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Great visual representation of the #AI Universe As AI continues to reshape our world, it's important to understand the intricate ecosystem of artificial intelligence techniques and domains. #ArtificialIntelligence AI refers to machines and systems designed to perform tasks that typically require human intelligence. Some of these tasks include: Natural Language Processing: Enabling machines to understand and respond to human language AI Ethics: Ensuring AI systems are developed and used responsibly Cognitive Computing: Simulating human thought processes in a computerized model. #MachineLearning This subset of AI involves systems that learn from data to make decisions and predictions. Key concepts include: Dimensionality Reduction: Simplifying data without losing significant information. Unsupervised Learning: Finding patterns in data without pre-labeled outcomes. Reinforcement Learning: Learning optimal actions through trial and error. Ensemble Learning: Combining multiple models to improve performance. #NeuralNetworks Delving deeper, we encounter Neural Networks, which are inspired by the human brain's structure. These are essential for many advanced AI capabilities. Components include: Perceptrons: The simplest type of neural network. Convolutional Neural Networks: Specialize in processing visual data. Recurrent Neural Networks: Handle sequential data, like time series. Multi-Layer Perceptrons: Networks with multiple layers between input and output. Activation Functions: Functions that determine the output of a neural network. Backpropagation: The method for training neural networks. #DeepLearning Within neural networks, we have the realm of Deep Learning. This subset involves networks with many layers (hence "deep") and includes: Deep Neural Networks: Networks with multiple hidden layers. Generative Adversarial Networks: Networks that generate new data similar to the input data. Deep Reinforcement Learning: Combining deep learning with reinforcement learning. #GenerativeAI At the core, we find Generative AI, which is about creating new content. This includes: Language Modeling: Predicting the next word in a sequence. Transformer Architecture: A model that handles sequential data efficiently, crucial for NLP. Self-Attention Mechanism: Allows models to focus on different parts of the input sequence. Natural Language Understanding: Comprehending and generating human language. Dialogue Systems: AI systems that can converse with humans. Transfer Learning: Using knowledge from one task to improve performance on another. #GenAI #DataScience Yves Dr. Khulood Dr. Marcell Antonio Giuliano Dr. Monika Eveline Franco Catherine Jennifer Prof. Dr. Ingrid Dr. Debashis Peggy Sally Avrohom Kevin Antonio Peggy Katarina Harold Gregory Hanadi Rommel Leonardo Daniel Fernando Bruno Bill Elitsa Eric Helen Tamara Nicolas Tony Tim Amitav Joseph Shyam Credits: Brij kishore Pandey https://bit.ly/4bfEW03 Shahab Anbarjafari https://bit.ly/3zgy0T5