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nullTop Computer Vision Opportunities and Challenges for 2024
nullAI-Driven LMS: The Future of Education

Imagine a classroom where each lesson seems tailor-made for you, the course curriculum adapts to your pace, the materials are selected according to your interests, and even your questions! Sounds futuristic? With the integration of AI power into traditional learning management systems (LMS), it’s already becoming a reality. With AI, an LMS becomes a full-fledged learning tool, offering exceptional learning experiences that even underachievers would be amazed by. Learn more about smart and incredibly personalized AI-based learning management systems. While traditional LMS can be compared to a classroom, where students communicate with a teacher, an AI-driven one is an individual tutor for each student. This digital tutor is always available, offers tailored learning resources for each student's unique needs, and corrects mistakes in assignments swiftly. Let’s see the contrast between traditional and AI-driven LMS in more detail: All the capacities of smart digital education are possible thanks to decision trees and neural networks integrated into AI-driven LMS: Teaching efficiency AI-driven LMS provides teachers with useful tools that simplify everyday tasks. This lets them focus more on improving teaching methods and developing customized learning paths for each student. Data-Driven Learning How do teachers analyze student performance in traditional education? Check their assignments and activities during lessons. It takes a lot of time, limits individual approaches to each student, and lacks real-time insights. Let’s see, how a data-driven approach offered by AI-powered LMS can tackle this challenge. Intelligent Course Management The old-school approach had educators wait for occasional student feedback and guess if it was too easy, too challenging, or just boring. With an AI-empowering LMS, timely feedback is now possible for teachers. This allows them to refine their course materials according to the needs of current students, not next semester. Deep learning models and recurrent neural networks track and analyze students’ interaction with the platform, helping to understand real engagement and comprehension rates. Advanced Natural Language Processing (NLP) algorithms can analyze student’s feedback and mark the content as engaging or boring, too difficult or too simple, etc. Let’s see how it can work in practice! Imagine that students often replay a specific video fragment. Perhaps, it’s because the explanation is not clear enough. What does AI do? Streamlined Administrative Routine As per McKinsey research, teachers work about 50 hours per week, spending only 49% of their time in direct interaction with students. Technology can help teachers reallocate 20-30% of their time for supporting students, instead of doing routine tasks: 1. AI-Driven Learning Solutions Developing all kinds of AI-driven solutions for educational institutions, EdTech companies, and internal training systems for businesses: 2. Data-Driven Education 3. Workflow Automation 4. Hi-Tech Learning Experience

nullAI Revolution in EdTech: AI in Education Trends and Successful Cases

The usage of Artificial Intelligence in the world of Education Technologies is totally changing the learning approach, offering truly innovative solutions that transform the experience of students and teachers. AI-based solutions are bringing the education industry to a new level by enhancing the learning experience, making it more personalized, interactive, and efficient. Through the last few years, AI has started gaining popularity in many fields, and the EdTech industry is not an exception: according to the report of Global Market Insights, AI in Education Market size reached USD 4 billion in 2022 and is projected to expand at over 10% CAGR from 2023 to 2032, owing to the growing inclination towards personalized learning. In this article, we analyze the most important trends of the EdTech industry in 2023, discuss the main advantages and disadvantages of AI in EdTech, the impact of Chat GPT on education, and successful cases of how top companies use AI on their platforms. Before we discuss specific cases, pros, and cons of AI in EdTech, let's first explore some trends in this field to be able to adjust to customer needs and requirements and outstand in the highly competitive market. One of the most revolutionary trends in educational technology is AI in personalized learning. There are many great general options for obtaining new skills or knowledge on the internet. However, they often do not comply with the expectations and needs of customers in terms of personalization. Here comes AI to solve this problem: AI can tailor educational content and experiences to suit the unique abilities and learning styles of individual students. This is a big shift from traditional teaching methods that often overlook individual learners' needs and capabilities, making education more enjoyable and accessible. This revolution has been made possible through the use of adaptive learning algorithms and intelligent tutoring systems. Basically, adaptive learning algorithms can adjust the difficulty, pace, and type of content based on the learner's individual performance. These systems take into account the strengths, weaknesses, and even the interest areas of a student to provide a learning pathway that keeps a person engaged and contributes to academic growth. Intelligent tutoring systems serve as personal tutors to students, providing personalized support and feedback. These systems use AI to spot a point where a student might be struggling and to provide detailed guidance on those specific areas. The main advantage of those systems is in their ability to deliver feedback in real-time. Personalized learning with AI is already showing promising results. Studies have shown that adaptive learning technology can increase student engagement and improve retention rates. Moreover, it allows students to learn at their own pace, reducing stress and making learning a more enjoyable experience. With AI becoming increasingly sophisticated, the potential for truly personalized learning experiences is growing exponentially, promising a future where every learner can access a tailored education that fully unlocks their potential. Here you can see a few free examples to experience personalized learning: EdApp, Schoox, WalkMe, Raven360, ExplainEverything. In simple words, learning analytics is the process of collection, processing, and analysis of data about students and their success. This process is performed to optimize learning and the environments in which it occurs. Learning analytics uses AI algorithms to understand how students are learning, what they are struggling with, and how their learning path can be made easier. This tool helps educators understand which teaching methods and content types are most effective for them. Artificial Intelligence can even predict students' future performance based on their current learning patterns and provide recommendations on how to enhance their experience. For instance, if a student consistently struggles with problems in Physics classes, AI can identify the pattern and suggest additional, customized practice in this area. On the other hand, if an entire class is struggling with a specific concept, this could indicate an issue with the methods of teaching. Learning analytics has tight bounds with personalized learning: AI enables the creation of personalized learning paths by providing insights into the learning patterns of each student. Virtual and augmented reality (VR and AR) technologies create interactive learning environments that significantly enhance the educational experience, especially for kids, as their attention often drops in seconds. VR and AR allow for dynamic and interesting interactions, making education more engaging and, thus, more effective. Augmented and virtual reality can transport students to any location: forest, ocean, countryside, or any other place just from their classroom or home. These technologies can provide simulations that make difficult and abstract concepts more accessible and easy to understand. For instance, students can explore the structure of a DNA molecule or learn history by walking through different places. A great example of those technologies was the Google Expeditions program, which allowed students to explore plants' and animals' anatomy, and hundreds of destinations, including museums, monuments, and even underwater exploration. It is worth mentioning that AR applications can bring interactive content into the real world, thus, making learning more engaging and interesting and allowing students to understand and memorize the information better. The use of AI in VR and AR in education is still in the early stages, but the potential of those technologies to transform the education industry is enormous. With the success of Chat-GPT and other AI-based chatbots, using intelligent chatbots has become a growing trend in the Education Technology field. AI-driven chatbots are great virtual assistants that can respond to students' questions instantly, providing 24/7 assistance, facilitating the learning process, and even helping manage administrative tasks such as scheduling and reminders. AI-based chatbots can perform a wide range of functions: from answering questions and providing explanations of complex concepts to offering personalized study tips and reminders. For example, a student can ask the chatbot to explain a particular rule, formula, or the meaning of a term. The chatbot, using its NLP capabilities, can understand the question, search its knowledge database, and provide a clear and helpful response. Furthermore, as we mentioned before, AI chatbots can offer 24/7 instant support and fill the gap when human teachers are unavailable. Chatbots are capable of providing instant feedback on assignments, recommending different resources for study, etc. Also AI-chatbots can also provide a platform to help students who hesitate to ask questions in a regular class. The usage of NLP as a subfield of AI in intelligent chatbots is set to redefine the landscape of learner support in education. By providing responsive, personalized, and accessible support, AI chatbots are reshaping how we engage with and facilitate learning, opening exciting new possibilities for Education Technology. Well, using Chat GPT in the education industry has its own pros and cons. Let’s start with the main advantages: Disadvantages of using Chat GPT in EdTech: Better Student Performance: The tools powered by AI can assist students in explaining complex concepts, thus, enhancing academic achievements and increasing graduation rates. Increased Efficiency: Automation of grading assignments and other administrative tasks saves up a lot of valuable time for teachers. This enables tutors to concentrate on teaching more and offer personalized support to students who may need it. Cost-effectiveness: AI-driven solutions can be more cost-effective than traditional educational approaches, especially in scenarios of remote or distance learning where physical resources might be limited and not that accessible. Greater accessibility: AI-based solutions can offer broader access to education for students who study remotely, enabling them to learn from the best lecturers and use educational resources from anywhere in the world. Adaptive Learning Platforms: Leading EdTech companies are using AI algorithms to create adaptive learning platforms that tailor and customize content and instructions to individual students' abilities and learning styles. Intelligent Tutoring Systems (ITS): EdTech companies also use intelligent tutoring systems that allow to simulate the experience of one-on-one tutoring by providing immediate feedback, clarifying doubts, and offering assistance based on each student's needs. Gamification and Learning Apps: To make studying more enjoyable and students more concentrated, many EdTech companies are integrating educational games into their platforms. By using gamification, we are changing the attitude of students towards studying: from a chore, it becomes an interactive and engaging experience, which can enhance motivation. For example, Duolingo and other companies use AI to create gamified learning experiences. Coursera: Coursera, one of the most popular online learning platforms in the world, uses AI to provide personalized course recommendations to learners. The system analyzes a user's past behavior, course history, and interactions on the platform to suggest the most relevant courses. Also, the platform uses an AI-powered grading system that identifies common mistakes and provides feedback to help students improve their understanding. Knewton: Knewton uses AI to provide personalized learning experiences to students by analyzing a student's performance and adjusting course material. Students receive personalized lesson plans, content recommendations, and study strategies. This allows them to learn more effectively at their own pace. Content Technologies, Inc. (CTI): CTI uses AI to create customizable textbooks that adjust to the needs of individuals. By using machine learning and natural language processing, they can transform any standard textbook into an interactive and adaptive learning resource. Duolingo: Duolingo, one of the most popular language-learning platforms, uses AI to personalize the process of language learning for its users. Its AI algorithms analyze user's learning patterns and choose the difficulty level of exercises accordingly. Duolingo also uses AI-driven chatbots for interactive language practice. Quizlet: is a popular online learning platform that uses gamification and AI to enhance student engagement and learning. The AI algorithms analyze student performance to recommend personalized study materials and games, catering to different learning styles and ensuring continuous learning challenges and support. These use cases show how AI is revolutionizing education and learning experiences today. By using the potential of AI, these platforms have successfully improved engagement, enhanced learning outcomes, and personalized education on a global scale. To sum up, the integration of AI in the EdTech industry can absolutely revolutionize the way we learn and teach. AI-based solutions prove that they can transform the learning process and make it more personalized, interactive, and efficient. In this article, we explored the biggest trends in AI in EdTech, such as personalized learning, learning analytics, virtual and augmented reality, and intelligent chatbots. By applying those technologies in the educational system, developed products will meet the expectations of the customers and solve their challenges. If you would like to implement AI in your education platform and unlock new opportunities in the industry, do not hesitate to contact us!

nullHow to Scale AI in Your Organization

According to WEKA's 2023 Global Trends in AI Report, 69% of organizations now have AI projects up and running, and 28% are using AI across their whole business. This shows a big move from just trying out AI to making it a key part of how companies operate and succeed. However, this is just the beginning as the major point is not to have only AI but to have it work to your benefit. Organizations have to address various challenges such as the collection of data, hiring the right skills, and fitting AI into their existing system. This guide serves both new companies and big businesses. It gives you clear examples and direct advice on how to work around these problems. We will discuss what specific things you can do to make the most of AI, whether you want to improve your processes, give better customer service, or make better business decisions. We could help you not only to use AI but to make the best use of it to lead the competition in your area. Artificial Intelligence (AI) and Machine Learning (ML) are two modern technologies that are restructuring the way businesses work. The AI study by 451 Research data has revealed that most companies start using AI/ML not just to cut expenses but to generate revenue as well. They are using AI/ML to revamp their profit systems, sharpen their sales strategies, and enhance their product and service offerings. This demonstrates the change of viewpoint, AI/ML becoming a driver of business growth and not just a hands-on tool. For integrating AI in your business to be effective, you need to have clear goals and plans of implementation. We have put together a short guide to get you started in a smart direction for your business. 1. Identify Objectives The first step in your AI integration is clearly stating your goals. This can be: 2. Assess Your Current Setup It's important to note that about 80% of AI projects don't move past the testing phase or lab setup. This often happens because standardizing the way models are built, trained, deployed, and monitored can be tough. AI projects usually need a lot of resources, which makes them challenging to manage and set up. However, this doesn't mean small businesses can't use AI. With the right approach, even smaller companies can make AI work for them, effectively bringing it into their operations. Computational Resources AI models, especially those using machine learning or deep learning, need a lot of computing power to work well to process large datasets. This is important for training the AI, doing calculations, and handling user queries in real-time. Small businesses that don't have massive infrastructure can choose cloud computing services like AWS, Google Cloud, or Microsoft Azure. They have the necessary hardware and can adjust your performance to your needs. Data Quality and Quantity AI requires access to a lot of clean and organized data that is essential for training AI to identify patterns, make correct predictions, and answer questions. Collecting and preparing this kind of high-quality, error-free data in large amounts can be difficult, often taking up to 80% of the time from the start of the project to its deployment. For businesses that don’t have massive amounts of structured data, the solutions can be as follows: Expertise Effective AI implementation requires a strong team capable of creating algorithms, analyzing data, and training models. It involves complex math and statistics and advanced software skills like programming in Python or R, using machine learning frameworks (e.g. TensorFlow or PyTorch), and applying data visualization tools. For businesses that can't afford to gather and maintain a professional AI team, the solution is to partner with niche companies that focus on AI development services, like SciForce. Specialized service providers have the necessary technical skills and business experience that allow them to create tailored AI solutions for your needs. Integration Integrating AI into existing business operations requires planning to ensure smooth incorporation with current software and workflows, avoiding significant disruptions. Challenges include resolving compatibility, ensuring data synchronization, and maintaining workflow efficiency as AI features are introduced. To overcome integration challenges, choose AI solutions with easy compatibility with standard business software, focusing on those with APIs and SDKs for seamless integration. Prefer AI platforms with plug-and-play features for CRM and ERP systems. SciForce offers integration services, specializing in AI solutions that integrate effortlessly with existing software, hardware, and operations with zero disruptions. Ongoing Maintenance and Updates Before engaging in the implementation of AI solutions in the company, remember that AI systems need regular updates, including consistent data stream and software improvements. This helps AI adapt, learn from new inputs, and stay secure against threats. Creating AI from scratch, you will need a permanent internal team to maintain it. If you opt for an out-of-the-box solution, the vendor will deliver automatic updates. Partnering with SciForce, you receive managed AI services with our professionals handling the maintenance and updates of your system. 3. Choose Your AI Tools and Technologies With a variety of AI/ML tools available in the market, it’s hard to choose the one that will suit your needs, especially if it’s your first AI project. Here we asked our ML experts to share top tools they use in their everyday work. Databases AI\ML can’t exist without databases that are the foundation for data handling, training, and analysis. SciForce top choice is Qdrant, a specialized vector database, that excels in this role by offering flexibility, high performance, and secure hosting options. It's particularly useful for creating AI assistants using organizational data. Machine Learning Here is our top choice of the tools that allow us to easier AI model management and deployment. Speech Processing Frameworks These tools help our team to refine voice recognition and teach computers to understand human language better. Large Language Models There are lots of tools for working with LLMs, but many of them are complex inside and not straightforward. Yet, our team picked some tools for you that simplify working with LLMs: Data Science Our Data Science team considers the DoWhy library a valuable tool for causal analysis. It helps to analyze and work with data in more depth, focusing on the cause-and-effect connections between different elements: 4. Start Small and Scale Gradually Begin with small AI projects to see what works best for your business. Learn from these projects and gradually implement more complex AI solutions. - Be focused Start with a small, well-defined AI project that addresses a specific business need or pain point. This could be automating a single task or improving a specific process. Define clear, achievable objectives for your initial AI project. This helps in measuring success and learning from the experience. - Gather a cross-functional team Assemble a team with diverse skills, including members from the relevant business unit, IT, and the ones with specific AI skills you need. This ensures the project benefits from different perspectives. You can also turn to a service provider with relevant expertise. - Use Available Data Begin with the data you already have. This approach helps in understanding the quality and availability of your data for AI applications. In case you lack data, consider using public datasets or purchasing ones. - Scale Based on Learnings Once you have the first results, review them and plan your next steps. To achieve your first goals, you can plan to expand the scope of AI within your business. - Build on Success Use the success of your initial projects to encourage the wider use of AI in your organization. Share what worked and what you learned to get support from key decision-makers. - Monitor and Adjust In managing AI initiatives, it's critical to regularly assess their impact and adapt as needed. Define key performance indicators (KPIs) relevant to each project, such as process efficiency or customer engagement metrics. Employ analytics tools for ongoing monitoring, ensuring continuous alignment with business goals. Read on to learn how to assess AI performance within your business. To make the most of AI for your business, it's essential to measure its impact using Key Performance Indicators (KPIs). These indicators help track AI performance and guide improvements, ensuring that AI efforts are delivering clear results and driving your business forward. 1. Defining Success Metrics To benefit from AI in your business, it's crucial to pick the right Key Performance Indicators (KPIs). These should align with your main business objectives and clearly show how your AI projects are performing: 1. Align with Business Goals Start by reviewing your business objectives. Whether it's growth, efficiency, or customer engagement, ensure your KPIs are directly linked to these goals. 2. Identify AI Impact Areas Pinpoint where AI is expected to make a difference. Is it streamlining operations, enhancing customer experiences, or boosting sales? 3. Choose Quantifiable Metrics Select metrics that offer clear quantification. This might include numerical targets, percentages, or specific performance benchmarks. 4. Ensure Relevance and Realism KPIs should be both relevant to the AI technology being used and realistic in terms of achievable outcomes. 5. Plan for Continuous Review Set up a schedule for regular KPI reviews to adapt and refine your metrics as needed, based on evolving business needs and AI capabilities. Baseline Measurement and Goal Setting Record key performance metrics before integrating AI to serve as a reference point. This helps in directly measuring AI's effect on your business, such as tracking improvements in customer service response times and satisfaction scores. Once you have a baseline, set realistic goals for what you want to achieve with AI. These should be challenging but achievable, tailored to the AI technology you're using and the areas you aim to enhance. Regular Monitoring and Reporting Regularly checking KPIs and keeping up with consistent reports is essential. This ongoing effort makes sure AI efforts stay in line with business targets, enabling quick changes based on real results and feedback. 1. Reporting Schedule Establish a fixed schedule for reports, such as monthly or quarterly, to consistently assess KPI trends and impacts. 2. Revenue Monitoring Monitor revenue shifts, especially those related to AI projects, to measure their direct impact on sales. 3. Operational Costs Comparison Analyze operational expenses before and after AI adoption to evaluate financial savings or efficiencies gained. 4. Customer Satisfaction Tracking Regularly survey customer satisfaction, noting changes that correlate with AI implementations, to assess AI's effect on service quality. ROI Analysis of AI Projects Determining the Return on Investment (ROI) of any project is essential for smart investment in technology. Here’s a concise guide to calculating ROI for AI projects: 1. Cost-Benefit Analysis List all expenses for your AI project, such as development costs, software and hardware purchases, maintenance fees, and training for your team. Then, determine the financial benefits the AI project brings, such as increased revenue and cost savings. 2. ROI Calculation Determine the financial advantages your AI project brings, including any increase in sales or cost reductions. Calculate the net benefits by subtracting the total costs from these gains. Then, find the ROI: 3. Ongoing Evaluation Continuously revise your ROI analysis to include any new data on costs or benefits. This keeps your assessment accurate and helps adjust your AI approach as necessary. Future Growth Opportunities Use the success of your current AI projects as a springboard for more growth and innovation. By looking at how these projects have improved your business, you can plan new ways to use AI for even better results: Expanding AI Use Search for parts of your business that haven't yet benefited from AI, using your previous successes as a guide. For example, if AI has already enhanced your customer service, you might also apply it to make your supply chain more efficient. Building on Success Review your best-performing AI projects to see why they succeeded. Plan to apply these effective strategies more broadly or deepen their impact for even better results. Staying Ahead with AI Keep an eye on the latest in AI and machine learning to spot technologies that could address your current needs or open new growth opportunities. Use the insights from your AI projects to make smart, data-informed choices about where to focus your AI efforts next. AI transforms business operations by enhancing efficiency and intelligence. It upgrades product quality, personalizes services, and streamlines inventory with predictive analytics. Crucial for maintaining a competitive edge, AI optimizes customer experiences and enables quick adaptation to market trends, ensuring businesses lead in their sectors. Computer Vision Computer Vision (CV) empowers computers to interpret and understand visual data, allowing them to make informed decisions and take actions based on what they "see." By automating tasks that require visual inspection and analysis, businesses can increase accuracy, reduce costs, and open up new opportunities for growth and customer engagement. - Quality Control in Manufacturing Computer Vision (CV) streamlines the inspection process by quickly and accurately identifying product flaws, surpassing manual checks. This ensures customers receive only top-quality products. - Retail Customer Analytics CV analyzes store videos to gain insights into how customers shop, what they prefer, and how they move around. Retailers can use this data to tailor marketing efforts and arrange stores in ways that increase sales and improve shopping experiences. - Automated Inventory Management CV helps manage inventory by using visual recognition to keep track of stock levels, making the restocking process automatic and reducing the need for manual stock checks. This increases operational efficiency, keeps stock at ideal levels, and avoids overstocking or running out of items. Case: EyeAI – Space Optimization & Queue Management System Leveraging Computer Vision, we created EyeAI – SciForce custom video analytics product for space optimization and queue management. It doesn’t require purchasing additional hardware or complex integrations – you can immediately use it even with one camera in your space. - Customer Movement Tracking: Our system observes how shoppers move and what they buy, allowing us to personalize offers, and improve their shopping journey. - Store Layout Optimization: We use insights to arrange stores more intuitively, placing popular items along common paths to encourage purchases. - Traffic Monitoring: By tracking shopper numbers and behavior, we adjust staffing and marketing to better match customer flow. - Checkout Efficiency: We analyze line lengths and times, adjusting staff to reduce waits and streamline checkout. - Identifying Traffic Zones: We pinpoint high and low-traffic areas to optimize product placement and store design, enhancing the overall shopping experience. Targeted for HoReCa, retail, public security, healthcare sectors, it analyzes customer behavior and movements and gives insights of space optimization for better security and customer service. Natural Language Processing Natural Language Processing (NLP) allows computers to handle and make sense of human language, letting them respond appropriately to text and spoken words. This automation of language-related tasks helps businesses improve accuracy, cut down on costs, and create new ways to grow and connect with customers. Customer Service Chatbots NLP enables chatbots to answer customer questions instantly and accurately, improving satisfaction by cutting down wait times. This technology helps businesses expand their customer service without significantly increasing costs. Sentiment Analysis for Market Research NLP examines customer opinions in feedback, social media, and reviews to gauge feelings towards products or services. These insights guide better marketing, product development, and customer service strategies. Automated Document Processing NLP automates the handling of large amounts of text data, from emails to contracts. It simplifies tasks like extracting information, organizing data, and summarizing documents, making processes faster and reducing human errors. Case: Recommendation and Classification System for Online Learning Platform We improved a top European online learning platform using advanced AI to make the user experience even better. Knowing that personalized recommendations are key (like how 80% of Netflix and 60% of YouTube views come from them), our client wanted a powerful system to recommend and categorize courses for each user's tastes. The goal was to make users more engaged and loyal to the platform. We needed to enhance how users experience the platform and introduce a new feature that automatically sorts new courses based on what users like. We approached this project with several steps: - Gathering Data: First, we set up a system to collect and organize the data we needed. - Building a Recommendation System: We created a system that suggests courses to users based on their preferences, using techniques that understand natural language and content similarities. - Creating a Classification System: We developed a way to continually classify new courses so they could be recommended accurately. - Integrating Systems: We smoothly added these new systems into the platform, making sure users get personalized course suggestions. The platform now automatically personalizes content for each user, making learning more tailored and engaging. Engagement went up by 18%, and the value users get from the platform increased by 13%. Adopting AI and ML is about setting bold goals, upgrading tech, smart resource use, accessing top data, building an expert team, and aiming for continuous improvement. It isn't just about successful competition — it's about being a trendsetter. Here at SciForce, we combine AI innovations and practical solutions, delivering clear business results. Contact us for a free consultation.

nullThe Art and Science of Conversation Design

Conversation design is key in making artificial intelligence (AI) easier and more natural for people to use. In this field, blending creativity with technical skills transforms AI interactions to feel more like talking to a human than a machine. Designers focus on making AI responses clear and relatable, using their knowledge of how language works. Their role is crucial in making advanced AI systems user-friendly, ensuring they fit smoothly into our daily lives. This article explores conversation design in artificial intelligence, emphasizing how designers make AI interactions more human-like and user-friendly by combining creative and technical skills, and a deep understanding of language. Conversational AI is revolutionizing the way businesses operate and interact with customers. This technology, encompassing both text-based and voice-based AI, is significantly enhancing business efficiency and customer service in the following industries: - Text-Based Conversational AI Text-based AI, such as chatbots, serves various sectors, such as healthcare and retail. In healthcare, they can help in scheduling appointments collect information about symptoms, and give some simple health advice. In retail, a chatbot can answer questions about product characteristics and help the customer make a purchase decision. Its use can cut customer service costs by up to 30%, handling up to 80% of routine customer inquiries. Conversational designers carefully design dialogue flows and responses to meet the specific needs of end users in each sector. - Voice-Based Conversational AI Voice assistants, enhancing daily life through smart device control, task management, and accessible internet for visually impaired users, are vital in technologies aiding those with disabilities. Conversational designers focus on user-centric interaction flows, contributing to the technology's market growth, projected to reach $19.57 billion by 2030 with a 16.30% CAGR. - Advanced Interactive Systems Conversational designers use AI and machine learning to enable these systems to understand and adapt to individual user preferences. This keeps the interaction relevant, engaging, and effective in solving client queries. In summary, Conversational AI is essential in driving technology forward, significantly enhancing user interactions and business efficiency, and proving vital in the AI industry development landscape. Conversation design in AI combines technical skills and human interaction insights to make AI systems user-friendly and effective. It's essential for creating AI that's both functional and practical for everyday use, with conversational designers playing a key role in this process. To develop effective conversation scripts for AI models, conversational designers need a wide range of technical skills: - Programming and Development In conversational design, programming skills, particularly in Python for AI algorithms and JavaScript for web integration, are crucial. Designers use these to develop and maintain AI's core functionalities, including natural language processing and system integration across platforms. - Understanding of AI and Machine Learning A deep understanding of AI principles and machine learning algorithms is essential for developing conversational AI. Conversational designers train and refine the models with user data and feedback, tailoring AI replies to user preferences. - Natural Language Processing (NLP) Expertise in Natural Language Processing (NLP) is key for enabling AI systems to understand and replicate human language. Conversational designers use NLP to develop AI that can interpret language variations, context, and sentiment, making conversations with users feel more natural and intuitive. - Data Analysis and Management Data analysis, including managing large datasets is essential in conversational design. Designers analyze this data to gain insights into user behaviors, preferences, and interaction patterns. Such analysis is crucial for tailoring AI responses to be more user-focused. - User Interface Design and Integration User Interface Design is crucial for conversational designers to create intuitive and engaging interfaces. This involves designing the dialogue flow and user navigation for chatbots or AI systems, ensuring interactions are natural and easy to follow. Proficiency in API integration enables designers to access external data for more dynamic and personalized interactions. A deep understanding of psychology is crucial in conversational design, influencing how AI systems interact with users. This involves three key areas: - User Empathy Empathy is essential in conversational design for understanding and anticipating user emotions and needs. For example, a designer using empathy can craft responses in a chatbot for a support helpline that not only provides solutions but also acknowledges and addresses the user's frustration or anxiety. - Behavioral Understanding es human behavior and psychology are key to crafting AI dialogues that are engaging and persuasive. Designers use this knowledge to influence user engagement and guide responses, making conversations more human-like and effective in achieving interaction goals. - Cultural and Social Awareness Conversational designers must understand and incorporate various cultural and social norms. This involves adapting AI communication styles to different regions and communities, ensuring that interactions are linguistically accurate, culturally sensitive, and inclusive. - Copywriting Proficiency Conversational copywriting is a vital part of conversational design, focusing on crafting AI dialogues and solving all client queries. Here the designer also shapes the tone of voice that will stick to brand identity and provide a more enjoyable user experience. - Crafting Dialogue Flows This involves structuring clear, concise, and context-relevant conversations. Designers structure dialogues to guide users naturally, tailoring content to fit the conversation's context and user intent. This process aims to mimic natural human interaction, making AI conversations intuitive and engaging. - Tone of Voice Development Developing an AI's tone and voice involves aligning it with the brand's identity and the preferences of the target audience. This includes choosing a style (professional, friendly, witty) and language (formal, casual, technical) that reflects the brand's identity and user expectations. - Interactive Scripting Interactive scripting for AI means creating dialogues that adapt to user input, like a chatbot shifting to empathetic responses for dissatisfaction or new product suggestions or loyalty rewards for positive feedback. - Feedback Incorporation The conversational designer shapes and refines the model based on user feedback: rephrasing confusing responses or shortening lengthy instructions Conversational designers combine technical skills with knowledge of human behavior and copywriting proficiency. This is how effective AI communication systems are developed. When clients need an AI developing or improving AI communication system, our skilled conversational designers already have a precise workflow to assist them: 1. Initial Client Briefing When we start a project to create or improve a conversational AI system, the first step is to build a strong foundation. Our goal is to fulfill the client's vision and turn it into a practical, well-defined plan. The key actions in involve: 2. User and Market Research This is an important stage for us to understand the target audience's request and current market trends to make a relevant and competitive product: 3. Defining AI Personality and Tone of Voice In this phase, we customize the AI’s personality and style to match the client’s brand and their audience’s preferences: 4. Designing Conversational Flows and Scripts This is where conversational design starts. Now, we design the AI's main conversation paths and alternative scenarios, ensuring a comprehensive and fluent user experience. 5. Prototyping and Iterative Testing When the conversational flow is ready, we develop initial prototypes of the conversational AI and then conduct iterative testing with real users: 6. Implementation and Integration In this stage, we integrate the conversational AI with the client's existing infrastructure. This means adjusting AI to interact smoothly with existing software and hardware. 7. Performance Monitoring and Optimization Here, we focus on regularly assessing and improving the AI system's efficiency, as well as staying updated with changing user needs and industry trends. 8. Ongoing Maintenance and Updates For each project, we regularly update the AI system in each project to keep pace with user needs and tech advancements.


null Ethical AI in Action: SciForce's Compliance to U.S. Regulations

President Biden has set new rules to make AI use safer and fairer, focusing on ensuring its safety and protecting privacy. This article will explore these new AI rules and their impact. We'll also discuss how SciForce follows these rules in our healthcare and education projects, like the Jackalope Project and our AI system for online learning. President’s new AI regulations are a critical step in guiding AI's future, focusing on safety, security, and privacy. This section explores these essential measures, highlighting how they aim to ensure AI's responsible growth and safeguard personal information, reflecting a commitment to managing AI's expanding impact effectively. The new AI regulations represent a major change, increasing oversight and responsibility in AI development. They require developers of sophisticated AI systems to share their safety test results with the U.S. government, in case the model might pose risks to the US economy, national security, or public health. New safety and security standards for AI, including extensive pre-release testing, are being established by the National Institute of Standards and Technology. These standards are specifically designed to protect critical infrastructure and ensure public safety before AI systems are made publicly available. Furthermore, the focus on protecting critical infrastructure and national security highlights the growing recognition of the potential risks AI poses, with the Department of Homeland Security ensuring these standards are applied effectively to safeguard key areas. The new focus on Privacy-Preserving Techniques and Measures in AI development involves several key aspects: 1. Data Privacy Legislation The U.S. government is supporting the development of AI techniques that preserve privacy during AI training, ensuring the confidentiality of data. This initiative aims to advance AI without compromising the privacy of training data and sensitive information. 2. Research in Privacy Technologies The U.S. government is working with the National Science Foundation to increase research in privacy technologies like cryptography. They are funding a network to speed up this research and promote its use in federal agencies, showing a strong commitment to improving digital privacy with advanced solutions. 3. Federal AI Data Privacy Review The government is set to review how federal agencies use commercial data, especially personal information bought from data brokers. This review is aimed at improving the way these agencies manage and protect personal data, addressing the privacy concerns that come with AI technology. 4. Federal Privacy Guidelines The government is focusing on creating guidelines to assess how AI systems preserve privacy. This effort will help establish a standard for how AI should be responsibly used, directing developers to build AI that respects user privacy. These efforts mark a significant change in how AI is developed, putting a strong emphasis on protecting privacy as a key part of AI's growth, balancing technological advancements with the crucial need to safeguard user privacy. At SciForce, we recognize the importance of aligning with President Biden's Executive Order on AI Safety and Privacy. To this end, we have established specific practices: 1. Advanced Safety Testing Protocols At SciForce, we prioritize safety in all our AI projects. Our approach includes conducting extensive safety tests on all AI projects. These tests, aligned with NIST's standards, comprehensively evaluate various scenarios to proactively detect and address potential risks, ensuring our AI systems are safe and reliable. 2. Privacy-Preserving AI Development In our AI development at SciForce, we use the latest technologies to protect user privacy. This involves encryption and anonymization methods to keep user data secure, allowing our AI to evolve and learn while maintaining the privacy of individual users. 3. Transparent Data Practices Transparency is key in our data handling. We communicate to our users how their data is used, stored, and protected in our AI systems, ensuring informed consent and trust. By implementing these measures, SciForce demonstrates its commitment to developing AI that is not only effective but also secure, ethical, and respectful of privacy. The Biden-Harris Administration, aware of AI's potential to increase discrimination in sectors such as justice and healthcare, has introduced measures like an AI Bill of Rights and an Executive Order to combat bias in algorithms. President Biden's additional directives focus on ensuring AI developments support fairness and civil rights. The U.S. government is taking action to promote fairness and prevent discrimination in fields such as housing, federal programs, and criminal justice. These measures are aimed at addressing biases caused by AI, ensuring its use is fair and equitable. 1. Guidance Against Discrimination The government is issuing guidelines to landlords, federal benefits programs, and contractors to prevent AI algorithms from increasing discrimination. 2. Fighting Algorithmic Bias The government's plan to fight AI bias involves working with the Department of Justice and civil rights offices and offering specific training. This strategy is designed to better identify and address civil rights issues caused by AI, leading to fairer AI usage. 3. Fair AI in Criminal Justice The government's effort to create best practices for AI in the criminal justice system focuses on making sentencing, parole, and other processes fairer. This plan aims to stop AI from creating biases, ensuring everyone is treated equally in the justice system. The government is working to reduce bias and unfairness in AI, showing its dedication to using AI responsibly. This is done by following set guidelines and best practices to ensure fairness and justice in AI applications. SciForce is dedicated to promoting equity and preventing bias in AI, reflecting the priorities of President Biden's Executive Order: 1. Diverse Data Sets We ensure the use of diverse and representative data sets in training our AI models, helping to prevent biased outputs. 2. Bias Prevention Our AI systems at SciForce are designed with special algorithms to find and fix any biases, ensuring our decisions are fair and equal. We also regularly check these systems to catch and correct any new biases, keeping our AI fair and reliable. 3. Inclusive Design Teams At SciForce, we emphasize diversity in AI development, combining varied team backgrounds with wide stakeholder engagement. This approach ensures our AI solutions are inclusive and unbiased. AI is transforming American workplaces, making them more productive but also leading to issues like surveillance, bias, and potential job losses. The Government is taking steps to protect workers' rights, strengthen their negotiating positions, and guarantee training opportunities for everyone. 1. Flexibility in AI Responses The project faced the challenge of developing AI models capable of handling a wide range of medical scenarios, each with its unique data structure. 2. Limited Labeled Data A common obstacle in healthcare AI is the scarcity of sufficiently labeled data, which is essential for training accurate and reliable AI models. 3. Complex Data Structures The complexity of medical data, often with variable and intricate structures, posed a significant challenge in developing effective AI solutions. To tackle these issues, we employed GPT models capable of interpreting complex medical data. This method effectively overcomes the lack of labeled data, as the models infer and accurately interpret data from context, useful in scenarios with challenging or insufficient conventional labeling. By implementing these solutions, we ensure that our AI models are accurate and unbiased, in compliance with the Order. SciForce's new project introduces an AI-driven question-answering system for online education. Designed to work with varied materials like PDFs and video transcripts, it aims to enhance the learning experience by providing instant, intelligent responses for students and educators. This system is a step towards more interactive and adaptable online learning: During this project, SciForce encountered several key challenges: Integrating Diverse Educational Content The challenge involved developing the system to effectively process and understand a wide variety of educational materials, from complex text in PDFs to spoken language in video transcripts. This required advanced natural language processing to handle the diverse formats and contextual nuances of each type of content efficiently. We also implemented advanced machine learning techniques to teach systems to adaptively learn from different educational materials. Maintaining Accuracy and Contextual Relevance The challenge involved ensuring the AI's responses were accurate and relevant across a wide range of academic queries and diverse educational content. The AI required advanced capabilities for accurate interpretation and response to complex topics in various subjects. To enhance the accuracy of the AI system, SciForce trained the model with robust datasets. We also used advanced Natural Language Processing techniques to boost the model's understanding of the subject and its ability to produce relevant outcomes. Additionally, regular updates and refinements, guided by real-world feedback, further enhanced the AI's interpretive accuracy and contextual awareness. Preventing Bias and Ensure Fair AI The challenge involved developing the AI system to be impartial, catering to its diverse user base. This necessitated the implementation of specialized algorithms to detect and rectify biases in the AI's responses. To avoid biased outputs, we implemented specialized bias detection algorithms and trained the model on diverse datasets. To ensure correct model performance, we regularly conduct audits and continuously refine it based on user feedback loops. At SciForce, we follow President Biden's Executive Order goals, which aim for AI to be safe, fair, and protect privacy. In our health and education projects, we meet these standards by solving problems and sticking to federal rules. We focus on making AI that's not only smart but also safe and fair for everyone, showing our dedication to responsible AI that helps society.

nullA New Era in AI: Insights from the OpenAI Developer Conference

In San Francisco, a city known for tech innovation, the OpenAI Developer Conference was a major event for the AI world. This conference brought together experts, developers, and technology leaders. Leading the event were Sam Altman, the CEO of OpenAI known for pushing boundaries in AI research, and Satya Nadella, the CEO of Microsoft, whose company has been a key player in advancing AI technology. OpenAI, under Altman's leadership, has been at the forefront of AI development, sparking curiosity and anticipation in the tech community about its next moves. We at SciForce have been closely monitoring OpenAI's trajectory, intrigued by the next steps of their advancements in the broader tech landscape. The conference was much more than just showing off new technology; it was a place for sharing big ideas about the future of AI. One of the main attractions was the unveiling of GPT-4 Turbo, a new development by OpenAI. The event was crucial for looking at how AI is growing and how it might change technology as we know it. GPT-4 Turbo sets a new benchmark with its ability to handle up to 128,000 context tokens. This technical enhancement marks a significant leap from previous models, allowing the AI to process and retain information over longer conversations or data sets. Reflecting on this enhancement, Sam Altman noted, "GPT-4 supported up to 8K and in some cases up to 32K context length, but we know that isn't enough for many of you and what you want to do. GPT-4 Turbo, supports up to 128,000 context tokens. That's 300 pages of a standard book, 16 times longer than our 8k context." GPT-4 Turbo enhances accuracy over long contexts, offering more precise AI responses for complex interactions. Key features include JSON Mode for valid responses, improved function calling with multi-function capabilities, and reproducible outputs using a seed parameter, enhancing control and consistency in AI interactions. At the OpenAI Developer Conference, new text-to-speech and image recognition technologies were revealed, marking major AI advancements.

SciForce medical team attended  OHDSI Symposium 20232023 OHDSI GLOBAL SYMPOSIUM

Since 2015, Sciforce has been an active contributor to the OHDSI scientific community. Our medical team is consistently at the forefront of OHDSI events, sharing groundbreaking research and driving advancements in health data harmonization that empower better health decisions and elevate care standards. The fall event was no exception: from October 20 to 22 Polina Talapova, Denys Kaduk, and Lucy Kadets were delegated as our company representatives to the Global OHDSI Symposium with more than 440 of our global collaborators together held in East Brunswick, New Jersey, USA. The symposium affords unique opportunities to dive deeper into the OMOP common data model standards and tools, multinational and multi-center collaborative research strategies, and insight into completed large-scale multinational research projects. Our Medical Team Lead, Polina Talapova presented the topic “Mapping of Critical Care EHR Flowsheet data to the OMOP CDM via SSSOM" during a lightning talk session. She emphasized the significance of mapping metadata generation and storage for producing trustworthy evidence In turn, Denys and Lucy participated in the OHDSI Collaborator Showcase, where they successfully presented a prototype and poster detailing the Jackalope Plus AI-enhanced solution. This tool streamlines the creation, visualization, and management of mappings, reducing manual effort and ensuring precision in capturing details from real-world health data. Our colleagues had an opportunity to meet in person with leading OHDSI researchers such as George Hripcsak, Patrick Ryan, Marc Suchard, Andrew Willams, Rimma Belenkaya, Paul Nagi, Mui Van Zandt, Christian Reich, Anna Ostropolets, Martijn Schuemie, Dmytro Dymshyts, Dani Prieto-Alhambra, Juan M. Banda, Seng Chan You, Kimmo Porkka, Alexander Davydov, Aleh Zhuk, among other distinguished individuals. The event was truly transformative and rewarding, expanding participants’ minds and horizons. The SciForce team is profoundly grateful to the OHDSI community for the opportunity to be a part of this fantastic journey!

nullThe Launch of the Toxin Vocabulary: Our Solution to the Medical Research Complexity

In such an evolving Medical field, the synthesis of reliable and insightful data is a basis for making innovation and progress possible. With its global adoption, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been an important tool for advancing drug safety monitoring and healthcare outcome prediction. However, it has a gap in the representation of toxic substances and environmental exposures, an aspect central to deepening our understanding of their impacts on human health. Meanwhile, the Toxin Vocabulary – our revolutionary solution, is designed to improve the representation of toxic substances within the OMOP CDM, enabling better analysis of the complex interplay between environmental factors and health outcomes. In this article, we will tell you about the approaches and collaborative efforts that powered the creation of our Toxin Vocabulary. The Vocabulary aims to empower researchers, healthcare professionals, and organizations to get deeper insights regarding environmental exposures and human health outcomes. Let’s explore what insights are possible with our Toxin Vocabulary! Now, let’s get into more detail about the current approach and the problems researchers face. The OMOP CDM was established as an open community data standard, created to harmonize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. It was widely adopted by researchers and healthcare organizations around the globe. That’s how OMOP CDM simplified drug safety monitoring, comparative effectiveness research, clinical trial design, and healthcare outcome prediction. However, the representation of toxic substances and environmental exposures within the OMOP CDM has been a crucial need in environmental epidemiology. At the same time, environmental epidemiology focuses on investigating the impacts of exposure to toxic substances on human health, considering both short-term and long-term effects. To support such studies, Geographic Information Systems (GIS) have been utilized to analyze the spatial distribution of exposures and assess their potential health consequences. While recent efforts have aimed to integrate GIS data with the OMOP CDM, insufficient standards have hindered the comprehensive evaluation of environmental exposures and their associated health risks. So, that is how we came up with the idea of solving this issue and developing a hierarchical Toxin Vocabulary as a solution to improve the representation of environmental exposomes within the OMOP CDM. This standardized terminology has been developed through a systematic review of toxicological literature, analysis of open toxin databases, and consultation with experts in the field. By synthesizing the most relevant and up-to-date toxin terminology, our Vocabulary aims to facilitate environmental exposure assessment, support toxicological and epidemiological research, and enable the integration of GIS-related data into the OMOP CDM. The journey of the Toxin Vocabulary development needed a systematic approach containing a comprehensive review of toxicological literature, analysis of open-source toxin databases, and consultation with domain experts. These steps were essential in synthesizing a thorough and accurate representation of toxic substances within the OMOP CDM. As we already mentioned before, firstly, we conducted a systematic review of toxicological literature to identify relevant terms and classifications associated with various toxins and their impact on human health. By examining a variety of research papers, regulatory documents, and many different authoritative sources, we reached a comprehensive understanding of the diverse range of toxins and their associated semantic attributes. And, simultaneously with the literature review, we performed an analysis of open-source toxin databases. A primary resource that stood out in terms of comprehensiveness and reliability was the Toxin and Toxin Target Database (T3DB). T3DB provided us with a vast repository of toxin terminology, including descriptions of over 3,000 toxins with 41,602 synonyms. This database encompassed a wide range of toxins, including pollutants, pesticides, drugs, and food toxins, and provided extensive metadata fields for each toxin record (ToxCard), such as chemical properties, toxicity values, molecular and cellular interactions, and medical details. The process of integration required using the information obtained from the literature review and the T3DB to develop the Toxin Vocabulary. Also, it involved automatically uploading the source data to the PostgreSQL database using Python. Afterward, we extracted essential metadata, established cross-term connections, and performed a semi-automated mapping of selected terms to the OMOP Vocabulary standards. To ensure compatibility and seamless integration with the existing OMOP CDM standard vocabularies, the Toxin Vocabulary was mapped to relevant terminologies, including SNOMED CT, RxNorm, and RxNorm Extension. During the mapping process, we needed to associate corresponding concepts from the Toxin Vocabulary with the appropriate standard concepts within the OMOP CDM. This created the link between toxin terms and established clinical concepts, enabling us to do a comprehensive analysis and integration of environmental exposures with other healthcare-related data. As unique vocabulary identifiers, we used CAS codes due to their alignment with GIS data and the CAS Registry, one of the largest registries encompassing around 204 million organic substances. For toxins without CAS codes, unique T3DB codes were assigned, ensuring proper identification and classification. We have seamlessly incorporated the Toxin Vocabulary into OMOP instance by methodically organizing the information in preliminary stages, following the standard OHDSI contribution process, and ensuring each piece of data is accurately placed and interconnected for optimal use. These staging tables were instrumental in incorporating the Toxin Vocabulary's semantic and syntactic aspects. In this way, we ensured the compatibility of the system with the existing OMOP CDM framework. In the picture below you can see how our vocabulary works, our decisions, and their subsequent impact on the OMOP CDM structure Our Toxin Vocabulary represents a hierarchical and expansive representation of toxic substances within the OMOP CDM. It has over 79,377 internal relationships and maps the complex interconnections between toxins, cellular structures, relevant diseases, biological processes, and more, offering researchers an unprecedented level of detail in their analysis. But the Vocabulary's strength doesn't end here. The integration with standardized vocabularies such as SNOMED CT and RxNorm strengthens its capabilities, creating a symbiotic relationship. Such a synergy is the foundation for a more deep and detailed exploration of the exposome and can offer better insights and create a more complex understanding of the toxin-health outcome dynamics. Furthermore, it opens up new sights in drug safety monitoring, clinical trial design, and health outcome predictions, empowering researchers and healthcare professionals to harness rich, GIS-related data for advancing toxicoepidemiological research. The Toxin Vocabulary is not just a tool – it's a gateway to a future with an insightful understanding of environmental impacts on health. In the world of open science, innovative approaches and tools play a key role. The Medical Team of our company, Sciforce, is truly proud to contribute to this development, focusing on OHDSI vocabularies. Our Vocabulary was first presented at the OHDSI GIS Working Group. And, after the validation of the vocabulary is completed, we would be happy to present it publicly at the Global OHDSI Symposium (New Jersey, USA) on October 20, 2023, and officially integrate it into the OHDSI ecosystem! This opens up new opportunities in the fields of Geographic Epidemiology and Toxicoepidemiology. We are truly happy to introduce our enhanced integration of the Toxin Vocabulary, setting a new standard for healthcare analytics and research.

nullMicroservices Saga – Orchestration vs Choreography

Microservices architecture has absolutely changed the approach of developers to building large and complex systems reliably. And, over the last few years, they are rapidly gaining popularity. According to the research conducted by Statista in 2021, 85% of companies utilize microservices. Well, one of the technical goals of building the application is making it scalable and secure, and microservices allow us to do this, simultaneously making the final product fault-tolerant. So, by breaking down applications into smaller, independently deployable services, microservices offer more flexibility and scalability than traditional monolithic architectures. Using this type of architecture, developers can deploy features that prevent cascading failures. Basically, there are 2 most popular approaches for implementation architecture using microservices: orchestration and choreography. And choosing the right one can be challenging. So, in this article, we would like to compare choreography and orchestration microservices architectures and discuss the projects those types of architecture are more suitable for. When it comes to microservices architecture, there are two common approaches to service coordination: orchestration and choreography. The picture above perfectly illustrates the key differences between those two approaches, and we would like to explain to you how each of them works: In this approach, a central orchestrator acts as the “brain,” or logic component, that assigns tasks to the microservices and manages and controls their interaction. In this case, the orchestrator is responsible for routing requests, coordinating service interactions, and ensuring that services are invoked in the correct order. Basically, here, the orchestrator is a central point of control and can enforce rules across the entire system. So, what are scenarios when using orchestration is more beneficial? This approach is an excellent decision when: Not suitable for large projects The first disadvantage is that the controller needs to communicate directly with each service and, after that- wait for the response of each service. The consequences here are the following: first of all, when the interactions are occurring across the network, invocations may take longer and can be impacted by downstream network and service availability. The second point is that this system can work okay in small projects, but everything can fall apart when we are talking about hundreds or even thousands of microservices. In such a case, you are creating a distributed monolithic application that will be too slow to function well. Tight coupling In the orchestration approach, microservices are highly dependent upon each other: basically, when they are synchronous, every service should respond to requests, thus, if a failure occurs - the whole process will be stopped. Moreover, if we are talking about microservices in an enterprise environment, hundreds or even thousands of microservices are attached to a single function. Therefore, this method won’t fulfill the demands of your business. Reliance on RESTful APIs Orchestration also relies on RESTful APIs, and the problem that occurs here is that RESTful APIs and orchestration can’t scale. RESTful APIs are usually created as tightly coupled services. This means that using such services increases the tight coupling of the architecture of your application. Moreover, if you would like to build new functionality, remember that it will cost a lot and have a high impact on the API. In this approach, there is no central orchestrator, and the situation is quite the opposite here: each microservice is responsible for its own behavior and coordination with other services. Services communicate with each other through events and messages without the need for a central point of control. Here, each service can react to events as they happen and can trigger events that other services can react to. To automatically apply this approach and ensure things go smoothly, you can try choreography tools like Kafka, Amazon SQS, and RabbitMQ. They all are Event Brokers, which is the main tool for this approach. A service mesh like Istio and runtime system DAPR is also used for the choreography approach. So, when should you use choreography? This approach is an excellent decision in the following cases: Avoiding the creation of a single point of failure or bottleneck is important for you. You need microservices to be autonomic and independent. You want to simplify the process of adding or removing services from the system without disrupting the overall flow of communication. Now, let’s discuss the benefits of choreography that are solving the problems that occur with orchestration: Loose service coupling for agility and fault tolerance Well, adding and removing services is much simpler using a choreographed microservices architecture. Basically, here you will only need to connect/disconnect the microservice to the appropriate channel in the event broker. So, using loose service coupling, the existing logic will not fall apart when you add or remove microservices. And this results in less development churn and flux. Moreover, because of the independence of each service, when one application fails, the whole system will work while the issue is rectified as choreography isolates microservices. Also, it is not required to have a built-in error handling system in case of failure of the network, as this responsibility lies on the event broker. Faster, more agile development As clients' requirements are higher every day and the market grows constantly, the speed of development and modifying the app is crucial. So, the development teams are impacted by changes to other services, and it is a common barrier to achieving agility. But, here, choreographed microservices enable development teams to focus on their key services and operate more independently. So, the services are easily shared between teams once they are created. This allows us to save labor, time, and resources. More consistent, efficient applications During the creation of microservices with specific functions, you can create a more modular codebase. In this case, each microservice by itself has its business function, and together, microservices perform a business process. Thus, your system will be consistent, and it will be easy to modify and create services because you can reuse your microservices and tap into code that’s already been proven to perform a given function. So, what are the different and similar features between microservices and orchestration? Let us briefly summarise this: Both approaches involve service coordination and communication and can be used to implement complex workflows and business processes and build scalable and maintainable microservices architectures. When deciding between orchestration and choreography, it's important to consider the specific needs of your project. Here are some of the most important factors to consider: