Tips – Brushed Steel Consulting

May 1 2024

AI Acronyms

Carlos Valcarcel AI, Carlos's Diner, Tips AI, AI Acronyms 0

I am at a loss for a better blog title.

Every day seems to bring more AI acronyms to our vocabulary and I am at a constant loss. I have found a few sites that have been doing their best to keep up with the flurry of 2 and 3 letter acronyms (also know as TLAs) and I just thought I would put together my own subset of acronyms with a list of posts that have pointed the way toward a clearer train of thought by understanding SOTA (State Of The Art) from SORA (you know, the video generator from OpenAI).

I will add acronyms as I see fit and even add some more references…as I see fit. The definitions will usually be from an LLM like ChatGPT.

The cat doesn’t find this amusing.

Acronyms

References

Machine Learning Acronyms
A Primer on Generative AIs Alphabet Soup of Acronyms
AI Terms Glossary
AI and the Future of Skills, Volume 1 : Capabilities and Assessments
TechScape: Seven top AI acronyms explained
Do you know these AI Abbreviations?

Just for Fun

Acronym Generator

AGI (Artificial General Intelligence)

AGI refers to a type of artificial intelligence that can understand, learn, and apply knowledge across a wide range of tasks, much like a human being. AGI would be capable of reasoning, problem-solving, and decision-making across diverse domains without specific task training, unlike narrow AI which is designed to perform specific tasks.

AI (Artificial Intelligence)

AI (Artificial Intelligence) refers to the capability of a machine to imitate intelligent human behavior. It involves the development of algorithms and models that enable computers to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.

AUC(Area Under the Curve)

BERT(Bidirectional Encoder Representations from Transformers)

BLEU(Bilingual Evaluation Understudy)

CNN (Convolutional Neural Network)

CRF(Conditional Random Field)

CTM (Conscious Turing Machine)

Conscious Turing Machine (CTM) is a theoretical model from the field of computer science that seeks to address the concept of consciousness within the framework of computational machines. The CTM is modeled in the spirit of Alan Turing’s original definition of a Turing Machine, aiming to understand consciousness through a simple yet effective computational model.

CUDA

CUDA stands for Compute Unified Device Architecture. It is a parallel computing platform and application programming interface (API) model created by NVIDIA. CUDA allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units).

DL (Deep Learning)

DNN(Deep Neural Network)

ELMo(Embeddings from Language Models)

FFNN(Feedforward Neural Network)

FLOPS(Floating Point Operations Per Second)

GAN (Generative Adversarial Network)

Generative Adversarial Networks consist of two neural networks, a generator and a discriminator, that compete against each other. The generator creates fake data, while the discriminator tries to distinguish between real and fake data, improving both through this process.

GPT (Generative Pre-trained Transformer)

GPT (Generative Pre-trained Transformer) is a type of artificial intelligence model developed by OpenAI that excels in generating human-like text based on the input it receives. It is trained on a diverse range of internet text and uses this training to produce text that can mimic various writing styles and perform tasks like translation, summarization, and answering questions.

HMM(Hidden Markov Model)

KNN (K-Nearest Neighbors)

LoRA (Low Rank Adaptor)

LoRa for Low Rank Adaptor. This typically refers to techniques in computational mathematics or computer science where adaptations or modifications are made to handle or approximate large datasets or matrices with lower rank representations. This approach helps in reducing the complexity and computational cost, making operations more efficient while preserving essential data characteristics.

LSTM (Long Short-Term Memory)

ML (Machine Learning)

ML (Machine Learning) is a subset of artificial intelligence that involves training algorithms on data to enable them to make predictions or decisions without being explicitly programmed to perform the task. This field utilizes statistical methods to give computers the ability to “learn” from past experiences or data trends.

MLOps(Machine Learning Operations)

NLP (Natural Language Processing)

NLP (Natural Language Processing) is a branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. This technology underpins a variety of applications, including speech recognition, machine translation, and sentiment analysis.

PCA (Principal Component Analysis)

Principal Component Analysis is a dimensionality reduction technique used in ML to transform a large set of variables into a smaller one that still contains most of the information. It helps in simplifying data visualization and speeding up the training process.

PPO(Proximal Policy Optimization)

QLoRA (Quantized Low Rank Adaptor)

QLoRa builds upon LoRa (Low Rank Adaptor) technology by quantizing the weights of the LoRa adapters to lower precision, such as converting 8-bit weights to 4-bit. This quantization further reduces the memory footprint and storage requirements, enhancing efficiency for tasks involving large datasets and matrix operations in computational settings.

RL (Reinforcement Learning)

RLAIF (Reinforcement Learning from AI Feedback)

Reinforcement Learning from AI Feedback involves using preferences labeled by an AI, typically a large language model (LLM), instead of human annotators. It aims to address the scalability and efficiency challenges associated with collecting high-quality human feedback, which is often a bottleneck in training AI models through reinforcement learning. RLAIF has shown to be effective in tasks like summarization, dialogue generation, and more, achieving performance comparable to or better than models trained with human feedback (Starling-7B) (Hugging Face).

RNN (Recurrent Neural Network)

RLHF (Reinforcement Learning from Human Feedback)

RLHF involves training machine learning models, particularly in the realm of reinforcement learning, by using feedback or guidance provided by humans. Instead of relying solely on predefined rewards or environmental signals, RLHF integrates human evaluations, corrections, or preferences into the training process.

ROC(Receiver Operating Characteristic)

SGD(Stochastic Gradient Descent)

SGI (Super (Artificial) General Intelligence)

SAGI stands for Super Artificial General Intelligence. It refers to a hypothetical form of artificial intelligence that not only possesses the ability to perform any intellectual task that a human can, but also far surpasses human intelligence across all cognitive domains, including emotional and social intelligence.Accordion content