Imagine a virtual replica of your entire factory floor, a power plant, or even a city – constantly learning, reacting, and predicting. This isn’t science fiction; it’s the power of digital twin technology.
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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 the 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 on the IT industry 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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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. The company partners with multiple
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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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The Growing Impact of AI in today’s Sports, Media & Entertainment Industries ATMECS – Content Team Fifteen years ago, the term AI or artificial intelligence was labeled a dream concept. A lot of individuals linked this to the various movies and shows from the sci-fi world. A good example would be the Steven Speilberg movie, A.I, where a highly-advanced robot boy dreamed to be human to regain his mother’s love. However, as the years have changed, so has humanity’s stance about this idea. AI is the process of inserting human intelligence into machines allowing them to think and learn as humans do. In addition, it has helped the continuously growing amount of data generated and stored, as well as capitalized on it. On a global scale, 54% of CEOs have attested to an increase and improvement in their productivity after introducing AI solutions in businesses. Not only does it help improve work efficiency in all industries, but it also reduces the risk factors that a person might face in dangerous circumstances. Apart from the health and finance industries, there are a lot of other industries that have also faced a growing impact of AI solutions. The growing impact of AI on sports Based on recent reports, the AI market in the sports industry is expected to see a growth rate of 28.7% CAGR between 2021 and 2026. This is based on the increase in the use of technologies like loT, and the commoditization of cloud tech, helping with the storage and processing of big data obtained from various sports. Over the years, there has been an increase in the use of AI to help players maintain their health and increase their performance. From one aspect, this can be seen through the use of artificial intelligence-based coaching (mapping motion of an athlete to spatial analytics & machine learning). This way, a player receives different types and forms of coaching compared to one coach’s style. The system can also analyze the game plan of other teams, coming up with the best possible solution to win a game. While sports are competitive on the field, they have also become competitive in the front office, using AI obtained data like the game’s complexity and speed to create a competitive advantage. Apart from the actual game, AI has also affected the sportswear and equipment industry. Through smart apparel that can monitor and increase a player’s performance, a lot of manufacturing companies are looking forward to major distribution. AI has also had a major growth impact on fans and their level of engagement in the sports industry. Unlike where everything was done by hand, with voice technology and chatbots, teams and clubs can learn more about their fans, what they expect, and vice versa. This, in turn, provides richer content for fans, making it more personalized and engaging. As a whole, content moderation and content recommendation software have now become dependent on AI to strive due to the sheer global scale of digital content adoption. The growing impact of AI on media & entertainment From 2011 to 2020, many media and entertainment companies have adopted AI solutions to push for investments and produce high-end content that can draw the audience’s attention. This includes major companies like Google, Walt Disney, Microsoft, Intel, Amazon, Netflix, and many more. A lot of professionals in the media and entertainment industry use AI as a new approach to creativity. A major area where AI has impacted the entertainment world is increasing user experience and personalization. A lot of individuals rely on online sources like Netflix to watch movies and shows. With over 93 million people across the world streaming movies, an AI system has made it possible to personalize and recommend movies and shows to each person. During the next five years, 72% of media and telecommunication companies expect their product offerings to receive a major impact due to AI solutions. This revolves around the designing, advertising, and planning areas. With the help of certain predictive measures and AI solutions, media marketing processes occur faster and more efficiently. For those looking to change their future through AI and next-gen technology, ATMECS is the place to go. Considered a technology accelerator, the company provides AI services that benefit clients and their end customers. With the continuous growth of AI in various fields, the world will soon reach a stance where one’s life experience will be enhanced due to AI solutions. Even with a significant impact on daily work life, 72% of business owners have stated that digital assistants have made life easier. Gaining a global scale audience, individuals who didn’t have easy access to AI, are now able to do so with today’s cloud technologies. ATMECS can help you – Let’s, Partner.
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AI Bands Together with Humans in the War against Pandemic Tushar Nayak – ATMECS Content Team Mother nature remains indomitable and unconquerable, but she also gave birth to human beings, our known universe’s most ingenious species. Looking back, we (humans) have been challenged time and time again, from surviving the ice age, to fighting the Antonine Plague of 165AD, The Black Death caused by the Bubonic plague of 1346 with a death toll of 200 million or Spanish flu of 1918 and the latest – Covid-19, we fought back with everything at our disposal and we have just begun to talk about wits and intelligence of humans. It was the creative spark in our ancestors, to do things efficiently, that brought about a change in human evolution no one ever anticipated; healing and healthcare being one of them. Yet, we cannot be ignorant of the fact that we have always remained a step behind and it is either because we are wired only to solve the problem(s) or maybe, mother nature does not trust us with 100% of our cerebral capacity yet she continues to leave a trail of breadcrumbs for us to look at the bigger picture. This concept of idea will remain debatable for years to come. Now that we have talked about healthcare we feel that we need help from an epidemiological level application that can not only solve problems but screen, diagnose and treat health problems while forecasting the future quicker than ever, in short, we need Artificial Intelligence to be the front runner. And, what’s more, surprising is that it was not humans who brought us news on Covid-19 first but an AI platform that scoured every inch of the internet and search indexes to find us a title that said, “7 found critically ill in Wuhan Province”. Unlike healthcare, AI has seen its rise in every sector from retail to banking from e-commerce to supermarkets, and continues to ease the stress on both organizations and end-customers. That said, “don’t you think that it will ease the burden of health workers and fight deadly viruses at the same time?” Seems like a crazy idea, well – “Every idea is crazy until it’s not.” Elon Musk. Let us look at the most critical elements Artificial Intelligence can take care of and believe us, we have factored in historical data against AI’s presence in the last few years and this is how it did (or, set out to do). With its strings of ever-evolving algorithms followed by magnetic resonance imaging (MRI) and/or Computed Tomography (CT) scan symptoms of ‘red flags’ thus alarm healthcare authorities. As humans, we are constantly looking for cost-effective solutions and AI can do just that by providing solutions on the go for faster decision making thus saving the life of the patients. Let us look at another scenario, the next time a patient comes knocking at the doctor’s office he or she can successfully trace the contact and monitor them for the future course of the disease and its reappearance, if at all. AI can track and predict the nature of the virus, its risk of infection, number of cases and death in a region, identify the most vulnerable regions through the available data and media platforms to find appropriate measures. It is also being used by research centres to analyze available data to fight Covid-19 thus speeding up drug testing in real-time. It is now a powerful tool to analyze, diagnose and test vaccination under-development. One of the areas where doctors and nurses spend most of their time is in the file room. Now, imagine finding all the information of a patient’s history on the click of a button. Then, multiply that times the number of walk-ins/interactions each day. It is hard to quantify the exponential savings AI could yield in terms of time and scale at which modern healthcare can impact saving lives and our communities. As a result oriented full-service engineering and R&D organization, all of us at ATMECS believe that Artificial Intelligence has been brought to existence for the good of humanity. It is our submission that AI, if nurtured right, will continue to become an essential part of our day to day lives and play a pivotal role in how we manage health care. We believe AI will increasingly become an enabler in detection, tracking and monitoring diagnosis not only on ground level but also on a scientific, systematic, and data-driven level reading through frequencies and patterns of health-related states and events (not limited to disease control). With that in mind, we strongly believe in bringing visible transformation for our clients through Automation, AI, adoption of leading edge integrated development platforms, CI/CD, DevOps, Cloud, and Big Data. To conclude, it is our humble belief that, through the above, neither are we overestimating AI’s presence in human life nor are we trying to undermine human intelligence but we are merely stating our opinion based on empirical evidence. Author – Tushar Nayak, ATMECS Content Team
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