The Alphabet Soup of AI

AI is the latest talk of the town. It is being widely discussed on all platforms and has become so much more than just a buzzword. With the growing chatter about AI, and the immense terminologies connected to it, sometimes reading an article can feel like deciphering a code. As AI continues to be the transformative force for now and the future, it is imperative to understand the vast range of expressions related to it. Let us discuss a few here:

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AI - Artificial Intelligence

Artificial Intelligence is the ability of computer systems to perform tasks commonly associated with human intelligence. These tasks include problem solving, natural language processing, speech and visual recognition etc.

ANI - Artificial Narrow Intelligence

ANI - Artificial Narrow Intelligence is also known as Weak AI. ANI is AI that is trained on a specific set of data and is programmed to perform a single task. Common example of ANI is Siri or Google Assistant and even Chat GPT is an exceptional example of ANI.

AGI - Artificial General Intelligence

AGI - Artificial General Intelligence is the next step after ANI. AGI is capable of portraying human like intelligence for a wide range of tasks. A hypothetical concept currently, AGI will be expected to perform complex tasks that it has not been explicitly trained on.

ASI - Artificial Super Intelligence

ASI - Artificial Super Intelligence is expected to operate above human-level intelligence. ASI is expected to self learn and improve resulting in an exponential growth in intelligence. This is another hypothetical concept that does not exist yet with all AI systems currently being weak AI or ANI.

ANN - Artificial Neural Networks

ANN or Artificial Neural Networks is also sometimes known as Simulated Neural Networks (SNN). It is a subset of Machine Learning (ML) and is inspired by the human brain. ANN consists of 3 layers - input layer, hidden layer and output layer. The layers comprises of many interconnected nodes that rely on the training data and processing information. It is called neural because the nodes mimic the neurons in the human brain.

Big Data

Big Data is structured, semi-structured and unstructured data that is too large, complex and diverse to be handled by standard data processing applications. This data can be mined for precious information. Tailored recommendations by Netflix is an example of using Big Data effectively to understand user behaviour.

Chatbots

Chatbot or Chatterbot is a computer program designed to simulate human conversation in either voice or text format.

Chat GPT

Chat GPT stands for "Chat Generative Pre-trained Transformer." It is an advanced AI language model developed by OpenAI that can engage in conversational exchanges and generate human-like responses.

CNN - Convolution Neural Networks

CNN or Convolution Neural Networks are a type of artificial neural network designed for processing structured grid data, such as images and video. CNNs are particularly effective at tasks involving visual recognition, image classification, object detection, and image generation.

DL - Deep Learning

Deep Learning is a subset of Machine Learning based on artificial neural networks with multiple layers that helps process complex data and perform human like tasks.

Generative AI

Generative AI, GenAI or Generative Artificial Intelligence is capable of creating various forms of content such as text, image, audio using generative models. Chat GPT is an example of Generative AI.

Generative Models

Generative AI Models use neural networks analyse input data and existing data to identify patterns and generate new and original content based on this analysis.

GAN - Generative Adversarial Networks

GAN or Generative Adversarial Networks are a subset of deep learning based generative models. They are designed to create new data that is similar to the existing data. They consist of 2 neural networks: the generator and the discriminator that work together to create new content.

K-Nearest Neighbour

K-Nearest Neighbours (K-NN) is a machine learning algorithm that makes predictions based on the average value of its data points from the training set that are nearest to a new one.

LLM - Large Language Models

LLM or Large Language Models are AI language models that have been trained on vast amounts of textual data to generate human-like response on given input. They have the ability to learn patterns, grammar, context, and nuances of language. Chat GPT is an example of an LLM.

LLaMa

LLaMa - Large Language Model Meta AI are a family of open source large language models released by Meta AI. It is free for research and commercial use and some of the uses can be to build chatbots and discuss code.

ML - Machine Learning

ML or Machine learning is a subset of artificial intelligence (AI) that works on developing algorithms and models that enable computers to automatically learn and improve from experience, rather than being explicitly programmed for specific tasks.

Machine Learning Models

Machine learning models are computer programs that are trained on data to learn patterns, relationships, and insights. Once trained, these models can make predictions, classifications, or decisions without being explicitly programmed.

NLP - Natural Language Processing

NLP or Natural Language Processing is a branch of AI that enables computers to understand, generate and manipulate human language in a way that is meaningful and useful. Chatbots and Language translations are some applications of NLP. NLP = NLU + NLG.

NLU - Natural Language Understanding

NLU or Natural Language Understanding is a subset of NLP that focuses on the computers ability to comprehend and interpret human language in a meaningful way.

NLG - Natural Language Generation

NLG or Natural Language Generation is also a subset of NLP that focuses on the computers ability to generate human language text as an output from structured data.

NN - Neural Networks

Same as ANN above, It is a subset of Machine Learning (ML) and is inspired by the human brain. ANN consists of 3 layers - input layer, hidden layer and output layer. The layers comprises of many interconnected nodes that rely on the training data and processing information. It is called neural because the nodes mimic the neurons in the human brain.

RNN - Recurrent Neural Networks

RNN or Recurrent Neural Networks are types of Artificial Neural Network designed for processing past data, input data and memorise things. Unlike traditional neural networks, RNNs have connections that loop back on themselves, allowing them to maintain a form of memory.

RL - Reinforcement Learning

RL or Reinforcement Learning is a type of machine learning where the system learns by interacting with an environment, receiving rewards for good actions and penalties for bad ones. It aims to find the best actions or strategies to maximise long-term rewards through trial and error.

OCR - Optical Character Recognition

OCR - Optical Character Recognition also known as text recognition or text extraction is a technology that converts images of text that is handwritten, typed, or printed, into machine-encoded text.

Weak AI

Weak AI is same as ANI - Artificial Narrow Intelligence. AI that is trained on a specific set of data and is programmed to perform a single task. Common example of ANI is Siri or Google Assistant and even Chat GPT is an exceptional example of ANI.