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Computer vision (CV) is a part of artificial intelligence that enables computers to analyze and understand visual information, both images and videos. It goes beyond plain “seeing” an image, but teaches computers to make decisions based on what they see. The AI-driven computer vision market is experiencing rapid growth, rising from $22 billion in 2023 to an expected $50 billion by 2030, with a 21.4% CAGR from 2024 to 2030. This technology imitates human vision but works faster using sophisticated algorithms, vast data, and cameras. Computer vision systems can quickly analyze thousands of items in huge areas, or detect tiny defects invisible to the human eye. This ability has found its application in lots of areas – and that’s what we will talk about in today's article! Computer vision empowers machines to interpret and make decisions based on visual information. It applies advanced methods to process and analyze images and videos, enabling computers to identify objects and respond accordingly. This section explains the key processes and techniques in computer vision, highlighting how it turns visual data into practical insights. Capturing Visual Data The first stage in teaching computers to see is the accurate capturing and preparing of visual data: - Data Acquisition Visual data is captured by cameras and sensors that act as a link between the physical world and digital analysis systems. They collect a wide range of visual inputs, from images to videos, providing the raw material for training CV algorithms. By converting real-world visuals into digital formats, they enable computer vision to analyze and understand the environment. - Preprocessing Preprocessing involves refining visual data for optimal analysis. This includes resizing images to consistent dimensions, standardizing brightness and contrast, and applying color correction for accurate color representation. These adjustments are crucial for ensuring data uniformity and improving image quality for further processing. Image Processing and Analysis The second stage involves identifying and isolating specific image characteristics, to recognize patterns or objects. - Feature Extraction This step focuses on detecting distinct elements such as edges, textures, or shapes within an image. By analyzing these features, computer vision systems can recognize various parts of an image and correctly identify objects and areas of interest. - Pattern Recognition The system uses the identified features to match them with existing templates, recognizing objects by their unique traits and learned patterns. This process enables the classification and labeling of various elements within images, helping the system to accurately interpret and understand the visual information. Machine Learning The third stage is Machine Learning which enhances the ability of systems to interpret and interact with visual data. - Supervised Learning Training models use labeled data to recognize and categorize images by learning from examples. Models learn to predict the correct labels for images by understanding patterns in the data and applying them to unknown objects. - Unsupervised Learning Allows computer vision models to sort and understand images without labels, by finding natural groupings or patterns in the data. This helps handle vast image sets without labels, detect anomalies and segment images. It enables models to spot unusual images or classify them by visual features, boosting their autonomous interpretation of visual data. - Deep Learning and Neural Networks Creating multi-layered neural networks that learn complex patterns in large amounts of data, like image recognition, NLP, and predictive analytics with high accuracy. Convolutional Neural Networks (CNNs) take this a step further, specifically in the realm of image data. They use layers with filters to automatically learn image features, from simple edges to complex shapes, by processing through many neuron layers. This method, inspired by human vision, excels in object identification, facial recognition, and scene labeling. Advanced Techniques The final stage in computer vision's development involves integrating advanced techniques that greatly expand its applications beyond basic image analysis. - Object Detection and Segmentation Object Detection and Segmentation pinpoint and differentiate objects in images, outlining each item to analyze scenes in detail. Essential for tasks like medical diagnostics, autonomous driving, and surveillance, these methods assess object shape, size, and position, providing a comprehensive visual understanding. - Real-time Processing Real-time Processing is essential for immediate decision-making in applications like autonomous driving. It demands fast, optimized algorithms and computing power to analyze traffic and obstacles instantly, ensuring safe navigation and effectiveness in critical scenarios like security and robotics. - Generative Models Generative Models, like GANs, enhance computer vision by crafting images nearly identical to real ones. By pairing a generator network with an evaluator, they refine outputs for applications such as video game development, AI training data, and virtual reality simulations. Computer vision is evolving quickly, creating opportunities in different industries to improve how they work, their accuracy, and how people interact with them. Retail Computer vision is significantly impacting the retail industry, projected to reach a market size of $33 billion by 2025, up from just $2.9 billion in 2018. Currently, 44% of retailers use computer vision to improve customer service, and it's expected to drive a 45% economic increase in the industry by 2030. The power of computer vision transforms various types of retail operations, from logistics to advertising. - Inventory Management Computer vision optimizes inventory management through real-time shelf analysis, identifying stock issues, and forecasting needs. This automates inventory tracking, preventing shortages, and maintaining organized shelves. - Space & Queue Optimization Tracking customer movements, computer vision cameras track customer movements and highlight high-traffic areas. This helps retailers understand customer behavior for improving layout and space usage and streamlining queue processing - Personalized Advertising Computer vision helps to analyze visual data of customer behavior and preferences: time spent in specific sections, products examined, purchase history, etc. This enables the development of personalized ads targeting customers with relevant promotions and products. The market for computer vision in healthcare, starting at $986 million in 2022, is predicted to skyrocket to $31 billion.) by 2031, growing at a rate of 47% annually. Such rapid expansion highlights the growing role of computer vision in enhancing medical diagnostics, improving treatment accuracy, and elevating patient care standards. - Automated Diagnostics & Analysis Computer vision boosts medical diagnostics by accurately detecting conditions like brain, breast, and skin cancers faster than traditional methods. It compensates for the shortage of radiologists by efficiently analyzing images. Research indicates that machine learning-trained computer vision systems surpass human radiologists in accuracy, especially in detecting breast cancer. - Surgical Assistance Computer vision technology supports surgeons by using specialized cameras that deliver live, clear images during procedures. This helps surgeons see and work with greater precision, improving the safety and success of surgeries. - Patient Monitoring Computer vision can be used for tracking health indicators and visual data, like wound healing or physical activity levels. It allows clinicians to assess patient health from afar, reducing the need for regular in-person visits. - Training and Education Computer vision enhances medical training with realistic simulations and case study analysis. It provides an interactive learning environment, improving trainees' diagnostic and surgical skills. A Deloitte survey reveals a strong trend towards adopting computer vision in manufacturing, with 58% of firms planning its implementation and 77% acknowledging its necessity for smarter, more efficient production. - Quality Control Computer vision systems can automate checking product quality by comparing them to set standards. These systems can find different flaws in one image, speeding up production by reducing manual inspections and increasing the quality of the final product. - Process Optimization Manufacturers lose 323 hours to downtime annually, costing $172 million per plant. Computer vision offers real-time insights to tackle inefficiencies, optimizing processes and machine use. - Predictive Maintenance In manufacturing, equipment often faces wear and tear from corrosion, risking damage and production stops. By detecting early signs and promptly alerting for maintenance, computer vision helps maintain uninterrupted operations. - Inventory Management Manufacturers now use computer vision for warehouse management, inventory tracking, and organizational efficiency. Companies like Amazon and Walmart are using CV-based drones for real-time inventory checks, quickly identifying empty containers to facilitate streamlined restocking. Agriculture, crucial for food production, is embracing digital innovation to tackle challenges such as climate change, labor shortages, and the impact of the pandemic. Technologies like computer vision are key to making farming more efficient, resilient, and sustainable, offering a path to overcome modern challenges. - Precision Farming By analyzing images from drones or satellites, farmers can closely monitor their crops' health and growth across vast areas. This detailed view helps catch problems like nutrient shortages, weeds, or insufficient water early, allowing for precise fixes. - Sustainable Farming AI-driven computer vision detects weeds early, reducing herbicide use and labor. The technology also aids in water and soil conservation, identifying irrigation needs and preventing erosion. - Yield Prediction Vital for large-scale farming, computer vision streamlines yield estimation, improving resource allocation and reducing waste. Using deep learning algorithms, it accurately counts crops in images despite challenges like occlusion and varying lighting. Computer vision is changing how machines understand images, but it faces several challenges, including ensuring data quality, processing data quickly, the effort needed for labeling data, scaling, and addressing privacy and ethical issues. Addressing these challenges effectively will ensure computer vision's advancement aligns with both tech progress and human values. Quality of Raw Material This addresses the clarity and condition of input images or videos, crucial for system accuracy. Specific challenges include poor lighting, obscured details, object variations, and cluttered backgrounds. Enhancing input quality is vital for the accuracy and reliability of computer vision systems: - Enhanced Image Capture: Use high-quality cameras and adjust settings to optimize lighting, focus, and resolution. - Preprocessing: Apply image preprocessing methods like normalization, denoising, and contrast adjustment to improve visual clarity. - Data Augmentation: Increase dataset diversity through techniques like rotation, scaling, and flipping to make models more flexible - Advanced Filtering: Use filters to remove background noise and isolate important features within the images. - Manual Inspection: Continuously review and clean the dataset to remove irrelevant or low-quality images. Real-Time Processing Real-time processing in computer vision requires powerful computing to quickly analyze videos or large image sets for immediate-action applications. This includes interpreting data instantly for tasks like autonomous driving, surveillance, and augmented reality, where delays can be critical. Minimizing latency and maximizing accuracy is critical for the need for a fast, accurate algorithm in live scenarios: - Optimized Algorithms: Develop and use algorithms specifically designed for speed and efficiency in real-time analysis. - Hardware Acceleration: Use GPUs and specialized processors to speed up data processing and analysis. - Edge Computing: Process data on or near the device collecting it, reducing latency by minimizing data transmission distances. - Parallel Processing: Implement simultaneous data processing to improve throughput and reduce response times. - Model Simplification: Model Simplification: Streamline models to lower computational demands while maintaining accuracy. Data Labeling Labeling images manually for computer vision demands significant time and labor, with the accuracy of these labels being critical for model reliability. The extensive volume creates a major bottleneck in advancing computer vision applications. Embracing automation and advanced methodologies in data labeling is key to creating effective datasets: - Automated Labeling Tools: Use AI to auto-label images, reducing manual effort and increasing efficiency. - Crowdsourcing: Use crowdsourced platforms to distribute labeling tasks among a large pool of workers. - Semi-Supervised Learning: Minimize labeling by combining a few labeled examples with many unlabeled ones. - Active Learning: Prioritize labeling of the most informative data that benefits model training, optimizing resource use. - Quality Control Mechanisms: Establish robust quality control checks for accurate label verification, mixing automation with expert human review. Scalability Scalability in computer vision faces challenges like adapting technologies to new areas, needing large amounts of data for model retraining, and customizing models for specific tasks.. To advance scalability across diverse industries, we need to focus on efficiency at each stage: - Adaptable Models: Create models that can easily adjust to different tasks with minimal retraining. - Transfer Learning: Use pre-trained models on new tasks to reduce the need for extensive data collection. - Modular Systems: Design systems with interchangeable parts to easily customize for various applications. - Data Collection: Focus on efficient ways to gather and label data needed for retraining models. - Model Generalization: Work on improving models' ability to perform well across diverse data sets and environments. Ethical and Privacy Concerns These issues highlight the need for careful handling of surveillance and facial recognition to safeguard privacy. Solving these challenges requires clear rules for data use, openness about technology applications, and legal support: - Data Protection Policies: Establish strict guidelines for collecting, storing, and using visual data to ensure privacy. - Transparency: communicate to users how their data is being used and for what purpose, fostering trust. - Consent Mechanisms: Ensure that individuals provide informed consent before their data is captured or analyzed. - Legal Frameworks: Create robust legal protections that define and enforce the ethical use of computer vision technologies. - Public Dialogue: Involve the community in discussions about the deployment and implications of computer vision to address societal concerns and expectations. Explore SciForce's expertise in computer vision, where we apply AI for enhanced efficiency, precision, and customer satisfaction in areas such as retail analytics, insurance, and agriculture. Retail: EyeAI Space Analytics EyeAI is SciForce’s own product, leveraging CV to transform existing cameras into a smart space analytics system. It helps to get real-time visitor behavior insights, optimize space usage, and deliver personalized service in retail, healthcare, HoReCa, and public safety. Using AI, EyeAI analyzes video data to help with space planning and queue management, making the whole process smoother without needing extra equipment. It includes the following advanced features: - Visitor Identification & Analysis Identifying visitors’ shopping behavior, and real-time route monitoring to improve layout and offer personalized promotions. - Space Usage Analytics Analyzing occupancy and facility usage data to ensure each square meter is used at its best. Delivering space optimization suggestions. - Queue Management Detecting queue length, movement speed, and crowd size in waiting areas. Analyzing client processing in checkout areas. It has been successfully used by a chain with over 80 supermarkets. The client faced challenges in managing their space effectively and keeping queues short, crucial for a good shopping experience. EyeAI turned their existing cameras into a smart system providing instant insights into visitor behavior. After adopting EyeAI, they saw better store organization and faster queues, leading to happier customers and more efficient operations. InsurTech: Roof Top Damage Detection Our client is an insurance company that wants to improve customer service and streamline claims processing. The main challenge was to accurately assess roof damagefrom photos for efficient claims processing, requiring analysis of location, size, shape, and type of damage without installing new hardware. We developed a system using advanced drone cameras and 3D imaging for precise evaluations from just two images. Utilizing algorithms like the 8-point algorithm and keypoint triangulation, our solution accurately maps damage and adjusts measurements to real-world dimensions, backed by a web service for easy image upload and damage annotation. Key Features: - Advanced Imaging It uses drone cameras and 3D models for precise evaluations of the extent, location, and nature of the damage by capturing and analyzing images from many angles. - Damage Detection Employs Mask RCNN for identifying damaged areas and calculates their size by detecting precise boundaries. - Efficient Processing Uses a REST API for seamless image uploads and the retrieval of detailed damage analysis, which include damage locations, dimensions, and other data. The implementation streamlined roof damage assessments for insurers, offering a tech-driven approach to claims processing. It improved operational efficiency and customer satisfaction by providing fast, accurate damage evaluations, and setting a new industry standard for insurance claims handling. Our client, a fintech startup at the intersection of finance and healthcare, specializes in managing insurance claims. The primary hurdle is the high rate of claim denials in the U.S. healthcare system, causing significant revenue loss and complex resolution process. Our solution addresses these challenges by automating claim assessments and streamlining processing with AI integration, computer vision, and predictive analytics. Key components include the CodeTerm for processing and structuring claim data and the HealthClaim RejectionGuard for predicting claim outcomes, and enhancing processing efficiency. Key Features: - Automated Claim Assessment Automates claim evaluations, identifying potential denials early in the process for proactive management. - AI-Integrated Processing Simplifies the complex claim processing workflow, reducing manual tasks, and freeing up staff time and resources. - Predictive Analytics for Prevention Allows healthcare institutions to foresee possible claim denials and implement preventative measures, shifting from reactive to proactive claim management. Our AI system makes processing more efficient by automating claims assessment and preventing denials. This leads to fewer rejections and better financial health for providers. Our client is an agriculture innovation company, aiming to increase farming productivity along with minimizing carbon footprint. Traditional methods were inefficient and imprecise, creating a demand for a tech solution capable of providing detailed, real-time insights on crop conditions and their environmental effects. We developed a system that analyzes satellite images to identify harvested sugar cane from fields and its sugar level. Using AI algorithms, our solution analyzes the crop's condition and expected output. Key features: - Satellite Imagery Analysis It uses high-resolution images from Sentinel2 and Planet satellites to monitor crop conditions across vast areas. - Yield and Sugar Content Prediction Analyzes satellite imagery and agro indices to forecast crop yields and sugar content, enabling precise agricultural planning and management. - Weather Data Integration Incorporates crucial weather parameters, such as precipitation and temperature, into models to refine predictions Our solution allows for the accurate and early identification of crop health problems and pest attacks, enabling quick and specific responses. Although the accuracy of harvest time and yield predictions varied by region because of data limits, the overall improvement in work efficiency and sustainable farming methods was notable. The shift from desktops to mobile devices has significantly changed content consumption, particularly boosting mobile video viewing. This trend has led to an increase in mobile video advertising, pushing advertisers to create shorter, yet engaging content suitable for various social platforms. The project's goal was to create a system that automatically edits and adjusts videos to fit the requirements of social media platforms like Instagram, YouTube, and Facebook. We aimed to shorten 30-second TV commercials to make them briefer and more engaging for these platforms. Key features: - Quick Video Trimming Turns 30-second ads into short 6 to 10-second clips, using motion analysis to pick out the most significant scenes. - Adaptive Resizing Adjusts videos to fit different social media, ensuring key details and visuals remain intact across all channels. - Object and Text Detection Uses sophisticated techniques to identify and keep important content and text during resizing, tailored to each social platform's needs. Our automated system simplifies video editing for mobile content, helping advertisers craft impactful ads more efficiently. It boosts ad relevance and viewer engagement, aligning with dynamic changes in digital advertising. Our client is a manufacturer of advanced image-acquisition devices and analytical tools for image processing. We cooperated with their team to develop the model for anomaly detection on images. The project aimed to improve how factories spot faulty parts without needing a person to inspect each one. Traditional manual checks took a lot of time and could cause defects. The client intended to automate this, speeding up inspections and catching more errors. The solution was based on the PaDiM (Patch Distribution Modeling) algorithm that identified the defects by comparing the items with normal parts. Its great benefit is that it doesn’t require a big dataset and can work with 240 images. We were lucky to have more that had a positive effect on model training. Here is how it worked: - Learning from the Good There was a dataset with pictures of items without any defects. It acted as a basis for further model training. - Checking for Distribution Differences The model then examined new images of details by comparing their feature distributions with the distribution learned from normal data during training. - Finding Faults If the system saw a big enough difference from the normal patches, it flagged the part as potentially defective. Introducing Computer Vision for the detail inspection process helped to speed it up and improve the efficiency and accuracy of defect detection, compared with human inspectors. Computer vision's impact on digital transformation is undeniable. Adopting smart systems of analyzing visual information, we drive forward plenty of industries, from more early and precise disease detection to strict quality control in manufacturing and environmental-friendly farming. SciForce has rich experience in introducing CV solutions to businesses in different areas. Contact us to explore new opportunities for your business.

Voice biometry is changing the way businesses operate by using distinctive features of a person's voice, like pitch and rhythm, to confirm their identity. This technology, a central part of Voice AI, turns these voice characteristics into digital "voiceprints" that are used for secure authentication. Unlike traditional methods such as fingerprint or facial recognition, voice biometry can be used remotely with just standard microphones, making it both practical and non-intrusive. This technology enhances security using advanced algorithms that block fraudulent attempts, making it a popular choice in various sectors requiring reliable and user-friendly authentication solutions, such as finance, healthcare, and customer support. The voice biometric market, valued at $1.261 billion in 2021, is expected to grow significantly, with a projected annual growth rate of 21.7%. By 2026, the market is anticipated to exceed $3.9 billion. Voice recognition is a valuable method capable of improving the security and customer service and offering rich personalization experience. Today we’ll explore, how it works and take a look on use cases in different areas of business Voice is produced when humans push the air from the lungs through the vocal cords, causing them to vibrate. Vibrations resonate in the nasal and oral cavity, releasing the sounds to the world. Each human's voice has unique characteristics, such as pitch, tone, and rhythm, shaped by the anatomy of their vocal organs. This makes the voice as unique as fingerprints, faces, or eyes. Voice recognition identifies individuals by analyzing the unique characteristics of their voice. This involves two key stages: Acoustic Analysis This stage involves analyzing the voice sample as an acoustic wave. Technicians use a waveform or a spectrogram to visualize the voice. Waveform displays the amplitude of voice, featuring the loudness, while spectrogram reflects the frequency, representing them in color or grayscale shading. Mathematical Modeling After analyzing the voice, its unique characteristics are transformed into numerical values through mathematical modeling. This step uses statistical and artificial intelligence methods to create a precise numerical representation of the voice, known as a voiceprint. Active & Passive Extraction Active Voiceprint Extraction requires the person to actively participate by repeating specific phrases. It’s used in systems that need very accurate voiceprints. Passive Voiceprint Extraction captures voice data naturally during regular conversation, like during a customer service call. It doesn’t require any specific effort from the user, making it more convenient and less intrusive. The choice between active and passive extraction depends on the needs of the system, such as the level of security required and how intrusive the process can be for users. Voiceprints are securely saved in a database, and each is stored in a unique format set by the biometrics provider. This special format ensures that no one can recreate the original speech from the voiceprint, protecting the speaker's privacy. Voiceprint Comparison When a new voice sample is provided, it is quickly compared to the stored voiceprints to check for a match, which is crucial for verifying identities. This comparison can happen in a few ways: Main Challenges Solution The language learning platform supports various types of exercises, including writing ones, guessing games, and pronunciation training. This module focuses on providing precise, unsupervised pronunciation training, helping the students to refine their pronunciation skills autonomously. How It Works When a student speaks, the system displays a visual waveform of their speech. This points out errors by highlighting incorrect words, syllables, or phonemes and offers the correct pronunciation. It also presents alternative pronunciations, providing learners with a broad understanding of different speaking styles. The pronunciation evaluation module uses artificial neural networks and deep learning to analyze speech patterns, while machine learning and statistical methods identify common errors. Decision trees analyze speech patterns against set linguistic rules to determine pronunciation accuracy, identify errors, and suggest corrections. Implementation The development team upgraded from traditional MATLAB-based ASR models to a more sophisticated, TensorFlow-powered end-to-end ASR system. This new system uses the International Phonetic Alphabet (IPA) to convert sounds directly into phonetic symbols, efficiently supporting multiple languages within a single system. Key features include: Conclusion Analyzing unique voice characteristics offers endless possibilities in various business areas. More secure than traditional passwords, voice recognition can safeguard customers’ money and sensitive information, like health records. Quick processing of client support requests, easy and non-intrusive authentication will both please the customers and make business more efficient. Voice recognition can even become a key selling feature in your product – like training pronunciation of language learners. SciForce has rich experience in speech processing and voice recognition. Contact us to explore new opportunities for your business.

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.

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

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.

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!

Chat GPT-4 has already become a very promising tool that many people across the globe use for different purposes. Usually, many of us use it to summarize generate text, or write code. Indeed, the potential of Chat GPT is enormous. So, today, we would like to discuss with you the projects that are implemented on top of Chat GPT- 4. Mayo Oshin, a data scientist who has worked on various projects related to NLP (natural language processing) and chatbots, has built GPT-4 'Warren Buffett' financial analyst. Basically, this chatbot can analyze multiple large PDF documents (~1000 pages) using GPT-4 and LangChain - a framework for developing applications powered by language models. So, this chatbot is designed to process and analyze financial data from multiple PDF files. Specifically, Mayo analyzed the 10-k annual reports of Tesla for the years 2020 to 2022. The reports contain a lot of information about Tesla's financial performance, operations, risks, and opportunities, which is obviously very time-consuming and overwhelming to read for humans. The chatbot uses GPT-4 to generate conversational responses to user queries about the data in the PDF files. Basically, you can ask any questions about the documents. Those questions can be simple, such as "What is this document about" and can be more specific such as "What are the main risks identified in this report?" or "What do you mean by (any term)?" And, as a result will receive the answer with the sources and references. Here you can see the PDF chatbot architecture: Also, here you can see the repository of this project and a video tutorial on chatting with PDF documents. This project demonstrates the potential of using AI-powered chatbots to automate complex tasks that require time, skills, and effort. Of course, the results of such chatting can be both enough for some types of work and require more attention and refinement, but still, the information extracted from the chat will simplify and optimize a lot of processes. We believe that such usage of AI can provide valuable insights for various fields, including finance, law, and healthcare. Creating a 3D space runner game from scratch is a challenging task that requires a deep understanding of game development, computer graphics, and programming. Indeed, It is really impressive that Ammaar Reshi was able to create a fully-fledged 3D game without prior knowledge of game programming using Chat GPT-4 and, here are the steps he took:

Technologies now revolutionize our experience in each sector of our lives: from creating Netflix recommendations to helping doctors with diagnostics. But what about the agriculture field and technologies? Precision agriculture is going to absolutely change the farmer's cultivation and harvest of their food with the help of different sophisticated technologies like AI and Machine Learning, GPS, GIS, UAVs, and Satellite Remote Sensing, collectively known as Precision Agriculture. Precision Agriculture, also known as smart farming or precision farming, is the application of advanced technologies and data-driven insights to optimize farming practices. It's all about making agriculture more accurate and controlled when it comes to growing crops. From a technical point of view, the results are achieved by using tools like GPS, GIS, remote sensing, satellite imagery, field mapping, artificial intelligence, and machine learning to monitor field conditions, make accurate predictions, and improve crop quality and profitability. Basically, precision agriculture employs data from multiple sources to improve crop yields and increase the cost-effectiveness of crop management strategies, including fertilizer inputs, irrigation management, and pesticide application. By using such technologies in the agriculture field, your business can participate in the development of sustainable agriculture and solve both: economic and ecological problems. The importance of Precision Agriculture is definitely more than just improving farm efficiency – It provides great solutions for some of the most pressing global challenges we face today. The research "2020 World Population Data Sheet", conducted by the Population Reference Bureau, shows that the world population is projected to increase from 7.8 billion in 2020 to 9.9 billion by 2050. And, when the world's population is growing so fast, the demand for food is growing at the same or even higher speed. However, the resources to produce this food, like land, water, and energy, are limited and must be used wisely. Here precision agriculture steps in: it enables farmers to use resources more efficiently, helps to increase yield and productivity, reduces waste, minimizes environmental impact, and ultimately, ensures food security. Actually, beyond those socio-environmental aspects, precision agriculture also allows demonstration of the potential of AI, machine learning, and other high-tech tools. Precision agriculture systems allow farmers to remotely manage all agricultural processes. Such systems can effectively control vast areas, making them viable for small farms with large fields or multiple small parcels of land. Implementation of precision agriculture remarkably enhances crop efficiency and escalates production while simultaneously conserving financial resources. Despite the prejudiceі that these technologies are expensive, in a long-term perspective, the savings are significantly higher than with traditional agricultural methods. Summarizing the Impact of Precision Agriculture, let's take a look at the benefits it can bring. Use cases of precision agriculture In its early stages, precision agriculture was primarily used for larger operations that could support the IT infrastructure and technology resources to use its benefits. However, with the widespread of mobile apps, intelligent sensors, drones, and cloud computing, precision agriculture has become accessible for farming cooperatives and even small-scale family farms. Some of the most popular uses of precision agriculture are the following: Agricultural Mapping and Field Scouting: Drones with cameras can generate high-resolution maps of fields, which can help to identify problems, monitor crops, and predict yield potential. Soil Sampling and Analysis: Mobile applications can collect data about soil composition, fertility, moisture levels, etc. This data can be used to make decisions concerning irrigation or other details of crop management. Weather Monitoring: Hyperlocal weather information can guide users on when it is the best time to plant, how to water the plants, and when is the optimal time for harvesting. Labor Management: Mobile apps with GPS can track the location and activity of field workers. This data can enhance workflows and ensure efficiency. Equipment Management: Farming equipment can be very expensive, and precision agriculture can help farmers maintain equipment inventories by scheduling preventative maintenance and planning for necessary repairs. Let's start our exploration of precision farming technologies with one of the most famous tools - the Global Positioning System or GPS. Basically, GPS provides precise positioning information based on satellite signals, which is critical for many aspects of modern life. In the context of precision farming, it offers a powerful means of location-specific data collection and analysis, facilitating everything from field mapping and crop scouting to guided tractor movements and variable rate applications. Next in our technological toolbox is the Geographic Information System or GIS. This system is designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data by creating maps. GIS technology is a valuable tool for agricultural management as it allows farmers to turn raw data into informative and easy-to-understand maps. The usage of a Geographic Information System empowers farmers to access and examine historical data, such as regional soil survey maps and characteristics of traditionally cultivated crops. Satellite imagery and aerial photography supplement this information, providing further insights. A particularly useful function of GIS is its ability to scrutinize various farm management strategies through the comparison and alteration of different data layers. UAVs equipped with multispectral cameras can capture high-resolution imagery and data of farm fields. They're particularly useful for monitoring crop health, detecting pests and diseases, and spraying pesticides or fertilizers in a targeted manner. The advent of Satellite Remote Sensing has truly globalized Precision Agriculture. By capturing images of the Earth's surface, satellites can provide comprehensive and continuous data on weather patterns, soil conditions, and crop health. This technology is especially valuable for farmers who manage large territories. While GPS, GIS, UAVs, and Satellite Remote Sensing are the backbone of precision agriculture, the field is constantly evolving with the development of other cutting-edge technologies. Machine learning and Artificial Intelligence, for instance, are often used for the analysis of the vast amounts of data generated by precision farming, leading to predictive models that guide farmers' decisions. In order to fully understand the impact of different technologies enabling precision agriculture, it's essential to pay attention to their strengths and weaknesses. Here, we will compare GPS, GIS, UAVs, and Satellite Remote Sensing, highlighting how each tool fits into the broader precision agriculture ecosystem. Our journey through the world of Precision Agriculture has revealed a fascinating convergence of technology and agriculture. GPS, GIS, UAVs, Satellite Remote Sensing, and many other technologies are bringing innovations to old practices of farming and transforming the industry. The usage of technologies in the agriculture field proves its economic profitability and environmental sustainability. Precision farming brings many great benefits today for many companies around the world. The main prospects of Precision Agriculture consist in its capacity to address one of our hardest challenges today - feeding a growing global population and preserving the health of our planet.

Two years after Generative Adversarial Networks were introduced in a paper by Ian Goodfellow and other researchers, including in 2014, Facebook’s AI research director and one of the most influential AI scientists, Yann LeCun called adversarial training “the most interesting idea in the last ten years in ML.” Interesting and promising as they are, GANs are only a part of the family of generative models that can offer a completely different angle of solving traditional AI problems. When we think of Machine Learning, the first algorithms that will probably come to mind will be discriminative. Discriminative models that predict a label or a category of some input data depending on its features are the core of all classification and prediction solutions. In contrast to such models, generative algorithms help us tell a story about the data, providing a possible explanation of how the data has been generated. Instead of mapping features to labels, like discriminative algorithms do, generative models attempt to predict features given a label. While discriminative models define the relation between a label y and a feature x, generative models answer “how you get x.” Generative Models model P(Observation/Cause) and then use Bayes theorem to compute P(Cause/Observation). In this way, they can capture p(x|y), the probability of x given y, or the probability of features given a label or category. So, actually, generative algorithms can be used as classifiers, but much more, as they model the distribution of individual classes. There are many generative algorithms, yet the most popular models that belong to the Deep Generative Models category are Variational Autoencoders (VAE), GANs, and Flow-based Models. A Variational Autoencoder (VAE) is a generative model that “provides probabilistic descriptions of observations in latent spaces.” Simply put, this means VAEs store latent attributes as probability distributions. The idea of Variational Autoencoder (Kingma & Welling, 2014), or VAE, is deeply rooted in the variational bayesian and graphical model methods. A standard _autoencoder_ comprises a pair of two connected networks, an encoder, and a decoder. The encoder takes in an input and converts it into a smaller representation, which the decoder can use to convert it back to the original input. However, the latent space they convert their inputs to and where their encoded vectors lie may not be continuous or allow easy interpolation. For a generative model, it becomes a problem, since you want to randomly sample from the latent space or generate variations on an input image from a continuous latent space. Variational Autoencoders have their latent spaces continuous _by design_, allowing easy random sampling and interpolation. To achieve this, the hidden nodes of the encoder do not output an encoding vector but_,_ rather, two vectors of the same size: a vector of means and a vector of standard deviations. Each of these hidden nodes will act as its own Gaussian distribution. The new vectors form the parameters of a so-called latent vector of random variables. The _i_th element of both mean and standard deviation vectors corresponds to the ith random variable’s mean and standard deviation values. We sample from this vector to obtain the sampled encoding that is passed to the decoder. Decoders can then sample randomly from the probability distributions for input vectors. This process is stochastic generation. It implies that even for the same input, while the mean and standard deviation remain the same, the actual encoding will somewhat vary on every pass simply due to sampling. The loss of the autoencoder is to minimize both the reconstruction loss (how similar the autoencoder’s output to its input) and its latent loss (how close its hidden nodes were to a normal distribution). The smaller the latent loss, the less information can be encoded that boosts the reconstruction loss. As a result, the VAE is locked in a trade-off between the latent loss and the reconstruction loss. When the latent loss is small, the generated images will resemble the images at train time too much, but they will look bad. If the reconstruction loss is small, the reconstructed images at train time will look good, but novel generated images will be far from the reconstructed images. Obviously, we want both, so it’s important to find a nice equilibrium. VAEs work with remarkably diverse types of data, sequential or nonsequential, continuous or discrete, even labeled or completely unlabelled, making them highly powerful generative tools. A major drawback of VAEs is the blurry outputs that they generate. As suggested by Dosovitskiy & Brox, VAE models tend to produce unrealistic, blurry samples. This is caused by the way data distributions are recovered, and loss functions are calculated. A 2017 paper by Zhao et al. has suggested modifications to VAEs not to use the variational Bayes method to improve output quality. Generative Adversarial Networks, or GANs, are a deep-learning-based generative model that is able to generate new content. The GAN architecture was first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.” GANs adopt the supervised learning approach using two sub-models: the generator model that generates new examples and the discriminator model that tries to classify examples as real or fake (generated). GAN sub-models