approach to providing resilient and reliable security services and preventing cyber threats. We understand business risks, evolve mitigation measures for data threats and attacks, and enable security posturing to ensure an efficient working system. We provide scalable services that handle all our clients’ cybersecurity needs. References 8 Huge Cybersecurity Trends (2022) – Link Alarming Cyber Statistics For Mid-Year 2022 That You Need To Know – Link 7 Top Trends in Cybersecurity for 2022 – Link TOP TRENDS IN CYBERSECURITY 2022 – Link DEFENDING THE EXPANDING ATTACK SURFACE

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When To Choose Edge Computing?

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When Should You Choose Edge Computing Over Cloud Computing? ATMECS – Content Team When Should You Choose Edge Computing Over Cloud Computing? Edge computing is a distributed IT architecture and computing framework that includes multiple devices and networks at or near the users. It processes data near the generation source and enables processing at a higher volume and speed resulting in real-time action-led results. Edge computing helps business organizations by offering faster insights, better bandwidth availability, and improved response times. The process enables organizations to improve how they use and manage physical assets and create interactive human experiences. How is edge computing different from cloud computing? Cloud computing involves the deployment of different resources like databases, storage, servers, software, networking, etc., through the internet. Edge computing, on the other hand, helps increase the responsiveness of the IT infrastructural resources by processing data near the generation source. Organizations and industry experts remain optimistic about cloud computing’s future growth, but a few others bet on the benefits of edge computing. Here is a breakdown of the differences between edge computing and cloud computing. Speedy and agility Edge computing uses computational and analytical powers close to the datacenter to increase responsiveness and perception speed and boost well-designed applications. On the other hand, a traditional cloud computing setup does not match the speed of configured edge computing networks. Edge computing solutions provide low latency, high bandwidth, device-level processing, data offload, and trusted computing and storage. In addition, they use less bandwidth because data is processed locally. Scalability Scalability, in edge computing, depends on device heterogeneity. This means performance levels vary across devices based on device specifications. However, cloud computing enables better scalability related to network, data storage, and processing capabilities through existing subscriptions or on-premise infrastructure. Productivity and performance The computing resources are close to end-users in edge computing, which means the client data can get processed through AI-powered solutions and analytical tools that require real-time streaming of data. The process helps ensure operational efficiency and heightened productivity. Cloud computing removes the requirement of patching software or setting up hardware related to onsite datacenters, which enhances IT professionals’ productivity, improves organizational performance and minimizes latency. Cloud computing offers IAAS, PAAS and SAAS models as offerings catering to the infrastructure needs of organizations regardless of size or IT staff/expertise. Examples of edge computing Edge computing helps bring storage capabilities and data processing closer to ensure an efficient ecosystem. As the costs of ‘storage’ and ‘compute’ have been reducing steadily, the number of smart devices that can carry out various processing tasks with edge computing is growing steadily as well. The variety of edge computing use cases are increasing along with the increasing capabilities of artificial intelligence (AI) and machine learning. Big Data, where volume, veracity, velocity and variety of data matters, is one area where edge computing is poised to have the best business applications and returns on investment. Here are some examples of edge computing use cases: Autonomous vehicles By collecting and processing data about the location, direction, speed, traffic conditions and more, all in real time, autonomous vehicle manufacturers use edge computing to enhance efficiency, improve safety, decrease traffic congestion, and reduce accidents.  Remote monitoring of oil and gas industry assets To enable careful monitoring of oil and gas assets, petroleum companies use edge technology to observe the oil and gas equipment, manage cost-cutting, and enhance productivity. The process also includes visual inspection or monitoring of remote sites. As edge computing enables real-time analytics with processing much closer to the asset there is less reliance on good quality connectivity to a centralized cloud. Smart grid technology Smart grid technology collaborates with edge computing to enable side-based decentralized storage and generation, optimize energy efficiency, innovate business models, predict maintenance in product lines, and improve overall  operational operational efficiency.  In-hospital patient monitoring Use of edge computing can allow the hospitals to process data locally to maintain data privacy. It also enables real-time notifications to practitioners of unusual patient trends or behaviours, and creation of 360-degree view patient dashboards for full visibility. Content delivery Edge computing enables fast, efficient and secure content delivery by leveraging APIs, websites, SaaS platforms, mobile applications, etc.  Benefits of edge computing Edge computing optimizes data-driven capabilities by enabling data collection, reporting, and processing near the end user. The framework incurs multiple benefits during the process. Speed and latency With edge computing, data analysis is confined to the source where it was created and thus eliminating latency. The process leads to faster response times and makes the data relevant and actionable. Security Critical business and operational processes rely on actionable data that may be vulnerable to breaches and cyber threats. Edge computing helps diminish the impact of potential system risks and analyze the data locally providing security to the entire organization. Cost savings Edge computing helps categorize data from a management perspective by retaining it and reducing the requirement of costly bandwidth to connect different locations. The framework optimizes data flow, reduces redundancy, and minimizes operating costs. Reliability Devices that utilize edge computing can store and process data locally to improve its reliability. It helps eliminate temporary disruptions in connectivity and ensures zero impact on smart device operations. Scalability Edge computing ensures scalability by deploying IoT devices with data management and processing tools in a single implementation. It forwards the data to a centrally located datacenter to analyze the information and execute actions for faster business growth. Future outlook Edge computing will continue to improve with advanced tech enhancements like 5G connectivity, artificial intelligence (AI), and satellite mesh in the foreseeable future. The framework will help commoditize advanced technology by enabling wider access to high performance networks and automated machines. From software-enabled improvements to advanced computing solutions – the edge computing framework will open up opportunities for achieving organizational IT efficiencies through powerful processors, cheaper storage facilities, and improved network access. ATMECS aims to bring visible transformation in systems through edge-integrated development platforms and automation services. 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Why is Graph Technology a Critical Enabler For Future Innovation?

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Graph Technologies – Why is Graph Technology a Critical Enabler For Future Innovation? ATMECS – Content Team Graph Technologies are one of the trending technologies nowadays to help analyze vast amounts of information. To understand why this is so, it may be useful to first understand what a graph is? A graph (or more commonly known as a network diagram) is simply a set of objects called nodes with interconnections called edges. And, why would one want/care to study graphs? Because they are everywhere. From a company’s internal email/chat data to complicated stock market trends, from social networks to information networks or even biological networks, graphs are ubiquitous. This is why gaining expertise in graph technology can set your company apart from competition.  What Is Metaverse?  Metaverse is not a single technology, solution, or platform. Instead, participating in the metaverse is all about using web 3.0 technologies to create an immersive experience for the audience. For businesses, investing in the metaverse is implementing newer internet technologies such as Extended Reality (XR), Virtual reality (VR), Mixed Reality (MR), Internet of Things (IoT), Augmented Reality (AR), and mirror worlds with digital twins to provide an interactive environment for the end user similar to the real-life interactions. It is a technology concept of mixing the physical and virtual worlds of the customers. The crux of the technology is to improve engagement through immersion. Currently, the video game industry is growing leaps and bounds with VR headsets and unimaginably realistic graphics. The introduction of Non-Fungible Tokens (NFTs) has also increased the popularity of the metaverse, where users can create, buy and sell NFTs. These portable digital assets continue to gain value and momentum, especially in the blockchain world. Users can use cryptocurrency to invest in NFTs. All evolving and established companies nowadays pay high salaries for graph analytics practitioners to help with their businesses and their clients. Graph technologies have different business aspects/challenges considered each time, making them a much sought field of expertise. Discerning relationships and interconnections we thought never existed now can be studied using graph technologies. Covid-19 proved that graph technologies to understand contact tracing were going to be very important to the future of technology. Digital marketers are breaking ground into behavioural analytics by studying the types of websites one visits in a given day through graphs. It is probably safe to surmise graph technology, while still in its nascent stages, can be guaranteed to be one of the top analysing techniques in the upcoming decades. Graph Technologies and all you need to know about them. Graph Technology is one of the most up-and-coming analytical technologies. It is often noticed that traditional graph analytics are not able to comprehend or discern patterns as the complexity and scale of today’s networks grows rapidly. Hence, the emergence of advanced graph technologies. Graphs aid in the visualization of data and maximize the understanding of the network relationships concepts. Since networks are easy to visually comprehend, the empirical observations of relationships or interconnections becomes straight forward. Graph Technology helps organizations with a new and effective way of processing, managing, and storing enormous amounts of data. It is an innovative approach leading to timely insights helping grow businesses. For ex: Think of studying a network of people you get emails from and ones to respond to in a given day. Extrapolating the idea across the organization, can help HR discern who the power centers are or who the next (hidden) leaders are in an organization. Imagine doing a similar study if you work in the travel desk of the organization. Understanding patterns in business travel with graph technology can save an organization millions of dollars every year. For deeper understanding, graph technologies can be divided into three sections. They are – graph theory, graph analytics, and graph databases. Graph Theory Herein the graphs are drawn up and used to connect different paths and links of the objects and their interlinked relationships. Almost everything can be studied through graph patterns and understood instantly. Graph theory is a prominent part of the process as it lays the foundation for the whole procedure to be carried out further. Graph Analytics Issues arising in different subjects can be resolved by observing the general trends of the graphs and predicting the upcoming course of the concerned area. One of the most common uses of such graph technologies can be seen in the stock market. If you are into speculation trading, understanding false positives and for that matter, even false negatives, can make you quite lucrative if you are an expert in graph analytics. Graph Databases Graph databases allow people to store the results produced after the process of graph analytics is completed. Previously held data can be compiled in the same database to be easily accessible afterward. Data collection is one of the most prevalent examples of graph databases. Few leading graph analytics tools and databases include but are not limited to: Amazon Neptune, IBM Graph, Neo4J (this author recommends), Oracle spatial and graph, DGraph, Data Stax, Cambridge Semantics Anzograph etc. Why will developers and analytics practitioners prefer Graph Technologies? Graph technologies have started growing in the past couple of years, but the real question is – Are graph technologies worth the hype? Traditional analytics are based on concepts with long codes and hours of programs whose results are promising and accurate but time-consuming. It has been observed that while a specific amount of data can take up to 1000-4000 lines of code, it can be overcomed easily by completing the task in less than 400 code lines in Graph analytics. Ease of learning, ease of understanding and use, ability to scale, ability to handle complexity are all compelling reasons why graph technologies have now become very attractive. As cloud computing matures, we will see more practitioners wanting to innovate in the graph technology space. Graph technologies have use cases across industry domains as networks exist virtually everywhere. Gaining expertise in graphing technologies will ensure an exciting career path.

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