Generative AI 101: A Complex world Simplified
A simple introduction to a complex world
Raveendiran RR
9/21/20242 min read
Artificial Intelligence (AI) has evolved from theoretical concepts in the 1950s, including Alan Turing’s foundational work, to early rule-based systems and neural networks in the 1980s. By the 2000s, machine learning surged, followed by deep learning breakthroughs in the 2010s. Today, Generative AI and large language models revolutionize industries globally
Artificial Intelligence refers to the simulation of human intelligence in machines programmed to think and learn like humans. AI enables computers to perform tasks such as understanding natural language, recognising patterns, and making decisions
Machine Learning (ML) is a subset of AI that focuses on enabling machines to learn from data without being explicitly programmed. ML algorithms improve their performance as they process more data.
Generative AI involves models that can generate new content, such as text, images, or music, that is similar to the data they were trained on...
Neural networks are computational models inspired by the human brain's neurons; they learn patterns from data to make predictions. Transformers are advanced neural network architectures that use self-attention mechanisms to efficiently process sequential data, excelling in natural language processing tasks.
Connected nodes with layers forming a neural network
Neural Network | Deep Learning
Transformers
Input = Encoders | Output = Decoders
RGB scatter plot gives a pictorial representation of vectors
Example of how transformers are used to predict information
Metrics to choose while considering Gen AI
Refer this link to get the comparison between popular AI Platforms
https://artificialanalysis.ai/
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no - code | Low Code tools for Gen AI
Prompt Engineering
Ollama set up
