ai & Acronyms

Some acronyms for the layman 

Some acronym's for the laymam :

GPT - Generative Pre Trained - 

§Generative: The model is capable of generating new content, such as text, based on the input it receives. It can create coherent and contextually relevant responses.

§Pretrained: The model is trained on large datasets before being fine-tuned for specific tasks. This pretraining allows GPT to learn general language patterns and structures, making it capable of understanding and generating text in a wide range of topics.

§Transformer: This refers to the underlying neural network architecture used in GPT. The transformer architecture is particularly effective for processing sequences of data (like text) and is the basis for many modern natural language processing models.

AI - Artificial Intelligence: The simulation of human intelligence in machines, enabling them to perform tasks like problem-solving, reasoning, and decision-making.

ML - Machine Learning: A subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data.

DL - Deep Learning: A subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in large datasets.

NLP - Natural Language Processing: A field of AI focused on enabling machines to understand, interpret, and generate human language.

NN - Neural Network: A computational model inspired by the human brain, used in machine learning and deep learning to process data and make predictions.

ANN - Artificial Neural Network: A type of neural network that uses algorithms to simulate the way a human brain processes information.

CNN - Convolutional Neural Network: A specialized type of neural network used primarily in image and video recognition tasks.

GAN - Generative Adversarial Network: A framework for training models that consist of two neural networks (a generator and a discriminator) that work in opposition to create new data instances.

RL - Reinforcement Learning: A type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. (Think of punishement & rewards !)

LSTM - Long Short-Term Memory: A type of RNN that is particularly good at learning and remembering over long sequences, useful in tasks like language translation and speech recognition.

SVM - Support Vector Machine: A supervised machine learning algorithm used for classification and regression tasks.

PCA - Principal Component Analysis: A technique used for dimensionality reduction while preserving as much variance as possible in the dataset.

CV - Computer Vision: A field of AI that enables machines to interpret and understand visual information from the world, such as images and videos.

API - Application Programming Interface: A set of protocols and tools that allow different software systems to communicate with each other. (The glue!)

AIoT - Artificial Intelligence of Things: The integration of AI technologies with the Internet of Things (IoT) to create smart devices that can learn from and interact with their environment.

TTS - Text to Speech: A technology that converts written text into spoken words using AI.

ASR - Automatic Speech Recognition: The process of converting spoken language into text, typically used in virtual assistants, transcription, and voice commands.

MLOps - Machine Learning Operations: Practices that combine machine learning and DevOps to automate and streamline the lifecycle of machine learning models.