Techniques & Methods

Adversarial Training

Improves AI robustness by training with deliberately challenging inputs to enhance model accuracy.
Techniques & Methods

Alignment

The process of ensuring AI behaviors and outputs adhere to human ethical standards and intentions.
Techniques & Methods

Attention

A mechanism in AI that allows models to weigh the importance of different pieces of information.
Techniques & Methods

Attention Mechanism

In AI, a technique that helps models focus on relevant parts of the input data, improving relevance.
Techniques & Methods

Autoregression

A statistical model that predicts future behavior based on past outcomes in time series data.
Techniques & Methods

Backpropagation

A method used in training artificial neural networks, adjusting weights based on error rates.
Techniques & Methods

Backward Chaining

A reasoning method that starts with the end goal and works backward to determine the solution path.
Techniques & Methods

Bandit Optimization

A strategy for balancing the exploration of new choices and the exploitation of known rewards.
Techniques & Methods

Beam Search

A search algorithm that efficiently finds the most likely sequences of outcomes in models.
Techniques & Methods

Chain-of-Thought

A prompting strategy that encourages AI to break down complex problems into manageable steps.
Techniques & Methods

Completion

The output produced by AI in response to a given input or prompt, completing the thought process.
Techniques & Methods

Coreference Resolution

The task in NLP of determining which words refer to the same entity in a text.
Techniques & Methods

Data Augmentation

A technique for increasing the amount of training data by adding slightly modified copies.
Techniques & Methods

Data Mining

The practice of examining large databases to generate new information and find hidden patterns.
Techniques & Methods

Decoding Rules

Guidelines that dictate how a language model translates its internal representations to output.
Techniques & Methods

Dependency Parsing

Analyzing the grammatical structure of a sentence to understand relationships between words.
Techniques & Methods

Distributed Training

A method where AI model training is spread across multiple computers or servers.
Techniques & Methods

Entity Annotation

The process of labeling text with information about entities, enhancing data structure.
Techniques & Methods

Entity Extraction

Identifying and classifying named entities in text into predefined categories.
Techniques & Methods

Evaluation Metrics

Quantitative measures used to assess the performance and effectiveness of AI models.
Techniques & Methods

Extractive Summarization

Creating summaries by extracting key sentences or fragments directly from the source text.
Techniques & Methods

Feature Extraction

Identifying and isolating useful information from data to improve model training and performance.
Techniques & Methods

Few-Shot Learning

The ability of a model to learn and generalize from a very small number of examples.
Techniques & Methods

Fine-Grained Control

The capability to precisely adjust the output or behavior of an AI model based on specific criteria.
Techniques & Methods

Fine Tuning

The process of adjusting a pre-trained model to perform well on a specific task or dataset.
Techniques & Methods

Forward Chaining

A logical reasoning method that starts with known facts and applies rules to reach new conclusions.
Techniques & Methods

Generation

The process of producing new content, such as text or images, based on learned patterns and data.
Techniques & Methods

Greedy Algorithms

Optimization algorithms that make the locally optimal choice at each step to find a global optimum.
Techniques & Methods

Hallucination

When AI generates information that is not grounded in reality, often due to training data issues.
Techniques & Methods

Heuristics

Problem-solving approaches that use practical methods or various shortcuts to produce solutions.
Techniques & Methods

Inference

The phase where a trained model is used to make predictions or decisions based on new, unseen data.
Techniques & Methods

Information Extraction

The process of automatically extracting structured information from unstructured text data.
Techniques & Methods

Joint Probability

The probability of two events happening at the same time in a probabilistic model.
Techniques & Methods

Knowledge Representation

The method by which AI systems model, store, and retrieve knowledge to solve complex tasks.
Techniques & Methods

Linguistic Annotation

The process of adding metadata regarding linguistic information to text, aiding in its analysis.
Techniques & Methods

Low Rank Adaption (LoRA)

A technique for fine-tuning large models in a memory and computationally efficient manner.
Techniques & Methods

Markov Decision Process

A mathematical framework for modeling decision-making in situations with random outcomes.
Techniques & Methods

Masked Language Modeling

A training technique where some words in the input are hidden, and the model predicts them.
Techniques & Methods

Multitask Learning

Training an AI model on multiple tasks simultaneously, leveraging commonalities across tasks.
Techniques & Methods

Named Entity Recognition (NER)

The process of identifying and classifying key information (entities) in text into predefined categories.
Techniques & Methods

Offline Reinforcement Learning (RL)

Learning optimal actions from a fixed dataset without further interaction with the environment.
Techniques & Methods

One-Shot / Few-Shot

Learning techniques where the model learns from one or a few examples, respectively.
Techniques & Methods

One-Shot Learning

The ability of a model to learn information from a single example or a few examples.
Techniques & Methods

Online Learning

A model training approach where the model updates continuously as new data arrives.
Techniques & Methods

Overuse Penalty

A technique to discourage repetitive or overly similar responses in generative AI models.
Techniques & Methods

Part-of-Speech Tagging (POS)

The process of marking up a word in a text as corresponding to a particular part of speech.
Techniques & Methods

Pre-training in AI

The initial training phase where a model learns from a large, general dataset before task-specific training.
Techniques & Methods

Prompt

A text input given to an AI model, designed to elicit a specific type of response or output.
Techniques & Methods

Prompt Engineering

The art of crafting prompts to effectively communicate with and elicit desired responses from AI models.
Techniques & Methods

Prompt Injection

A technique used to influence or manipulate the behavior of AI systems through specially crafted inputs.
Techniques & Methods

Proximal Policy Optimization (PPO)

A reinforcement learning algorithm that balances exploration and exploitation in policy learning.
Techniques & Methods

Query

A request for information or action made to a database, search engine, or AI model.
Techniques & Methods

Regularization

Techniques used to prevent overfitting by penalizing complex models during the training process.
Techniques & Methods

Reinforcement Learning from Human Feedback (RLHF)

Training approach where models are refined based on feedback from human evaluators.
Techniques & Methods

Response Quality

An evaluation of how well an AI system's responses meet the criteria of relevance, coherence, and accuracy.
Techniques & Methods

Retrieval Augmented Generation (RAG)

Combining retrieval of relevant information with generative models to produce informed responses.
Techniques & Methods

Scaling Laws

Observations that as AI models increase in size, their performance improves according to predictable patterns.
Techniques & Methods

Self-Attention

A mechanism that allows models to weigh the importance of different parts of the input data relative to each other.
Techniques & Methods

Semantic Annotation

The process of adding semantic metadata to content, making it easier for AI to understand and process information.
Techniques & Methods

Semantic Similarity

The measure of how much two pieces of text are related in terms of meaning, used in various NLP tasks.
Techniques & Methods

Sequence Generation

The process where AI models produce a sequence of items, such as words in text generation, based on learned patterns.
Techniques & Methods

Supervised Fine-Tuning

The process of refining a model's performance on specific tasks by training it further with labeled data.
Techniques & Methods

System Prompt

Internal cues or instructions that guide the behavior of an AI model, influencing how it processes and responds to input.
Techniques & Methods

Text Classification

The task of assigning predefined categories to text, used in applications like spam detection and sentiment analysis.
Techniques & Methods

Topic Modeling

A statistical model to discover abstract topics within a collection of documents, aiding in content organization and discovery.
Techniques & Methods

Training

The process of teaching a machine learning model to make predictions or decisions, typically by exposing it to a large dataset.
Techniques & Methods

Transfer Learning

Leveraging knowledge gained while solving one problem to solve a different but related problem in machine learning.
Techniques & Methods

Upstream Sampling

A technique in generative AI where multiple outputs are generated and the best one is selected based on certain criteria.
Techniques & Methods

Validation

The process of evaluating a model's performance with a separate portion of the data not used in training, to gauge its accuracy.
Techniques & Methods

Variation

Different expressions or phrasings that convey the same intent or meaning, important in understanding natural language variability.
Techniques & Methods

Vector Representation

The encoding of words or phrases as numerical vectors, enabling mathematical operations and comparisons by AI models.
Techniques & Methods

Word Embedding

A technique in NLP where words are represented as vectors in a high-dimensional space, capturing semantic similarity.
Techniques & Methods

Zero-Shot Learning

The ability of a model to correctly perform tasks it has not explicitly been trained to do, demonstrating generalization.