Blending the principles of Agile and Lean has transformed the game in the continually evolving project management arena. Agile’s adaptability and Lean’s focus on efficiency combine to provide a practical approach for modern-day project managers seeking to improve productivity and add value. This blog post will examine how adopting Agile and Lean concepts can transform application development & project management procedures.
1. Understanding Agile Principles
Adaptive project management is based on agile concepts, prioritizing iterative delivery, customer collaboration, and flexibility. Agile helps teams prioritize customer happiness and produce incremental value by cultivating a flexible mentality. This approach thrives on continuous feedback, allowing projects to evolve with shifting requirements. Teams looking to navigate the ever-changing world of modern project management with efficiency and flexibility can benefit significantly from adhering to agile principles.
The Agile mindset in project management champions adaptability, collaboration, and continuous improvement. Embracing change as a constant fosters a culture of iterative progress, open communication, and teamwork. Agile empowers project managers to navigate uncertainties and achieve success in dynamic environments by valuing flexibility and delivering incremental value.
2. The Lean Philosophy
The core elements of the lean philosophy are efficiency, minimizing waste, and value delivery enhancement. Toyota introduced this concept in the Toyota Production System, which was developed by Japanese engineers Taiichi Ohno and Eiji Toyoda. It originated in the industrial sector but is now a flexible strategy that works across industries.
Lean emphasizes streamlining processes, continuous enhancement, and eliminating tasks that don’t provide value. Lean aims to increase productivity, shorten lead times, and promote a waste-reduction culture by adopting concepts like value stream mapping and Kanban. Ultimately, this means providing consumers with more value while consuming fewer resources.
3. Integration of Agile and Lean
Integrating Agile and Lean methodologies creates a robust framework for project management, combining Agile’s flexibility with Lean’s focus on efficiency. Lean’s continuous improvement and waste reduction principles align with Agile’s iterative development and customer input. This integration improves overall project efficiency, streamlines workflow, and accelerates delivery. By combining these approaches, teams can better adapt instantly to changing needs and foster a continuous enhancement culture, ensuring that projects are completed on time and aligned closely with customer expectations.
4. Benefits for Project Managers
Project management can benefit significantly from a diversified strategy incorporating Agile and Lean principles. Agile’s iterative approach accelerates time to market, allows quick adaptation to changing requirements, and fosters better teamwork & collaboration. Lean concepts simultaneously improve workflows and promote efficiency by getting rid of waste. Stakeholder satisfaction, risk management, and project visibility are all boosted by this dual integration.
Project managers benefit from streamlined processes, reduced lead times, and a culture of continuous improvement, empowering teams to deliver high-quality products with increased speed and adaptability, ultimately aligning projects more closely with project objectives and exceeding customer expectations.
5. Overcoming Challenges
Implementing Agile and Lean methodologies is challenging. Acknowledging these hurdles is crucial for project managers. Organizational resistance is frequently caused by a change in perspective or a fear of the unknown. Cultural changes might encounter resistance, requiring judicious change management. As teams move to these techniques, specialized training becomes necessary.
Project managers should engage in thorough training programs, encourage open communication, and establish a welcoming environment for change to navigate these challenges. Highlighting the enduring advantages and presenting successful case studies can assist groups in accepting the transformative path, transforming obstacles into chances for development and enhancement.
6. Tools and Techniques
Project managers who implement Agile and Lean techniques benefit significantly from a collection of tools and techniques to improve collaboration and productivity. Jira, Trello, and Asana are a few examples of project management software that offer dynamic platforms for task tracking, sprint planning, and team discussion. Visual management tools, like Kanban boards, provide transparency and prioritizing by giving an exact picture of each workflow stage. Slack and Microsoft Teams are two examples of collaboration platforms that promote real-time communication and knowledge sharing, dissolving barriers to collaboration and improving teamwork.
Together, these solutions improve overall project efficiency, facilitate collaboration, and increase transparency in project contexts. Through the seamless integration of these tools into Agile and Lean techniques, project managers empower their teams to adjust to changing requirements quickly, stay focused on delivering value, and promote a culture of continuous improvement throughout the project lifecycle.
7. Cultivating an Agile and Lean Culture
Developing a Lean and Agile culture is essential for long-term project management success. It involves encouraging a mindset of collaboration, adaptation, and continuous improvement. Leadership is critical to promote these ideals and motivate teams to welcome change and grow from experiences. Companies and organizations can establish a culture that promotes creativity, efficiency, and a shared goal of offering customers maximum value by emphasizing transparency, open communication, and individual empowerment.
Conclusion
Combining Agile and Lean principles in project management results in a powerful and flexible approach that maximizes efficiency, minimizes waste, and promotes efficient product development. Organizations can achieve a streamlined project management methodology by combining Agile’s emphasis on adaptability and iterative development with Lean’s focus on eliminating non-value-adding activities and optimizing processes. This integration enables teams to respond swiftly to changing requirements, deliver high-quality products or services, and enhance overall project success. Agile’s collaborative and customer-centric nature, coupled with Lean’s commitment to continuous learning and efficiency, creates a balanced framework that aligns with the dynamic demands of modern project environments. Project managers should blend Agile and Lean principles to harness the strengths of both methodologies. The combination enables project managers to enhance overall project success by marrying flexibility with efficiency in a dynamic and ever-changing business landscape.
By Ashwin Palaskar, Agile Project Manager-Scrum Master, NeoSOFT
Artificial Intelligence (AI) has emerged as a game-changer in software development, revolutionizing how applications are built and enhancing their capabilities. From personalized recommendations to predictive analytics, AI has the power to transform traditional applications into intelligent systems that learn from data and adapt to user needs. This blog will explore the diverse facets of constructing Smart applications by integrating AI within development endeavours. We’ll delve into the various AI types, their advantages for software applications, and the efficient steps to infuse AI seamlessly into your development process.
What does AI in software development include?
AI in software development encompasses a variety of techniques and technologies that enable applications to mimic human intelligence. Machine Learning forms the foundational element of AI, allowing the applications to glean insights from data and make forecasts devoid of explicit programming instructions. Natural Language Processing (NLP) empowers applications to understand and interpret human language, giving rise to chatbots and virtual assistants.
On the other hand, Computer Vision allows applications to process and analyze visual data, enabling tasks like facial recognition and image classification. Deep Learning, a subset of ML, uses artificial neural networks to process vast amounts of complex data, contributing to advancements in speech recognition and autonomous vehicles.
What are the benefits of incorporating AI into development projects?
Integrating AI into development projects brings many benefits that enhance applications’ overall performance and user experience. Personalized Recommendations, enabled by AI algorithms that analyze user behaviour, lead to tailored content and product suggestions, significantly improving customer satisfaction and engagement. Automation is another key advantage, as AI-driven processes automate repetitive tasks, increasing efficiency and reducing human error. Leveraging AI models, Predictive Analytics empowers applications to anticipate forthcoming trends and results grounded in historical data, contributing to informed decision-making and strategic foresight.
How to prepare your development team for AI integration?
Before embarking on AI integration, preparing your development team for this transformative journey is essential. Assessing the AI skills and knowledge gap within the team helps identify areas for training and upskilling. Collaboration with data scientists and AI experts fosters cross-functional Learning and ensures a cohesive approach to AI integration. Understanding data requirements for AI models is crucial, as high-quality data forms the foundation of practical AI applications.
How to select the right AI frameworks and tools?
Choosing the appropriate AI frameworks and tools is paramount to successful AI integration. TensorFlow and PyTorch are popular AI frameworks for ML and deep learning tasks. Scikit-learn offers a rich set of tools for ML, while Keras provides a user-friendly interface for building neural networks. Selecting the proper framework depends on project requirements and team expertise. Additionally, developers should familiarize themselves with AI development tools like Jupyter Notebooks for prototyping and AI model deployment platforms for seamless integration.
What are AI models?
AI models are computational systems trained on data to perform tasks without explicit programming. They encompass a range of techniques, including supervised learning models for predictions, unsupervised learning for data analysis, reinforcement learning for decision-making, and specialized models like NLP and computer vision models. These models underpin many AI applications, from chatbots and recommendation systems to image recognition and autonomous vehicles, by leveraging patterns and knowledge learned from data.
What is the data collection and preprocessing for AI models?
Data collection and preprocessing are vital components of AI model development. High-quality data, representative of real-world scenarios, is essential for training AI models effectively. Proper data preprocessing techniques, including data cleaning and feature engineering, ensure the data is ready for AI training.
Addressing data privacy and security concerns is equally crucial, especially when dealing with sensitive user data.
What do developing AI models for your applications include?
Building AI models is a fundamental step in AI integration. Depending on the application’s specific requirements, developers can choose from various algorithms and techniques. Training AI models involves feeding them with the prepared data and fine-tuning them for optimal performance. Evaluating model performance using relevant metrics helps ensure that the AI models meet the desired accuracy and effectiveness, which helps boost the performance of your application.
Why is integrating AI models into your applications important?
Integrating AI models into applications requires careful consideration of the integration methods. Embedding AI models within the application code allows seamless interaction between the model and other components. Developers address real-time inference and deployment challenges to ensure that the AI models function efficiently in the production environment.
Why is testing and validation of AI integration crucial?
Rigorous testing and validation are critical for the success of AI-integrated applications. Unit testing ensures that individual AI components function correctly, while integration testing ensures that AI models work seamlessly with the rest of the application. Extensive testing helps identify and address issues or bugs before deploying the application to end users.
The journey of building intelligent applications continues after deployment. Continuous improvement is vital to AI integration, as AI models must adapt to changing data patterns and user behaviours.
Developers should emphasize constant Learning and updates to ensure that AI models remain relevant and accurate. Model monitoring is equally important to identify model drift and performance degradation. Developers can proactively address issues and retrain models by continuously monitoring AI model performance in the production environment.
Addressing ethical considerations in AI development
As AI integration becomes more prevalent, addressing ethical considerations is paramount. AI bias and fairness are critical areas of concern, as biased AI models can lead to discriminatory outcomes. Ensuring transparency and explainability of AI decisions is essential for building trust with users and stakeholders. It is critical to manage privacy and security issues about user data properly to protect user privacy and comply with applicable legislation.
Conclusion
In conclusion, building intelligent applications by incorporating AI into development projects opens up possibilities for creating innovative, efficient, and user-centric software solutions. By understanding the different types of AI, selecting the right frameworks and tools, and identifying suitable use cases, developers can harness the power of AI to deliver personalized experiences and predictive insights. Preparing the development team, integrating AI models seamlessly, and continuously improving and monitoring the models are crucial steps in creating successful AI-driven applications. Moreover, addressing ethical considerations ensures that AI applications are intelligent but also responsible and trustworthy. As AI technology advances, integrating AI into software development projects will undoubtedly shape the future of applications and pave the way for a more intelligent and connected world.
Micro Frontends are revolutionizing the traditional approach to building, deploying, delivering, and maintaining web applications. In the conventional model, these tasks required large-scale developer teams and complex, centralized systems. However, the rise of Micro Frontends is changing the game. This innovative design approach involves breaking down a front-end app into individual, semi-independent “micro apps” that collaborate loosely, much like microservices.
By adopting this new technique, organizations can achieve significant benefits. Firstly, it enables the decoupling of large teams to empower smaller groups to develop strategies and make decisions autonomously on their projects.
Additionally, it offers several advantages:
Reducing cross dependencies: Micro Frontends help minimize the dependencies between different teams or services, allowing them to work more independently and efficiently.
Separating deployment plans for individual services/applications: With Micro Frontends, deployment plans can be tailored to each specific service or application, facilitating faster and more targeted releases.
Splitting the front-end codebase into manageable pieces: By breaking the front-end codebase into smaller, more manageable pieces, developers can focus on specific functionalities or features without being overwhelmed by the entire codebase.
Organizations can supercharge speed, ignite innovation, and ensure fail-safe operations with Micro Frontends. Centralization often leads to team frustrations, as external dependencies become challenging to resolve, given that one team’s work can heavily impact another’s. Micro frontends address this issue by promoting autonomy and reducing interdependencies.
Architecture Of Micro Frontend: Say Goodbye to Monoliths!
Addressing codebase growth with Micro Frontends: As the product expands, the codebase grows in complexity, necessitating delegating different features to separate teams.
However, when multiple teams consistently work on the same monolithic codebase, it often leads to conflicts and delays in the CI/CD pipeline. To mitigate these challenges, breaking down the monolithic architecture into Micro Frontends empowers individual teams to take ownership of feature development and appropriately leverage the framework for their specific product requirements.
Unlike microservices, there is no standardized approach or architecture for Micro Frontends. We have adopted a Single Page Application (SPA) Micro Frontend architecture, which ensures scalability within a distributed development environment.
The diagram provides an overview of the Micro Frontend architecture, showcasing the relationship between Micro Frontend source control, deployment through the CI/CD pipeline, and the host app consisting of Micro Frontend services:
Our host app integrates Micro frontend applications within their codebases, servers, and CI/CD pipelines. These mini-apps are divided based on routes, allowing our DevOps team to efficiently build and continuously deploy various feature updates to the production environment without impacting the entire product.
When breaking down the application, we follow a value-driven approach, ensuring that each mini-app delivers value on its own. This approach allows for greater flexibility and targeted development efforts within the micro frontend architecture.
What are the benefits of Micro Frontends?
By leveraging the appropriate tools and components, any team can surpass the challenges of monolithic applications and simplify them into individual release features. The fear of unintended consequences causing application breakdown becomes obsolete. Independent groups can collaborate seamlessly, focusing on distinct front-end features and developing them comprehensively, from the database to the user interface. Micro Frontends enable the following possibilities:
Facilitate autonomous teamwork: Each team can concentrate on their specific part of the project without extensive coordination or dependency on other groups.
Build independent applications: Micro Frontends allow the creation of self-contained applications that operate without relying on shared variables or runtime, even if multiple teams employ the same framework or codebase.
Enhance versatility: With teams working independently, there is greater flexibility in exploring diverse ideas and designs.
Develop cross-team APIs: Micro frontends encourage native browsers for communication and enable the creation of APIs across different teams.
Flexible updates and upgrades: The user-centric nature of Micro Frontends streamlines the process of releasing new updates, making it more efficient, quicker, and responsive.
Decrease codebase complexity: By clearly defining the goals of each component within an application, the codebase becomes cleaner and easier to work with, often avoiding problematic coupling between components that can occur otherwise.
Implement autonomous deployment: Micro Frontends support continuous delivery pipelines, where teams can independently build, test, and deploy their code without worrying about the status of other code within the application.
Scalability and extensibility: Micro frontends, developed in smaller units, provide developers with better control over their projects, allowing for more effortless scalability and the ability to toggle features on and off to manage complexity effectively.
Embrace the single responsibility principle: Each module in Micro Frontends adheres to the principle of having a single responsibility, contributing to cleaner and more maintainable code.
Improve user experience: With the independence of cross-functional teams, every aspect of the user experience and application can be meticulously thought through, resulting in an enhanced user experience.
Micro Frontends herald a paradigm shift in software development, granting teams the autonomy to work independently. Promoting efficient development practices enables streamlined workflows and faster iteration cycles. This approach ultimately leads to improved user experiences and more manageable applications. With Micro Frontends, organizations can embrace a modular architecture that empowers teams, fuels innovation, and enhances productivity.
Challenges with Micro Frontends
While Micro Frontends offer numerous advantages, specific issues need to be considered and addressed:
Increased code duplication and framework complexity: Each team can choose their technologies, and the browser may download multiple frameworks and duplicate code to impact performance and improve the overall complexity of the application.
Balancing autonomy and shared dependencies: There is a tension between allowing teams to independently compile their applications and the desire to have common dependencies for efficient code reuse. However, introducing changes to shared dependencies may require additional efforts to accommodate one-off releases.
Consideration of the development environment: When developing Micro Frontends in a non-production-like climate, it becomes essential to regularly integrate and deploy them to environments that closely resemble the production environment. Additionally, thorough testing, both manual and automated, in these production-like environments is crucial to identify and address integration issues as early as possible.
Leveraging Micro Frontends to address complex codebases
Micro Frontends offer a valuable solution for tackling complex codebases and scaling architectures. They serve as an effective component model, providing a modular approach to application development, streamlining development processes, and facilitating faster project delivery. While numerous solutions are available in the market, it’s crucial to consider the variety of patterns and carefully evaluate factors such as team size and communication between components and frameworks.
By adopting Micro Frontends, organizations can develop targeted solutions for specific challenges within their applications. Transforming an extensive front-end application into a Micro Frontend architecture can significantly reduce technical friction and enhance overall efficiency.
Mastering Micro Frontends
Enter Micro Frontends – a game-changing architectural pattern that allows for the independent development and deployment of smaller, self-contained frontend modules. With Micro Frontends, teams can effectively decouple their front-end codebase, enabling seamless collaboration, faster development cycles, and improved scalability. This approach opens possibilities, empowering organizations to create highly modular, maintainable, and adaptable web applications. As we embark on this exciting journey, let’s delve into the road ahead for Micro Frontends and discover its boundless potential for the future of front-end development.
By Sumit Munot (Delivery Manager – Javascript Fullstack, NeoSOFT)
The increasing demand for mobile apps gets every business to look for the best and robust solution. Understanding the pros and cons of each platform is necessary. In this blog, we share key comparative insights on the popular cross-platform technologies – React Native and Flutter.
React Native was built and open-sourced by Facebook in 2015 with easy access to the native UI components and the code is reusable. A hot reload feature is available with access to high-quality third-party libraries.
Flutter is an open-source technology launched by Google which has a robust ecosystem and offers maximum customization.
Programming Language
React Native mainly uses JavaScript as the programming language, which is a dynamically typed language. ReactJS is a JavaScript library mainly used for building user interfaces. ReactJS is used across various web applications, a specific pathway to build out its forms has to be used which is accomplished by using – ReactJS lifecycle.
On the other hand, Flutter uses Dart which was introduced by Google in 2011. It is similar to most other Object-Oriented Programming Languages and has been quickly adopted by developers as it is more expressive.
Architecture
React Native uses the JavaScript bridge, which is the JavaScript runtime environment that provides a pathway to communicate with the native modules. JSON messages are used to communicate between the two sides. This process requires a smooth User Interface. The Flux architecture of Facebook is used by React Native.
Flutter contains most of the required components within itself which rules out the need for a bridge. Frameworks like Cupertino and Material Design are used. Flutter uses the Skia engine for its purpose. The apps built on Flutter are thus more stable.
Installation
React Native can easily be installed by someone with little prior knowledge of JavaScript. It can be installed by using the React Native CLI- which needs to be installed globally. The prerequisites for installing React Native are NodeJS and JDK8. The yarn needs to be installed to manage the packages.
Installing Flutter is a bit different. The binary for a specific platform needs to be downloaded. A zip file is also required for macOS. It is then required to be added to the PATH variable. Flutter installation does not require any knowledge of JavaScript and involves a few additional steps in comparison with React Native.
Setup and Project Configuration
React Native has limitations while providing a setup roadmap and it begins with the creation of a new project. There is less guidance while using Xcode tools. For Windows, it requires JDK and Android Studio to be preinstalled.
Flutter provides a detailed guide to installing it. Flutter doctor is a CLI tool that helps developers to install Flutter without much trouble. Flutter provides better CLI support and a proper roadmap to setting up the framework. Project configuration can be done easily as well.
UI Components and Development API
React Native has the ability to create the Native environment for Android and iOS by using the JS bridge. But it relies heavily on third-party libraries. The React Native components may not behave similarly across all platforms thereby making the app inconsistent. User Interface rendering is available.
Flutter provides a huge range of API tools, and the User Interface components are in abundance. Third-party libraries are not required here. Flutter also provides widgets for rendering UI easily across Android and iOS.
Developer Productivity
The React Native codes are reusable across all the platforms. JavaScript is supported by all editors. React Native also provides the Hot Reload feature. This means that any changes in the backend will be directly visible on the front end, even without recompilation.
Flutter also offers the Hot Reload feature. The compilation time on Flutter is shorter as compared to React Native. This affects Flutter VS React Native development speed comparison. But all editors do not support Dart as it is not common.
Community Support
Communities also help in sharing knowledge about specific technology and solving problems related to it. Since being launched in 2015, React Native has gained popularity and has increasing communities forming across the world, especially on GitHub.
Flutter started gaining popularity in 2017 after the promotion by Google and the community is relatively smaller, but a fast-growing one. Currently, React Native has larger community support, however, Flutter is being acknowledged globally and is also fast-trending.
Testing Support
The React Native framework does not provide any support for testing the UI or the integration. JavaScript offers some unit-level testing features. Third-party tools need to be used for testing the React Native apps. No official support is provided for these tests.
Flutter provides a good set of testing features. The Flutter testing features are properly documented and officially supported. Widget testing is also available that can be run like unit tests to check the UI. Flutter is hence better for testing.
DevOps and CI/CD Support
Continuous Integration and Continuous Delivery are important for apps to get feedback continuously. React Native does not offer any CI/CD solution, officially. It can be introduced manually, but there is no proper guideline to it and third-party solutions need to be used.
Setting up a CI/CD with Flutter is easy. The steps are properly mentioned for both iOS and Android platforms. Command Line Interface can easily be used for deploying them. React Native DevOps is properly documented and explained. DevOps lifecycle can also be set up for Flutter. Flutter edges React Native in terms of DevOps and CI/CD support because of the official CI/CD solution.
Use Cases
React Native is used when the developer is accustomed to using JavaScript. The more complicated apps are created using the React Native development framework.
If the User Interface is the core feature of your app, you should choose Flutter. Flutter is used for building simple apps with a limited budget. Thus you should consider the main use case of your app before finalizing the technology stack. The target of Google is to improve Flutter’s performance for desktops mainly. This will allow developers to create apps for the desktop environment. React Native may use the same codebase to develop apps for both Android and iOS.
Conclusion
React Native and Flutter both have their pros and cons. React Native might be the base of a majority of currently existing apps, but Flutter is quickly gaining popularity within the community since its inception, a fact further boosted by the advancement of the Flutter Software Development Kit (SDK) which makes the framework more advanced and preferable. The bottom line is to use the right platform after a thorough need-analysis is done. Contact NeoSOFT Technologies for a free consultation to help you get ready for a ‘mobile-journey’.
How AR and AI Work Together to Build Unique Mobile Experiences?
The intriguing partnership of Augmented Reality (AR) and Artificial Intelligence (AI) is a match made in the digital heaven. An AR application can become more beneficial when AI is incorporated into it. The natural bridging of AR and AI enables mobile app developers to build more interactive and intriguing apps. This article explores a few practical ways in which AR and AI can be combined to build incredible mobile experiences.
Ways AI and AR Complement Each Other
The partnership between AR and AI is likely to have a profound impact on customer experience. Companies are developing next-generation applications for mobiles that employ AR and AI technologies. In fact, AI is the heart of practically all AR platforms.
Though Artificial Intelligence and Augmented Reality have distinct technologies, they can sync with one another on a variety of applications. They can leverage each other’s best features and aspects to build innovative mobile experiences. AI enables AR to have a multidimensional interaction with the physical environment. It allows you to manipulate 2D and 3D virtual objects with your words, eyes, and hands.
It is anticipated that the demand for AR-based apps is bound to soar in the next four to five years. Hence, the search for appropriate Software Development Kits (SDK) and Application Program Interfaces (API) for AI and AR is ongoing.
Current State of SDKs and APIs for AR and AI
As the capabilities of current SDKs (Software Development Kits) and APIs (Application Programming Interfaces) rapidly expand, the number of commercial opportunities increase exponentially. Consider a few examples:
Vuforia: This is an Augmented Reality SDK that enables app developers to build mobile-centric, immersive AR experiences. It is capable of supporting both iOS and Android devices, allowing brands to develop apps with minimal commercial and technical risks.
ARCore: This is Google’s proprietary AR SDK. It enables developers to get their AR apps up and running on mobile devices. ARCore supports iOS devices and allows developers to build rich and immersive AR experiences supported by mobile devices.
Core ML: This is a Machine Learning framework used across multiple Apple devices. This API allows you to perform real-time predictions of live images on your device. Its low latency and near real-time results are its biggest advantages. Core ML is an application that can be run without network connections.
TensorFlow Lite: This is an open-source deep learning framework focused on mobile device inference. TensorFlow Lite enables developers to insert their own custom models.
Practical Ways to Combine AR and AI
The marriage of AR and AI opens up endless opportunities. Here are a few ways in which this combination is deployed to create digital miracles.
1. Speech Recognition: As an AI model listens to what you say, AR effects appear in front of you. An example would be when you say ‘pizza,’ a virtual pizza slice appears in front of your mouth on the app screen.
2. Image Recognition and Image Tracking: It allows customers to see how an object would look and fit in a given space. Combining AR with AI technology allows users to move still photos of items into a still image of a room and assists them in making a decision. Example: the popular IKEA Place.
3. Human Pose Estimation: It is a technique that detects human figures and poses. It predicts the positions of a person’s joints in an image or video. This can be used in controlling AR content. Yopuppet.com is one example.
4. Education: It allows students to gain new perspectives by interacting with virtual reality. For example, they can visualize and interact with a 3D life-size version of the human body.
5. Recognizing and Labelling: When the camera is pointed to a scene or an image, the AR app displays a label that indicates the object or the item when it recognizes it.
6. Car Recognition: Using a smartphone camera, this tech-application allows its customers to sit inside the car and explore the car’s interiors. There isn’t even a need to download the application.
7. Object Detection: AR-AI combination can be applied to automatically learn and detect the position and extent of the objects within an image or a video. This mobile-friendly model facilitates interaction between physical and digital objects.
Take Away
The bridging of AR and AI is offering businesses an opportunity to empower their customers with ways to sharing information in captivating ways. Together, AR and AI continue to enhance mobile experiences, enabling developers to design richer, more intuitive, and relevant experiences for their diverse consumers in numerous ways.
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