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Ramesh Ranjan posted an update
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/3zgy0T5bit.ly
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
