Artificial Intelligence (AI) – Refers to the development of computer systems that can perform tasks that typically require human intelligence
Machine Learning (ML) – A subset of AI that allows computers to learn from data, identify patterns, and make predictions without being explicitly programmed.
Deep Learning – A subfield of machine learning that focuses on training deep neural networks. These models are characterized by having multiple hidden layers, referred to as deep neural networks, between the input and output layers.
Natural Language Processing (NLP) – NLP is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP involves a range of tasks, such as language translation, sentiment analysis, text summarization, speech recognition, and language generation. It combines techniques from linguistics, machine learning, and computational linguistics to process and analyze textual and spoken data.
Large Language Models – A type of AI model used for understanding and generating human language. They are trained on vast amounts of text data and can generate human-like text based on the patterns they’ve learned.
Generative Adversarial Networks (GANs) – GANs are a class of AI algorithms used in unsupervised machine learning. GANs consist of two neural networks: a generator, which creates new data instances, and a discriminator, which tries to distinguish between real and fake data. The two parts are trained together, with the generator trying to produce realistic data examples to deceive the discriminator, while the discriminator aims to correctly identify real data from fake.
Variational Autoencoders (VAEs) – VAEs are generative models that belong to the family of autoencoder neural networks. Unlike traditional autoencoders, VAEs aim to generate new data points rather than reconstructing the input data. They work by learning the underlying distribution of the input data and then sampling from that distribution to generate new data points. VAEs are widely used in applications like image generation, data compression, and generating new samples in various domains.
Cloud Computing – Cloud computing is a paradigm for delivering computing resources and services over the internet. Instead of hosting applications and storing data on local servers or personal computers, cloud computing allows users to access and utilize computing power, storage, and other services through remote data centers. Cloud services provide scalability, flexibility, and cost-efficiency for various applications, including AI, as users can dynamically allocate resources based on their needs.
Edge Computing – Edge computing is a decentralized computing model that brings computation and data storage closer to the location where it is needed, such as near IoT devices or end-users. In the context of AI, edge computing enables AI algorithms to run locally on edge devices, reducing the need for constant data transmission to centralized servers. This approach is particularly beneficial for real-time applications where low-latency processing is critical and for reducing the load on cloud infrastructure.