To test this feature, visit your live site. Categories All Posts My Posts AI Artist Community A New Era of Creativity Add Media Egyptian Follow Views Posts 14 Strict geometry, bright colors, hieroglyphs, mythological scenes. Roman Follow Views Posts 17 Strict forms, ideal proportions, historical and mythological subjects Romanesque Follow Views Posts 15 Massive walls, rounded arches, strict forms, religious themes. Gothic Follow Views Posts 19 Elegant gloom, bats, castles, the Middle Ages. Rococo Follow Views Posts 18 Delicate pastels, playful ornamentation, elegant figures. Black and White Follow Views Posts 22 The art of contrast, texture and tone. Melancholy Follow Views Posts 12 Sadness, longing, and introspection. Cubism Follow Views Posts 14 Geometric shapes, fragmentation of objects. Cyberpunk Follow Views Posts 13 A combination of high technology and a dark atmosphere. Cartoon Follow Views Posts 25 Bold lines, bright colours. Pop Art Follow Views Posts 10 Bright colors, iconic images from pop culture. Digital Art Follow Views Posts 16 Pixel Art, 3D modeling, generative Art. Steampunk Follow Views Posts 14 The world of steam, mechanisms and the Victorian era. Technology Follow Views Posts 3 The world of machines through the eyes of an artist. Space and Universe Follow Views Posts 12 Infinite canvas. Micro/Macro Follow Views Posts 26 The invisible becomes visible. Nature Follow Views Posts 0 Living colors, Еternal Beauty. Interior Follow Views Posts 1 Art of space, a reflection of the Soul. Sculpture Follow Views Posts 7 A three-dimensional work of art. Video Follow Views Posts 0 Art in video format. Greek Follow Views Posts 9 Idealized figures, harmonious proportions, mythological subjects. Byzantine Follow Views Posts 14 Synthesis of ancient and oriental art. Renaissance Follow Views Posts 12 Harmonious compositions, idealized images. Baroque Follow Views Posts 18 Dramatic lighting, strong contrasts, lavish ornamentation. Impressionism Follow Views Posts 39 Loose brushstrokes, focus on light and atmosphere. Minimalism Follow Views Posts 10 Reduced shapes, clean lines, focus on the essentials. Surrealism Follow Views Posts 20 Dreamlike scenarios, illogical combinations of elements. Abstract Art Follow Views Posts 16 Non-representational forms, experiments with color and texture. Fantasy Follow Views Posts 17 Mythical creatures, magical landscapes. Anime Follow Views Posts 23 Japanese animation with a unique atmosphere and deep plots. Watercolor Follow Views Posts 7 Dancing colors born of water. Street Art Follow Views Posts 11 Graffiti, stencils, installations. Photorealism Follow Views Posts 4 Works of art that are so realistic that they look like photographs. People Follow Views Posts 0 Hyperrealistic portraits of people. Old Photo Follow Views Posts 31 A sense of time and history. Animals Follow Views Posts 0 The picturesque World of Fauna. Fashion Follow Views Posts 2 Trends, style, individuality. Landscape design Follow Views Posts 0 Creation of a harmonious space for man and nature. Architecture Follow Views Posts 2 Synthesis of functionality, aesthetics and engineering solutions. Forum - Frameless Tretyak 1h The melancholy of being Tretyak 1h Broken Heart Tretyak 1h Rain Tretyak 1h Foggy distance Tretyak 1h Quiet sadness Tretyak 1h Melancholy of the Rain Tretyak 1h Hours of Silence Tretyak 1h Sunset of hopes Tretyak 1h Sadness without reason New

Why do FAQs matter? FAQs are a great way to help site visitors find quick answers to common questions about your business and create a better navigation experience. What is an FAQ section? An FAQ section can be used to quickly answer common questions about your business like "Where do you ship to?", "What are your opening hours?", or "How can I book a service?". Where can I add my FAQs? FAQs can be added to any page on your site or to your Wix mobile app, giving access to members on the go. How do I add a new question & answer? To add a new FAQ follow these steps: 1. Manage FAQs from your site dashboard or in the Editor 2. Add a new question & answer 3. Assign your FAQ to a category 4. Save and publish. You can always come back and edit your FAQs. How do I edit or remove the 'Frequently Asked Questions' title? You can edit the title from the FAQ 'Settings' tab in the Editor. To remove the title from your mobile app go to the 'Site & App' tab in your Owner's app and customize. Can I insert an image, video, or GIF in my FAQ? Yes. To add media follow these steps: 1. Manage FAQs from your site dashboard or in the Editor 2. Create a new FAQ or edit an existing one 3. From the answer text box click on the video, image or GIF icon 4. Add media from your library and save. The future is AI, and AIWA is your guide to this future. Welcome to AIWA-AI Нome World of AI Application Customer Service Healthcare Marketing Education Economic Transportation Finance Entertainment Art Manufact Law Everyday Life World of AI Tools Education Health Productivity Business Communication Video Photos Image Music Translation Information Travel Entertainment AI Artist Community Rules Members Dictionary terms About Us AI Artist Community

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Community Rules The aiwa-ai.com community was created to unite people who are passionate about art created with the help of artificial intelligence. The goal of the community is to share experiences, support creative endeavors and create a friendly atmosphere for the development of AI art. Participation rules: Respect: To other community members, regardless of their skill level and views. To copyright. Copying other authors' works without permission is prohibited. To the opinion of the moderators. The moderators' decisions are final. Constructive criticism: Criticism should be aimed at improving the work, and not at insulting the author. When criticizing, you should indicate specific aspects of the work that can be improved. Activity: Regularly share your works and participate in discussions. Support other participants by leaving comments and likes. Community topic: The topics under discussion should be related to AI art, its creation, use and development. Off-topic and spam are prohibited. Content: Published content must correspond to the community theme and not violate the law. It is prohibited to publish content containing: Violence, pornography, discrimination. Advertisements not agreed with the administration. Viruses and other malware. Nickname: The nickname must be respectful and not violate the community rules. Confidentiality: Do not disclose the personal information of other participants without their consent. Responsibility of participants: Each participant is responsible for the content of their publications. In case of violation of the rules, the administration has the right to: Delete the violating content. Issue a warning. Temporarily or permanently block the offender's account. Moderation: Moderators monitor compliance with the community rules. Moderators' decisions are final. Changing the rules: The administration reserves the right to make changes to the community rules. Conclusion Following these rules will help create a comfortable and creative atmosphere in the aiwa-ai.com community. Let’s Work Together Get in touch so we can start working together. First Name Last Name Email Message Send Thanks for submitting!

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Dictionary of AI terms A Activation Function: A mathematical function within a neuron that determines its output, crucial for enabling networks to learn complex patterns. Algorithm: A set of instructions that a computer follows to perform a task. Artificial General Intelligence (AGI): A hypothetical type of AI that possesses human-level intelligence and adaptability across various domains and tasks. Artificial Intelligence (AI): The broad field encompassing the simulation of intelligent behavior in computers, with the aim of creating systems that can learn, reason, and act autonomously. B Backpropagation: The core method for calculating errors and adjusting weights within neural networks during the training process. Bias: The tendency of a model to favor certain outcomes over others, potentially leading to unfair or discriminatory results. Big Data: Datasets that are exceptionally large and complex, requiring specialized techniques for processing and analysis. C Chatbot: A computer program designed to engage in conversations with human users, often through text or voice interactions. Classification: A machine learning task where a model learns to assign categories to data points (e.g., classifying an email as spam or not spam). Clustering: A machine learning task focused on grouping similar data points without pre-defined labels. Cloud Computing: The on-demand delivery of computing resources, including AI tools and platforms, over the internet. Computer Vision (CV): The field of AI that enables computers to extract meaningful information from images and videos. D Data: The raw information used to train and evaluate machine learning models Data Preprocessing: The essential process of cleaning, transforming, and preparing data for use in machine learning models. Dataset: A structured collection of data used for training and testing machine learning models. Deep Learning (DL): A subset of machine learning that uses multi-layered artificial neural networks to learn complex representations from data. Deepfake: Manipulated media (images, videos, audio) created using AI techniques, often with the intent to deceive. E Embeddings: Mathematical representations of words or other data points that capture their semantic meaning and relationships. Explainable AI (XAI): Techniques and methods aimed at understanding and interpreting the decision-making processes of AI models. F Feature: A measurable characteristic or property of a data point used as an input to a machine learning model. G Generalization: The ability of a machine learning model to perform accurately on new, unseen data. Generative Adversarial Network (GAN): A deep learning architecture where two neural networks compete: one generates samples, the other tries to distinguish between real and fake. Gradient Descent: An iterative optimization algorithm commonly used to minimize errors and find the best parameters for a machine learning model. H Hyperparameter: A configuration setting for a machine learning model that is set before the training process begins (e.g., learning rate, number of layers in a neural network). I Inference: The process of using a trained machine learning model to make predictions or decisions on new data. Internet of Things (IoT): A network of interconnected devices with sensors that can collect and exchange data. L Label: In supervised learning, the target output or correct answer associated with a data point, used to guide the model's learning. M Machine Learning (ML): A subset of AI focused on algorithms and techniques that enable computers to learn from data without being explicitly programmed. Model: A mathematical representation of patterns learned from data, used for making predictions or decisions. N Natural Language Processing (NLP): The field of AI concerned with the interaction between computers and human language, including understanding and generation. Neural Network: A type of machine learning algorithm inspired by the structure of the biological brain, composed of interconnected nodes (neurons). Neuron: The basic computational unit within an artificial neural network. O Overfitting: A situation where a model learns the training data too well, including noise and anomalies, leading to poor performance on new data. P Precision: A performance metric in classification tasks. Measures the proportion of true positive predictions out of all positive predictions made by the model. R Recall: A performance metric in classification tasks. Measures the proportion of true positives correctly identified by the model. Regression: A machine learning task where the model predicts a continuous numerical value (e.g., house price prediction). Reinforcement Learning (RL): A type of machine learning where an agent learns through trial and error by interacting with an environment and receiving rewards or punishments. S Supervised Learning: A machine learning paradigm where models are trained on labeled datasets (input-output pairs are provided). T Transfer Learning: A technique where a model pre-trained on one task is re-purposed for a related task, improving efficiency and performance. U Unsupervised Learning: A machine learning paradigm where models discover patterns in data without explicit labels. V Validation Set: A portion of the dataset used to tune model hyperparameters and help prevent overfitting. W Weights: The adjustable parameters within a neural network that determine the strength of connections between neurons. Learning involves updating these weights. X XAI (Explainable AI): The field focused on developing techniques to understand and explain the decisions made by AI models, fostering transparency and trust. ​ ​ Less Common (But Important!) Terms Adversarial Examples: Inputs to machine learning models intentionally crafted to cause misclassification, exposing vulnerabilities. ​ ​ Autoencoder: A type of neural network used for unsupervised learning, often for dimensionality reduction or feature representation. ​ ​ Backtracking: A search algorithm that systematically explores potential solutions, reversing direction when a dead-end is reached. ​ ​ Bayesian Inference: A statistical approach to updating beliefs about a hypothesis as new data becomes available. ​ ​ Capsule Networks: A type of neural network architecture designed to better handle hierarchical relationships and viewpoints. ​ ​ Dimensionality Reduction: Techniques for transforming data into a lower-dimensional representation that preserves essential information. ​ ​ Domain Adaptation: The ability to adapt a model trained in one context (domain) to perform well in a different but related domain. ​ ​ Ensemble Learning: The process of combining multiple machine learning models to improve overall predictive performance. ​ ​ Evolutionary Algorithms: Optimization methods inspired by biological evolution, used for finding solutions to complex problems. ​ ​ Fuzzy Logic: A type of logic that deals with degrees of truth rather than simply true or false, useful for handling uncertainty. ​ ​