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ITRex Group

itrexgroup

“Quality is not an act, it is a habit.”— Aristotle Automation is not the next big thing. It’s the future that is now. It is easy to understand why. Technology does the job quicker and often better while freeing us for more creative tasks. When it comes to that helps us get rid of nasty bugs and make users happy, the benefits of are pretty much the same. A higher speed, reduced costs, and fewer errors. software testing automation testing However, many are slow to adopt. According to a , 25% of testers from across industries are just starting on their test automation journey or have no automation whatsoever. recent survey Common automation testing challenges range from a lack of skilled resources or knowledge to identify the right methods and tools to changing software requirements. Meanwhile, testing remains the number one reason for delays in software delivery cycles. The question is: Whether the laggards are just late adopters? It is possible as polls suggest that more than 40% of leading digital enterprises are looking to invest in QA automation solutions in the short term. If you are thinking about test automation as well but don’t know how to get started, stay tuned. Tips and practical examples are below. Benefits of automation testing, first things first So, exactly what are the benefits of automation testing? Above all, they are about higher test coverage and way faster release cycles for your product. It is because automated tests can be run 24/7. They can be run in hundreds in a matter of seconds. They can be run continuously and in parallel, something which is impossible to imagine with manual testing. With no human intervention, automated checks return more accurate results. And they can be reused, saving you money. What project can benefit from test automation? Any. But especially complex projects that require checking the same areas over and over again. If you are still not convinced, dig into more details. 1. You go to market faster With , you test new software features continuously - and deliver continuously as well. QA automation In fact, automated checks enable you to be truly agile, powering your CI/CD pipeline. Once you add automated tests to the process, tests are run the moment new code is complete, and your developers no longer have to spend days on trying to remember and fix code written many weeks ago. Teams detect errors and bottlenecks early, putting high-quality software on a faster track to market. Only good code passes the pipeline. But you want to make sure that your tests are working and you have the skill sets to prepare and prioritize new test cases for code updates. 2. You test more, quicker, and cheaper The more tests you perform, the fewer bugs escape to annoy your users. However, with manual testing, you may need either extra hands and extra time to repeat the same tests over and over again. Through the implementation of automated tests, it’s easier to run thousands of test cases all at the same time. Unattended. Overnight. Oh, another point worth mentioning. Once developed, the same test suite can be used repeatedly, either at subsequent release stages or on a totally new project, delivering significant cost savings. What’s not to like about that? How it works in practice: automation testing examples Many of our clients across industries have benefited from automation testing introduction. Below are just a few real-world examples from : our portfolio Leading global retailer A robust automation testing framework built by our QA team from scratch for a leading global retailer has accelerated the delivery of their complex big data platform. Their team can now run 200 automated UI regression tests in just seven minutes, with the previous 2 or 3 days of manual testing replaced with 30 minutes’ testing. Developer of a popular mobile dating app Our solution has automated more than a dozen tests for the registration/authentication flows and live-streaming functionality of a dating mobile app with millions of users. Test execution time reduced from one hour to 10 minutes as a result, freeing resources for more business-oriented tasks. Elearning company The development of an automation testing framework for a leading eLearning company has helped automate over 300 tests for their cloud data analytics platform. Manual testing time reduced by more than 25 hours in each release cycle. Like other senior-level IT executives from around the world, our clients also enjoy both reduced test cycle time and reduced test costs from best automation testing practices. Automation makes lofty promises and truly delivers on them. On top of that, it can be set up quickly, with immediate benefits. Expert advice on how to implement automation testing fast Is QA automation hard? Slava Markovski, QA Automation Team Lead at ITRex with 15+ years of experience in a global tech giant, agrees that test automation can indeed be challenging. Advanced skills are required, from test case design to knowledge of smart automation tools and automated test environments in agile. But once implemented, dependency on such skills reduces, while automation testing can be implemented really, really quickly. Here are his 5 great tips on how to do automation testing easily from scratch: Start with creating small test cases instead of spending a huge amount of time designing your intricate QA automation framework up front Smoke tests are an imperative Use remote machines to run tests from the very beginning Make sure you’ve got a well-thought-out plan for running tests in parallel and as part of your CI/CD pipeline Set up capabilities to analyze test results on a daily basis "Our automation testing strategy helps teams realize the first benefits from test automation in a matter of weeks," Slava says, and explains how: Keep it simple Some QA engineers start working on a new automation project with designing bulky scenarios. A costly mistake! Requirements evolve, along with business needs, and at the end of the day, your top-notch framework can be simply good for nothing. It’s essential to get complete product knowledge first and determine what you want to cover with automated checks. So, we start small. We develop small test cases that are easy to debug and reuse, while working on a dynamic framework that will keep up with app changes. Smoke tests Smoke testing is a software testing process that determines whether the deployed software build is stable or not. This basic health check run on each build is intended to confirm that we can proceed with more rigorous tests without wasting time and resources from flawed artifacts. In any good CI/CD pipeline, smoke tests should be automated due to their repetitive nature. While working on smoke test cases, our team also gets a chance to understand the app’s architecture and business logic better, which is important for building a robust QA automation framework. Remote test execution Remote test execution means deploying remote machines to perform tests across multiple systems, browsers, and system configurations. A key advantage is the ability to simultaneously execute hundreds of automated tests in different test environments. This approach helps us not only save time but increase coverage as well, and prevent our local systems from collapsing under the load. Parallel Testing Parallel testing also reduces test execution time dramatically. To test your app against a dozen devices, you need to write only ONE script and “push the button,” as soon as developers submit new code. Parallel testing boosts your ROI, as it brings increased test coverage to the table. We create test cases with parallelization in mind, trying to avoid test dependencies and hard-coding. CI/CD Automation testing enables continuous testing, which in its turn enables CI/CD in an agile or DevOps environment. In fact, you cannot achieve efficient CI/CD without a QA automation strategy. We make sure that we take full advantage of CI/CD practices to increase quality while allowing developers to deliver software faster. This means that testing occurs earlier and more frequently before release, with everyone in the team instantly notified if something goes wrong within the app. Tests are started by the timer. Reporting is automated. Remote and parallel test execution are in place, too. Daily Tests Analysis Regression tests, performed to ensure that a change in the code has not negatively affected the previously working code, are normally run on a nightly basis, with test results available in the morning. We start our working day with a thorough analysis of failed tests. So, failures are fixed early without delaying delivery. That said, efficient automation implementation is actually not that hard if you follow these simple rules, Slava adds. Conclusion The benefits of automation testing are obvious, but for many companies, test automation is still not at the level it should be. However, with users’ zero tolerance to bugs and increasing pressure on teams to speed up release cycles for fixes and updates, automation testing can be the only way out of this challenge. Technologies unlock hidden value and make you fly. All you need is the right strategy and the right skills to set up test automation best practices. Machines will do the rest. If you require advice on your automation journey,  please do not hesitate our Test Automation Team Lead Slava Markovski for more information. to contact Also published on: https://itrexgroup.com/blog/automation-testing-benefits-best-practices/

The Benefits of Automation Testing and How it Works

With the help of a facial recognition system, federal agents could capture a person suspected of illegal activity. One example of this in action is the following: The tool may detect a person-of-interest in the background of someone else’s selfie at the gym -- perhaps in the mirror in the background. So, the agents are able to get to that gym, ask about the person, and eventually capture them. This real-life story example, and many others, encourage businesses to benefit from and deploy facial recognition systems. AI services The global facial recognition market size was evaluated at and is expected to reach $8.5 billion in 2025, growing at a CAGR of 17.2%. $3.8 billion in 2020 However, the costs of facial recognition software are challenging to estimate. There are hidden expenses that companies tend to overlook and, as a result, surpass the allocated budget. This article breaks down the factors influencing the total price and gives tips on reducing expenditure. So, how much does a facial recognition system cost? What is a facial recognition system, and how does it work? Facial recognition systems are among biometric identification forms, such as fingerprints and iris recognition. considered the most reliable But the technology has its challenges. The facial recognition process often occurs in an uncontrolled environment with variable lighting conditions and dynamic backgrounds. Other factors affecting recognition quality include facial expressions, a person’s age, and ethnicity. A facial recognition system is composed of five main parts: includes servers and devices responsible for capturing images. Hardware: allows hardware devices to transmit images for further analysis either to the cloud or to other devices on-premises. Connectivity technology: a biometric tool that extracts faces from images and matches them to the existing database of faces for identification. Facial recognition software: a collection of identities, such as an employee database or a hub for social media images. Database of faces: an interface that enables users to view the results Client-side web/mobile app: Here is how facial recognition systems work: After detecting a face, facial recognition software reads the facial geometry, which encompasses around 80 different elements. The key features include the distance between the eyes, eye socket depth, cheekbone shape, and jawline length. When the analysis is complete, the tool will generate a facial signature as a mathematical formula and will compare it against other faces in the repository. According to a recent report by the , facial recognition algorithms have a favorable average error rate of 0.08%, up from 4.1% in 2014. National Institute of Standards and Technology (NIST) Facial recognition systems have many exciting . One simple example of this technology is photo tagging in Google Photos, where the tech giant compares faces in one image to a preexisting database of uploaded photos to identify users. applications in different industries Which Factors Influence Facial Recognition Systems’ Prices? Hardware Companies need to carefully consider their hardware choices to avoid getting their hopes high with a software solution that their devices can’t handle. But at the same time, firms shouldn’t overpay for computational resources that they will not use. To have a working facial recognition system, you will need to procure cameras, switches, kilometers of cables, and servers for data storage and processing (unless you’re planning to use the cloud). All these utilities will add up to your facial recognition system’s price. Camera Options The camera type and its location depends on the desired coverage, image quality, and angle of view. For example, if the device needs to capture a person standing one meter away, a 3-8mm lens camera is recommended. Examine the location where you want to install cameras. In the case of poor lighting conditions, opt for devices with built-in features that can compensate for the lack of light and still produce images that your facial recognition software can work with. Also, some facial recognition algorithms . You might consider purchasing cameras that come with pre-installed computer vision software installed and can accomplish tasks like pre-processing and face detection. require 3D cameras This arrangement will take some load off your custom facial recognition software, thereby increasing its speed. Such cameras can cost $100 per device. Here is an example of what such gadgets can do: https://www.youtube.com/watch?v=HNAeBwNCRek Hardware Configuration There is no standard hardware configuration suitable for every facial recognition task. So, organizations need to consider their requirements carefully. If the chosen format is not powerful enough, it will cause delays and quality degradation. Let’s assume the task at hand is to identify a person posing in front of a camera. To achieve this, we need two neural network models – a face detector and a face recognizer. We can process around six frames per second. With these requirements, one could make do with a low-cost graphics processing unit (GPU), and even a central processing unit (CPU) might suffice. However, if we complicate this task by tracking people’s trajectories and actions or even increasing the number of cameras, we will have to procure a more powerful and expensive GPU. Facial Recognition Software Different types of ready-made facial recognition software are available on the market. These tools vary in their features and pricing models. Here are a few examples to help you gauge how much does off-the-shelf facial recognition software costs: You can see those different vendors offer variable pricing arrangements allowing clients to select the suitable model based on the number of transactions planned per month, the speed of image processing, and the classifiers used. It is crucial to note that face recognition software costs cited above account for subscription fees only, which are just a part of the overall expenses. Facial recognition solution vendors allow client companies to use their APIs, but you still need to integrate them into your system. You can turn to custom software vendors to facilitate integration and build a client-side application, which allows you to reap the full benefits of the facial recognition software. For example, integrating a tool that only does face detection and identification will cost you at least $3,000, and this number will increase with the scope of the facial recognition solution. You might also require additional features, such as enhanced security or on-premises storage for sensitive applications where you can’t risk transmitting your data to the vendor’s cloud system. can help you build additional layers of functionality to supplement ready-made facial recognition solutions. For example, as a part of a large project, we developed a microservice for anti-spoofing as middleware between the client-side application and the facial recognition software API. Such microservice costs around $10,000 - $15,000, including development, training, and deployment expenses. Computer vision development companies So, your final facial recognition software costs will accumulate everything mentioned above. Looking solely into software vendors’ licensing prices can be misleading. Ready-made solutions are an excellent choice when you want to launch your product fast and avoid spending money on infrastructure. If you want a solution with specialized requirements, think of a facial recognition system at a hospital where some people wear a mask and some don’t. Then it is best to invest resources into building and training a custom facial recognition tool. Connectivity Requirements If a company operates from a remote location where different communicating objects are positioned far apart, you will need to establish a reliable communication channel. Such high-quality cables can be even more expensive than servers and cameras and will account for a large portion of the total costs of a facial recognition system. One tip to reduce connection bandwidth requirements is to install the server responsible for video pre-processing close to the cameras. In this case, the server will analyze the stream, extract images of interest, and transmit them further to the main server, instead of streaming the whole video. This configuration can operate on 1-2 Mbit per second, even with multiple cameras, if there are no strict requirements on responsiveness and stability. The Complexity of Your Facial Recognition Solution Logically, the more features you want to include, the higher the price is. Some off-the-shelf solutions, such as Face++, set their prices based on the classifiers used, with more complex classifiers being more expensive. The same applies to custom facial recognition solutions. The system may include classifiers, such as face detection, face verification, face grouping, similar face search, etc. The more models you accumulate, the more it will cost. But the number of classifiers is only one of several attributes impacting complexity. Other parameters include the solution’s scalability, number of images being processed, security requirements, availability, and fault tolerance. A simple solution that merely counts the number of faces in a picture will take a few days to build and train. The costs of developing this type of face recognition apps are around $1,000, while more complex facial recognition tools can cost tens and even hundreds of thousands. Here is one example of a rather complex facial recognition system. An enterprise risk management company based in the US reached out to ITRex to develop a comprehensive to identify people based on their unique facial features. biometric-powered cybersecurity solution Our team opted for Microsoft Face API to deliver a solution with a diverse set of functions. The resulting system designed cyber credentials based on biometric parameters for all user groups contained anti-spoofing measures, offered a secure data transfer channel, and had its proprietary cybersecurity protocol that can be used by third-party software. It also operated in high-load cloud environments, including microservices that enabled painless scaling and change implementation. The costs associated with such facial recognition solution could easily surpass $500,000. How to Minimize Facial Recognition System Costs in the Long Run? System complexity directly influences the costs of facial recognition software. You can deploy a tool with limited functionality for a few thousand dollars, while a highly complex and secure solution will cost you up to $1,000,000 and even more. The location of operations also influences the total price. If you need to survey large warehouses with hundreds of cameras, hardware costs will form a large part of your expenditures. But even with complex solutions, you can plan ahead to ensure the allocated budget is wisely spent. Don’t make the mistake of opting for a cheaper tool to end up spending on security fines more than you saved on the software itself. And those fines can be rather hefty. Not long ago, Clearview AI, a prominent American facial recognition company, was by the UK as it failed to obtain consent when gathering publicly available photos of British citizens and using them in training datasets. charged with £17 million in penalties Here are a few tips that will help you make cost-effective decisions: Opt for a ready-made solution if you have standard requirements and want something to be up and running in a matter of days without spending a lot on infrastructure. However, if you have unique and complex requirements, then it is best to invest in a custom facial recognition solution. Invest in your system’s security. If your favorite ready-made solution doesn’t offer any reliable options, you can to build an anti-spoofing middleware and additional security features. Also, make sure you are familiar with the data protection laws in the countries of your operation. hire a custom software development vendor If you decide to provide a training dataset yourself or compose it with your vendor, work on . Many open-source datasets are skewed towards the white male population. So, make sure the data you use represents your target population faithfully. eliminating bias Don’t procure hardware with barely enough power and storage capacity to cover your current needs. Leave a margin for estimation errors and possible business expansion. Also, as your facial recognition software updates, it might become more demanding. To sum up, the amount of money necessary for facial recognition software depends on multiple factors. And if you can take all of them into account, you will have a more realistic feel of what you can get with your budget. Don’t limit yourself to what is available on the market. If you have rather specific requirements, turn to custom software developers for tailored solutions. First published here: https://itrexgroup.com/blog/how-much-does-a-facial-recognition-system-cost/ Thinking about deploying a facial recognition system? ! ITRex computer vision experts will help you integrate with the selected vendor’s API, build client-side software, and even develop a custom facial recognition application if needed. Get in touch!

Things You Need to Know Before Installing a Facial Recognition System

Back in 2018, the American Civil Liberties Union found out that Amazon’s Rekognition, face surveillance technology used by police and courting departments across the US, shows AI bias. During the test, the software 28 members of Congress with the mugshots of people who have been arrested for committing a crime, and 40% of the false matches were people of color. incorrectly matched Following mass protests wherein Amazon’s employees refused to contribute to AI tools that reproduce facial recognition bias, the tech giant has announced a one-year moratorium on law enforcement agencies using the platform. The incident has stirred new debate about bias in artificial intelligence algorithms and made companies search for new solutions to the AI bias paradox. In this article, we’ll dot the i’s, zooming in on the concept, root causes, types, and ethical implications of AI bias, as well as list practical debiasing techniques shared by our that worth including in your AI strategy. AI consultants But let’s start with the basics. What is AI bias, and why does it occur? A simple definition of AI bias could sound like that: an anomaly in the output of AI algorithms. Bias in artificial intelligence can take many forms — from racial bias and gender prejudice to recruiting inequity and age discrimination. The underlying reason for AI bias is human misbeliefs, either conscious or unconscious, lurking into at different stages of their development. So, an AI solution adopts and scales the prejudiced assumptions of the human brain, both individual and societal. AI algorithms One potential source of this issue is Psychologists claim there’re about 180 cognitive biases, some of which may find their way into hypotheses and influence how AI algorithms are designed. prejudiced hypotheses made when designing AI models or algorithmic bias. An example of algorithmic AI bias could be assuming that a model would automatically be less biased when not given access to protected classes, say, race. In reality, removing the protected classes from the analysis doesn’t erase racial bias from AI algorithms. The model could still produce prejudiced results relying on related non-protected factors, for example, geographic data — the phenomenon is known as . proxy discrimination Another common reason for replicating AI bias is The training data may incorporate human decisions or echo societal or historical inequities. the low quality of data on which AI models are trained. For instance, if an employer uses an AI-based recruiting tool trained on historical employee data in a predominantly male industry, chances are AI would replicate gender bias. The same applies to natural language processing algorithms. When learning on real-world data, like news reports or social media posts, AI is likely to show language bias and reinforce the existing prejudices. This is what happened with Google Translate, which tends to be biased against women when translating from languages with gender-neutral pronouns. The AI engine powering the app is more likely to generate such translations as “he invests” and “she takes care of the children” than vice versa. AI bias can stem from The mistakes data scientists may fall prey to range from excluding valuable entries to inconsistent labeling to under- and oversampling. Undersampling, for example, can cause skews in class distribution and make AI models ignore minority classes completely. the way training data is collected and processed as well. Oversampling, in turn, may lead to the over-representation of certain groups or factors in the training datasets. For instance, crimes committed in locations frequented by the police are more likely to be recorded in the training dataset simply because that is where the police patrol. Consequently, the algorithms trained on such data are likely to reflect this disproportion. A no less important source of AI bias is People may reinforce bias baked in already deployed AI models, often without realizing it. For example, a credit card company may use an AI algorithm that mildly reflects social bias to advertise their products, targeting less-educated people with offers featuring higher interest rates. These people may find themselves clicking on these types of ads without knowing that other social groups are shown better offers, thus, scaling the existing bias. the feedback of real-world users interacting with AI models. What are the four common types of bias in artificial intelligence? The most common classification of bias in artificial intelligence takes the source of prejudice as the base criterion, putting AI biases into three categories — algorithmic, data, and human. Still, AI researchers and practitioners urge to look out for the latter as human bias underlies and outweighs the other two. Here’re the most common types of AI bias that creep into the algorithms. 1. Reporting bias This type of AI bias arises when the frequency of events in the training dataset doesn’t accurately reflect reality. Take of a customer fraud detection tool that underperformed in a remote geographic region, marking all customers living in the area with a falsely high fraud score. an example It turned out that the training dataset the tool was relying on claimed every historical investigation in the region as a fraud case. The reason was that because of the region’s remoteness, fraud case investigators wanted to make sure every new claim is indeed fraudulent before they travel to the area. So, the frequency of fraudulent events in the training dataset was way higher than it should have been in reality. 2. Selection bias This type of AI bias occurs if training data is either unrepresentative or is selected without proper randomization. An example of the selection bias is well illustrated by conducted by Joy Buolamwini, Timnit Gebru, and Deborah Raji, where they looked at three commercial image recognition products. The tools were to classify 1,270 images of parliament members from European and African countries. The study found that all three tools performed better on male than female faces and showed more substantial bias against darker-skin females, failing on over one in three women of color — all due to the lack of diversity in training data. the research 3. Group attribution bias Group attribution bias takes place when data teams extrapolate what is true of individuals to entire groups the individual is or is not part of. This type of AI bias can be found in admission and recruiting tools that may favor the candidates who graduated from certain schools and show prejudice against those who didn’t. 4. Implicit bias This type of AI bias occurs when AI assumptions are made based on personal experience that doesn’t necessarily apply more generally. For instance, if data scientists have picked up on cultural cues about women being housekeepers, they might struggle to connect women to influential roles in business despite their conscious belief in gender equality — an example echoing the story of Google Images’ gender bias. Why should businesses engage in solving the AI bias problem? With the growing use of AI in sensitive areas, including finances, criminal justice, and we should strive to develop algorithms that are fair to everyone. Businesses, too, have to work on reducing bias in AI systems. healthcare, The most apparent reason to hone a corporate debiasing strategy is that a mere idea of an AI algorithm being prejudiced can turn customers away from a product or service a company offers and jeopardize the company’s reputation. On the flip side, relying on an AI solution that performs accurately for the whole spectrum of genders, races, ages, and cultural backgrounds is much more likely to deliver superior value and appeal to a broader and more diverse pool of potential customers. Another point that could motivate businesses to dedicate themselves to overcoming AI bias is the growing debate about AI regulations. Policymakers in the EU, for example, are starting to develop solutions that could help keep bias in artificial intelligence under control. Certifying AI vendors could be one of such solutions. And along with regulating the inclusiveness of AI algorithms, obtaining an AI certification could help tech enterprises stand out in the saturated marketplaces. How to reduce bias in machine learning algorithms Solving the problem of bias in artificial intelligence requires collaboration between tech industry players, policymakers, and social scientists. And the tech industry has a long way to go before it could eliminate AI bias. Still, there are practical steps companies can take today to make sure the algorithms they develop foster equality and inclusion. Some industries and use cases are more prone to AI bias and have a previous record of relying on biased systems. Being aware of where AI has struggled in the past can help companies improve fairness, building on the industry experience. 1. Examine the context. Before actually designing AI algorithms, it makes sense to engage with humanists and social scientists to ensure that the models you create don’t inherit bias present in human judgment. Also, set measurable goals for the AI models to perform equally well across planned use cases, for instance, for several different age groups. 2. Design AI models with inclusion in mind. That would require establishing procedures and guidelines on how to collect, sample, and preprocess training data. Along with establishing transparent data processes, you may involve internal or external teams to spot discriminatory correlations and potential sources of AI bias in the training datasets. 3. Train your AI models on complete and representative data. While testing your models, examine AI’s performance across different subgroups to uncover problems that can be masked by aggregate metrics. Also, perform a set of stress tests to check how the model performs on complex cases. In addition, continuously retest your models as you gain more real-life data and get feedback from users. 4. Perform targeted testing. AI can help reveal inaccuracies present in human decision-making. So, if AI models trained on recent human decisions or behavior show bias, be ready to consider how human-driven processes might be improved in the future. 5. Hone human decisions. Additionally, keep in mind the adjacent issue of understanding how AI generates predictions and what features of the data it uses to make decisions. Understanding whether the factors supporting the decision reflect AI bias can help in identifying and mitigating prejudice. 6. Improve AI explainability. AI explainability: The trends in tacking AI bias Tech leaders across the globe are taking steps to reduce AI bias. And leveling out the demographics working on AI is one of their priorities. Intel, for example, is taking steps in improving diversity in the company’s technical positions. Recent data shows that women make up 24% of the company’s AI developers, ten percentage points higher than the industry average. Google has also rolled out AI debiasing initiatives, including featuring advice on making AI algorithms fairer. At the same time, a nonprofit dedicated to increasing diversity and inclusion in AI education, research, and development, breeds new talent for the AI development sector. responsible AI practices AI4ALL, Other industry efforts focus on encouraging assessment and audit to test algorithms’ fairness before AI systems go live and promote legal frameworks and tools that can help tackle AI bias. If you want to develop an AI solution that is bias-free, the ITRex team, and we’ll connect you with our AI experts. contact Also published on https://itrexgroup.com/blog/ai-bias-definition-types-examples-debiasing-strategies/ .

Solving the Problem of Bias in Artificial Intelligence

Hey Hackers! I’m Tony the T-Rex, and I’m the mascot at the ITRex Group. First of all, a huge thank you to the HackerNoon community and staff for nominating me for a 2021 Noonies award! I’ve been nominated in the following categories (please do check out this award page and vote): : HackerNoon Contributor of the Year - CONTINUOUS-INTEGRATION https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-continuous-integration HackerNoon Contributor of the Year - HEALTH: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-health HackerNoon Contributor of the Year - HEALTHCARE: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-healthcare HackerNoon Contributor of the Year - TECHNOLOGY: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-technology HackerNoon Contributor of the Year - TECHNOLOGY-TRENDS: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-technology-trends HackerNoon Contributor of the Year - STRATEGY: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-strategy HackerNoon Contributor of the Year - TECH: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-tech HackerNoon Contributor of the Year - AI: https://noonies.tech/award/2021-hackernoon-contributor-of-the-year-ai As someone from the information technology industry, I believe that today’s most exciting technology is artificial intelligence because it’s the cornerstone of transformation and innovation in business! Learn more about my thoughts and opinions on AI and other emerging technologies and my journey in the tech industry via the interview below. 1. What do you do and why do you do it? (tell us your story) As a spirit animal, I encourage my fellow solution architects, software developers, designers, QA engineers, business analysts, and innovation consultants to create emerging tech solutions for our clients. is an enterprise software development company operating in the USA and Eastern Europe. Our know-how spans all things artificial intelligence, data science, cloud computing, IoT, and extended reality (XR) technologies. ITRex Group 2. Tell us more about the things you create / write / manage / build! We care. That’s our motto. And that’s why we always go the extra mile to help enterprises detect inefficiencies, automate processes, boost productivity, and reduce operating costs. We also enjoy building next-gen software and hardware solutions from the ground up, helping innovative startups launch new products and services. Most often, the articles we share on our corporate blog and HackerNoon highlight the challenges we’ve encountered in the process and provide practical tips for solving them. 3. How did you end up on your current career path? Do you like it? I’m a T-Rex. When a humongous asteroid collided with Earth 65 million years ago, I thought it was a good idea to hide somewhere and hibernate. I woke up in 2009 when ITRex Group was founded. I got super excited about all the present technological advancements and thought, “Hey, these guys could benefit from my superpowers!” And here I am 12 years later, sharing tech insights on HackerNoon! 4. What tech are you most excited or passionate about right now and why? I believe in artificial intelligence. Had my ancestors mastered this powerful technology, they could’ve solved most of the challenges they’d been facing, including climate change and food scarcity. Also, I believe in data and the lessons we can learn from it. 5. What tech are you most worried about right now and why? Again, it’s artificial intelligence! A flawed algorithm can administer an incorrect drug or dose to a patient or accuse a person of a crime they did not commit! The sooner humans take a responsible approach to , the less harm algorithms will cause. AI development and implementation 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? I have all sorts of biases here, but I could do with some intelligent prosthetic devices to make my T-Rex arms a little longer! Are there any IoT startups working on that problem? Please let me know! 🦖 7. What are you currently learning? Tony the T-Rex is a constant learner! When the pandemic broke out, we thought it was high time to up our game in the healthcare and wellness sectors, and that’s what ITRex Group has been doing ever since. Right now, we’re focusing on emerging tech like telecare, the Internet of Bodies, and bioengineering. 8. What’s the best advice you’ve ever given someone? In case you’re a T-Rex, don’t try yoga! With arms like these, downward dog is a freaking waste of time! 9. What’s the best advice you’ve ever received? “Hurry up, hide, and hibernate!” That’s what my uncle said when he saw the asteroid coming. About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

Our Ancestors Could Have Solved Food Scarcity With AI - Noonies Nominee TRex

“I’ve never been able to predict the future of anything”, said Bob Edwards, one of the most accomplished American journalists. Even though this quote sits well with most people, the field of analytics is advancing, and it offers a great opportunity to look into some future events. Consequently, organizations are searching for to build solutions that will help them reduce costs, protect their data assets, and tailor treatments to individual patients. healthcare data analytics service providers So, how exactly does predictive analytics contribute to healthcare? Which risks are hospitals facing when deploying such tools? Keep reading this article to learn which type of events predictive analytics can reliably forecast. What is predictive analytics in healthcare? According to Deloitte, predictive analytics can be described as a branch of advanced analytics used to predict unknown future activities. Unlike traditional approaches to analytics that simply present processed information from the past, predictive analytics uses historical and real-time data to forecast future events and identify trends in patient care. To achieve this, predictive analytics employ various of techniques including data mining, statistics, . Simply put, predictive analytics solutions answer the question: what can happen next? AI, and ML What can predictive analytics accomplish in healthcare? In healthcare, predictive analytics solutions rely on big data and artificial intelligence. Predictive analytics in healthcare aggregates vast amounts of patient data incoming from electronic health records (EHR), insurance claims, administrative paperwork, medical imaging, etc., and processes it searching for patterns. With predictive analytics, healthcare providers can figure out: Which diseases are patients likely to develop? How will they respond to different treatments? Will they be a no-show in their next medical appointment? Will they return to the hospital within 30 days of discharge? Here’s what Vinnie Ramesh, CTO, and Co-founder of Wellframe, says about predictive analytics: “Combining these new datasets with the existing sciences of epidemiology and clinical medicine allows us to accelerate progress in understanding the relationships between external factors and human biology—ultimately resulting in enhanced reengineering of clinical pathways and truly personalized care.” Benefits of predictive analytics in healthcare Here are some examples of how healthcare leaders can benefit from predictive analytics: Reducing costs on appointment no show and readmission penalties Speeding up administrative tasks such as discharge procedures and insurance claims submission Preventing ransom and other cyberattacks by analyzing ongoing transactions and assigning risk scores Proactively preparing for upcoming population health trends Acquiring new patients through personalized campaigns 7 examples of predictive analytics in healthcare Predictive analytics is transforming the medical field in multiple ways. Here are seven applications that offer the most value to healthcare providers: Preventing readmissions Managing population health Enhancing cybersecurity Increasing patient engagement and outreach Speeding up insurance claims submission Predicting suicide attempts Forecasting appointment no-shows 1. Preventing readmission The costs of hospital readmission are rather high. According to HealthcareDive, Medicare spends around on readmissions. Additionally, hospitals are subject to under Medicare’s Hospital Readmission Reduction Program, which adds financial incentives to combat readmission. As research shows, in this program received this type of penalty. Healthcare predictive analytics can identify patients with traits indicating a high possibility of readmission so that doctors can allocate additional resources for follow-ups and personalize discharge protocols to prevent a quick turnaround. UnityPoint Health, a network of healthcare facilities, wanted to understand why patients are being readmitted. They simply asked them, “Why do you think you are back?” The answers varied from not having the means to buy medication to not being able to book follow-up appointments. UnityPoint Health aggregated these answers into a predictive model that would assign a readmission risk score to every visiting patient. A family physician, Patricia Newland, used the UnityPoint algorithm to prevent the readmission of one of her patients. The tool predicted this patient would experience symptoms during the next 13 to 18 days after discharge. Newland shared the results with her patient and instructed her to call the clinic as soon as the described symptoms appear. Sure enough, the patient called during the predicted time frame. Newland, who had already allocated time and resources, was able to see the patient on the same day and change her medication, thereby preventing readmission. UnityPoint Health could reduce patient all-causes readmission by of deploying the predictive analytics tool. Another example comes from Utah-based Health Catalyst, which provides data and analytics technology to the healthcare sector. The company developed a predictive health analytics solution to reduce readmissions. If you want to deploy their software, your data scientists will collaborate with the vendor to integrate your data into the program and establish a dashboard where medical staff can log in to see the likelihood of admitted patients developing a central line-associated bloodstream infection (CLABSI), which would ensure readmission. Thanks to Health Catalyst, the University of Kansas Health System . $26 billion annually heavy penalties 82% of hospitals How predictive analytics in healthcare reduces hospital readmission 40% within 18 months reduced their all-causes readmission by 39% 2. Managing population health This is another prominent healthcare predictive analytics example, which covers three aspects. Predictive analytics in healthcare helps medical institutions identify people at risk of developing chronic conditions and give them preventive care before the disease progresses. This type of analytics assigns scores to patients based on various factors, including demographics, disabilities, age, and past patterns of care. Diabetes Care published a study demonstrating that predictive analytics models for healthcare can determine a five to ten-year life expectancy for older adults with diabetes, enabling doctors to craft customized treatment plans. Additionally, predictive analytics in the healthcare industry helps identify potential population health trends. The Lancet Public Health journal published a study that used predictive analytics to uncover health trends. This study found that unless alcohol consumption patterns change in the US, alcohol-related liver diseases will rise, causing deaths. When speaking of outbreak predictions, one can’t help but ask, “could predictive analytics have foreseen the COVID-19 pandemic?” The answer is yes. BlueDot, a Canadian company building predictive analytics and AI solutions, about the rise of unfamiliar pneumonia cases in Wuhan on December 30, 2019. Only nine days later, the World Health Organization released an official statement declaring the novel coronavirus emergence. To this day, predictive analytics in healthcare helps authorities and ordinary people to have a view on the pandemic. For instance, a research team at the University of Texas Health Science Center at Houston (UTHealth) developed a predictive analytics-based tool for COVID-19 tracking. This program produces and maintains a public health dashboard that shows current and future trends of the virus. Shreela Sharma, Ph.D., and a member of the UTHealth research team outlined the benefits of this predictive analytics tool: “The dashboard identifies the current hot spots, predicts future spread both at the state and county level, and houses relevant public health resources. It can effectively inform decision-makers across Texas to help mitigate the spread of COVID-19.” Spotting chronic diseases with predictive analytics for healthcare Identifying public health trends with predictive analytics Detecting disease outbreaks with predictive healthcare analytics issued a warning 3. Enhancing cybersecurity According to the HIPAA Healthcare Data Breach Report, cyberattacks are frequent in the healthcare sector. The report revealed that most ransomware attacks involved data theft before encryption. April 2021 sustained in the healthcare sector. Seven of them resulted in compromising over 100.000 medical records each. 62 data breaches Cybersecurity predictive analytics in healthcare can positively contribute to this situation. Healthcare organizations can use predictive analytics coupled with to calculate risk scores for different online transactions in real-time and respond to events based on their scores. For example, during a login event, the system will grant access to a low-risk process and block a high-risk process or challenge it for multi-factor authentication. Furthermore, predictive modeling in healthcare can monitor data access and sharing to identify any changes in patterns, which could indicate an intrusion. Predictive analytics in cybersecurity comes in two high-level types. Each has many subtypes of its own: artificial intelligence solutions for the medical sector are searching for weaknesses in the healthcare system that can be exploited. These vulnerabilities range from misconfigurations to Common Vulnerabilities and Exposures (CVEs) that were not patched. Vulnerability-based solutions that look for potential threats. Threat-focused platforms Saif Abed, CEO of Clinical Cyber Defense Systems, advises healthcare clinics to consider outcome-based variants of both types. Here is what he said, “I recommend healthcare providers focus on outcomes-based analytics: solutions that can correlate technical findings with actual clinical and business metrics. If a solution can chart, forecast, and predict whether patient harm will happen or whether services will be shut down, then it’s a solution able to capture the attention of hospital executives at the highest levels irrespective – of their technical acumen.” 4. Increasing patient engagement and outreach To receive quality care, patients need to actively participate in their well-being. Another benefit is that studying behavior can help spot when patients are becoming non-compliant. Having this information, doctors can be proactive in their approach instead of waiting until non-compliance reflects on patients’ health. Medical facilities can use predictive analytics in healthcare to engage patients and strengthen their relationships with physicians. These tools can help create patient profiles, send customized messages, and craft strategies that would be more impactful on each individual. Lillian Dittrick, Fellow of the Society of Actuaries, said on the usage of predictive analytics: “When we use predictive models to look at all the variables, it helps us prioritize those patients who are really going to be receptive to changing something in their lifestyles, such as nutrition or exercise.” Hospitals and pharmacies can use predictive algorithms to analyze patient data and identify dynamic customer personas with their distinct preferences and patterns. Afterwards, physicians can relate to these personas while planning targeted outreach messaging on, for instance, treatment adherence and drug efficacy. The marketing department can use these personas to craft their email campaigns. patient engagement Predictive healthcare analytics enable targeted marketing 5. Speeding up insurance claims submission Health insurance can also benefit from predictive analytics. Such tools can help hospitals prepare insurance claims faster while minimizing errors. One example comes from Apixio. Headquartered in California, this company builds analytics-powered tools for healthcare. Apixio developed software that helps hospital coders to identify the correct codes for insurance claims. Those codes will determine how much insurance companies need to pay. Traditionally, hospital coders sift through vast amounts of information to find the right codes. Apixio’s tool mines patient medical records for relevant data and presents hospital coders with the selected pieces that will help them determine the best code options. 6. Predicting suicide attempts Suicide is in the US, taking lives of 14 Americans out of 100,000 annually. To improve this sad situation, a research team at the Vanderbilt University Medical Center (VUMC) developed a predictive analytics model that uses patients’ electronic health records to forecast the likelihood of suicide attempts by particular patients. The tool was tested at VUMC for 11 months. While doctors were receiving patients, the algorithm was running in the background, predicting the risk of patients returning for attempted suicide treatment. the tenth leading cause of death This predictive analytics in healthcare tool classified patients into 8 groups based on their estimated risk factors. The VUMC research team noticed that the highest-risk group accounted for over 33% of all suicide attempts. The team concluded that anyone who is placed into the high-risk groups should be checked for suicidal tendencies. Colin Walsh, assistant professor of Biomedical Informatics, Medicine and Psychiatry, described the benefits of such predictive models, "Today across the Medical Center, we cannot screen every patient for suicide risk in every encounter — nor should we. But we know some individuals are never screened despite factors that might put them at higher risk. This risk model is the first pass at that screening and might suggest which patients to screen further in settings where suicidality is not often discussed." 7. Forecasting appointment no-shows The US healthcare system is losing about on appointment no-shows, not to mention the associated administrative burden. For individual practitioners, the costs are averaging $200 per missed appointment. Predictive analytics in healthcare can improve cost efficiency by allowing hospitals and private clinics to forecast which patients are likely to skip appointments without prior notice. Consequently, they can prevent revenue loss and increase provider satisfaction. $150 billion per year Examples of predictive analytics for medical appointments no-shows Researchers from Duke University developed a predictive modeling tool that analyzes patients’ EHRs to identify potential no-shows. This software captured 4819 points of no-show in the Duke health system. The research team believes the algorithm performed well because it was trained specifically for this medical clinic. They strongly recommend retraining the acquired algorithms on the local clinical data. This will yield better results than when depending on the vendor’s training. Some healthcare organizations are taking it a step further and reaching out to the patients with a potential no-show record to make sure they are fine and offer care remotely. Community Health Network partnered with CipherHealth, a health tech company headquartered in New York, to deploy a data analytics solution that would reduce no-shows and facilitate patient outreach. With the new system in place, Community Health Networks can not only predict a no-show event but also reach out to the corresponding patient via their preferred means of communication (text, phone, email) to offer a brief remote consultation to satisfy the patient’s needs. Predictive analytics surely benefits healthcare providers. But there are things to consider Predictive analytics in healthcare offers many benefits and, as the industry amasses more and more data, the algorithms will improve in accuracy. However, these types of tools bring their own risks that medical facilities will need to address before unlocking their full potential. Deloitte describes three main risk factors associated with implementing predictive analytics solutions in healthcare: Healthcare providers are finding themselves in a constant need to advance their computer skills. And with predictive analytics, they will not only need to access dashboards but also keep capturing and processing patient data. It can be a challenge to find the balance between patient care and data collection during appointments. Gaining doctors’ acceptance. To overcome this challenge, one can involve doctors in the algorithm development process to ensure the solution addresses their needs. As Oscar Marroquin, Chief Clinical Analytics Officer at the University of Pittsburgh Medical Center, stated, “Being able to win acceptance of your models by clinicians—who are skeptics by nature—is often a challenge. By making end-users part of the whole process, we’ve been able to overcome that in the majority of cases. One manifestation of moral hazard is that people are faster to take risks when they know they are insured. Some doctors may no longer put a lot of thought into making decisions as they believe predictive analytics is responsible for the consequences. Ethics and moral hazards. To minimize this risk, everyone involved needs to understand that some decisions made by analytical tools are not binding but merely a suggestion. Clinicians still need to think them through and discuss with patients if necessary. There are several that can impact predictive analytics’ performance on particular datasets. What makes things harder is that there are no explicit regulations governing algorithm development. At the moment, the responsibility of producing fair tools falls on the vendor’s shoulders, and healthcare facilities are counting on the vendor’s goodwill. Algorithm bias and lack of regulations. types of algorithm bias To reduce the risk of bias, vendors can use feedback loops to improve their tools and eliminate any bias that still gets in. Also, organizations that are using predictive analytics in healthcare need to conduct regular audits to make sure the algorithms are still relevant. The global predictive analytics in the healthcare market keeps growing and is expected to hit . It provides plenty of opportunities for healthcare providers and health tech companies. If you are a medical facility manager, search for ways to deploy predictive analytics at your organization and ensure doctors and executives are on board. If you are a health tech startup founder, make sure the algorithms you develop are fair and suitable for the targeted population segments. $7.8 billion by 2025 Looking for your way to save time and money with predictive analytics? ITRex analytics and healthcare experts will craft a solution tailored to your unique needs and wants. Get in touch! Previously published on . https://itrexgroup.com/blog/predictive-analytics-in-healthcare-top-use-cases/

Top 7 Use Cases of Predictive Analytics in Healthcare

Augmented reality (AR) is popular among gamers. But can it be used in more serious settings, such as the healthcare sector? Since COVID-19 struck, more people seek physical and psychological medical help, while hospitals are looking to compensate for staff shortage and movement restrictions. Augmented reality in healthcare emerged as a viable solution to both problems. As an increasing number of healthcare facilities are turning to augmented reality development companies to craft custom AR solutions, the global AR in the healthcare market is expected to So, what is in healthcare? How does it add value to your operations? Where is the novel technology headed in times of COVID-19? And, finally, how to get started with AR in the medical field? These are some of the issues we’re going to address in this article. generate $1.918.6 million by 2026. augmented reality What is augmented reality, and why is it getting traction in healthcare? Augmented reality (AR) is a technology that blends elements from the real world with the virtual ones by overlaying computer-generated content on top of a live user environment. Speaking of the medical sector, AR aggregates health-related information, such as patients’ medical records or 3D models of internal organs composed from patients’ medical imaging data, and projects all this as a virtual component for the doctor to view while examining a patient in real-time. is the umbrella term that covers all immersive technologies: AR, VR, and MR. ) removes users from their surroundings and fully immerses them in an artificial digital environment with the help of a VR headset. , or hybrid reality, allows virtual and real-world objects to coexist and interact together in real time. During the surge of COVID-19, augmented reality . AR is just one type of existing immersive technologies. Extended reality (XR) Virtual reality (VR Mixed reality (MR) is more relevant than ever With the lockdown and social distancing measures in place, it is difficult to arrange the presence of external consultants during surgical procedures and to conduct group trainings for new healthcare practitioners. Moreover, hospitals are overloaded, and doctors have less time to spend on each appointment. Augmented reality, with its use cases in healthcare, caters to the needs of the medical sector during the pandemic, as you will learn from the following section. 5 medical applications of augmented reality AR helps conduct complex surgeries AR improves mental health therapy AR facilitates diagnostic procedures AR takes medical training and education to another level AR contributes to pain management 1. AR helps conduct complex surgeries It has been a challenge to bring AR to surgery, as superimposing computer-generated images (CGI) on top of a camera or smart glasses’ view leads to performance and perception issues. If you want to put virtual objects into focus, then real objects will become blurry and appear at different distances, which is a recipe for disaster during medical procedures. Another option for augmenting surgeries involves displaying virtual information on a separate screen, which would cause surgeons to constantly take their eyes off the patient. Incorporating augmented reality into healthcare allows projecting virtual objects within the doctor’s line of sight, and it doesn’t interfere with how real-life objects appear. took place in February 2020 at Policlinico Universitario S. Orsola of Bologna. The lead surgeon wore VOSTARS, which is a visor integrated with AR. The device enabled him to view virtual elements, such as the patient’s heart rate, body temperature, breathing rate, and some CT and MRI scans taken prior to the surgery. Dr. Philip Pratt, a Research Fellow in the Department of Surgery and Cancer at Imperial College London, speaks about the benefit of augmented reality tools for surgery: The first-ever augmented reality-assisted surgery “This technology allows us to experience the data that we have collected from patients before their operation in the most realistic and natural way. You look at the leg and essentially see the inside of it.” One FDA-approved augmented reality tool for spine surgery is Xvision Spine System, developed by Chicago-based Augmedics. This is a navigation system that guides surgical tools’ movements. Xvision determines the position of surgical tools in real time and builds a virtual trajectory imposing it on the patient’s CT data. The whole model is then projected on the surgeon’s retina through the headset. That way, the surgeon can see both the patient and the navigation data at once. Dr. Frank Phillips, Director of the Division of Spine Surgery at Rush University Medical Center, was the first to use Xvision to perform a lumbar fusion with spinal implants. The tool enabled Phillips to see the patient’s spine anatomy in 3D through the skin. Here’s how Dr. Phillips described his experience: Augmented reality in spine surgery “Traditional surgical navigation platforms have been shown to improve accuracy of implant placement, however using augmented reality allows for the advantages of traditional (non-3D) navigation plus the ability to visualize the patients spinal anatomy in 3D through the skin.” An example of AR-assisted brain surgery comes from LA-based Surgical Theater. The healthcare startup has partnered with Medtronic, a medical device company, to enhance its augmented reality technology with Medtronic’s StealthStation S8 surgical navigation system. This new technology will enable neurosurgeons to visualize brain structures and test different surgical equipment virtually before entering the operating room. EchoPixel, a medical visualization software provider, introduced an intraoperative program that delivers a 3D anatomic visualization of the human heart. This model is composed of different types of a patient’s medical images and displays blood vessels and other heart structures. This invention allows the surgical team to interact with the 3D model as if it was a real-world object. They can see patient-specific anatomy, capture accurate measures and distances. Augmented reality in brain surgery Augmented reality in heart surgery With the help of augmented reality in healthcare, patients can see how their appearance changes after a reconstructive surgery, even before entering the operating room. One example comes from dentistry. New Jersey-based SmarTek developed an augmented reality medical app, which uses a smartphone’s camera to apply virtual depictions of an improved set of teeth to the current patient’s photo. That way, the patient knows what to expect, and together with the dentist, they can adjust the features based on the patient’s preferences. Plastic surgery with augmented reality The pandemic presents a unique challenge for the healthcare industry, as the novel virus is highly contagious, and the risk of infecting the surgical team is rather high. Also, due to limitations imposed on travel, senior surgeons are not always able to attend and advise their junior colleagues. Augmented reality-assisted telesurgery looks like a viable option in these circumstances. The British Journal of Surgery describes who was admitted to a hospital with a persistent cough and medical history of diabetes and other disorders. His COVID-19 test turned positive, and the patient needed an immediate surgical intervention to alleviate COVID-related complications. Despite the difficulty of this case, a junior attending surgeon performed the operation. He used an AR-enabled telesurgery platform, which let him consult two other surgeons who could not be physically present. Augmented reality surgery in the COVID-19 era the case of a 59-years old patient 2. AR improves mental health therapy Augmented reality is diverse in its medical applications as it spans over the realm of mental well-being. For example, augmented reality can treat phobias. In safe settings, doctors can help patients immerse into a virtual reality that would trigger negative responses, such as fear and anxiety. Under a professional’s supervision, patients learn to control their feelings, thereby overcoming the mental health condition. Additionally, AR can be used for relaxation and meditation. The coronavirus infection and the subsequent lockdowns created extreme circumstances and . During the pandemic, reported symptoms of depression or/and anxiety, compared to only one in ten experiencing similar symptoms during the period of January-June 2019. Augmented reality treats mental illnesses during the pandemic took a toll on people’s psychological behavior four out of ten adults The Journal of Medical Internet Research published a paper describing the positive impact of playing augmented reality games during the pandemic. Within this study, reported that playing AR games was beneficial for their mental health. The participants also mentioned that AR games encourage them to exercise more often. Just recently, the University of Alberta developed YOU-AR-OK, an augmented reality healthcare app that prompts users to reflect on their feelings. This program features an avatar in the form of a dog. As soon as users create an account, this virtual dog is transported into their physical space. The animal asks users questions such as “how are you feeling?”, “did you get enough sleep?”, etc. With each question, people will open up without fear of judgment and recognize their psychological patterns. three out of four participants 3. AR facilitates diagnostic procedures Clearly communicating the symptoms plays a vital role in accurate diagnosis. However, many patients struggle to describe their symptoms realistically without downplaying or amplifying them. Augmented reality in healthcare can help both patients and doctors understand medical conditions better. Moreover, AR can help doctors handle other diagnosis-related tasks, such as taking medical notes. Every person’s anatomy is different, and with some patients, it is rather hard to spot their veins. According to statistics, miss the vein on the first attempt. AccuVein, a global producer of medical imaging solutions, has solved this problem. The company developed a device that uses AR to illuminate veins, enabling medical professionals to view the vein map clearly on patients’ skin while administering intravenous injections. While diagnosing patients, physicians must constantly look up relevant information on their screen, scattering their attention between their PC and the patient. Atheer, a California-based startup, developed augmented interactive reality (AIR) glasses that are connected to an Android-powered computer. These glasses can display relevant patient data within physicians’ field of view during checkups. Additionally, doctors can interact with the presented information via voice commands and hand gestures. Augmented reality-based healthcare apps permit users to perform self-checks, monitor their conditions, and contact a professional when needed. SkinVision uses machine learning, a , and augmented reality to assess any suspicious spots or moles on the skin. All you need to do is to take a photo of the affected area using the app, which will analyze the spot and classify it as high/medium/low risk. You can set a reminder to repeat the procedure to continuously monitor the progress. All these images are stored in the SkinVision database. Making veins easier to spot 40% of intravenous injections Bringing important data into view Enabling self-diagnosing subset of AI Taking notes during patient examination is a time-consuming task that prolongs the time allocated per patient. During the pandemic, hospitals are trying to reduce the duration of each office visit to be able to serve more patients in one day. Augmedix, a startup headquartered in San Francisco, provides tools for remote medical documentation. This product is designed specifically for the busy emergency department environment during the pandemic. All the virtual scribes are properly trained and use Augmedix note builder technology to produce consistent documentation. They capture key information from a patient's speech and suggest any educational material that a patient might find beneficial. To let physicians benefit from augmented reality in healthcare, the company integrates their remote scribing tool with Google Glass to display relevant patient information to the doctor in real time. Augmented diagnosis in the coronavirus era 4. AR takes medical training and education to another level This is one of the most relevant augmented reality examples in healthcare, given the lockdown and ubiquitous shift to eLearning. Augmented reality in healthcare can create 3D models of human organs. Medical students and trainees can interact with these projections, try to examine, cut, and treat them. Some advanced tools even allow physicians to perform complex surgeries virtually for the purpose of training, without the need to interact with the real human tissue. For example, TouchSurgery, a health tech company headquartered in London, developed a groundbreaking augmented reality surgery training platform that offers step-by-step guidance for over 300 surgical procedures. It is used globally in 160 surgical residency programs. AR for students and residents training AR-based solutions for patient education help individuals better understand their anatomy and the impact a disease or a treatment can have on their body. Medical Augmented Intelligence, a digital health company, headquartered in Taiwan, developed a tool called DigiTwin that enables patients to learn from their digital twins. The solution converts a patient’s medical images, such as CT and MRI scans, into a 3D model in a matter of . Patients engage with their digital twins under doctors’ supervision. They experiment and decide on treatments together. Since the hit, employers are looking for novel ways to conduct classes without gathering people in one location. With augmented reality-based medical training, every physician can learn individually from their office or home. Trainees can virtually practice medical procedures as described above. And they can also learn how to use medical equipment remotely by means of digital twins that mirror the functionality of real-life complex medical devices. Patient education with AR 30 seconds Augmented reality-supported medical education in the COVID-19 era pandemic 5. AR contributes to pain management Augmented reality in healthcare is emerging as a strong drug-free pain management tool. This technology allows patients who experience persistent pain to immerse themselves in a safe, relaxing environment with audible and visual stimulations. Such virtual surroundings alter people’s experiences, providing a distraction from pain. In addition to visualization, deep breathing is another powerful remedy against pain. Neon, an immersive technology provider, developed an application that enables people with chronic pain to use deep breathing as a pain relief technique. When patients put the Neon headset on, they are asked to inhale deeply. Upon exhaling, they see the surrounding virtual leaves move in response to their breath. This tool offers both visual and breath-based pain distractions. COVID-19 and the consequent lockdown add stress and anxiety, making chronic pain sufferers even more vulnerable. Several medical clinics are opting for virtual and augmented reality in healthcare to support pain management. For example, in response to the pandemic, Stanford Children Health Pediatric Rehabilitation Program supplies its young patients with VR and AR equipment to use during their telehealth sessions with doctors, as well as independently. Pain management with AR during the pandemic Tips to get started with augmented reality in healthcare If your medical facility is looking to adopt augmented reality to deliver superior patient care, provide accessible training, and improve internal workflows, we have some tips that will help you get started. Start small and simple: many augmented reality tools and vendors are available on the market, making it easy for AR newbies to lose focus. The only way to avoid costly mistakes is to start small and concentrate your attention on one tool or solution with a well-defined business case. Purchase/develop this tool, test it with your clinicians to see if it brings about the expected benefits (e.g., shortens patient examination time, supports decision making during surgery, etc.). Investigate whether your training program was successful and if your medical staff are willing to use the tool. Start with a simple tracking approach that has proven itself, like marker-based or marker-less AR driven by SLAM algorithms, which deliver perfect tracking quality in most cases. Think about legacy system integration: when introducing a new augmented reality solution in healthcare, check if it can be seamlessly integrated with your existing systems. If your hospital uses legacy software, choose an AR solution that can merge with the old system without disrupting other internal operations. Upgrade your technology: augmented reality in healthcare is becoming increasingly sophisticated. So, medical facilities need to make sure their infrastructure (software, hardware, cloud services, etc.) can keep up. Select the right software development kit (SDK) vendor: a vendor’s SDK will determine the set of features you can expect from your augmented reality application. If your AR tool requires a complex feature, such as recognizing a dynamic target, you need to discuss this in advance to ensure your selected vendor offers a kit that can support this requirement. Start with a Proof of Concept (PoC): this phase allows you to verify if the chosen technology/SDK works as expected before you commit time and resources to a full-blown project. Determine your content strategy: decide where to obtain content for your AR tool and how often it will be updated (if relevant). Research the prices of content creation. Sometimes, content costs are significantly higher than actual tool development costs. Work on safety and security: this aspect is particularly relevant if your AR data will be stored in the cloud. You need to secure data transmission between the cloud and the AR device. Think about how much of a problem Internet interruption can cause and how likely it is to happen. Can your solution work in offline mode (e.g., with the hospital’s restricted network)? When choosing a cloud vendor, opt for someone who is familiar with the healthcare sector and its demands, including compliance and data sensitivity. What to expect for AR in the medical field? Even though the augmented reality in is still in its early stages, it evolves fast and will soon make its way to operating rooms, doctor offices, and medical universities. According to Richard Chua, an MD from Northwest NeuroSpecialists, Arizona, As with many of our other enabling technologies, AR will find a place and penetrance. It may initially be best for training surgeons, but as high tech gets more and more integrated with artificial intelligence, machine learning, and data analytics, AR may become the new image guidance or robotic-assisted technology to gain popularity.” While thinking about improving your clinic and building a business case, remember that acquiring AR-based is not only a matter of development costs. You also need to consider deployment and merging with the existing systems, staff training, and content creation. healthcare medical software solutions Wondering if augmented reality can help you streamline clinical processes and benefit your patients? Their AR and healthcare experts will develop a solution that corresponds to your unique needs. Drop ITRex a line! Also published on . https://itrexgroup.com/blog/augmented-reality-in-healthcare-applications-examples-tips

A Healthier Reality: The Impacts of AR in Healthcare 

Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged. Delivering process automation and accuracy, computer vision technology is expected to have even greater momentum due to the COVID-19 pandemic as organizations have rushed to adopt automation on a larger scale. More nuanced use cases of computer vision in different industries are also predicted to emerge with the next evolutionary leaps in ; a field computer vision is a part of. Let’s look at the inroads already made by computer vision, its vast applications in different industries, and its benefits. artificial intelligence development What is computer vision, and how has it evolved? Computer vision (CV) is a that enables systems to interpret information from digital images and react to it with action or recommendations. The goal of computer vision technology is to emulate human vision for performing monotonous or complex visual tasks faster and even more efficiently. subset of AI , it all started with a lot of manual coding until machine learning progressed enough to allow developers to program smaller computer vision applications and apply statistical learning algorithms for specific CV-related tasks such as pattern recognition. With AI making major strides, ML algorithms have been largely replaced by deep learning or hybrid models that rely on neural networks to transform patterns into mathematical equations for information classification. After the launch of the first commercial software back in the 1970s, computer vision applications have evolved from enabling reading devices for the blind to transforming entire industries. Historically Some systems powered by computer vision have achieved 99% accuracy and can even surpass human performance (for instance, in ). diagnostic radiology The key drivers behind the surge in computer vision applications are: Spreading mobile technology that allows us to create and share billions of images a day Increasingly affordable computing power to process this massive amount of visual data New hardware A burst of deep learning algorithms What can computer vision do? — Major computer vision techniques With the growth in visual data and advances in computing power to process it, present-day computer vision applications rely on the following technology capabilities: to assign objects in a photograph or video to predefined classes. With binary classification, the system answers a simple question whether a particular object belongs to, say, a class of apples, tourist attractions, or cats. Object classification to locate an object in an image by enclosing it into a bounding box. Object localization to do both of the above to many objects in an image, assigning labels and localizing objects by drawing bounding boxes around them. Object detection to understand every pixel of an image and associate it with a class label (a car, a person, etc.) by creating object masks, with objects of the same class presented as a single entity. Semantic segmentation to do semantic segmentation and additionally identify different instances of the same class so that you don’t get just a one-color mask for three parked cars in a street view photo but label the vehicles with three different colors, identifying their boundaries. Instance segmentation Computer vision applications — Benefits and real-world examples Applications for computer vision have been expanding at a rapid pace over the last decade to reach a frantic level with the onset of the COVID-19 pandemic. Organizations now invest heavily in AI-driven solutions, from and to advanced manufacturing and government systems. Many are even concerned that AI technology is moving too fast for them, according to a retail software healthcare platforms survey of business leaders conducted by KPMG. While there is still an overall lack of AI regulation that comes with and bias risk, computer vision technology is a safer playing field due to its maturity. Given the investment speed and immediate benefits delivered by computer vision implementation, the worldwide market of computer vision solutions is to $19.1 billion from 2020 to 2027. AI explainability challenges projected to grow at a CAGR of 7.6% We’ve compiled a list of industries and use case examples to demonstrate how companies leverage advanced computer vision techniques to boost their results: Retail & Ecommerce Education Healthcare Fitness & Sports Precision Agriculture Manufacturing & Mining Cross-Industry Applications Computer vision applications in retail and eCommerce Benefits : Minimizing human interactions in stores to boost safety, deliver and lower labor costs digital shopping experience, Personalizing customer experience for increased engagement and more effective upselling and cross-selling strategies Leveraging next-gen in-store analytics to prevent stockouts, enhance store layout designs, and optimize staff scheduling Use cases: combine computer vision techniques with shelf sensors and deep learning to recognize shoppers, detect items they place into their carts, and automatically charge them on their accounts for items bought when they leave the store. Apart from the famous example, automated stores using sophisticated computer vision technology have also been launched by Chinese giants , , and . In the startup space, AI provider self-scanning scale solution can automatically identify fresh produce, allowing customers to check out without the need for barcodes or packaging. Automated checkouts and cashierless stores Amazon Go Lenovo JD Alibaba Tiliter’s powered by computer vision algorithms, can find the most efficient route to products on a customer’s in-app shopping list and recalculate the route if the person decides to look at other items. Currently, home improvement chain tests such an indoor mapping app developed with Google. Combining computer vision and the app reportedly locates users in an aisle more accurately than Wi-Fi or mobile phone systems while also providing access to product reviews. In-store navigation systems, Lowe’s augmented reality software, feature mobile barcode scanning software based on computer vision that recognizes objects for shoppers to get a personalized offer or information on the product in the store. Product information display apps Scandit’s that leverage deep learning algorithms make it more convenient for online shoppers to discover products, returning visually similar results. was among the first to offer its customers this engaging computer vision experience. Visual search solutions eBay are enabled by an array of computer vision-based systems. Among them are virtual mirrors that use augmented reality, allowing shoppers to try on various clothes virtually (like on site) or experiment with makeup products (like via app or the ). In-store virtual dressing room technology, embedded in Findmine’s solution, offers shoppers a touchscreen display with a CV-powered camera that recognizes items they are wearing to create an outfit based on images from the retailer’s catalog. Ecommerce sites are eyeing computer vision-enabled body scanning technology like the service to provide size or clothing recommendations. The skin care brand Neutrogena invites their customers to try the app that measures their skin health by assessing facial attributes and skin pixels and recommends a skin care routine. candy store uses facial recognition to identify regular shoppers for offering them personalized product recommendations or loyalty discounts. Personal recommendations Amazon’s Sephora’s Bourjois Magic Mirror Complete the Look Bodygram Skin360 The Lolli & Pops based on computer vision methods help retailers improve inventory management. Examples range from the technology that uses shelf-mounted computer vision cameras to alert staff about out-of-stock or incorrectly displayed items to the mobile robot that not only notifies shop assistants of gaps on shelves but also can detect damaged packaging and inaccurate pricing. Inspection systems Shelfie Tally use computer vision-powered cameras to analyze customer journeys and get product-related insights. Among them are famous pop-up store infused with capabilities to analyze dwell time, demographics, and other customer data, and Serbian fashion retailer store built in collaboration with Deloitte to gauge customer movement heatmaps. Connected stores Samsung’s Legend World Wide’s Computer vision applications in education Benefits: Understanding students’ learning behaviors to drive personalization and improve learning experiences Automating classroom monitoring to deter cheating in tests Assessing students’ papers to reduce the burden on educators Use cases: With computer vision-powered platforms, educators can measure students’ mood and behavior to capture signs of engagement or distraction both online and in a classroom. Solutions in this space include that uses facial recognition to analyze students’ emotional response to online content and the learning app that also reads facial expressions to detect frustration or boredom and adapt learning content. In addition, computer vision-driven insights help teachers regroup students into a more comfortable environment to improve learning. Student engagement detection and personalization. Emotuit Little Dragon has been made easy with computer vision-powered webcams that are used to identify students and flag cheating behaviors by tracking their postures or eye movements. Examples include with multi-factor authentication launched by the proctoring service and automated proctoring solutions like and . Attendance monitoring and automated online proctoring UAuto ProctorU Respondus Monitor Mettl is an area where computer vision is also expected to shine, as advanced algorithms are able to not only recognize responses written by students but also assist with their automatic evaluation. Handwritten character recognition Computer vision applications in healthcare Benefits: Improving patient identification to prevent wrong person procedures Delivering more accurate diagnosis through medical imaging analysis Providing assistance in surgery training and real-world surgeries for better outcomes Delivering rehabilitation assistance to patients Use cases: use computer vision-based cameras that help improve facial authentication of patients from check-in to discharge to prevent wrong person procedures. Patient identification systems assisted by computer vision is transforming radiology, helping practitioners interpret X-ray, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Computer vision applications in medical imaging also feature solutions to estimate a human pose in analyzing symptoms of neurological and neuropsychiatric disorders, monitor blood loss to optimize blood transfusions, diagnose eye conditions, and even detect COVID-19 (a deep convolutional neural network called has showed 90% accuracy in diagnosing COVID-19 based on chest X-ray images). Medical image analysis COVID-Net leverages computer vision technology to increase surgical precision. Apart from assistant surgical robot systems, there are solutions like that combine computer vision with ML and VR to create 3D visualizations for surgeons in the operating room. In the surgical training space, is a famous mobile simulator that provides a detailed guide to surgical procedures. Surgical simulation and assistance Proprio Vision Touch Surgery feature computer vision systems that are being developed to supervise exercise routines as part of at-home rehabilitation for rheumatoid arthritis, sports injury, brain injury, or stroke. Rehabilitation applications Computer vision applications in fitness and sports Benefits: Capturing performance data to aid coaches in training sessions and athletes in self-training Introducing advanced player or ball tracking methods to improve viewing experience or help referees in decision-making Collecting performance statistics for scouts, sports betting sites, and other industry professionals Use cases: powered by computer vision-enabled cameras detect and track moving players or balls in an array of games such as soccer, tennis, baseball or golf. Top examples include , designed by Sentio for soccer player tracking and analysis, and the optical tracking technology that gives football coaches a holistic view of matches. Computer vision-based systems are also used for improving shooting accuracy in basketball training ( ), help swimmers improve their techniques by collecting data from stroke rates to real-time velocity and turn times ( ), and even can take over in part the job of the umpire in professional tennis matches (the ball tracking solution). Tracking systems SentioScope SportVU 2.0 Noah System FINIS LaneVision Hawk-Eye that are based on computer vision techniques like pose estimation for motion analysis can help users improve their self-training, with , for instance, claiming that its fitness app powered by computer vision and ML can recognize yoga asanas with 95% accuracy. Such fitness solutions can also use markers as the intelligent fitness mirror does, unlocking data on user performance with the help of weight sensors to offer intelligent suggestions. Self-training solutions Zenia Carbon analyze the actions of players on the ice or field, producing meaningful insights either for building better game strategies or making smart decisions on players in the scouting market, or engaging viewers. Among famous examples are the system that collects raw data from video feeds to produce game models and the solution that can analyze any recorded basketball game for performance improvement and scouting insights. In-depth data analytics platforms Sportlogiq AutoSTATS Computer vision applications in precision agriculture Benefits: Identifying pests and weeds with greater accuracy to optimize the application of chemicals Monitoring crop development and the environment to maximize yields and produce better quality according to rising customer expectations Automating livestock management to prevent flock and herd losses and reduce the need for feet in the field Use cases: feature Blue River Technology’s y solution equipped with intelligent cameras that can distinguish between crops and weeds to apply herbicides to the right plants. Computer vision methods are also used for automatic identification and counting of flying insects and even for identifying apple diseases early. Pest and weed detection systems See & Spra are developed to detect the ripeness of fruit, including through color ratings of cherries in an outdoor environment or pick vegetables in a greenhouse using robots. Computer vision-aided monitoring solutions include , a 100% autonomous solar-powered drone system from XSun, that provides farmers with HD images capturing crop and soil conditions. AI solutions from Brazil’s can gauge information about the color, shape, and texture of crops. High-definition cameras from are equipped with sensors to help monitor soil moisture for yield prediction. Observation, harvesting, and prediction systems SolarXOne Cromai SWIR Vision Systems using drone technology can perform automatic counts, detect sick or injured animals, find strays, spot grazed areas, and even move cattle. A fair example is autonomous drone technology developed by Israeli firm to herd cattle. Livestock management systems BeeFree Agro Computer vision applications in manufacturing and mining Benefits: Implementing automated quality control to increase manufacturing accuracy, improve productivity, and produce better quality Deploying monitoring solutions to cut inspection time, minimize safety risks, improve operator productivity, and increase cost-efficiency Reduce human involvement to protect workers from hazardous environments Use cases: can be enabled by intelligent computer vision-powered cameras directed at a manufacturing line. Examples include machinery for the pharmaceutical industry to automatically count tablets or capsules on production lines and , a surface inspection system that identifies defects in items, stores images, and collects image-related metadata to classify errors by type and grade. Сomputer vision methods are also used to guide assembly operations, like assembly verification solutions from that measure product components versus manufacturing specifications, check caps and fill levels, and verify packaging components. Next-level quality control Pharma Packaging Systems’ WebSPECTOR Acquire Automation guided by machine vision automatically load or unload boxes and items to and from pallets. Robot palletizing systems assisted by computer vision technology and sensors have made it much easier to track the condition of critical infrastructure and determine when maintenance is needed. For example, FANUC’s takes photos and collects metadata to uncover any potential problems in the machine. Oil and gas giants such as Shell, ExxonMobil, and BP are using computer vision-powered predictive maintenance to anticipate failures in their equipment. Predictive maintenance systems Zero Down Time including drone-assisted systems, allow companies to conduct remote inspections of their sites and assets. This application of computer vision is especially important in mining, an unsafe industry for workers, where operators need to collect visual data in difficult areas. Visual inspections of well sites using the system, for instance, have enabled operators to reduce routine site visits by half. Intelligent monitoring solutions, Osprey Reach Computer vision in cross-industry applications systems using computer vision technology reduce inventory times from hours to minutes, delivering huge savings on operational costs. Such systems feature the computer vision-based platform that uses drones and connects to IoT devices for scanning and counting inventory. Another example is Amazon rolling out the robot technology at its sorting centers, claiming that the robot can improve sorting accuracy by 50%. Warehouse management Gather AI Pegasus enables a contactless delivery process for retail, logistics, post, and parcel businesses, transforming smartphones and other smart devices into computer vision-enabled barcode scanners like solutions from . The frictionless way to pick items from warehouses, distribution centers, or retail rooms is growing in popularity in the age of COVID-19. In retail, barcode scanning software is used for order fulfillment while allowing shoppers to safely collect items with one contactless mobile scan. Mobile computer vision Scandit using computer vision help keep public spaces safe during the pandemic by detecting ill employees or students as well as monitoring social distancing or exposure times. At construction sites and on the manufacturing floor, computer vision systems like monitor behavior-based safety and can protect people working around hazardous zones by alerting machine operators of dangerous events or incidents. Safety monitoring solutions IRIS What's next? It might be hard to believe, but we can uncover more computer vision applications and benefits with the advancement of technology such as edge computing, emotion AI, mixed reality, and embedded vision. And they can be quite incredible as artificial intelligence will get as sophisticated as we humans are. if you want to explore the benefits that computer vision can bring to your organization. Their AI experts will be happy to help you with complex or straightforward computer vision initiatives to address your specific business needs. Drop ITRex a line Previously published on https://itrexgroup.com/blog/computer-vision-applications-in-different-industries/

Computer Vision Applications are Everywhere: Top Use cases in 2021

Online learning has been there for years. Yet today, eLearning technology poses a real challenge to the traditional education models with more and more schools and universities turning to . Still, many educational institutions feel skeptical about adopting online learning tools. “A lack of in-person interaction sucks all the joy out of education” and “innovations in online education are too complex to handle” are the most commonly cited barriers halting the future of online and blended learning. artificial intelligence solutions for education The good news is that artificial intelligence can help overcome these barriers and transform eLearning as we know it. To prove that, we’ve summarized key online education trends giving a look over the applications of AI for eLearning and offering a blueprint for a successful AI adoption. So, let’s waste no time and dive right in. AI and eLearning: Top 4 trends in online education 1. Personalized learning With artificial intelligence in eLearning systems, educators can create personalized learning pathways and provide students with tailored content following their academic performance and learning goals. An AI engine may be integrated into a school’s LMS system, turning a simple data repository into a real-life version of the Hogwarts’ sorting hat. The only difference is that it puts students into learning buckets, like visual and aural learners or loners and team players. So, students who don’t easily succeed in a standard one-size-fits-all paradigm can, for example, take a survey that would identify their learning styles, preferred educational resources, and other performance-influencing factors. And the AI engine powering the survey would draft recommendations on how to adjust the curriculum and the instructions to make the student engage. Take, for example, the that is currently implemented across schools in Flanders, Belgium. Flemish educators leverage AI and online education to embrace personalized learning. They’ve partnered with a tech vendor to create a portal for deploying various EdTech apps for different domains, topics, proficiency levels, and learning profiles. The portal is to be rolled out in at least 10% of the Flemish schools by September 2022. i-Learn program 2. Skills-oriented learning Educators often think that online education lacks practice. But tapping in eLearning trends, schools can implement interactive online learning with a focus on building up vital skills. AI-powered chatbots, interactive mobile apps, and virtual assistants can help students master hard and soft skills, like speaking a foreign language, diagnosing a patient, or leveling up their communication and problem-solving skills. Students who are embarrassed to speak a foregin language, for example, can tame their anxiety conversing with AI-powered chatbots. The chatbots — now accessible through popular language learning apps like Duolingo — allow learners to go through life-like communicative scenarios and prepare for offline conversations. In turn, online assistants like (Social Agent Mediated Interaction) can help address a lack of social contact and emotional engagement for online students, informing them about interests and backgrounds they share with classmates. custom-made SAMI One of the hottest online education trends, Coursera-like online educational platforms are often used to enable skills-based learning. Colleges and universities, too, are and switching to fully online degrees based on similar eLearning platforms. In such cases, AI steps in to improve student retention and keep online learners engaged. AI technology in online education can monitor how consistently a student attends online lectures and submits assignments. And if the algorithms predict a possible drop-out, they give the student a push to go forward with learning. The experience of edX, for example, proves that a timely nudge accounts for a 30% higher student retention. increasingly adopting tech 3. Automated administrative tasks AI technology used in online education spares tutors and instructors the trouble of manually reviewing and grading assignments and monitoring students during online tests so instructors can have more time to develop quality eLearning content and interact with students. AI-powered reviewing and grading software can independently “read” and grade assignments building on the grades given by human instructors. After training, such software can bulk-check student papers with the precision of a human. Grading software has already in science, technology, engineering, and math classes, unbiasedly grading two times more assignments than a human professor during the same time. However, grading systems are more challenging to apply in social sciences and humanities. At least 21 states in the US to evaluate essays, from middle school to college level, yet AI algorithms powering the adopted software remain biased. For example, using advanced words tricks algorithms into thinking an essay is well-written when in fact, it doesn’t have any substance. The algorithms have also turned out to be biased against students from different nationalities and cultures, deeming their essays as poorly written. proved its effectiveness use technology When it comes to preventing cheating at online exams, AI-powered remote proctoring software can help. Educators may rely on proctoring solutions to automatically detect suspicious behaviors by recording students during tests via their computer’s camera or preventing students from switching browser tabs. An Educause poll that 54% of institutions are already using eLearning tech for proctoring, and another 23% are considering using it. However, some educational organizations failed to weigh out the ethics of remote proctoring, which caused severe resistance from students and parents. Still, there are institutions whose approach to remote proctoring can be named a best practice. Florida State University, for example, encrypts all of the data collected by proctoring software, and it is professors, not the AI algorithms, that make the final decision on whether a student cheated or not. suggests 4. Eliminating barriers International students or students with special needs benefit from artificial intelligence eLearning tools as well. Translation tools, for instance, can accurately translate a lecture into a student’s native language. Voice recognition and speech-to-text software can transcribe lectures. And referencing tools can help students grasp complex content, say, breaking down a lengthy reading assignment into a set of simple, easily digestible, and structured pieces. Many education institutions worldwide have already adopted assistive learning technology, including the University of Massachusetts and Sheffield University. Both schools use similar speech-to-text software that transcribes live speech and makes it easier for online students to digest educational content. Incorporating AI into eLearning systems: 4 steps towards success If you’re planning to enrich your eLearning software with AI capabilities, but don’t know where to start, here’s our high-level AI adoption plan you may rely on. Study what technology tools and software you already have to estimate the scope of work you’ll have to carry out when adopting AI for online education. Some eLearning solutions, for example, have built-in AI capabilities, which only need to be configured, and some require relatively simple customization. Other software solutions, in turn, can be enhanced via third-party or custom plugins. But occasionally, you might have to go for custom , giving your IT infrastructure a complete overhaul. 1. Conduct an inventory of your eLearning technology tools and systems. eLearning software development The key to AI’s successful performance is thorough training of AI algorithms on the large volumes of quality data. So, you’ll have to put effort into aggregating your educational data, including the information from your eLearning systems, a corporate website, social media, student surveys, and other sources. However, mind that additional data restoration and cleaning may be required to eliminate bias in many cases. 2. Collect your data. Identify how you can leverage AI to improve your online education process without causing critical disruption to your current operations while guaranteeing economic efficiency. Then, put down a specific use case, like task automation or curriculum personalization, mapping it to your objectives and thinking out general compliance and security factors. And if you think you lack the experience to do so, it might be better to turn to a consultant. 3. Elaborate on an AI use case. Create a high-level AI implementation strategy, putting down desired outcomes and KPIs, amending current operations to include AI, and choosing tools that will help you monitor both adoption progress and AI performance. 4. Design an AI adoption strategy. Final thoughts Artificial intelligence will continue to transform online learning, making it more enjoyable and results-driven. It could also help educators transition to new, more effective educational models with an emphasis on competencies, not grades. But to maximize the benefits of AI, both educators and EdTech providers will need to balance the AI’s impact and ethics, eliminating bias and constantly improving algorithm accuracy. If you think about tapping into the future of online learning and expanding your eLearning software with AI capabilities, drop us a line! Previously published at https://itrexgroup.com/blog/online-education-trends-will-ai-become-future-of-elearning/

4 Ways AI Can Improve Online Education

People, processes, and tools are the three foundational pillars of DevOps — the term coined by Patrick Debois in 2009 to describe a new culture of collaboration and shared ownership in software development. With DevOps adoption, software is developed and delivered in short cycles supported by automation and tools. But is not all hard stuff. According to Patrick Debois, DevOps is more . Or, as Viktor Farcic put it in his book The DevOps Paradox, it’s at its roots. DevOps implementation a human problem a people game In the old pre-DevOps days, developers would only tell the operations team after completing a piece they have been working on for months, “Here’s version 2.0. Make it run.” And Ops would run it to find out that everything has crashed. Finger-pointing was also commonplace. DevOps processes, however, invite everyone to the party, making teams assume collective responsibility for user experience with new software. In the world of DevOps, Ops engineers are saying, "Fine, we get it, we should ship this piece tomorrow, we’ll figure out how to meet the deadline." And Dev people are saying, "We don't want to be messaged in the middle of the night that something has gone terribly wrong, so we will help ship it safely." In the DevOps environment, everyone is working together toward the same goal — building better quality software faster. With no blame game. Everyone means people from product owners, developers, testers, and IT operations to even marketing and sales that bring customer feedback to the table. An important thing to know is, you don’t set up a DevOps department between Dev and Ops to implement DevOps. This way, you can get only a third silo. Instead, you change the culture of Dev and Ops to break down the walls between them. What is exactly the DevOps mindset? DevOps implementation above all means a cultural shift. Because DevOps is mostly about : soft stuff Empathy and collaboration Empathy, meaning the ability to think about things from another’s perspective, is believed by many to be the true DNA of DevOps. It allows Ops people to understand the importance of quick releases, while developers can better appreciate problems caused by bad code. Plus, both parties are putting users at the center of everything they are doing. Empathy powers collaboration. Collaboration powers productivity, encouraging everyone to commit to software development quality and speed. Mutual responsibility DevOps implementation involves setting up small cross-functional groups who will work autonomously, taking mutual responsibility for continuous delivery. Loosely coupled product-centered teams, consisting of developers, operations engineers, testers, and project managers, enable microservices style, reducing the number of handoffs along the value stream — from version control to production deployment. However, individually, developers are still responsible for a piece of code they’ve written. “ ,” Amazon CTO Werner Vogels once famously said. You build it, you run it Knowledge sharing Scrum standup meetings and retrospectives allow teams to understand what was done yesterday, what was successful, and what can be improved. In a knowledge-sharing environment, DevOps engineers learn new things and grow continuously, adjusting their competencies. At a bare minimum, everyone on the team should have on-demand access to source code, logs, tools, test servers, production builds, and postmortem reports. Continual experimentation DevOps is hypothesis-driven development. Failing fast to learn faster through a tighter feedback loop is key to DevOps implementation. DevOps teams write code in small batches and push it to production as quickly as possible. This way, they have an immediate chance to put the solution right by fixing defects swiftly or removing a certain feature altogether. As The DevOps Handbook co-author Jez Humble said, “ ” So, the DevOps mindset is about starting with intuition and learning lessons as you move forward to arrive at a data-driven decision. Users don’t know what they want. Users know what they don’t want once you’ve built it for them. Customer centricity The DevOps culture relies on a product-first approach that cares about the needs of real users. DevOps teams should understand what they build and understand the people for whom they build. Continuous feedback is crucial. This often means that (i) Ops take part in developing service-level agreements with users to understand user requirements, (ii) developers heed to end-user requests with fast updates, leveraging a loosely coupled, service-oriented architecture that allows utmost flexibility, and (iii) testers set up user focus groups for a controlled UAT, interacting with users directly. Automation A very strong emphasis in building DevOps is put on automation that reduces human error and amplifies speed, cutting overhead costs. Automation in DevOps processes is extended virtually to everything, from development and testing to infrastructure provisioning, configurations, network tasks, databases, deployment, and monitoring. Netflix is known as a fantastic example for DevOps implementation, with its fully automated deployment processes allowing engineers to run code thousands of times per day. Why implement DevOps According to the , organizations see a number of benefits from DevoOps implementation, including: 2020 DevOps Trends Survey by Atlassian Speaking of tangible DevOps benefits, an example from our experience would be a 5x increase in the speed of delivery of new features that we helped achieve in . gaming app development DevOps implementation best practices It’s impossible to oversell the importance of automation for a successful DevOps implementation roadmap. Automation, however, cannot be done overnight. It’s a journey with multiple stops and requires careful planning. According to the Boston Consulting Group's (BCG) classic article, , organizations implement DevOps in four automation steps: Leaner, Faster and Better with DevOps Source Continuous Integration An essential part of agile processes, CI allows DevOps teams to always work with the latest code version. In the CI process, developers push new pieces of code into a shared repository at least once a day for validation through an automated build and testing, which typically includes unit, integration, and basic security tests. Builds and tests are triggered by a version control tool for code check-ins which the server is watching. So, CI is a fully automated process run within a few seconds or minutes to allow developers to know immediately if their code is right and has successfully integrated with others’ code or has broken the build. Continuous Delivery Continuous Delivery automates the build-test-release process, allowing new builds to be released with one button push. While CI is focused on the quality of code to make sure it passes all testing before being merged into the master build, the objective of CD is to ensure through more testing that the software (the master build) behaves as intended in the real world. CD is a short cycle, completed in a few hours, enabling the team to automate everything in the life of a code. In essence, it consists of two pipelines: a CI pipeline and continuous testing. Continuous Deployment In a Continuous Deployment process, code changes are deployed automatically after successfully passing the complete testing cycle. There is no need for human intervention to decide what to release and when. Continuous Deployment differs from Continuous Delivery in only one thing: it involves an automated push to production environments. Continuous Deployment, however, requires setting up a rigorous automated testing framework. Continuous Monitoring If an organization’s environment is automated, ensuring its stable performance will be imperative. In DevOps infrastructure, there are a lot of things you can monitor, but it is, however, impossible to keep track of all of the services and procedures. So, when implementing DevOps, you need to understand which are your primary monitoring targets. These can include the efficiency of your CI/CD pipeline, deployment milestones, automated handling of vulnerabilities, server health, user activity, application logout output, and application performance. You do the job either through application monitoring tools like Datadog or Newrelic or by going manually through application logs or by configuring the notifications and alarms. Important metrics to watch are failures, usage patterns, performance, availability, scalability, and custom telemetry. Plus, Configuration Management This process in DevOps implementation ensures optimal configuration of multiple testing environments, set up for unit testing, integration testing, load testing, acceptance testing, end-user testing, and more. Unlike in the traditional organization where configuration management is done through formal documentation, this process in a DevOps environment is automated to a great degree — configurations are encapsulated in code or scripts and controlled through a version control tool. Configuration management systems have given rise to: , which codifies all infrastructure resources in a code or a script, with the text files stored in a version control tool like Git. Infrastructure as a code , which defines server configurations as a code or script and commits them to version control. Configuration as a code Infrastructure automation enables automated infrastructure provisioning, configuration, and scaling, increasing productivity, saving costs, and freeing engineers for more creative tasks. Step-by-step DevOps implementation plan Drawing on our vast hands-on experience, we’ve compiled a list of practical recommendations on how to implement DevOps activities successfully: 1. Start a DevOps initiative DevOps adoption should start with a strong business justification. Your DevOps initiative should be aligned with hard-boiled business goals, rather than driven by a fear of missing out. The CIO plays a key role, initiating DevOps transformation as part of the organization’s digital activities and planning financial investments and human resources. 2. Build a DevOps strategy Program managers are responsible for developing the company’s DevOps implementation strategy. They need to identify its vision based on challenges like production defects, complicated deployments, or team burnout, and figure out how to make everything in a software development cycle continuous. It’s also critical to set KPIs for your DevOps team. A common set of metrics, proposed by Nicole Forsgren, Jez Humble, and Gene Kim in their book Accelerate, includes: – the time it takes to go from code committed to code successfully running in production Lead time for changes – software deployment to production or an app store Deployment frequency – mean time to recover (MTTR)Change failure rate – how often deployment failures occur in production that requires an immediate rollback Time to restore service 3. Set up a competent team No matter whether your company chooses to go with an in-house or outsourced model, people on your team should be qualified and eager to learn new things. They should be experts in advanced DevOps tools, able to think out of the box when configuring complex scenarios, and… have the patience to troubleshoot and debug as many times as needed to find the root cause of a problem. Consider including a release manager, automation experts, testers, QA lead, and SecDevOps engineers in your team. The right people are key. According to the 2020 DevOps Trends Survey, a lack of skills is the number one inhibitor to successful DevOps implementation. 4. Choose the right tools There are many good DevOps tools available today, from release planning to building, testing, and safely delivering applications. In fact, there is an overwhelming amount of them that keeps growing, so it’s easy to get confused. Get expert help if needed and don’t be. When choosing DevOps tools, be also sure to understand how they will fit together to avoid ending up with what Gartner calls disconnected islands of automation. What you will need is an integrated toolchain and systematic automation implemented with tools aligned at each stage. 5. Containerize your applications Сontainerization makes your application easy to execute. Containers, implemented with tools like Docker or Kubernetes, are basically an isolated process for an executable file running on a Linux machine. They include code, dependencies, binaries, libraries, and other things your software needs, presenting an environment that can follow the application from development to testing to production deployment. Containers are considered the best choice for a microservices architecture that splits your application into multiple microservices focusing on smaller tasks and providing scalability, flexibility, easier automated testing, and rapid deployment. 6. Integrate infrastructure automation with CI/CD You should run a number of application services integrations to make infrastructure automation tools such as Kubernetes, Chef, or Puppet work with CI/CD tools (Jenkins, Bamboo, or other). Seamless integrations will arm you with a consistent set of services applied automatically. As a result, you will be able to deploy new infrastructure configurations and release new software way faster. 7. Automate testing To achieve DevOps-style speeds and agility with DevOps implementation, it is important to automate testing as much as possible but as appropriate. Once you add to the process, checks are run the moment new code is complete, and your developers no longer have to spend days trying to remember and fix code written many weeks ago. automation testing According to Slava Makovsky, QA Automation Team Lead at ITRex with more than 15 years of experience with a global tech giant, there are few things to remember when implementing test automation: Start with creating simple test cases, rather than designing a complex automation framework upfront. Smoke tests are an imperative Use remote machines to run tests from the very beginning Make sure you’ve got a well-thought-out plan for running tests in parallel and as part of your CI/CD pipeline Set up capabilities to analyze test results on a daily basis 8. Start Small Before configuring any complex scenarios, it is safer and more beneficial to begin with small initiatives in DevOps implementation. Pick carefully your first app and scale only when you are ready. There are basically no restrictions for applying DevOps, but web-enabled agile applications are a popular choice for the first moves. These applications are often architectured from the beginning with a lean or agile mindset that complements DevOps. 9. Collect feedback “ ,” Microsoft Founder Bill Gates said. We all need people who will give us feedback. That’s how we improve There are two types of feedback you might want to seek: feedback on bugs and incorrect usage. Apart from collecting customer-centric data, such as the load time of your application, the maximum number of concurrent users, or performance differences between web, mobile app, and wearables, you can garner feedback directly from your users. You can either build user feedback mechanisms within your application using emojis, star ratings, or binary-type questions, or ask users through your app or in an email to join a survey, or study public discussions. Feedback is an essential part of the DevOps loop, informing the next steps and enhancements. A final note While it may be cliche to say that every business should implement DevOps today if they want to keep up with the pace of innovation and stay competitive, almost half of organizations are only starting with DevOps, practicing it for less than three years, according to the 2020 DevOps Trends Survey. DevOps transformation indeed can be complicated. It is not an initiative that can be undertaken once and for all. It is not even done after you implement continuous practices. These will require continuous improvement as well. Also published at https://itrexgroup.com/blog/how-to-implement-devops/

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