Application Programming Interface (API):
An API enables different software programs to communicate and exchange information, serving as an intermediary that allows them to interact seamlessly, regardless of their programming languages or technologies.
Artificial Intelligence (AI):
AI involves machines displaying intelligence similar to humans, including learning, problem-solving, and decision-making. It is achieved through algorithms and systems that analyze large data sets to make informed decisions.
Compute Unified Device Architecture (CUDA):
Developed by NVIDIA, CUDA allows for parallel computing in GPUs, enhancing computational efficiency by tackling large problems in smaller, simultaneous tasks.
Data Processing:
This involves preparing raw data for analysis in machine learning by cleaning, transforming, and normalizing it to ensure it is usable for models.
Deep Learning (DL):
A subset of machine learning, deep learning utilizes multi-layered neural networks to discern complex patterns in data.
Embedding:
A technique in natural language processing where words are converted into numerical representations, allowing computers to understand and process language based on the contextual relationships between words.
Feature Engineering:
The process of selecting, modifying, and creating new features from raw data to enhance the performance of machine learning models.
Freemium:
A pricing strategy where a product or service is provided free of charge with limited features, with additional features available at premium tiers.
Generative Adversarial Network (GAN):
A framework for training two neural networks in competition, where one generates new data and the other evaluates its authenticity. This method is used to create realistic multimedia content.
Generative Art:
Art created with the use of algorithms, often incorporating elements of randomness and complexity to produce unique, unpredictable outputs.
Generative Pre-trained Transformer (GPT):
Developed by OpenAI, GPT is a large language model that generates coherent and contextually relevant text based on a given prompt.
Giant Language Model Test Room (GLTR):
A tool designed to detect if a text was written by a human or a machine by analyzing the predictability of word usage within the text.
GitHub:
A platform for hosting and collaborating on software development projects, utilizing Git for version control.
Google Colab:
An online platform that allows the execution and sharing of Python code in the cloud, facilitating collaborative software development.
Graphics Processing Unit (GPU):
A specialized processor designed to accelerate the rendering of images, videos, and animations in computing devices. It is particularly effective in tasks requiring substantial parallel processing power.
Langchain:
A library that facilitates the integration of AI models with external data sources, enabling the creation of sophisticated AI agents and chatbots.
Large Language Model (LLM):
A machine learning model trained on extensive text data capable of generating natural language text that mimics human writing.
Machine Learning (ML):
The science of programming computers to learn from data through algorithms that improve automatically through experience.
Natural Language Processing (NLP):
A field of AI focused on enabling machines to understand, interpret, and generate human language.
Neural Networks:
Algorithms modeled loosely after the human brain that are designed to recognize patterns and make decisions.
Neural Radiance Fields (NeRF):
A deep learning model used for creating lifelike images and understanding complex spatial relationships in visual data.
OpenAI:
A research organization that develops advanced AI technologies aimed at promoting and ensuring AI safety and benefits for society.
Overfitting:
A modeling error in machine learning that occurs when a function is too closely fit to a limited set of data points, resulting in poor predictive performance on new data.
Prompt:
A text input used to guide the output of a language model, ensuring relevance and contextuality in its responses.
Python:
A versatile, high-level programming language known for its ease of use and broad applicability in programming, especially in artificial intelligence.
Reinforcement Learning:
A type of machine learning where algorithms learn optimal actions through trial and error, using feedback from their own actions and experiences.
Spatial Computing:
The practice of enhancing physical spaces with digital data and interfaces, encompassing technologies like augmented reality and virtual reality.
Stable Diffusion:
An open-source AI model that generates detailed images from textual descriptions, available for use and modification by the general public.
Style Transfer :
A technique in AI video generation where the style of one image (e.g., a painting) is applied to another image or video frame. This method is frequently used to create artistic effects.
Frame Rate :
The number of frames displayed per second in a video. Higher frame rates generally produce smoother motion. AI video generators may optimize frame rates to balance quality and performance.
Resolution :
The number of pixels in each dimension that a video displays. Higher resolution typically means more detail and clarity. AI video generators may upscale videos to higher resolutions using algorithms like super-resolution.
Super-Resolution :
A technique used in AI to enhance the resolution of an image or video. It involves generating high-resolution outputs from low-resolution inputs, often using deep learning techniques.
Temporal Consistency :
Ensuring that visual elements remain coherent and stable across consecutive video frames. This is crucial for avoiding flickering or jarring transitions in AI-generated videos.
Data Augmentation :
The process of modifying existing data to create new training examples. In video generation, data augmentation can involve altering frame sequences, colors, or adding noise to improve the robustness of the model.
Latent Space :
A high-dimensional space where data is represented in a compressed form. In video generation, exploring the latent space can help in generating diverse and novel video content.
Inference :
The process of using a trained model to generate outputs based on new inputs. In AI video generators, inference is the stage where the model creates video content from given data.
Training Data :
The dataset used to teach machine learning models. For AI video generators, this data might include videos, images, and annotations that help the model learn patterns and features relevant to video creation.
Render Time :
The amount of time it takes to generate the final video output. Efficient AI video generators aim to minimize render time without compromising on quality.
Video Synthesis :
The process of generating video content from scratch using AI algorithms. This involves creating new sequences of frames that form a coherent video.
Semantic Segmentation :
A computer vision technique that involves partitioning an image into regions based on their semantic content. This can be used in video generation for tasks like background replacement or object manipulation.
Real-time Processing :
The capability of generating or modifying video content instantaneously. Real-time processing is essential for applications like live video effects and interactive media.
Supervised Learning:
A machine learning technique where models are trained using labeled data to predict outcomes based on input data.
Temporal Coherence:
The consistency of patterns or behaviors over time, crucial in applications like video analysis, natural language processing, and time-series forecasting.
Unsupervised Learning:
A machine learning approach where models infer patterns and relationships from unlabeled data.
Webhook:
A method for one application to provide real-time information to another application over the internet via custom callbacks.
Heuristic:
A technique designed to solve a problem more quickly when classic methods are too slow, or to find an approximate solution when classic methods fail to find any exact solution.
Bias in AI:
Refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
Decision Trees:
A decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
Ensemble Learning:
A machine learning technique that combines several base models in order to produce one optimal predictive model.
Backpropagation:
A method used in artificial neural networks to improve the accuracy of predictions through training, by adjusting the weight of edges.
Transfer Learning:
A research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
Convolutional Neural Network (CNN):
A class of deep neural networks, most commonly applied to analyzing visual imagery.
Recurrent Neural Network (RNN):
A class of neural networks where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior.
Natural Language Generation (NLG):
The process of producing meaningful phrases and sentences in the form of natural language from some internal representation.
Semantic Analysis:
The process of understanding the meaning and interpretation of words, phrases, and sentences in the context of a given language.
Anomaly Detection:
The identification of rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.
Autoencoders:
A type of artificial neural network used to learn efficient codings of unlabeled data.
Bayesian Networks:
A type of statistical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
Capsule Networks (CapsNets):
A type of artificial neural network that uses groups of neurons (capsules) to represent the instantiation parameters of a specific type of entity such as an object or an object part.
Explainable AI (XAI):
Refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by humans.
Federated Learning:
A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers without exchanging their data samples.
Hyperparameter Tuning:
The process of finding the optimal set of parameters for a machine learning algorithm, usually with the goal of maximizing the performance of the model on some metric.
Model Deployment:
The process of integrating a machine learning model into an existing production environment to make practical use of its predictions.
Quantum Machine Learning:
An emerging interdisciplinary research area at the intersection of quantum physics and machine learning.
Reinforcement Learning Environment:
The setting in which an agent learns how to behave in the world through actions and feedback from those actions.
