Functions used in neural networks to introduce non-linearity into the model, enabling it to learn complex patterns. Common Activation Functions include ReLU, sigmoid, and tanh. Each function has its own properties and is chosen based on the specific task.
A technique that involves actively selecting the most informative data points to label and train a Machine Learning model.
The rise of AI Answer Engines has led to the development of Answer Engine Optimization (AEO), a practice focused on optimizing content to provide direct answers to user queries. This evolution reflects a shift in how information is retrieved, emphasizing the need for businesses to adapt their content strategies to remain visible in AI-driven search […]
AI (Artificial Intelligence): The development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. [Read more](https://www.example.com/what-is-ai)
An AI Answer Engine is a system designed to provide direct answers to user queries, distinguishing itself from traditional search engines that typically return a list of web pages. Unlike search engines, which require users to sift through links for information, answer engines deliver concise and accurate responses directly within the search results. This capability […]
The use of AI and ML tools to automatically write computer code based on user-provided descriptions of desired functionality. It leverages large language models and Generative AI techniques to suggest or produce code, making coding faster and more accessi
The practice of using AI to assist in writing, reviewing, or optimizing code. This can include code completion, bug detection, and even generating entire code snippets or programs.
The branch of ethics that deals with the moral implications of creating and using AI systems, addressing issues like Bias, privacy, and accountability.
A set of rules or instructions given to an AI system to help it learn on its own. Algorithms can be rule-based or learned through Machine Learning techniques.
In linguistics, an Anaphora is a reference to a noun by way of a pronoun, such as “he” in “John didn’t like the appetizers, but he enjoyed the entr‚e”.
Artificial Intelligence (AI) The simulation of human intelligence in machines programmed to think and learn like humans. AI encompasses various subfields and technologies aimed at creating intelligent systems.
A computing system inspired by the human brain’s neural networks, consisting of interconnected nodes that process information.
A technique used in neural networks to focus on specific parts of the input sequence when making predictions. It has been highly successful in improving the performance of NLP models. The Attention Mechanism is a key component in Transformer models. Learn
A method in AI Code Generation where the AI tool attempts to complete the code based on patterns learned from a training dataset.
Tools and techniques used to automate the application of Machine Learning, including data preprocessing, model selection, and Hyperparameter Tuning.
An Algorithm used to train neural networks by computing the error gradient of the loss function with respect to the model’s parameters.
A technique to improve the training of deep neural networks by normalizing the inputs of each layer. This helps in stabilizing and accelerating the training process. Batch Normalization also acts as a regularizer to reduce Overfitting. Learn more.
A Transformer-based Machine Learning technique for NLP pre-training developed by Google. It’s designed to help computers understand the context of words in search queries.
Systematic errors in AI model predictions that can result from skewed training data or flawed algorithms. Bias can lead to unfair or discriminatory outcomes.
A software application that simulates human conversation using predefined rules or AI technologies like NLP.
A field of study that focuses on enabling computers to interpret and understand visual data from images and videos. It involves tasks such as Object Detection, Image Classification, and Facial Recognition. [Read more](https://www.example.com/computer-vis
A type of Neural Network architecture particularly well-suited for image and signal processing tasks. It uses convolutional and pooling layers to extract features.
An AI-powered code completion tool developed by GitHub and OpenAI that suggests code snippets and functions based on the context of what a developer is writing.
An approach to examining AI that focuses on reflective assessment and critique to understand and challenge existing structures within AI.
An image generation model developed by OpenAI that creates images from text descriptions using a variation of the GPT-3 architecture.
A technique to increase the amount of training data by adding slightly modified copies of existing data, improving model performance.
A subset of ML involving neural networks with many layers (deep networks). It is particularly effective for complex tasks like image and speech recognition. Examples include convolutional neural networks (CNNs) for image processing and recurrent neural ne
Predicting the distance of objects in an image from the camera, crucial for 3D scene understanding.
A class of Deep Learning models used for generating images. They work by gradually adding noise to data and then learning to reverse this process.
A method in AI Code Generation where developers interact with the AI via a chat interface to request specific code or bug fixes.
Running AI algorithms on end devices like smartphones or IoT devices, allowing real-time processing and privacy.
A technique in NLP where words or phrases are mapped to vectors of real numbers. These vectors capture semantic meanings and relationships between words. Common methods include Word2Vec, GloVe, and contextual Embeddings like those produced by BERT. Learn
The field of AI focused on developing models and algorithms that are transparent and can explain their decision-making processes to humans.
A biometric technology that uses AI to map facial features from a photograph or video and compare them to a database of known faces.
The process of identifying and isolating distinctive attributes in images for further analysis.
A Machine Learning technique that trains an Algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them.
Similar to One-shot learning, but the model is trained on a small number of examples for each class. This approach balances the need for few training examples with achieving good generalization. Learn more.
The process of taking a pre-trained model and further training it on a specific task or dataset. This allows the model to adapt to the new task while leveraging the knowledge it gained during pre-training. Fine-Tuning is commonly used in Transfer Learning
A type of Deep Learning model that consists of two neural networks: a generator and a discriminator. The generator generates new data, while the discriminator evaluates the generated data and provides feedback to the generator.
A type of AI that can create new content, such as text, images, or music, rather than simply analyzing or classifying existing data.
A type of Large Language Model developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, translate languages, and perform various NLP tasks.
An optimization Algorithm used for minimizing the loss function in Machine Learning models. Variants include stochastic Gradient Descent (SGD), mini-batch gradient descent, and adaptive methods like Adam and RMSprop. Learn more.
The phenomenon where AI models, particularly large language models, generate outputs that are nonsensical, factually incorrect, or unrelated to the given input. This can occur due to insufficient training data, Bias in the training data, or the model’s te
The process of optimizing the parameters of an ML Algorithm that are not learned during training, such as learning rate or Regularization strength.
Parameters that define the structure and training process of a Machine Learning model. Unlike model parameters, Hyperparameters are set before training and include values like learning rate, batch size, and number of epochs. Learn more.
AI models that generate textual descriptions of images.
The task of assigning predefined categories or labels to an input image.
AI models capable of creating new images from text descriptions or modifying existing images. Examples include DALL-E, Midjourney, and Stable Diffusion.
An AI technique for reconstructing missing or damaged parts of an image.
The process of dividing an image into multiple segments or objects, often used in medical imaging and autonomous vehicles.
AI models that enhance the resolution and quality of low-resolution images.
The creation of new, artificial images using AI models.
In Artificial Intelligence (AI), Inference refers to the process of applying a trained Machine Learning model to new, unseen data to make predictions or decisions. This phase follows the training phase, where the model learns from a large dataset to recognize patterns and relationships.
A more advanced form of Object Detection that involves identifying individual instances of objects and creating pixel-perfect masks for each instance.
The process of training a language model on a large corpus of text data before Fine-Tuning it for specific tasks. LMPT allows models to acquire general language understanding that can be adapted for various applications.
(LLM) A specific category of NLMs that are trained on vast amounts of text data to generate human-like text. These models, such as GPT-3 and BERT, are capable of understanding and generating text based on context. They are used in various applications inc
A specific category of NLMs that are trained on vast amounts of text data to generate human-like text. These models, such as GPT-3 and BERT, are capable of understanding and generating text based on context. They are used in various applications including
Large Language Model: A type of NLM that is trained on vast amounts of text data to generate highly accurate and coherent language understanding and generation capabilities. Examples include language translation, text generation, and conversational AI.
A type of RNN architecture that uses memory cells to learn long-term dependencies in sequential data.
A type of AI that enables computers to learn from data without being explicitly programmed. It involves training algorithms on data to make predictions, classify objects, and make decisions. [Read more](https://www.example.com/machine-learning)
A subset of AI that involves training algorithms to learn from and make predictions or decisions based on data. Common techniques include Supervised Learning, Unsupervised Learning, and reinforcement learning. Examples include spam filters, recommendation
An NLP task that involves identifying and classifying named entities (people, organizations, locations, etc.) in text.
A subfield of NLP focused on generating human-like text from structured data or other inputs. NLG is used in applications like chatbots, report generation, and creative writing.
A method in AI Code Generation where developers describe their coding intentions in natural language, prompting the AI tool to generate corresponding code.
A field of AI focused on the interaction between computers and human language. It involves the development of algorithms to process and analyze large amounts of natural language data. Applications include Sentiment Analysis, machine translation, and speec
A type of model within AI that uses neural networks to understand, generate, and manipulate human language. Examples include OpenAI’s GPT series and Google’s BERT. NLMs are widely used in applications like chatbots, translation services, and text summariz
A computational model inspired by the structure and function of the human brain. It consists of interconnected nodes (neurons) that process and transmit information.
Natural Language Model: A type of AI model that is trained on large amounts of text data to generate language understanding and generation capabilities. Examples include language translation, Sentiment Analysis, and text summarization.
A Computer Vision task that involves identifying and locating specific objects within an image or video.
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A type of learning where a model is able to make predictions on tasks it has never seen before. This is achieved by leveraging semantic information and relationships between known and unknown classes. Examples include language models generating responses
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A technology that recognizes and extracts text from images, converting it into machine-readable text data.
A scenario where a Machine Learning model performs well on the training data but poorly on unseen test data. Overfitting indicates that the model has learned noise and details specific to the training set rather than general patterns. Learn more.
The task of determining the position and orientation of objects or human bodies in images or videos.
The practice of crafting effective input prompts to guide the output of language models. Prompt Engineering is key to generating high-quality and contextually relevant text from LLMs.
Retrieval-Augmented Generation: A type of AI model that combines the strengths of retrieval-based and generation-based models to generate more accurate and informative text.
A class of neural networks designed for processing sequential data. RNNs are used in applications like language modeling, speech recognition, and time series prediction. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) address th
Techniques used to prevent Overfitting by adding constraints to the model. Common methods include L1 and L2 Regularization, dropout, and early stopping. Regularization helps in improving the generalization of the model. Learn more.
A type of ML where an agent learns to make decisions by performing actions and receiving rewards or penalties. The goal is to maximize cumulative rewards. Examples include AlphaGo, which learned to play the game Go, and autonomous driving systems. Learn m
A hybrid model that combines retrieval-based methods with generative models to produce more accurate and contextually relevant outputs.
A mechanism in Transformer models that allows the model to assign importance to different words in an input sequence, crucial for understanding context in language tasks.
A Computer Vision task where each pixel in an image is classified into a specific class or category.
An ML approach that combines elements of supervised and Unsupervised Learning, using a small amount of labeled data along with a larger amount of unlabeled data.
The capacity for subjective experience, feeling, or awareness. In the context of AI, Sentience refers to the hypothetical ability of a machine to have conscious experiences, such as emotions, self-awareness, and qualia. Currently, there is no scientific c
An NLP technique used to determine the sentiment expressed in a piece of text, such as categorizing tweets as favorable, unfavorable, or neutral.
An open-source image generation model that uses diffusion techniques to create high-quality images from text descriptions.
A technique that allows AI to adapt the style of one image to the content of another, e.g., recreating a photo in the style of a famous painter.
A type of ML where the model is trained on labeled data. The goal is for the model to learn the mapping from inputs to outputs based on example input-output pairs. Common algorithms include linear regression, decision trees, and support vector machines (S
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AI models that create images based on textual descriptions, such as DALL-E or Midjourney.
In NLP, a Token refers to a single unit of language data, typically a word or subword in a text document.
The process of breaking down text into smaller units (tokens), such as words, subwords, or characters. Tokenization is a crucial step in NLP pipelines for transforming text into a format that can be processed by models. Examples include word tokenization
A technique in ML where a pre-trained model is reused on a new but related task. This can significantly reduce the amount of data and computation needed for the new task. Examples include using a model trained on ImageNet for other Image Classification ta
A Deep Learning model architecture introduced in the paper “Attention is All You Need,” commonly used in NLP tasks such as machine translation and text generation.
A Neural Network architecture that has revolutionized NLP tasks. Transformers use Self-attention mechanisms to weigh the importance of different words in a sentence, leading to better language understanding.
A type of Deep Learning model introduced in the paper “Attention is All You Need” by Vaswani et al. It uses Self-attention mechanisms to process input sequences in parallel, making it highly efficient and effective for NLP tasks. Notable examples include
A scenario where a Machine Learning model performs poorly on both training and test data. Underfitting indicates that the model is too simple to capture the underlying patterns in the data. Learn more.
A type of ML where the model is trained on data without labels. The goal is to find hidden patterns or intrinsic structures in the input data. Common algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA). L
A database optimized for storing and querying high-dimensional vectors, often used in AI applications for efficient similarity searches.
AI systems designed to perform specific tasks within a limited domain, lacking general intelligence or consciousness.
A way of representing words or phrases as numerical vectors in a high-dimensional space. Word Embeddings capture semantic relationships between words, allowing models to understand word meanings better.
A method in NLP that maps words to high-dimensional vectors, capturing relationships and similarities between words based on their context.
A popular real-time Object Detection system that applies a single Neural Network to the full image, dividing it into regions and predicting bounding boxes and probabilities for each region.
A type of learning where a model is able to make predictions on tasks it has never seen before. This is achieved by leveraging semantic information and relationships between known and unknown classes. Examples include language models generating responses