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      • Profile photo of Ramesh Ranjan

        Ramesh Ranjan posted an update

        a year ago

        Great visual representation of the hashtag
        #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
        #ChatGPT #chatbots #chatgpt4 #chatgpt3 #GenerativeAI #ArtificialIntelligence #AI ‪#MachineLearning‬ #‪ML‬

        Credits v/ Marcus Borba
        Brij kishore Pandey https://bit.ly/4bfEW03
        Shahab Anbarjafari https://bit.ly/3zgy0T5

        bit.ly

        Brij kishore Pandey on LinkedIn: As AI continues to reshape our world, it's crucial to understand the… | 44 comments

        As AI continues to reshape our world, it's crucial to understand the sprawling universe of artificial intelligence techniques and domains. This infographic… | 44 comments on LinkedIn

        Madhuri Yeditha and Unnathi S Vashista
        0 Comments
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